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Mechanisms associated with cognitive impairment and
mood in elderly heart failure patients.
Doctor of Philosophy
Christina Elizabeth Kure
Centre for Human Psychopharmacology
Faculty of Life and Social Sciences
Swinburne University of Technology
Melbourne, Australia
2013
Abstract
i
ABSTRACT
Cognitive impairment is a prevalent comorbidity seen in elderly heart failure (HF) patients
and is linked to poor quality of life, reduced self-care abilities and increased hospital
readmissions, possibly due to a reduced capacity to understand and follow complex treatment
protocols. In addition to cognitive impairments, HF patients also endure depressed mood and
increased anxiety.
In order to establish the most effective treatment for improving or ameliorating cognitive
impairment and mood in older HF patients, a better understanding of the basic physiological
mechanisms is necessary. Previous research suggests that mechanisms underlying cognitive
impairments in these patients include reduced cerebral blood flow but little is known about
the influence of other processes on their cognitive impairments.
The aim of the present thesis was to examine whether inflammatory, antioxidant, oxidative
stress and arterial stiffness explain cognitive deficits and depressed mood and anxiety in
elderly HF. A further aim was to explore additional cognitive domains that may be impaired
in HF using a well-validated computerised neuropsychological assessment battery.
In this thesis, 36 patients with HF (NYHA class II, III or IV) aged 60 years and above were
compared to 40 age- and sex- matched controls on tests of cognitive function, mood and
several biological mechanisms including cerebral blood flow, arterial stiffness, inflammation,
oxidative stress and antioxidant markers.
The results indicated that Power of Attention is an additional cognitive domain impaired in
elderly HF patients. Determinable reactive oxygen metabolites (DROMs) are significantly
elevated in HF compared to controls. Furthermore, the results indicated that reduced common
carotid arterial blood flow velocity, arterial stiffness and reduced coenzyme Q10 levels were
related to poor attention and psychomotor abilities in elderly HF patients. Common carotid
arterial blood flow velocity and reduced circulating coenzyme Q10 were associated with
reduced executive function in HF. Although inflammation and oxidative stress were
significantly elevated in the HF group, there was no indication these biomarkers were related
to cognitive function. Finally, the results indicated that cerebral blood flow, arterial stiffness,
antioxidants and inflammatory makers did not relate to depression or anxiety levels in HF.
In conclusion, this thesis confirmed the existence of cognitive impairments in HF, and
suggested that Power of Attention may be a further, previously undescribed impairment.
These findings also indicate that DROMs are a useful measure of oxidative stress in older HF
patients, and those interventions that reduce central pulse pressure, increase cerebral blood
Abstract
ii
flow and elevate coenzyme Q10 levels may improve attention and executive function in
elderly HF patients.
Acknowledgements
iii
ACKNOWLEDGEMENTS
I would like to gratefully acknowledge the cardiologists and Heart Failure nurses at the
Alfred Hospital Heart Centre for their assistance and support with patient recruitment for this
investigation. In particular, I would like to acknowledge Professor David Kaye and Dr Peter
Bergin for their enthusiasm, advice and valuable contribution to the project. I would also like
to offer a special thanks to the participants who volunteered their time to take part in the
study.
I would like to thank the researchers at the Centre for Human Psychopharmacology,
Swinburne University who assisted with collecting the control data and who created a fun,
supportive and inspiring work environment. I would like to thank my family and friends for
their support throughout the duration of my candidature. In particular, my mum Julijana Kure
for her love, support and encouragement to help me overcome obstacles and reach my goals.
I also wish to thank Matthew Hughes in particular for his love, support, encouragement,
patience, understanding, assistance with generating appealing scatter plots, proof reading,
making me laugh and for ‘lifting the load’ during trying times.
Most importantly, I would like to thank my supervisors for their continuing support and
invaluable contribution to the thesis:
Professor Con Stough for his support, providing solutions to multiple issues, being a mentor,
for reading drafts and providing guidance in becoming a confident researcher.
Professor Franklin Rosenfeldt who provided invaluable medical and cardiovascular input into
the project, for reading drafts, being a mentor and for teaching me the importance of
perseverance and persistence.
Professor Andrew Scholey for his ongoing support, offering advice on methodology and
statistical analysis, his inspiration and for being a positive mentor.
Dr Andrew Pipingas for his guidance, support, advice on methodology and for reading drafts.
Professor Stephen Myers for his guidance, enthusiasm, passion and advice.
I would also like to acknowledge Professor Denny Myers for her advice on statistical
analyses, Professor Kevin Croft from the University of Western Australia for his contribution
to the project design, Professor Keith Wesnes who provided in-kind contribution for the use
of the Cognitive Drug Research® computerised test battery and Nestec™ for their support with
collecting control data.
Signed declaration
iv
SIGNED DECLARATION
I declare that this thesis does not contain material which has been accepted for the award of
any other degree or diploma; and to the best of my knowledge this thesis contains no material
previously published or written by another person except where due reference is made in the
text. I further declare that where the work in this thesis is based on joint research or
publications, relative contributions of the respective workers or authors are disclosed.
Name: Christina Elizabeth Kure
Signed:
Date:
Table of contents
v
TABLE OF CONTENTS
TITLE PAGE
ABSTRACT i
ACKNOWLEDGMENTS iii
SIGNED DECLARATION iv
TABLE OF CONTENTS v
LIST OF FIGURES xv
LIST OF TABLES xvi
LIST OF ABBREVIATIONS xx
CHAPTER 1 OVERVIEW 1
CHAPTER 2 BACKGROUND AND OVERVIEW OF HEART FAILURE 2
2.1 Chapter overview 2
2.2 Definition of heart failure 3
2.3 Heart failure diagnosis, classifications, signs and symptoms 4
2.4 Heart failure pathophysiology 6
2.5 Heart failure treatments 6
2.6 Economic costs of heart failure 7
2.7 Prevalence and incidence of heart failure 8
2.8 Prevalence and incidence of cognitive impairment in heart failure 9
2.9 Impact of cognitive impairment in heart failure 11
2.9.1 Heart failure prognosis and mortality 11
2.9.2 Hospital readmissions 12
2.9.3 Treatment compliance 13
2.9.4 Quality of life 14
2.9.5 Daily living and self-care abilities 15
2.9.6 Effects of poor sleep on cognitive function 16
2.10 Summary 17
CHAPTER 3 COGNITIVE IMPAIRMENT AND MOOD IN HEART FAILURE 18
3.1 Introduction 18
3.2 Global cognitive function 18
3.3 Neuropsychological function, specific cognitive domains 19
3.3.1 Memory 19
3.3.2 Attention 20
3.3.3 Executive functioning 20
3.4 Studies using comprehensive neuropsychological test batteries 21
3.4.1 Longitudinal studies 21
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3.4.2 Comparison studies: cognitive function in heart failure
compared with healthy controls 23
3.4.3 Comparison studies: cognitive function in heart failure
compared with other diseases 25
3.4.4 Comparison studies: cognitive function in heart failure
compared with normative data 27
3.5 Effects of disease severity on cognitive function in heart failure 28
3.6 Psychological parameters and mood disturbances in heart failure 29
3.6.1 Mood disturbances and cognitive function 30
3.7 Factors that improve or worsen cognitive dysfunction in heart failure 32
3.7.1 Pharmaceuticals 32
3.7.2 Exercise programs 33
3.7.3 Educational programs 34
3.8 Summary 35
CHAPTER 4 MECHANISMS ASSOCIATED WITH COGNITIVE IMPAIRMENT
AND MOOD IN OLDER HEART FAILURE PATIENTS 36
4.1 Introduction 36
4.2 Vascular mechanisms 37
4.3 Cerebral haemodynamic factors 38
4.3.1 Transcranial Doppler 39
4.4 Cerebral circulation and cognitive function 40
4.4.1 Cerebral blood flow and mood 42
4.4.2 Brain imaging studies 43
4.4.3 Summary 44
4.5 Arterial stiffness 44
4.5.1 Arterial stiffness and heart failure 45
4.5.2 Arterial stiffness and cognitive function 47
4.5.3 Arterial stiffness and mood 49
4.5.4 Arterial stiffness and cerebral circulation 50
4.5.5 Summary 51
4.6 Oxidative stress in heart failure and in cognitive impairment 51
4.6.1 The oxidative stress pathway 51
4.6.2 The role of antioxidants in the body 53
4.6.3 Oxidative stress in heart failure 53
4.6.4 Oxidative stress in cognitive impairment and mood 55
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4.7 Inflammation in heart failure and in cognitive impairment 58
4.7.1 The inflammatory pathway 58
4.7.2 Inflammation and heart failure 59
4.7.3 Inflammation and cognitive impairment 60
4.7.4 Inflammation and mood 60
4.8 Summary 62
CHAPTER 5 RATIONALE, RESEARCH QUESTIONS AND HYPOTHESES 63
5.1 Study Aims 63
5.2 Rationale 63
5.3 Methodological issues 64
5.3.1 Patient selection 64
5.3.2 Research environment 65
5.3.3 Neuropsychological test batteries 65
5.3.4 Practice effects 66
5.4 Summary of biological mechanisms 66
5.4.1 Vascular 66
5.4.2 Oxidative stress and antioxidants 67
5.4.3 Inflammation and omega-3 dietary intake 68
5.5 Mechanisms for changes in mood 68
5.6 Hypotheses and research questions 69
CHAPTER 6 METHODS 72
6.1 Introduction 72
6.2 Participants 72
6.2.1 Heart failure patients 72
6.2.2 Healthy control volunteers 73
6.3 Power analysis 73
6.4 Materials 74
6.4.1 Case Report Form (CRF) 74
6.4.2 Cognitive measures 74
6.4.2.1 Cognitive Drug Research 74
6.4.2.2 Stroop word task 79
6.4.2.3 Trail Making Test (TMT) 80
6.4.3 Screening Measures 81
6.4.3.1 Mini Mental State Examination (MMSE) 81
6.4.3.2 Wechsler Abbreviated Scale of Intelligence
Scales (WASI) 82
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6.4.4 Mood and quality of life measures 82
6.4.4.1 Profile of Mood States (POMS) 82
6.4.4.2 Short Form-36 Item (SF-36) 83
6.4.4.3 Chalder fatigue scale 84
6.4.4.4 General Health Questionnaire 84
6.4.4.5 Speilberger's State Trait Anxiety Inventory 84
6.4.5 Oxidative stress, antioxidant, inflammatory and omega-3
samples 85
6.4.5.1 F2-isoprostanes 85
6.4.5.2 Determinable reactive oxygen metabolites
(DROMs) 86
6.4.5.3 Coenzyme Q10 86
6.4.5.4 Glutathione peroxidase 86
6.4.5.5 High-sensitive C-reactive protein (hs-CRP) 87
6.4.5.6 Polyunsaturated fatty acid questionnaire 88
6.4.6 Cardiovascular Measures 88
6.4.6.1 Endothelin-1 analysis 88
6.4.6.2 Transcranial Doppler (TCD)
Ultrasconography 89
6.4.6.3 SphygmoCor® Px: pulse pressure and
augmentation index 90
6.4.7 Study design 93
6.4.8 Experimental Design 96
6.4.8.1 Testing environment 96
6.4.8.2 Data safety and monitoring 96
6.4.8.3 Equipment 96
CHAPTER 7 RESULTS: DEMOGRAPHIC CHARACTERISTICS 98
7.1 Introduction 98
7.2 Data screening 98
7.3 Demographic variables 99
7.4 Quality of Life 102
7.5 Summary 103
CHAPTER 8 RESULTS: GROUP DIFFERENCES BETWEEN COGNITIVE
MEASURES, MOOD AND BIOMARKERS 105
8.1 Introduction 105
8.2 Cognitive tasks 105
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8.2.1 Introduction 105
8.2.2 Data Screening 105
8.2.3 Selecting covariates 107
8.3 Results 111
8.3.1 Introduction 111
8.3.2 Attention domains 111
8.3.3 Psychomotor function 113
8.3.4 Cognitive Drug Research task subsets 113
8.3.5 Summary for attention and psychomotor function 115
8.4 Memory tasks 115
8.4.1 Introduction 115
8.4.2 Results 115
8.4.3 Summary for memory function 116
8.5 Executive function domains 117
8.5.1 Introduction 117
8.5.2 Results 117
8.5.3 Summary for executive function 118
8.6 Mood measures 118
8.6.1 Introduction 118
8.6.2 Results 118
8.6.3 Summary of mood measures 119
8.7 Vascular variables 120
8.7.1 Introduction 120
8.7.2 Data Screening 120
8.7.3 Results 120
8.7.4 Summary 122
8.8 Oxidative stress, antioxidant and inflammatory biomarkers 122
8.8.1 Introduction 122
8.8.2 Results 123
8.9 Relationships between the vascular, oxidative stress, antioxidant and
inflammatory biomarkers 124
8.9.1 Introduction 124
8.9.2 Oxidative stress measures and vascular, antioxidant and
inflammatory markers 124
8.9.3 Antioxidant and vascular measures 125
8.9.4 Antioxidant and inflammatory measures 125
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8.9.5 Inflammatory and vascular measures 125
8.9.6 Summary 128
CHAPTER 9 RESULTS: RELATIONSHIPS BETWEEN COGNITIVE MEASURES
AND BIOMARKERS 129
9.1 Introduction 129
9.2 Examination of the relationships between cognitive and vascular
measures 129
9.2.1 Introduction 129
9.3 Relationships between common carotid and middle cerebral arterial blood
flow and cognitive function 130
9.3.1 Global cognition 130
9.3.2 Attention 130
9.3.3 Memory 134
9.3.4 Executive function 135
9.3.5 Summary 137
9.4 Relationships between arterial stiffness and cognitive performance 137
9.4.1 Introduction 137
9.4.2 Global cognition 137
9.4.3 Attention 137
9.4.4 Memory 139
9.4.5 Executive function 139
9.4.6 Summary 141
9.5 Relationships between cognitive performance and oxidative stress,
antioxidant and inflammatory markers 142
9.5.1 Introduction 142
9.5.1.1 Global cognition in heart failure 142
9.5.2 Relationship between oxidative stress and cognitive
function 144
9.5.2.1 Attention 144
9.5.2.2 Memory 144
9.5.2.3 Executive function 144
9.5.2.4 Summary 144
9.5.3 Relationship between antioxidants and cognitive
function 144
9.5.3.1 Attention 144
9.5.3.2 Memory 146
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9.5.3.3 Executive function 146
9.5.3.4 Summary 147
9.5.4 Relationship between inflammation and cognitive
function 147
9.5.4.1 Attention 147
9.5.4.2 Memory 148
9.5.4.3 Executive function 148
9.5.4.4 Summary 148
9.6 Multiple regression analysis examining the effect of vascular, oxidative
stress and inflammatory predictors on cognitive function 148
9.6.1 Introduction 148
9.6.2 Attention tasks 149
9.6.2.1 Hierarchical multiple regression analysis
examining vascular and antioxidants
predictors on congruent Stroop
performance 149
9.6.2.2 Hierarchical multiple regression analysis
examining the effect of vascular predictors
on Power of Attention 152
9.6.2.3 Summary 153
9.6.3 Executive function 154
9.6.3.1 Hierarchical multiple regression analysis
examining the effect of vascular and
antioxidant predictors on incongruent
Stroop 154
9.6.3.2 Summary 157
CHAPTER 10 RESULTS: RELATIONSHIPS BETWEEN MOOD AND
BIOMARKERS 158
10.1 Introduction 158
10.2 Relationships between mood and vascular function 158
10.2.1 Introduction 158
10.2.2 Results 159
10.2.3 Summary 159
10.3 Relationships between mood and oxidative stress 160
10.3.1 Introduction 160
10.3.2 Results 160
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10.3.3 Summary 161
10.4 Relationships between mood and antioxidants 161
10.4.1 Introduction 161
10.4.2 Results 161
10.4.3 Summary 162
10.5 Relationships between mood, inflammation and dietary omega-3
intake 162
10.5.1 Introduction 162
10.5.2 Results 162
10.5.3 Summary 162
CHAPTER 11 DISCUSSION 163
11.1 Introduction 163
11.2 Summary of the main findings 163
11.3 Demographics and clinical characteristics 170
11.4 Summary of cognitive measures 170
11.4.1 Global cognition and screening for dementia 170
11.4.2 Attention and psychomotor speed 171
11.4.3 Quality of Episodic Memory, Quality of Working Memory
and Speed of Memory 173
11.4.4 Executive Function 175
11.5 Summary of vascular measures 176
11.5.1 Cerebral blood flow 176
11.5.2 Arterial stiffness 177
11.5.3 Vasoconstriction: endothelin-1 177
11.6 Summary of oxidative stress measures 178
11.7 Summary of antioxidant measures 178
11.8 Summary of inflammation and dietary omega-3 fatty acid 179
11.9 Summary of the relationships between vascular, oxidative stress and
inflammatory biomarkers 180
11.9.1 Vascular measures and oxidative stress, inflammation and
antioxidant biomarkers 180
11.9.2 Oxidative stress, inflammation and antioxidants 181
11.10 Relationship between cerebral blood flow and cognitive function 182
11.10.1 Global cognition 182
11.10.2 Attention 183
11.10.3 Memory 184
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11.10.4 Executive function 185
11.11 Relationship between arterial stiffness and cognitive function 185
11.11.1 Global cognition 185
11.11.2 Attention and psychomotor function 186
11.11.3 Memory 186
11.11.4 Executive function 187
11.12 Relationship between oxidative stress and cognitive function 188
11.12.1 Global cognition 188
11.12.2 Attention 188
11.12.3 Memory 189
11.12.4 Executive function 190
11.12.5 Summary 190
11.13 Relationship between antioxidant measures and cognitive function 191
11.13.1 Global cognition 191
11.13.2 Attention 191
11.13.3 Memory 191
11.13.4 Executive function 192
11.13.5 Summary 192
11.14 Relationship between inflammatory measures, dietary omega-3
and cognitive function 192
11.14.1 Global cognition 192
11.14.2 Attention 193
11.14.3 Memory 193
11.14.4 Executive function 194
11.15 Relationship between vascular, oxidative stress, antioxidant and
inflammatory measures and cognitive function 194
11.16 Summary of the relationships between cognitive function and
physiological measures 197
11.17 Mood measures 198
11.18 Relationship between vascular measures, oxidative stress,
antioxidant and inflammatory measures on depression and
anxiety 198
11.18.1 The relationship between cerebral blood flow and
arterial stiffness with depression and anxiety 198
11.18.2 The relationship between oxidative stress and
antioxidant measures with depression and anxiety 199
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11.18.3 The relationship between inflammatory measures
and depression and anxiety 201
11.19 Possible mechanisms for changes in mood in heart failure 202
CHAPTER 12 STUDY LIMITATION, STRENTHS AND FUTURE
DIRECTIONS 203
12.1 Study Limitations 203
12.1.1 Patient recruitment and sample size 203
12.1.2 Methodological issues 204
12.1.3 Biological markers 205
12.2 Study strengths 206
12.3 Directions for future research 208
REFERENCES 212
LIST OF APPENDICES 234
List of figures
xv
List of Figures
Figure 1 Generation of reactive oxygen species 52
Figure 2 Antioxidant defence mechanism 54
Figure 3 Formula for calculating mean blood flow velocity. MV = mean velocity;
PV= peak systolic blood flow velocity; EDV = end diastolic blood flow
velocity 89
Figure 4 Frontal view of the ultrasound probe (Transcranial Doppler; TCD
transducer) placed at the zygomatic arch/temporal window, directed
toward the middle cerebral artery 90
Figure 5 Formula for calculating central pulse pressure defined by subtracting the
central diastolic blood pressure (CDBP) from the central systolic blood
pressure (CSBP) 91
Figure 6 Augmentation index (AIx), defined as a percentage of augmentation
pressure (AP) and pulse pressure (PP) 91
Figure 7 A central aortic pressure waveform 91
Figure 8 Scatter plots of Power of Attention and common carotid arterial blood
flow velocity in the HF and control groups 132
Figure 9 Scatter plots of common carotid blood flow velocity and incongruent
Stroop in the HF and control groups 135
Figure 10 Scatter plots of coenzyme Q10 and congruent Stroop in the HF and
control groups 145
Figure 11 Scatter plots of coenzyme Q10 and incongruent Stroop in the HF and
control groups 146
List of tables
xvi
List of Tables
Table 1 New York Heart Association and American College of
Cardiology/American Heart Association (ACC/AHA) classifications for
heart failure 5
Table 2 Description of subsets from the Cognitive Drug Research (CDR) test
battery and the order in which the tasks were presented 75
Table 3 Composite scores for the five cognitive domains 78
Table 4 Summary of neuropsychological tests 81
Table 5 Summary of vascular, oxidative stress, antioxidant and inflammatory
measures 92
Table 6 Testing protocol timeline 95
Table 7 Demographic characteristics (age, gender and education), dementia
screening, general health questionnaire-12 item, premorbid IQ and
vitals for HF and control participants. 100
Table 8 Clinical characteristics for HF group - NYHA class, aetiology, common
comorbidities. 101
Table 9 Group differences for SF-36 subscales and Chalder fatigue scale 103
Table 10 Transformations selected for analysis for non-normally disturbed
cognitive variables 106
Table 11 Pearson’s correlation coefficients between neuropsychological and
mood variables for the HF Group 109
Table 12 Pearson’s correlation coefficients between neuropsychological and
mood variables for the control group 110
Table 13 Means and standard deviations of attention, memory and executive
function tasks for HF and controls after adjusting for premorbid IQ and
mood covariates 112
List of tables
xvii
Table 14 Group differences for Cognitive Drug Research subtests for HF and
control group 114
Table 15 Analysis of variance, means and standard deviations for each
experimental group for Profile of Mood States subscales and state trait
anxiety inventory 119
Table 16 Means and standard deviations of the vascular measures for HF and
control groups 121
Table 17 Pearson’s correlations between endothelin-1, high-sensitive C-reactive
protein and each of the each of the arterial stiffness measures
(augmentation index, central pulse pressure) in the heart failure group. 122
Table 18 Means and standard deviations of the oxidative stress, antioxidant and
inflammatory measures for HF and control groups 123
Table 19 Correlations between oxidative stress, antioxidant, inflammatory and
vascular measures for the HF group 126
Table 20 Correlations between oxidative stress, antioxidant, inflammatory and
vascular measures for the control group 127
Table 21 Correlation matrix for neuropsychological domains and blood flow
velocities, arterial stiffness and vascular function 131
Table 22 The effect of group on congruent Stroop reaction time after adjusting
for premorbid IQ and common carotid blood flow velocity 133
Table 23 The effect of group on Power of Attention domain after adjusting for
premorbid IQ and common carotid blood flow velocity 134
Table 24 The effect of group on incongruent Stroop reaction time adjusting for
premorbid IQ and common carotid blood flow velocity 136
Table 25 The effect of group on congruent Stroop reaction time after adjusting
for premorbid IQ and central pulse pressure 138
Table 26 The effect of group on Power of Attention domain after adjusting for
premorbid IQ and central pulse pressure 139
List of tables
xviii
Table 27 The effect of group on Trail Making-B after adjusting for premorbid IQ
and central pulse pressure 140
Table 28 The effect of group on incongruent Stroop reaction time after adjusting
for premorbid IQ and central pulse pressure 141
Table 29 Correlation coefficients between oxidative stress, antioxidant and
inflammatory markers with cognitive measures in each experimental
group 143
Table 30 The effect of group on congruent Stroop after adjusting for premorbid
IQ and CoQ10 145
Table 31 The effect of group on incongruent Stroop after adjusting for premorbid
IQ and CoQ10 147
Table 32 Hierarchical multiple regression analysis summary predicting congruent
Stroop reaction time from common carotid blood flow velocity and
central pulse pressure after controlling for premorbid IQ in the HF
group 150
Table 33 Hierarchical multiple regression analysis summary predicting congruent
Stroop reaction time from common carotid blood flow velocity and
CoQ10 after controlling for premorbid IQ in the HF group 151
Table 34 Hierarchical multiple regression analysis summary predicting Power of
Attention from common carotid blood flow velocity and central pulse
pressure after controlling for premorbid IQ in the HF group 153
Table 35 Hierarchical multiple regression analysis summary predicting
incongruent Stroop from common carotid blood flow velocity and
central pulse pressure after controlling for premorbid IQ in the HF
group 155
Table 36 Hierarchical multiple regression analysis summary predicting
incongruent Stroop from common carotid blood flow velocity and
CoQ10 after controlling for premorbid IQ in the HF group 156
Table 37 Correlation matrix for mood measures and blood flow velocities,
arterial stiffness and vascular function 159
List of tables
xix
Table 38 Correlation matrix for mood measures and oxidative stress, antioxidant
and inflammatory biomarkers 161
Table 39 Summary of the cognitive performance in the heart failure group
compared with healthy controls 164
Table 40 Summary of the biomarker values in the heart failure group compared
with healthy controls 165
Table 41 Summary of the relationships between cognitive and mood measures
with biomarkers in the heart failure group. 167
Abbreviations
xx
List of Abbreviations
ACE Angiotensin converting enzyme
AD Alzheimer’s disease
AIx Augmentation index
AMT Abbreviated Mental Test
ANCOVA Analysis of covariance
BDI Beck depression inventory
BP Blood pressure
CABG Coronary artery bypass grafting
CAMCOG Cambridge cognitive capacity scale
CARR Carratelli Units
CCA-BFV Common carotid arterial blood flow velocity
CDR Cognitive Drug Research
CFS Chalder fatigue scale
CHD Coronary heart disease
CI Cognitive impairment
CoQ10 Coenzyme Q10
CPP Central pulse pressure
CVLT Californian Verbal Learning Test
DHA Docosahexaenoic acid
DROM Determinable reactive oxygen metabolites
EDS Excessive daytime sleepiness
EPA Eicosapentaenoic acid
ET-1 Endothelin-1
Abbreviations
xxi
GDS Geriatric Depression Scale
GHQ General Health questionnaire
GPx Glutathione peroxidase
HADS Hospital Anxiety and Depression Scale
HF Heart failure
hs-CRP High-sensitive C-reactive protein
IL Interleukin
IQ Intelligence quotient
LVEF Left ventricular ejection fraction
MCA Middle cerebral artery
MDA Malondialdehyde
MDD Major depressive disorder
MMSE Mini Mental State Examination
MLWHF Minnesota Living With Heart Failure
MoCA Montreal Cognitive Assessment battery
NYHA New York Heart Association
PICF Participant Information and Consent Form
POMS Profile of Mood States questionnaire
PUFA Polyunsaturated fatty acid
PWA Pulse wave analysis
PWV Pulse wave velocity
QOL Quality of life
RAVLT Rey’s Auditory Verbal Learning Test
RBANS Repeatable Battery for the Assessment of Neuropsychological States
Abbreviations
xxii
ROM Reactive oxygen metabolite
SF-36 Short Form questionnaire - 36 item
SOD Superoxide dismutase
SPECT Single-photon emission computed tomography
STAI Speilberger State-Trait Anxiety Inventory
TCD Transcranial Doppler
TMT Trail making task
TNF-α Tumour necrosis factor alpha
WASI Wechsler Abbreviated Scale of Intelligence
WASI Vocabulary Wechsler Abbreviated Scale of Intelligence Vocabulary subset
Chapter 1: Overview
1
CHAPTER 1 OVERVIEW
This thesis begins with an overview of heart failure (HF) in elderly individuals in Chapter 2.
Chapter 2 provides a definition of HF, an outline of the classifications for HF and the
pathophysiology, aetiology, prevalence and incidence of this condition. Furthermore, the
prevalence and incidence of cognitive impairment in HF are outlined and a discussion of the
negative impact of cognitive impairments on elderly patient’s quality of life, self-care
abilities and treatment compliance is provided. This leads onto Chapter 3, which discusses
the literature pertaining to cognitive function in elderly HF patients’ global cognitive function
and specific cognitive domains including attention, episodic memory, working memory,
visuospatial abilities, psychomotor function and executive function. Included are previous
studies that have evaluated cognitive functions among HF patients in comparison to other
groups such as healthy controls and patients with diseases other than HF such as
cardiovascular disease. Chapter 3 also outlines the few studies that have explored possible
treatments for improving cognitive function in HF patients. Chapter 4 discusses possible
physiological mechanisms associated with cognitive impairments in elderly HF patients.
Specifically, known vascular mechanisms including cerebral blood flow using Transcranial
Doppler measurement and brain imaging studies are outlined. Chapter 4 also discusses
possible biological mechanisms associated with cognitive impairment including arterial
stiffness, oxidative stress, antioxidant and inflammatory markers. Here linkages are made
between studies that have investigated imbalances in these biological markers in conditions
associated with cognitive impairments and in HF. The rationale and aims of this thesis and
hypotheses and research questions asked are presented in Chapter 5. The selection of
participants, power analysis and sample size calculation, materials, procedures, and the
experimental design utilised in this thesis are outlined in Chapter 6. Demographic and quality
of life results between experimental groups are presented in Chapter 7. Results from the
statistical analyses for group differences between cognitive, mood and biomarker variables
are presented in Chapter 8. Results from the statistical analyses examining the relationships
between cognitive measures and biomarkers are presented in Chapter 9 and mood measures
and biomarkers are presented in chapter 10. The final chapters provide a discussion of the
results (Chapter 11), limitations and strengths of the thesis, directions for future research and
concluding comments (Chapter 12).
Chapter 2: Background and overview of heart failure
2
CHAPTER 2 BACKGROUND AND OVERVIEW OF HEART FAILURE
2.1 Chapter overview
Heart Failure (HF) is one of the leading causes of hospitalization and mortality in the
Western world especially in the elderly population (Schwarz, 2007; Tendera, 2004; Vogels,
Scheltens, Schroeder-Tanka, & Weinstein, 2007). HF is a common condition principally seen
in the elderly. This chapter will provide a brief overview of the prevalence, incidence and
economic burden of HF with a focus on a definition of the disease, the pathophysiology and
treatments commonly used to improve symptoms and treat the condition. Cognitive
impairment is commonly seen in older HF patients and the negative consequences of poor
cognitive function on factors such as hospital readmission rates, mortality rates and
compliance with treatment regimens will be discussed.
Despite effective treatments for improving HF symptoms, there are no effective treatments
for cognitive impairments in these patients. Life expectancy is increasing, with 10 per cent of
the population in the year 2000 aged 60 years or older and this percentage is expected to
double, reaching 21 per cent by the year 2050 (World Health Organisation [WHO], 2003).
With improvements in living conditions and an aging population, together with
improvements in medical treatments and diagnosis for HF, the number of older individuals
diagnosed with conditions such as HF is expected to increase (Thomas & Rich, 2007).
The ageing process not only involves changes in the cardiovascular system but also other
systems and organs (Rengo et al., 1996). Patients often seek medical attention for other
conditions and with further examination heart failure (HF) is detected. In a large study
(n=122,630) examining chronic HF patients aged 65 years and over, it was revealed that 40%
of these patients had five or more non-cardiac comorbidities. Elderly patients are therefore
more likely to be suffering from co-morbidities that may imitate or mask HF making
diagnosis difficult. Elderly patients are most likely to suffer from hypertension (55%),
diabetes mellitus (31%), coronary obstructive pulmonary disease, bronchiectasis (26%) and
ocular disorders (24%). Other common comorbidities seen in elderly HF inpatients (65-98
years) include chest disease (30%), incontinence (29%), cerebrovascular disease (26%) and
musculoskeletal problems (41%; Lien, Gillespie, Struthers, & McMurdo, 2002). Other less
common comorbidities include hypercholesterolemia (21%), atherosclerosis (16%),
osteoarthritis (16%), other chronic respiratory disorders (14%), thyroid conditions (14%),
asthma (5%) and osteoporosis (5%; Braunstein et al., 2003). Furthermore, mental changes
Chapter 2: Background and overview of heart failure
3
including cognitive impairment (e.g. Alzheimer’s disease), depression and affective disorders
(8%) also exist with HF (Braunstein et al., 2003).
Of these comorbidities, mental disorders such as cognitive impairment are of particular
interest in the current investigation as cognitive function in HF patients is often overlooked.
Cognitive abilities are important in order for patients to effectively plan and remember to take
their medications and to understand and comprehend treatment regimens (Wolfe, Worrall-
Carter, Foister, Keks, & Howe, 2006). Since HF is predominantly seen in the elderly and
cognitive function is known to decline with age (Riddle & Schindler, 2007) assessment of
cognitive abilities in these patients is an important area of research. Despite effective
diagnostic techniques and treatments for HF, cognitive impairments in these patients are
often unnoticed. This chapter will discuss evidence suggesting that cognitive impairments in
HF influence prognosis, self-care and treatment compliance.
The following chapters will review the literature on specific cognitive domains impaired in
elderly HF patients and possible mechanisms known to be related to these impairments. The
outcome of this review will lead on to the current investigation which will expand on the
current literature by examining possible mechanisms for cognitive decline using instruments
which can be easily administered in a clinical setting.
2.2 Definition of heart failure
Numerous definitions are used to describe heart failure (HF) with various classifications to
categorise the severity of the disease. Heart failure, which has previously been referred to as
“congestive HF”, is commonly defined as a complex clinical syndrome resulting in a
structural or functional cardiac disorder that impairs the ability of the left ventricle to fill with
or eject blood (Tendera, 2004). According to the European Society of Cardiology (ESC)
guidelines, HF is defined as:
“….a syndrome in which the patient should have the following features: symptoms of
HF, typically shortness of breath at rest or during exertion, and/or fatigue; signs of
fluid retention such as pulmonary congestion or ankle swelling; and objective evidence
of an abnormality of the structure of function of the heart at rest.” (Dickstein et al.,
2008).
and
Chapter 2: Background and overview of heart failure
4
“HF is a complex clinical syndrome that can result in any structural or
functional cardiac disorder that impairs the ability of the ventricle to fill with
or eject blood.” (Hunt et al., 2009)
As a result of a failing heart, cardiac output (amount of blood being pumped out of the heart)
is impaired, and the amount of blood reaching organs is insufficient to meet the body’s
metabolic requirements. Due to a poor blood supply, other organs weaken and become
compromised leading to presenting symptoms.
2.3 Heart failure diagnosis, classifications, signs and symptoms
Heart Failure (HF) classifications are used to describe the severity of the disease, which assist
with administering the appropriate treatment protocol. The New York Heart Association
(NYHA) criterion is a commonly used diagnostic tool to classify the disease into four
categories based on the severity of symptoms (Dickstein et al., 2008). Classifications range
from no HF (NYHA class I) to severe HF (NYHA class IV). Patients diagnosed with NYHA
class I are asymptomatic and have no physical activity limitations. Patients classified with
mild HF (or NYHA class II) have slight limitation of physical activity causing fatigue,
palpitations or dyspnoea. Patients with moderate HF severity (NYHA class III) have
noticeable physical limitations causing fatigue, palpitations or dyspnoea. Finally, patients
with severe HF (NYHA class IV) suffer breathlessness even at rest, are unable to carry on
with physical activity and may be hospitalised (Dickstein et al., 2008: Table 1).
Another classification for HF has been proposed by the American College of
Cardiology/American Heart Association (ACC/AHA) where stages of HF are determined by
structural changes and damage to the heart muscle (Hunt et al., 2009). The ACC/AHA
classification ranges across four phases from Stage A (high risk) to Stage D (an advanced
stage). Patients at an early stage or Stage A have a high risk for HF such as hypertension,
diabetes, obesity, metabolic syndrome but do not have any clinical symptoms of HF. Patients
who have some structural heart disease associated with the development of HF (e.g. previous
myocardial infarction, low EF) but do not have any signs or symptoms associated with HF
are classified as Stage B. The next level of disease severity is Stage C where patients are
symptomatic and HF is related to underlying structural heart disease. The final stage is where
advanced structural heart disease and symptoms of HF (e.g. symptoms at rest) is present,
requiring hospitalisation and intervention (Stage D; Hunt et al., 2009: Table 1). It is thought
that inconsistencies in research findings examining factors such as cognitive function in HF
patients between studies are due to various definitions of the disease and physicians using
Chapter 2: Background and overview of heart failure
5
different classifications to categorise the disease. The NYHA criterion is the classification for
HF predominantly used by Australian cardiologists.
Table 1 New York Heart Association and American College of Cardiology/American Heart
Association (ACC/AHA) classifications for heart failure (Hunt et al., 2009).
New York Heart Association
classification
American College of Cardiology/American
Heart Association (ACC/AHA)
Class Description Stage Description
NYHA I asymptomatic, no physical
activity limitations and
symptoms are well
controlled
Stage A having a high risk for HF without
any clinical symptoms of HF
NYHA II slight limitation of physical
activity causing fatigue,
palpitations or dyspnoea
Stage B some structural heart disease
associated with the development
of HF (e.g. previous myocardial
infarction, low EF) but no signs
or symptoms
NYHA III noticeable physical
limitations causing fatigue,
palpitations or dyspnoea
Stage C symptomatic HF related to
underlying structural heart
disease
NYHA IV breathlessness upon rest, an
inability to carry on with
physical activity and
possible hospitalisation
Stage D advanced structural heart disease
and symptoms of HF (e.g.
symptoms at rest), requiring
hospitalisation and intervention
HF classifications are objective measures based on presenting signs and symptoms and
although they provide a sound assessment of the disease severity and required treatments, a
complete evaluation using a combination of laboratory assessments and non-invasive
neuroimaging tests (e.g. echocardiography) is required for a specific diagnosis.
Heart failure (HF) can be described as systolic or diastolic, acute or chronic, compensative
and low output or high output. During HF ventricular remodelling (dilation) occurs to
compensate for the reduction in blood volume ejected from the heart. In diastolic HF, the left
Chapter 2: Background and overview of heart failure
6
ventricular wall becomes stiffer blood and filling is impaired. Conversely, during systolic HF,
ejection is reduced. A measurement used to differentiate systolic from diastolic HF is ejection
fraction or the stroke volume divided by the end-diastolic volume for that heart chamber.
Patients with diastolic HF for example, may present with signs and symptoms of HF although
the echocardiograph will indicate normal left ventricular ejection fraction (LVEF) at rest
(Dickstein et al., 2008; Swedberg et al., 2005). Acute HF refers to a new or sudden onset of
the condition, or new symptoms or signs with or without previously known cardiac
dysfunction. Acute HF can also refer to sudden deterioration or worsening of stable/chronic
HF, oftentimes requiring hospitalisation and urgent treatment (Nieminen et al., 2005). In the
patient with chronic HF, the condition can be compensated where it is persistent and stable,
or decompensated where there is deterioration and an increase in symptoms such as
breathlessness (Dickstein et al., 2008; Millane, Jackson, Gibbs, & Lip, 2000).
2.4 Heart failure pathophysiology
The pathophysiology of heart failure (HF) is complex and has been explained by models that
attempt to explain the mechanisms behind the poor functional ability of the heart. HF is a
progressive disorder, which over time involves destruction of heart myocytes and a resultant
reduction in the heart’s functional capacity to contract normally (Mann, 2011). As a result of
the reduced ability of the heart to pump, cardiac output is reduced and an insufficient supply
of blood is transported to tissues and organs. Because of poor blood supply, the tissues
become deficient in essential oxygen and nutrients and over time, bodily organs fail to work
properly. With diminished cardiac output, the brain in turn receives less energy, oxygen and
nutrients required for adequate functioning, possibly resulting in pathologies such as poor
cognitive function. Early stages of HF may remain asymptomatic and it is not until a chronic
lack of heart function, resulting in a reduced supply of oxygen and nutrients, that clinical
symptoms start to occur. Lack of cardiac output also causes organ dysfunction and patients
may experience symptoms that warrant them seeking medical attention and even
hospitalisation in severe cases.
2.5 Heart failure treatments
Given that HF is a multifaceted condition affecting various systems in the body it is no
surprise that the treatment protocol for HF is complex. Treatment of HF requires a team of
medical professionals and comprehensive treatment plans to improve symptoms. Tailored
individual treatment involves a combination of medication, dietary interventions (such as
fluid restriction, low sodium diet, diet consisting of adequate nutrients and limiting alcohol),
Chapter 2: Background and overview of heart failure
7
monitored physical activity, risk factor monitoring, patient education and symptom
management (Dickstein et al., 2008; Krum & Abraham, 2009). HF medication is vital in
order to treat this complex syndrome and in one report almost 90% of HF patients who were
admitted to hospital had been taking four or more medications (Lien et al., 2002).
Medications commonly prescribed include: i) angiotensin converting enzyme (ACE)
inhibitors which enhance survival in heart failure (HF), reduce the rate of hospitalisation,
improve neurohormonal levels and reduce the incidence of recurrent angina and myocardial
infarction in coronary artery disease; ii) diuretics, in particular loop diuretics that promote
dieresis and have been found to improve ventricular function and symptoms associated with
fluid overload in left ventricular systolic dysfunction and both decrease dyspnoea and
increase exercise tolerance (Swedberg et al., 2005); iii) digoxin (digitalis) a weak inotrope
that increases myocardial contractility, improves left ventricular function and renal blood
flow; and iv) beta-blockers (e.g. bisoprolol and succinate) which inhibit the sympathetic
activity of the nervous system and block adrenergic receptors thus reducing heart rate, blood
pressure and directly affect the myocardium (Jessup & Brozena, 2003). Additionally, beta-
blockers reduce hospitalisation, improve functional class and minimise worsening of HF
(Swedberg et al., 2005). Anticoagulant medicines including aspirin and warfarin are used to
prevent the risk of developing thromboembolism.
2.6 Economic costs of heart failure
As a large proportion of the elderly population are diagnosed with HF, the health care costs
of this disease pose a great burden on the economy. Requiring ongoing costs such as regular
outpatient visits, care and education from nurses and hospitalisations, HF is the most
expensive cardiovascular disorder in the USA (Thomas & Rich, 2007). Although the exact
health care costs associated with HF are unknown it is estimated that in the western world the
cost is between 1% and 2% of annual national healthcare expenditure (Liao, Allen, &
Whellan, 2008). Estimated annual health care costs associated with HF are as high as 1
billion dollars in Australia (Clark, McLennan, Dawson, Wilkinson, & Stewart, 2004) and
37.2 billion dollars in the USA (Lloyd-Jones et al., 2009). The bulk of costs of HF are related
to hospitalizations including inpatient, outpatient and emergency departments. In Australia it
is estimated that two thirds of the total expenditure for HF is attributed to hospitalisations
(Krum et al., 2006), and just over half ($20 billion) of the total HF costs in the USA (Lloyd-
Jones et al., 2009). Furthermore, in Australia, $4.5 billion (12%) of the total costs are related
to nursing home expenses and $2.4 billion (6.4%) to physicians and other professionals.
Chapter 2: Background and overview of heart failure
8
Approximately 80% of heart failure (HF) related hospitalisations in Australia involve elderly
patients aged 65 years and over (Clark et al., 2004). Not only is there a greater number of
elderly HF patients requiring hospital care compared to younger patients, the cost per
hospitalisation is greater for elderly patients. Demonstrating this, a large retrospective study
showed that in American hospitals the average cost per hospitalisation for older HF patients
(≥ 60 years; mean age = 72.7 years) was $18,086 compared to the overall average of $15,293
per patient (Titler et al., 2008). The additional costs required by elderly patients were related
to increased costs of medical procedures, medications and extra nursing interventions. The
authors suggest that these costs may be underestimated since some patients have HF as a
secondary not a primary diagnosis and other comorbidities may therefore further increase the
expenses (Wang, Zhang, Ayala, Wall, & Fang, 2010).
2.7 Prevalence and incidence of heart failure
The prevalence of HF in developed and developing countries is increasing with increases in
the number of older people in society (Abhayaratna, Smith, Becker, Marwick, Jeffery, &
McGill, 2006; Dickstein et al., 2008; Ho, Pinsky, Kannel, & Levy, 1993; Krum & Abraham,
2009; Tendera, 2004). The overall prevalence in Australia, United States of America and the
European countries is estimated to be between 2% to 3% (Abhayaratna et al., 2006; Dickstein
et al., 2008; Ho et al., 1993). In the year 2000 an estimated 565,000 Australians had HF of
whom 325,000 had symptomatic and 240,000 had asymptomatic HF (Clark et al., 2004). At
least 300,000 Australians over the age of 45 years are estimated to have chronic HF and
approximately 30,000 new cases are reported each year (Australian Institute of Health and
Welfare [AIHW], 2003). Studies in Western populations have revealed that there is a 4.4 fold
increase in people suffering the condition aged between 60 and 64 to the 80-86 age group
(Abhayaratna et al., 2006). In particular, the Framingham study, which examined the long-
term trend of HF from the years 1950 to 1999, showed that the prevalence of HF rises with
each decade (Bleumink et al., 2004). The occurrence of HF was 0.9% in individuals aged
between 55 and 64 years, 4% in those aged between 65 and 74 years, 9.7% aged between 75
and 84 years, and 17.4% aged 85 years and over (Bleumink et al., 2004).
The incidence of heart failure (HF) has been shown to increase with age in both men and
women. The rising incidence of HF with age was shown in a large study by Cowie et al.
(1999) that included a sample of 150,582 members from a London district who visited a
general practice. In their study, Cowie et al. (1999) found the incidence of new HF is
0.02/1000 in those aged 25-34 years and 11.6/1000 in those aged 85 years and over.
Examining the elderly population annually 10.6/1,000 persons aged 65-69 years and
Chapter 2: Background and overview of heart failure
9
42.5/1,000 for those aged ≥ 80 years are diagnosed with the condition (Gottdiener et al.,
2000). Furthermore, the overall incidence of HF is approximately two times higher in men
than women (Bleumink et al., 2004; Gottdiener et al., 2000). Ho et al. (1993) demonstrated in
an older sample that the annual incidence of HF increased from 3/1000 men and 2/1000
women aged 50-59 years to 27/1000 men and 22/1000 women aged 80-89 years of age (Ho et
al., 1993).
It is difficult to establish an accurate account of the incidence and prevalence of HF and it is
possibly underestimated with many undiagnosed individuals living in society (Abhayaratna et
al., 2006). Furthermore, inconsistent diagnostic criteria are used among researchers and
practitioners, different age groups and cohorts are examined, and different research designs
are implemented (e.g. population based and patients visiting a general practitioner; Krum &
Abraham, 2009). Although currently there is limited data on the incidence and prevalence of
HF, the incidence is expected to increase in the future. A projected future increase in the
incidence and prevalence of HF is due to multiple factors including expected increases in the
aging population, survival rates due to improvements in medical interventions of those with
coronary artery disease and acute cardiac disease such as myocardial infarctions and
improvements in diagnostic techniques (Krum et al., 2006; McMurray & Stewart, 2000;
Thomas & Rich, 2007).
2.8 Prevalence and incidence of cognitive impairment in heart failure
Studies examining global and specific cognitive abilities in HF have demonstrated that
impairments exist in this patient group, although few researchers have assessed the
prevalence and incidence of cognitive impairments. In an early study Cacciatore and
collegues (1998) examined the relationship between global cognitive impairment as measured
by an Italian version of the Mini Mental State Examination (MMSE; score of < 24 out of 30)
in a large population study in southern Italy. In this trial older individuals (73.9±6.2 years)
randomly selected from the electoral roles, were contacted at their home or institution and
interviewed. Of the 1075 individauls included in the trial the prevalence of HF based on the
NYHA guidelines was 8.2% (n=88), with hypertension as the most common eitiology (69%).
MMSE scores of < 24 were more prevalent in HF patients compared to the non-HF sample
(20.2% vs 4.6%). This study showed that HF patients have 1.96 times the risk of cognitive
impairments compared to individuals without HF with 56.8% of those with HF having
cognitive impairments compared to 20% of those without HF (Cacciatore et al., 1998).
Moreover, studies have revealed a prevalence of cognitive impairments (MMSE < 24) in
elderly HF patients to be as low as 13% (Di Carlo et al., 2000) to just over half (53%) in
Chapter 2: Background and overview of heart failure
10
patients with NYHA II and III (Zuccalà et al., 1997). However, no age matched control
groups were included in these studies. Furthermore, since the researchers employed a cut off
score for dementia only, they failed to detect and include patients with mild cognitive
impairments (MCI). When Debette and colleagues (2007) increased the Mini Mental State
Examination (MMSE) cut off score to include probable mild cognitive impairment, the
proportion of patients initially considered to be cognitively impaired increased from 31%
(MMSE < 24) to 61% (MMSE ≤ 28), when education levels were higher than eight years and
≤ 26 otherwise (Debette et al., 2007). Debette et al. (2007) examined adult patients (17-98
years), however, they failed to compare cognitive scores between various age groups to
explore whether MMSE scores change across the age groups. However, it demonstrates that a
simple screening tool for dementia may not be appropriate and tools that are more rigorous
are required to detect dementia in these patients. Since cognitive function changes over the
lifespan, reporting group differences between young, middle aged and older participants in
the Debette et al. (2007) study would have provided useful information regarding whether
evident cognitive impairments (MMSE < 24) and mild cognitive impairments are different
across age groups. Debette et al. (2007) did however use regression modelling to see if
MMSE scores of < 24 but not mild cognitive impairments are associated with age. However,
their study demonstrates that due to discrepancies in cut off scores, for cognitive impairment
across studies, it is difficult to establish an accurate prevalence for cognitive impairments in
HF. One study by Incalzi et al. (2003) for example revealed that MMSE had poor sensitivity
and specificity compared to verbal memory abilities suggesting that screening tests may not
be the best tools to detect declines in cognition (Incalzi et al., 2003). Although many studies
have assessed global cognitive function, these tests do not adequately detect cognitive
domains impaired in HF patients.
An assessment of global cognitive measures and dementia screening tools provide an
overview of cognitive abilities although do not differentiate between specific cognitive
domains. Riegel et al. (2002) demonstrated in a small sample (n=42) that 2.4% of HF patients
(NYHA class I, II, III and IV) had global cognitive impairments (MMSE < 24). Although
when using the standardized T scores of cognitive screening tests (draw a clock, commands
subset, MMSE and complex ideational material subtest), 28.6% of HF patients were
considered to have cognitive impairments (Riegel et al., 2002). This study demonstrates that
utilizing a global assessment tool as a single measure of cognitive impairment may
underestimate the prevalence of impairments and/or fail to detect impairments in research or
clinical settings. This study may have underestimated the number of patients with cognitive
impairments as they included asymptomatic or NYHA class I patients who may not present
with factors contributing to CI seen in HF. In a large Italian study, Trojano et al. (2003)
Chapter 2: Background and overview of heart failure
11
established that the prevalence of CI, as reflected by abnormal scores on 2 or more cognitive
tasks, was higher in patients with severe HF (NYHA class III or IV) followed by patients
with mild HF (NYHA class II) and no HF (57.9%, 43% and 34.3%, respectively).
Furthermore, employing an overall cognitive score (comprising memory, executive function,
visuospatial, language and memory speed and attention domains), Vogels et al. (2007),
showed cognitive impairment in 25% of HF patients (NYHA class III to IV; EF < 40%), 15%
in individuals with a history of ischemic cardiac disease but no symptoms of HF, and 4% of
the healthy controls. It is clear that cognitive deficits exist in HF and that the prevalence is
greater compared to healthy controls. The impact of cognitive impairments in patients is a
research imperative as it impacts negatively on the patients as a whole.
2.9 Impact of cognitive impairment in heart failure
2.9.1 Heart failure prognosis and mortality
Over the years, the numbers of hospitalisations, hospital stay durations and mortality rates
due to heart failure (HF) have decreased. The average hospital stay duration due to HF has
decreased from 21.1 days in 1980 to 12.9 days in 1999 and the in-hospital mortality has
declined from 18.6% to 13.5% during this period (Mosterd, Reitsma, & Grobbee, 2002). This
decline in hospital stay duration and mortality is likely due to advancements in diagnostic
techniques and improvements in medications and treatments. Despite a decline in mortality
rates, the number of patients dying from HF has increased possibly due to the increasing
incidence of the condition and the increasing elderly population in whom this condition is
frequently seen. Mortality rates have been investigated in various countries and there is
variability in the mortality rate statistics possibly due to inconsistent diagnostic methods used
between studies. For a general view of mortality, compared to older individuals without heart
failure (HF), older patients with HF (≥ 57 years) have a significantly worse 1-year (74% vs
97%), 2-year (65% vs 94%), 5-year (45% vs 80%) and 7-year (32% vs 70%) survival rates
(Van Jaarsveld et al., 2006). Physiological risk factors including dementia and
cerebrovascular disease are associated with increased mortality rates 30 days and 1 year
following discharge due to acute HF (D. S. Lee et al., 2003).
Various factors have been shown to influence mortality rates and prognosis in elderly HF
patients including depression (Jiang et al., 2004; Testa et al., 2011), and cognitive
impairments (Zuccalà et al., 2003). Assessments of cognitive function in hospitalised HF
patients have provided insight to the effects of cognitive impairments on mortality rates post
discharge. Overall, 6 and 12 months post-discharge mortality rates in elderly HF patients with
global cognitive impairments are higher than in patients without cognitive impairments (6
Chapter 2: Background and overview of heart failure
12
months: 35.6% vs 19% NYHA class III or IV; 12 months: 27% vs 15%; NYHA class IV;
Rozzini, Sabatini, Trabucchi, Zuccalà, & Bernabei, 2004; Zuccalà et al., 2003). Furthermore,
HF patients with global cognitive impairments have a greater 6-month mortality rate
compared to patients without HF but with cognitive impairments (35.6% versus 31%).
Finally, McLennan, Pearson, Cameron, and Stewart (2006) showed during a 5 year follow up
that compared to HF patients who were cognitively intact using a higher cut off (MMSE
>26), those with cognitive impairments (MMSE 19–26) had a 1.4-fold increased risk of being
admitted to hospital or dying. Furthermore, cognitively impaired patients at baseline had a
significantly higher mortality rate than those who were cognitively intact (96.3% vs 68.2%;
McLennan et al., 2006).
Examinations of specific cognitive domains have shown to relate to poor prognosis and
mortality. For example, baseline performance on global cognition, working memory, delayed
memory recall (verbal learning-Hopkins verbal learning test delayed recall), psychomotor
speed (Digit symbol scale and Trail Making-A), visual spatial ability and executive function
(as measured by the Trail Making-B), predicted 12 month mortality in outpatients with
chronic systolic HF (left ventricular ejection fraction < 40%; n=145, mean age 65.2±13.4;
Pressler, Kim, Riley, Ronis, & Gradus-Pizlo, 2010). Cardiovascular measures predicting 12-
month mortality were lower left ventricular ejection fraction mean, lower systolic blood
pressure, longer duration of HF and pacemaker implantation. Although no group differences
were observed in depressive symptoms, quality of life, NYHA class and age, patients who
died had poorer functional capacity (as measured by the Duke Activity Status Index) at
baseline compared to patients who survived (Pressler, Kim, et al., 2010). Patients with severe
HF (≥ 64 years; NYHA class IV) who failed to improve in memory, attention and
concentration from 4 days before to 6 weeks after discharge, also had worse 2-year prognosis
(24.7±2.9 months; Ochiai et al., 2004).
2.9.2 Hospital readmissions
Overall, in the year 2000 there were 100,000 admissions in Australian hospitals with
conditions associated with HF, for approximately 1.4 million days in total and costing more
than 1 billion dollars (Clark et al., 2004). Of the HF patients who are hospitalised about 80%
involve patients aged 65 years or older and this older age group stays in hospital longer than
younger age groups (89% of hospital stay time; Clark et al., 2004). Aspects such as
worsening of symptoms due to medication non-compliance can lead to increased
hospitalisation in HF patients. Studies in HF have shown that sodium retention and not
complying with medication and diet were the main factors contributing to increased hospital
admissions (Bennett et al., 1998; Happ, Naylor, & Roe-Prior, 1997). It is possible that poor
Chapter 2: Background and overview of heart failure
13
cognitive abilities together with other factors such as not recognising worsening symptoms
contribute to poor self-management and in turn hospital readmissions (Pressler, 2008). A
recent study demonstrated that older HF patients with reduced attention, executive function
and language abilities were more likely to adhere poorly to medical regimes, diet and exercise
(Alosco, Spitznagel, Van Dulmen, et al., 2012). Although cognitive impairments are related
to poor treatment adherence, impairments were not associated with length of hospital stay in
elderly patients (Zuccalà et al., 2003). However, when examining specific cognitive tasks,
poor executive function, memory, and processing speed in decompensated and compensated
patients related to increased hospital readmissions (14±2 months; Kindermann et al., 2012).
Furthermore vascular (LVEF) and inflammatory factors (C-reactive protein) predicted
hospital readmissions in patients. Hospitalisation not only causes great misery to patients and
a burden on their families and carers, it also poses a great burden on the economy.
With the elderly population projected to increase and a relative increase in the number of HF
patients and hospitalisations especially in this age group, the economic burden of HF is
expected to rise (W. C. Lee, Chavez, Baker, & Luce, 2004). Preventing hospital readmissions
will provide economic relief and potentially save an exorbitant amount of money per
admission. In conclusion, strategies to reduce hospital readmissions in elderly HF patients
such as improving cognitive function needs to be considered in order to help ease the
economic burden especially in elderly HF patients.
2.9.3 Treatment compliance
As outlined previously, medical regimes for the treatment of HF are complex as HF is a
multifaceted condition, although not all patients are compliant with these treatment regimes.
Elderly patients oftentimes misinterpret or forget medical advice, have difficulties adhering to
medical regimes, dietary advice and daily monitoring of weight (e.g. Schwarz, 2007). Poor
compliance with drug treatment and diets are leading factors for contributing to acute
decompensation in older patients with chronic HF and without dementia (75.4±9.9 years;
NYHA II: 8.9%; III: 33% and IV 58.1%; Michalsen, König, & Thimme, 1998). When asked
to recall information about the treatments patients received at discharge, more elderly patients
(NYHA class 2.5±0.9; n=22; 79±6 years) remembered verbal (91%) than written (23%)
information at the one-month post discharge follow up (Cline, Björck-Linné, Israelsson,
Willenheimer, & Erhardt, 1999). Interestingly, half of the patients were unable to name their
treatments and approximately two thirds (64%) were unable to state at what time of the day
and when in relation to meals they were required to take their treatments (Cline et al., 1999).
This was a small study, with ischemic heart disease as the main aetiology, therefore a larger
study with a broader representation of HF aetiology would help substantiate these findings.
Chapter 2: Background and overview of heart failure
14
During a longer post discharge follow up of 12 weeks, a large multicentre trial by Lainščak et
al., (2007) revealed that only 46% of older patients (68±12 years) correctly recalled advice
given to them in hospital and 67% correctly followed this advice (Lainščak et al., 2007). Of
the comprehensive treatment regime provided, patients were more likely to recall dietary and
exercise advice compared to factors such as daily weighing, reducing salt intake and avoiding
non-steroidal anti-inflammatory drugs (Lainščak et al., 2007). Older patients (aged ≥ 65
years) however were less likely to comply with exercise and dietary advice compared to
younger patients (aged < 65 years). This is possibly due to older patients finding compliance
to these treatments more challenging (Evangelista et al., 2003). Given that elderly HF patients
in these studies were more likely to have ischemic HF (Lainščak et al., 2007) it is possible
that the cerebral ischemia contributed to treatment non-compliance rather than the patient’s
age. Interestingly, patients who recalled fewer items of health advice (≤ 4) were significantly
older (70±12 years) than those who correctly recalled more items (> 4; 64±11 years; Lainščak
et al., 2007). Although elderly patients tend to be more compliant with taking medicine
compared to younger patients (Evangelista et al., 2003; Lainščak et al., 2007), non-
compliance in older patients still exists and is believed to be a major contributor to worsening
of their condition and leading to increased hospital readmissions. In contrast other researchers
suggest that non-compliance is greater in older HF patients and this may be due to
comorbidities including excessive daytime sleepiness (Riegel et al., 2011) and poor cognitive
functioning which impairs understanding and recall of treatments regimes (Wolfe et al.,
2006). It is important that patients have intact cognitive abilities to understand and follow
their treatment regimes.
2.9.4 Quality of life
The negative effects of the HF syndrome on the psychology and body of patients influence
patients’ overall quality of life. Compared to asymptomatic patients (NYHA class I), those
with mild and moderate HF (NYHA class II or III) have greater levels of physical limitations,
emotional distress and poor quality of life (QOL) as a result of their disease (Gorkin et al.,
1993). Furthermore patients with mild and moderate HF (NYHA class I, II and III) scored
lower on the physical health subset of the 36 item Short Form (SF-36) QOL questionnaire
and the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A) compared
to patients with coronary heart disease (Almeida, Beer, et al., 2012). Furthermore, increased
disease severity has been related to increased disruption to daily living (Pressler,
Subramanian, et al., 2010a). However, few studies have examined whether a relationship
between cognition and QOL in elderly patients exists. Although Gorkin et al., (1993) found
that patients with mild and moderate HF performed significantly slower on psychomotor
Chapter 2: Background and overview of heart failure
15
(Trail Making-A), working memory and attention (digit span), but not on executive
functioning (Trail Making-B) tasks, the authors failed to report whether a relationship existed
between cognition and QOL. Pressler and colleagues (2010a) on the other hand demonstrated
that worse executive function (Trail Making-B) performance weakly related to increased
disease disruption to QOL as measured by the Minnesota Living with Heart Failure
(MLWHF) Questionnaire. In this study, the variance on the MLWHF questionnaire was
explained by disease severity, age and depressive symptoms. However, memory (as measured
by Hopkins Verbal Learning Tests total recall) was only a minor contributor to MLWHF and
did not contribute to the variance in disease severity and quality of life (Pressler,
Subramanian, et al., 2010a). This study enrolled patients aged 20 years and over and did not
compare older and younger patients. It is important to compare the difference in QOL
between younger and older patients as it has been reported that younger (≤ 64 years) HF
patients have greater emotional and physical disruptions to life as measured by MLWHF than
older patients do (> 64 years; Gottlieb et al., 2004). Additionally, younger patients exhibit
worse mental functioning and bodily pain as measured by the SF-36.
2.9.5 Daily living and self-care abilities
Researchers have emphasised the effects of cognitive function on quality of life and self-care
abilities of HF patients. Patients are provided with a large amount of important information
concerning how to manage their condition and recognize and act on worsening symptoms
(e.g. shortness of breath and weight gain) to prevent potential hospital readmission. It is
thought that patients poorly comprehend, retain, recall and utilize the information provided
by their cardiologists, GP, nurses and other health care workers (Wolfe et al., 2006). Older
HF patients (NYHA class II, III and IV) with greater verbal memory impairments appear to
score worse on daily living tasks (Incalzi et al., 2003). A large retrospective study found that
decreased cognitive function as measured by MMSE scores and psychomotor function (Trail
Making-A) performance predicted the ability of older patients (68±9.43; NYHA class II and
III) to independently drive a car and accurately take medications (Alosco, Spitznagel, Raz, et
al., 2012). Sufficient self-care maintenance, which is important for patients to make
approprate decisions about their symptoms, requires adeqate cognitive capacitites. When only
global cognitive function measures were assessed, mild cognitive impairment (MCI; MMSE
< 27) did not predict poor self-care maintenance 6±5 days post hospital admission in older
HF patients (NYHA class III or IV; 73±11 years; Cameron, Worrall-Carter, Riegel, Lo, &
Stewart, 2009). Although, when combining two global cognitive function measures (MMSE
and Montreal Cognitive Assessment; MoCA), MCI explained 20% of the variance of self-
care management (Cameron et al., 2009). However, cognitive function as measured by
Chapter 2: Background and overview of heart failure
16
MMSE was not found to be a significant predictor of self-care maintenance or management
in 50 hospitalised elderly chronic HF patients (mean 73 years; 6 days±5 days; Cameron et al.,
2009). This indicates that some tests for cognition may fail to detect impairments when they
are actually present. During a 6 month post-hospitalisation follow up, patients (> 65 years)
who did not participate in a nurse-directed structured treatment program had lower MMSE
scores at baseline (25.5±2.2) compared to those who did participate (26.3±4.8) in the program
(Ekman, Fagerberg, & Skoog, 2001). The structured program comprised of self-care
management factors known to prevent hospital readmissions including education about HF,
treatment, weight control advice, awareness of symptom deterioration and adherence to
medication. Patients with impaired cognitive functions have shown to perform worse on daily
living tasks and ability to take medications and self-care capacities. Treatments for improving
cognition in these patients will help improve self-care abilities and daily living.
2.9.6 Effects of poor sleep on cognitive function
The detrimental effects of disturbed sleep on cognitive function in adults are well
documented, and recent studies have investigated the relationship between these variables in
heart failure (HF). Riegel and Weaver (2009) proposed a conceptual model linking sleep
deprivation with cognitive impairments and poor self–care. This model illustrates that poor
sleep, due to the effects of HF medication and sleep disordered breathing, may stimulate a
cycle whereby the resulting excessive daytime sleepiness (EDS) leads to cognitive
impairment. Based on the conceptual model, the resultant cognitive impairment in turn leads
to poor self-care capacities and long-term clinical outcomes. Depression in this model relates
independently to these aforementioned variables (Riegel & Weaver, 2009). Poor sleep, is
common in older HF patients (50-85 years) and has shown to relate to reduced executive
function and attention but not language abilities in this patient group (Garcia et al., 2012).
Interestingly, compared to patients that do not have EDS, patients with EDS are less likely to
adhere to medications (Riegel et al., 2011). Furthermore, of the HF patients with EDS, those
with MCI were slightly less likely to comply with treatments compared to those without MCI
(2.5 verses 2 times more likely). More recently, Riegel et al. (2012) demonstrated that
cognition influenced HF patients’ quality of life only when considered together with
excessive daytime sleepiness. The authors suggest that the impact on quality of life is
therefore due to HF symptoms instead of changes in cognitive function. However, this notion
is novel and further studies are required to substantiate this hypothesis.
Chapter 2: Background and overview of heart failure
17
2.10 Summary
It is evident that cognitive impairments are prevalent in HF and to a large degree impact
negatively on the patient’s treatment compliance, quality of life, prognosis, mortality and
hospital readmissions. Adequate cognitive processes are necessary for patients to understand
and remember their treatment regime, plan when to take medications in relation to meal times
(Cline et al., 1999; Wolfe et al., 2006) and decide what to do when symptoms worsen
(Pressler, 2008; Wolfe et al., 2006). In order to determine the best treatment approach for
physicians to facilitate improvements in patient’s cognitive function, it is important to
establish which of these cognitive facets are impaired.
Chapter 3: Cognitive impairment and mood in heart failure
18
CHAPTER 3 COGNITIVE IMPAIRMENT AND MOOD IN HEART FAILURE
3.1 Introduction
Cognitive impairments have a detrimental effect on the prognosis, ability to follow treatment
regimes, quality of life and increase hospital readmission in HF patients. Numerous
researchers have concluded it is essential to acquire a solid understanding of the physiological
mechanisms associated with the relationship between HF and cognition in order to ascertain
appropriate memory enhancing and preventative interventions (e.g. Alosco, Spitznagel, Raz,
et al., 2012; Incalzi et al., 2003; Lavery et al., 2007). However, prior to establishing the
mechanisms for cognitive impairments a clear understanding of the cognitive factors, which
are compromised is required to appropriately target treatment regimes.
Studies investigating this topic to date have explored global cognitive function exclusively or
have employed a more advanced approach to clarify which specific cognitive factors are
impaired. Accurately detecting cognitive impairments and associated risk factors in patients
is necessary to help establish the best treatment protocol to improve these cognitive factors.
Improving cognitive performance in patients may improve mortality rates and decrease
hospital readmissions especially in the elderly patient cohort, where cognitive impairments
and hospitalisations are highest. This chapter will review the cognitive factors found to be
impaired and preserved in these patients especially in the older population. Additionally, an
assessment of the cognitive screening tools used in these studies will determine whether
appropriate tools are being used to detect these impairments or whether impairments are
missed due to the use of inappropriate tools.
3.2 Global cognitive function
Global cognitive function is a measure commonly used to assess overall mental processes.
Various brief assessment tools are preferred in clinical or research settings to obtain a quick
overview of individual’s cognitive status. These tools help determine whether an individual
has signs of dementia, including mild cognitive impairment, and whether further referrals are
necessary (Lezak, 2012). The Mini Mental State Examination (MMSE) has been widely used
as a dementia screening tool for HF patients in clinical settings and research trials. The
MMSE is a brief screening tool used worldwide to provide a global cognitive score by means
of summing performance on short-term memory, orientation, concentration, and visuospatial
tasks (Folstein, Folstein, & McHugh, 1975). Additionally, the Hodkinson Abbreviated
Chapter 3: Cognitive impairment and mood in heart failure
19
Mental Test (AMT; e.g. Zuccalà et al., 2005) and more recently the Montreal Cognitive
Assessment battery (MoCA; e.g. Athilingam et al., 2012; Cameron et al., 2009) provide an
assessment of global cognitive function in HF patients. With an overall cognitive score and
cut off values indicating possible dementia and mild cognitive impairments, these
aforementioned measures provide useful information about the patient’s general level of
functioning.
Although HF patients show impairments on global cognitive function such as the MMSE
compared to age matched controls, this measure is an assessment of global cognitive function
and does not evaluate performance on specific cognitive domains. Despite global cognitive
measures providing useful information about cognitive abilities, more sensitive and inclusive
neuropsychological assessment batteries are necessary to provide a better understanding of
which cognitive domains are impaired (Cameron et al., 2010; McKee, Castelli, McNamara, &
Kannel, 1971). Using various testing methods, an assessment of the various cognitive
functions in a HF patient can be determined. A thorough assessment of the various classes of
cognitive functions impaired in HF will assist in devising targeted treatments aimed to
improve cognition in these patients. Cognitive function refers to a broad range of brain
processes including memory, attention, and executive functions (Lezak, 2012). These
functions, known to deteriorate with ageing are pertinent to an elderly patient’s ability to
successfully perform daily tasks and activities (Alosco, Spitznagel, Cohen, et al., 2012;
Incalzi et al., 2003). This next section will provide definitions of mental processing in various
domains that fit under this umberella term. Additionally, neuropsychological measures used
to test these factors will be outlined.
3.3 Neuropsychological function, specific cognitive domains
3.3.1 Memory
Memory is a cognitive process where information, is stored, processed and retrieved. The
term ‘memory’ is divided into ‘working (or short-term) memory” and “episodic (or long-
term) memory”. Using short spurts of attention, information in working memory is
temporarily stored over a period of seconds by means of manipulation through rehearsal or
repeating information (Baddeley, 2000). Long-term (episodic) memory, on the other hand
involves storing information over minutes, days and years and this information can be
retrieved after a long period of time. Storage of information in long-term memory requires
effortful or focused activity (Lezak, 2012). During ageing, attention and memory decline
(Riddle & Schindler, 2007). Tests commonly used in trials to assess memory as in immediate
Chapter 3: Cognitive impairment and mood in heart failure
20
and delayed recall in HF patients include Rey’s Auditory Verbal Learning Test (RAVLT),
California verbal learning test (CVLT), digit span and Hopkins verbal learning test. In these
tests, patients are presented with a list of words or numbers and are asked to recall as many
items as they can remember from the list immediately after the words are presented, or after
some delay with or without a distracting task. Many elderly HF patients have deficits in
short-term, long-term and immediate memory (Almeida, Garrido, et al., 2012; Hjelm et al.,
2011; Kindermann et al., 2012). Such deficits are believed to contribute to the patient
forgetting to take their medications or remembering when in relation to meal times they are
supposed to take their medication (Cline et al., 1999).
3.3.2 Attention
Attentional processes are required for storing information into working memory. Attention
refers to an individual’s ability to disengage and alter their focus and to be responsive to
surrounding stimuli (Lezak, 2012; Paarasaman, 1998 in Lezak, 2012, p. 36). Various
functions defining attention include vigilance, which is the process of sustained attention, and
information processing. Studies exploring attention in HF have typically employed the digit
span test, Rey’s Auditory Verbal Learning Test (RAVLT), Visual Scanning Test, Repeatable
Battery for the Assessment of Neuropsychological States (RBANS) subtest, Trail Making-A
and Trail Making-B and Stroop word naming task.
3.3.3 Executive functioning
Executive functions are higher order cognitive processes involved in planning, attentional
control, and response inhibition. These functions are required for an individual to live
independently, be productive, carry out goal orientated and self-motivated behaviours, and
undergo successful social interactions (Lezak, 2012). Impairments in executive functions
have been observed in HF patients (e.g. Hoth, Poppas, Moser, Paul, & Cohen, 2008; Lavery,
Vander Bilt, Chang, Saxton, & Ganguli, 2007; Pressler, Subramanian, et al., 2010b).
Measures generally used to assess executive function in HF include the Trail Making-B task
and Stroop interference test as measures of response inhibition. Additionally, verbal fluency
(Lavery et al., 2007; Stanek et al., 2009) and digit symbol coding (Almeida & Tamai, 2001a;
Pressler, Kim, et al., 2010; Pressler, Subramanian, et al., 2010a) are commonly used as
measurements of executive function. It is evident that all or some of these cognitive
functions are necessary for a HF patient, especially an elderly patient, to remember and
adequately comply with treatments. The following section will focus on research that has
examined specific cognitive domains in elderly HF patients.
Chapter 3: Cognitive impairment and mood in heart failure
21
3.4 Studies using comprehensive neuropsychological test batteries
A growing number of researchers have administered comprehensive neuropsychological test
batteries in order to provide an enhanced assessment of cognitive function in HF patients.
These tests are superior to global cognitive assessments, which as mentioned in the previous
section, only provide a general cognitive assessment. Neuropsychological assessment
batteries however determine which cognitive domains are impaired and which remain intact.
An understanding of the status of these cognitive abilities will in turn provide insight as to the
appropriate treatment approach required to improve impaired cognitive domains in these
patients. The next section will critically review studies that have employed
neuropsychological test batteries to assess cognitive function in HF patients. The review will
outline the evidence in both outpatients and inpatients from prospective, retrospective and
case control studies. The structure of this section will review the literature on cognitive
impairments in elderly HF patients based on longitudinal, prospective and baseline studies
that have included or omitted control groups.
3.4.1 Longitudinal studies
Few researchers have conducted longitudinal studies to examine whether cognitive function
in older heart failure (HF) patients changes over time. Longitudinal studies using a
comprehensive neuropsychological assessment battery will provide valuable information on
whether cognitive function is intermittent or stable over time (Riegel et al., 2002). Hjelm and
colleagues (2011) investigated cognitive changes in octogenarians every 2 years over a 10-
year period. At baseline, participants diagnosed with HF exhibited worse performance on
visual spatial ability (block design), short-term memory (digit span backward and forward)
and episodic memory tasks (Prose Recall test, Thurstone’s picture and memory-in Reality)
compared to non-HF in the same age group (Hjelm et al., 2011). Interestingly, compared to
baseline measures HF patients improved on the semantic and episodic memory tasks during
the 4th (years 7 & 8) and 5th (years 9 & 10) testing periods. The authors suggested that these
cognitive improvements could be due to better health and more enhanced cognitive abilities
from surviving patients (Hjelm et al., 2011). Over the 10 year observatory period, HF patients
performed significantly worse than non-HF on the visual spatial task (Block design) when
dementia was both included and excluded in the model. Furthermore, episodic memory and
short-term memory performance was worse in HF patients compared to non-HF when
dementia cases were included in the model, even when adjusting for other variables (sex, age,
educational levels and diabetes with and without including arterial hypertension and
smoking). HF episodic memory performance showed a longitudinal decline when all these
Chapter 3: Cognitive impairment and mood in heart failure
22
variables were adjusted for in the model and when the analysis did not include patients with
dementia (Hjelm et al., 2011). Overall, compared to non-HF individuals, significantly more
HF patients were taking Furosemide and diuretics, had myocardial infarctions, strokes and
vascular dementia, and lower systolic blood pressure (BP). Therefore, it is possible that HF
medications and comorbidities influenced cognitive decline in these patients. The authors did
not report the number of patients with mild, moderate or severe HF and hence the relationship
between HF severity and cognition is therefore unknown.
Almeida, Beer, et al. (2012) examined cognitive function over a 2 year period in an older
sample (45-86 years) of patients with HF (NYHA class I to III; EF < 40%), a clinical history
of coronary heart disease (CHD; EF > 60%) and a control group (EF > 60%) that did not have
a history of CHD. At baseline assessment, patients with HF performed worse than healthy
controls on total scores and immediate and delayed recall (Cambridge cognitive capacity
scale; CAMCOG total score, immediate recall, short and long delayed recall of the
Californian Verbal Learning Test; CVLT). At the two-year follow up assessment, HF patients
scored significantly worse than healthy controls on the total CAMCOG measure. After
correcting for age, gender, IQ and mood HF patients CAMCOG scores declined over time
compared to controls. However, scores on the word recall, digit coding and digit copying
however did not significantly change at the two-year follow up. The authors did not find any
interactions between cognition and age. However since cognitive impairment increases with
age, a comparison between older (e.g. > 60 years) and younger adults (e.g. 45-59 years)
would help differentiate cognitive functions and severity of decline over time between the
two groups. In addition, since no biological measures were obtained, the researchers were
unable to establish likely physiological mechanisms associated with eventual cognitive
decline seen in their HF cohort (Almeida, Beer, et al., 2012).
Stanek et al. (2009) examined cognitive changes employing the dementia rating scale in
patients over a 12-month period. Compared to a well-matched cardiovascular disease control
group whose cognitive performance remained unchanged, dementia rating scale scores in HF
patients (NYHA class II or III; 69.08±8.74) improved 12 months following baseline
assessment. Additionally, over a 12-month period HF patients significantly improved on
attention, working memory, verbal fluency and reasoning. However, scores on memory and
construction subscales of the dementia rating scale did not change over time in HF patients.
Furthermore, the finding that higher baseline diastolic blood pressure in HF patients was
related to better dementia rating scale scores, suggests that controlling blood pressure may
improve cognitive performance in these patients (Stanek et al., 2009). This study screened for
dementia and excluded patients who scored MMSE < 24, however the authors did not report
Chapter 3: Cognitive impairment and mood in heart failure
23
information on medication, thus the effects of drug treatment on cognitive performance were
not established. Researchers have suggested that longer observational studies are required to
assess cognitive changes over time. This will help determine factors that predict decline in
HF and establish severity of the decline (Beer et al., 2009).
These longitudinal studies have shown that specific cognitive deficits including visual spatial
abilities, episodic and short-term memory are seen in HF patients. Deficiencies in specific
cognitive domains may impair a patient’s ability to understand and comply with treatment
regimens and few studies have directly examined whether these cognitive domains are a
reliable measure for predicting mortality in these patients.
3.4.2 Comparison studies: cognitive function in heart failure compared with healthy
controls
A large number of studies comparing HF performance with age matched control groups have
helped ascertain whether cognitive impairments seen in HF are due to the normal cognitive
ageing or due to the HF itself. Sauvé, Lewis, Blankenbiller, Rickabaugh, and Pressler (2009)
reported cognitive impairments in patients who had stable HF for greater than 6 months
(n=50; age > 30 years; 63±14 years; NYHA II-IV; 4.8±5.1 years) compared to healthy gender
and age (not clinically significant) matched community dwelling controls (n=50; 62.5±14
years). Notably patients displayed significantly impaired performance on attention and
immediate and delayed word recall tasks as measured by Rey’s Auditory Verbal Learning
Test (RAVLT) including the distracter item. Error rates on attention (Visual Scanning Test)
and immediate recall (Memory Scanning Test) were significantly higher in patients.
Supporting this, Beer et al. (2009) found that HF patients (n=31; 54.3±10.6 years; NHYA
class II; EF ≤ 40%) performed significantly worse than healthy controls (n=21; 56.1±8.2
years; EF ≥ 40%) on short and long delayed verbal learning (Californian Verbal Learning
test), visual memory (Brief Visuospatial Memory) as well as on general intelligence (overall
CAMCOG score). These findings represent slower mental processes in patients when
responding to difficult processes requiring concentration and the ability to store or retrieve
information (Sauvé et al., 2009). However, another study did not observe differences between
an elderly group of patients (≥ 65years) with (n=68; 78.8±7.2 years) and without HF (n=286;
77.2±6.5 years) on verbal learning and recall (Hopkins) or delayed recall, although worse
performance on visual immediate recall (clock drawing) after adjusting for age, sex, race and
education were observed (Lavery et al., 2007).
Additionally, Almeida and Tamai (2001a) compared global cognitive function upon hospital
admission and six weeks following standard treatment in patients with severe HF to that of
Chapter 3: Cognitive impairment and mood in heart failure
24
geriatric controls. Compared to geriatric controls, significant impairments upon hospital
admission in older inpatients (> 60 years) with severe HF (NYHA class IV; ejection fraction
< 45%) were seen on attentional scores, global cognitive function (MMSE), CAMCOG, digit
span and symbol, letter cancellation and Trail Making-B (but not Trail Making-A). However,
following six weeks of standard medical treatment that was targeted to the individual patient,
significant improvements were seen on visuo-motor function (digit symbol) and visual
scanning (letter cancellation) and patient functional class as measured by the 6-minute walk
test following treatment (Almeida & Tamai, 2001a). No significant group differences were
seen in post treatment cognitive scores. Based on the findings the authors suggested that
cognitive impairment in HF is reversible and that their cognitive function may equate to that
of geriatric patients in the same age group.
Examining motor abilities, HF patient’s performance on tapping rate variability as
determined by performance on the attention and immediate recall (Visual and Memory
Scanning Tests) was worse than healthy controls (Sauvé et al., 2009). In the elderly patient
cohort however, HF patients showed no significant impairments on psychomotor functions
(Trail Making-A) compared to patients without HF (Lavery et al., 2007). Nevertheless, HF
patients performed worse than controls on visuo-spatial abilities (Block design) and executive
function as measured by Trail Making-B and Verbal fluency (Lavery et al., 2007). However
Lavery et al. (2007) conducted neuropsychological testing at the patient’s home and the
uncontrolled testing environment may have confounded the results.
Studies examining HF patients following standardised treatment such as cardiac
resynchronisation in patients who do not respond to treatments and examining the effects of
treatments on decompensated patients suggest that cognitive dysfunction in HF is reversible.
Recently Kindermann et al. (2012) provided insight on how cognition in decompensated
patients changes following cardiac compensation. In their study, Kindermann et al. (2012)
tested cognitive performance in decompensated patients who had moderate or severe HF
(NYHA class III or IV) for 6 months prior to testing. Assessments of patients’ cognitive
performance were conducted within 48 hours after hospital admission and again when
compensation was apparent following intravenous diuretics and/or vasodilators and/or
inotropes (14±7 days). Cardiac compensation was shown to significantly improve patients’
short-term verbal (digit span forward) and episodic memory, speed of information processing
and executive control but not verbal working memory (digit span backward), quality of life or
depression levels. Decompensated patients performed significantly worse than stable HF
patients and healthy controls on short-term memory, working memory, speed of information
processing and executive control (Stroop interference test). However, patients with stable HF
Chapter 3: Cognitive impairment and mood in heart failure
25
performed similarly to healthy controls on the Stroop interference. Additionally, patients with
stable HF performed worse than healthy controls on working memory, episodic memory and
speed of information processing. The two control groups who were recruited from a HF
outpatient clinic and a psychology clinic participated in two testing sessions, which were on
average the same number of days apart as the experimental group. In their study, although the
healthy controls performed significantly better in the post-test working and episodic memory,
the authors fail to suggest these improvements were due to practice effects (Kindermann et
al., 2012).
3.4.3 Comparison studies: cognitive function in heart failure compared with other diseases
Researchers have also compared cognitive functions with those of patient groups that have
similar risk factors to that of HF patients. Hoth et al. (2008) for instance compared cognitive
performance of patients (aged > 55 years) with HF (NYHA class II, III or IV; 69.1±8.5 years;
LVEF < 40%) to those with cardiovascular disease but no HF (68.9±8.5 years), matched for
age, sex and years of education. When exploring raw data it was revealed that HF patients
performed significantly worse than cardiovascular disease controls on attention and
psychomotor speed (Trail Making-A), executive function (Trail Making-B) and response
inhibition (Stroop interference) tasks (Hoth et al., 2008). However, no group differences were
seen on immediate or delayed memory recall, visuospatial, language or attention indexes (as
measured by the RBANS).
In a study by Trojano et al. (2003), cognitive data was compared from elderly hospitalised
patients (≥ 65 years) with heart failure (HF) to that of patients with cardiovascular disease but
no HF. The authors aim was to assertain cognitive performance in HF patients when
controlling for possible confounding factors such as neurological disease and organ damage.
A greater percentage of patients with severe HF showed abnormal performance on 2 or more
cognitive tasks followed by patients with mild HF and no HF (57.9%, 43% and 34.3%,
respectively; Trojano et al., 2003). Patients with mild HF (NYHA II) scored significantly
worse than controls with cardiovascular disease on verbal fluency (verbal attainment,
indicating frontal lobe function). Patients with moderate and severe HF (NYHA III-IV) on
the other hand scored significantly worse than cardiovascular controls on global cognitive
function as measured by MMSE, attention matrices (sustained attention), immediate and
delayed word recall tasks. In this sample the mean global cognitive scores (MMSE) were
generally low, with the mean for each group below 25 (no CHF 24.7±4; mild HF 23.7±4.2
and severe HF = 22.4±5.6) and significant differences seen only between the no HF and
severe HF groups. These low MMSE scores could have been due to the severity of the HF
Chapter 3: Cognitive impairment and mood in heart failure
26
since the patients were inpatients. An additional cognitive assessment during a follow up
period when the HF condition was more stable would have provided a better long-term
assessment of cognitive function in these patients when their HF improved (Trojano et al.,
2003). Since HF symptoms improve post discharge and cognitive performance is correlated
with HF severity, it is more appropriate to assess cognitive performance at follow up
outpatient visits when the HF condition is stable.
Recently, Almeida, Garrido, et al. (2012) found that HF patients scored lower on immediate
and long-term memory and slower psychomotor speed (as measured by the CVLT) compared
to healthy controls. However, these observations were not seen between patients with HF and
ischemic heart disease. HF patients however also had significantly lower CAMCOG and digit
code scores to those of healthy controls only when controlling for possible confounding
variables.
Cognition in hospitalised patients also differs from that of controls and HF outpatients.
Almeida and Tamai (2001b) explored cognitive performance differences between elderly
patients (≥ 60 years) with severe heart failure (HF; NYHA class III and IV) who were
admitted to hospital. Compared to patients attending a geriatric outpatient clinic (n=30;
ejection fraction > 60%), HF inpatients (n=50; ejection fraction < 45%) performed
significantly worse on overall CAMCOG scores and 5 of the 7 subscales (orientation,
language, memory, praxis and abstraction; Almeida & Tamai, 2001b). Overall, almost 75%
of the HF patients and 30% of the older controls demonstrated cognitive impairments as
described by a CAMCOG score of less than 80. There were no variations in performance on
attention tasks of the CAMCOG neuropsychological test battery.
Almeida and Tamai (2001b) assessed inpatients within 72 hours following admission,
therefore factors commonly seen in hospitalised patients and not in geriatric outpatients may
have contributed to poor cognitive performance in the HF group. Furthermore, factors known
to influence cognition were present in a greater number of HF patients than controls. For
instance more HF patients than controls had a history of stroke (14% versus 0%), were heavy
smokers (52% versus 23%) and regularly consumed alcohol (28% versus 0%). Finally, these
studies did not investigate whether certain medications were possible confoundering factors
on cognitive performance.
Case controlled studies using greater than one control group have provided insight into
whether cognitive deficiencies in heart failure (HF) are due to the HF itself, normal ageing or
result from comorbidities commonly seen in HF (Pressler, Subramanian, et al., 2010b).
Pressler, Subramanian, et al. (2010b) compared cognitive function of adults predominantly
Chapter 3: Cognitive impairment and mood in heart failure
27
with mild HF (n=249, NYHA class I – IV), a healthy control group living independently in
the community (n=63) and a group of patients diagnosed with a chronic condition other than
HF (n=102). In this study, the groups were matched for intellectual function (IQ) and global
memory scores (MMSE) but not age as the healthy control group was significantly younger
(53.3±17.2 years) than the HF (62.9±14.6 years) and clinical patient groups (63.0±11.9
years). Compared to the two control groups, HF patients had inferior scores on total verbal
memory recall (as measured by Hopkins Verbal learning test), psychomotor speed (as
measured by digit symbol substitution and Trail Making-A) and executive function (as
measured by Trail Making-B). These observations were seen between the HF and non-HF
patient groups when exploring Z scores. Additionally compared to non-HF patients those
with HF recalled fewer words on the delayed memory recall task (Hopkins Verbal learning
test). However, no group differences were seen in global cognition (MMSE) or visuospatial
and working memory tasks (digit span). This study included asymptomatic HF patients
(NYHA class I) in the analyses, hence it is unknown whether significant group differences
were present in patients with mild, moderate and severe HF. Of this participant cohort, almost
one-fifth HF patients exhibited impairments in total verbal memory recall (23%) and
executive function (19%) as opposed to approximately 10% of the healthy and non-HF
medical groups. Likewise, almost a quarter (24%) of HF patients showed impairments on
greater than three cognitive domains followed by the healthy (14%) and medical non-HF
controls (12%; Pressler, Subramanian, et al., 2010b).
In a small study, Grubb, Simpson, and Fox (2000) compared neuropsychological functioning
in patients aged 53-75years who had a previous episode of myocardial infarction with
(NYHA class II or IV; LVEF < 40%) and without (LVEF > 50%) stable HF. No significant
group differences were observed on any of the cognitive measures (Rivermead and digit span
tests). Although compared to myocardial infarction controls, the HF patient group scored
significantly higher on depression and anxiety, and scored lower on intelligence quotient (as
measured by the NART). It is unreasonable for the authors to conclude that patients with
stable heart failure and a history of myocardial infarction do not have memory problems,
since the control group consisted of patients with a history of myocardial infarctions, which
are associated with cognitive impairments. A control group without factors known to effect
cognition are preferred when examining cognitive abilities and any deficits in HF patients.
3.4.4 Comparison studies: cognitive function in heart failure compared with normative data
Other researchers have compared cognitive results of heart failure (HF) patients to normative
data rather than a control group. In one study HF patient’s (72±12 years) performance on
Chapter 3: Cognitive impairment and mood in heart failure
28
immediate and delayed memory (RBANS), language, attention, executive function and
psychomotor speed (Trail Making-A) was shown to be significantly lower than normative
means on respective tests (Bauer et al., 2011). Executive function, as measured by Trail
Making-B and visual constructional measures, were not different to those of normative data.
In a small study, Wolfe et al. (2006) found that compared to means of age matched normative
data, older patients HF patients (n=38; 76% male; 64±7.62 years) showed impairments on
immediate and delayed memory and reduced total RBANS index score. However, the authors
did not stipulate whether impairments in memory scores were visual or auditory.
Furthermore, HF patients’ executive function scores, as measured by the Winston card-
sorting test, were significantly higher than those of the age expected norms. Although
significant observations were not found on premorbid intelligence (IQ), language or
attentional abilities, patients showed a trend towards impaired performance on attention and
visuospatial/constructional index scores on the RBANS test battery compared to normative
data (Wolfe et al., 2006). A thorough assessment of the HF disease severity such as NYHA
classifications in the Wolfe et al. (2006) study would have provided a more accurate
diagnosis of HF and enabled better comparison of cognitive performance with findings from
other studies. In addition, this study did not assess depression and carer support that may
influence coping styles and patient performance on cognitive tests. Finally, comparing HF
patient results from age adjusted normative data rather than an age and IQ matched control
group further decreases the reliability of the findings from this study.
3.5 Effects of disease severity on cognitive function in heart failure
The influence of disease severity on cognitive impairment in heart failure (HF) has been
broadly studied. Based on studies that have pooled patients with various degrees of HF in
their analysis, there is clear evidence that cognitive impairments exist in this patient group.
However, few studies have compared cognitive performance amongst patients with mild,
moderate and severe HF in order to ascertian whether disease severity has an impact on
cognitive ability. Disease severity has been shown to predict of poor cognitive performance
in patients with chronic HF and cardiac controls (Vogels, Oosterman, et al., 2007). Studies
have shown that reduced attention, immediate recall (memory scan and RAVLT; Sauvé et al.,
2009), memory, visuospatial ability, psychomotor speed, and executive function (Pressler,
Subramanian, et al., 2010b) is associated with a higher NYHA functional class in older
patients. Even after controlling for variables known to affect cognition (e.g. age, atrial
fibrillation, diabetes mellitus, hypertension, and depression), Incalzi et al. (2003)
demonstrated that global cognition as measured by the MMSE reduced significantly with
Chapter 3: Cognitive impairment and mood in heart failure
29
increasing NYHA class in older patients with stable HF. Supporting this, Bauer et al. (2011)
showed that poorer performance on psychomotor speed (Trail Making-A), executive function
(Trail Making-B), attention (RBNS) and global cognition (RBNS total scores) were related to
increased disease severity. Furthermore, Trojano et al. (2003) showed that mild HF patients
recalled significantly more words on the Rey’s immediate recall task compared to patients
with severe HF (NYHA class III-IV), however no other group differences on cognitive tasks
were observed. Interestingly, Sauvé et al. (2009) failed to find a relationship between HF
severity and cognitive impairments in older patients with chronic HF (> 6 months; NYHA
class II – III).
Heart failure (HF) patients impaired in global cognitive scores are more likely to be older and
less educated compared to controls, suggesting that education has a protective role in global
cognitive function (Cacciatore et al., 1998; McLennan et al., 2006). Interestingly, disease
severity in HF is also associated with increasing age (Trojano et al., 2003). Compared to
patients who do not have severe cognitive impairment (MMSE score of ≥ 24), older HF
patients with severe cognitive impairment or probable dementia (MMSE score of < 24) have
been shown to be older, less educated and present with higher depression scores (Geriatric
Depression Scale; Cacciatore et al., 1998). Addtionally, HF patients with severe cognitive
impairment (MMSE score of ≤ 24) are less likely to have an education level of more than 8
years (Debette et al., 2007). However, other smaller studies which included asymptomatic
patients and no control group have failed to find an association between age, education,
NYHA class or hypotenstion with cognitive impairment (Riegel et al., 2002). Significant
differences in years of education between severe (NYHA class III or IV) and mild (NYHA
class II) HF groups was not observed in a large Italian study, however signficiant differences
were found with severe HF patients who had lower numbers of school years compared to
patients without HF (Trojano et al., 2003).
3.6 Psychological parameters and mood disturbances in heart failure
Few researchers have examined the relationship between depression and cognitive functions
in HF. Psychological factors known to effect cognitive function such as depression, anxiety
and fatigue, are greater in older HF patients (e.g. Stephen, 2008). Some authors have shown
no significant differences in depression among patients with mild HF (NYHA class II; EF ≤
40%; 54.3±10.6 years) and healthy controls (EF > 40%; 56.1±8.2 years) using a brief mood
scale (Beer et al., 2009). Whereas other authors have reported significantly greater levels of
depression (Patient Health Questionnaire; PHQ-8) in patients with mild, moderate or severe
HF (NYHA class I, II, III and IV) compared to healthy controls and a group of patients
Chapter 3: Cognitive impairment and mood in heart failure
30
without HF (Pressler, Subramanian, et al., 2010b). In addition, a recent study by Almeida,
Beer, et al. (2012) reported increased levels of depression and anxiety in HF patients (NYHA
I to III; EF < 40%) who also exhibited cognitive decline over a two year period. Although the
authors found changes in negative affective states and cognition over time, they failed to
explore whether a relationship exists between depression or anxiety and cognitive function
(Almeida, Beer, et al., 2012). Given the association between mood and cognitive function, it
is crucial to examine how these psychological states might affect patients’ cognitive function.
3.6.1 Mood disturbances and cognitive function
Some researchers have reported that depression is independently related to cognition, sleep
issues (poor sleep and excessive daytime sleepiness), impaired daily living capabilities and
poor quality of life (Garcia et al., 2012; Riegel & Weaver, 2009). Based on their findings
authors suggest that there is no association between cognitive impairment and depression in
HF. In an early study Cacciatore and coworkers found that although HF patients had
significantly higher levels of depression compared to patients with no HF, that global
cognitive impairment (measured by the MMSE) was independent of depression (and gender,
age, educational level, diabetes, hypertension, alcohol consumption, smoking, atrial
fibrillation, BP, heart rate; Cacciatore et al., 2008). Other authors have reported that
depression and anxiety does not explain performance on cognitive measures (Pressler,
Subramanian, et al., 2010b; Sauvé et al., 2009). Interestingly with Sauvé et al. (2009) found
that psychologically distressed patients were significantly younger, had greater difficulties
performing their physical roles, and had less social support (as measured by the SF-36). This
suggests that younger patients may be more aware of their physical limitations than older
patients are and react with anxiety and depression. However, given that the controls were
aged 55 years and older with 90% of the controls aged 65 years and HF patients aged > 30
years, caution must be taken interpreting these results. Despite evidence suggesting that
cognitive deficits seen in HF occurs independently of common comorbidities such as mood,
cognitive impairments may therefore be due to secondary factors (Almeida & Flicker, 2001;
Cacciatore et al., 1998; Pressler, Subramanian, et al., 2010b).
In addition to depression, other mood states including fatigue and anxiety increase in severity
in older HF patients. Fatigue has been related to depression, emotional health and poor
physical health in HF (Evangelista et al., 2008) with levels increasing with increasing disease
severity (Fink et al., 2012). The fatigue subset of the Profile of Mood States questionnaire
(POMS) has been used to demonstrate that fatigue is prevalent in elderly patients (> 65 years)
with systolic dysfunction (LVEF ≤ 40%; NYHA class II or III) or compensated HF patients
Chapter 3: Cognitive impairment and mood in heart failure
31
scoring on average 11.5 (range 5 – 25; Stephen, 2008). However, without a control group in
the study by Stephen (2008), it is unknown whether these observations are unique to HF or
related to the age of this older group. In another study Fink et al. (2012) found that HF
patients with reduced ejection fraction (57±1.7 years) were more fatigued (POMS) than
controls, however this result was not observed when depressive symptoms was included as a
covariate. Furthermore, in addition to increased depression and anxiety scores, reduced levels
of vigour using the POMS questionnaire was reported in female HF patients (Riedinger,
Dracup, & Brecht, 2002).
There is some evidence suggesting a relationship between depressed mood and cognition in
HF. Incalzi et al. (2003) showed that mood, as measured by the Geriatric Depression Scale
(GDS), was associated with a decrease in the number of words recalled by elderly HF
patients (NYHA II, III and IV) in the delayed recall task. Recently, Garcia et al. (2011) found
that when adjusting for sex, hypertension and cardiac fitness (2MWT), depression (as
measured by the Beck depression inventory; BDI-II) predicted cognition in HF (NYHA class
II and III; aged 68.53±9.3 years; range 50-85 years; 36.5% female). Specifically, higher
depression scores predicted scores on global cognition, attention and executive function
(frontal assessment battery, Trail Making-A, Trail Making-B and digit substitution), memory
(CVLT), language (Boston and Animal naming tests) and motor (grooved pegboard test)
composite scores. Furthermore, increased depression was related to poorer performance on
attention (Trail Making-A), executive functioning, (Trail Making-B, digit symbol coding),
motor abilities (grooved pegboard test) and language tasks (animal naming task) but not
memory (CVLT), cardiac fitness (2MWT) or anterior and middle cerebral blood flow
velocity (Garcia et al., 2011). Although patients were clinically impaired on the depression
scores and cognitive tasks, the authors did not report whether these impairments were
statistically significant. These findings suggest that depression relates to a decrease in
performance on global and specific cognitive function measures in older HF patients (Garcia
et al., 2011).
Like cognitive function, higher levels of depression in HF has been related to reduced levels
of quality of life (Pressler, Subramanian, et al., 2010a), and predicts treatment compliance
(Glazer, Emery, Frid, & Banyasz, 2002). It is therefore necessary to examine the influence of
depression when assessing mechanisms for cognitive impairment in HF to ascertain whether
these factors are linked.
Chapter 3: Cognitive impairment and mood in heart failure
32
3.7 Factors that improve or worsen cognitive dysfunction in heart failure
3.7.1 Pharmaceuticals
A limited number of studies have explored treatments for improving cognitive function in
HF. It is possible that medications taken at different stages of the disease may impact on
patients cognitive capacities. There is evidence that heart failure (HF) medication does not
influence cognitive function in older patients (65-95 years) with low (MMSE < 24; mean age
76.7±6.9) or high (> 24; mean age 72.6±5.5) global cognitive function (Cacciatore et al.,
1998). In contrast, a more recent study by Hoth et al. (2008) showed that patients taking
angiotensin converting enzyme (ACE) inhibitors performed worse on psychomotor speed
(Trail Making-A) compared to those who were not taking this drug, although no differences
were seen on the executive function (Trail Making-B or Stroop interference task).
Interestingly, the opposite findings were observed with patients taking non-steroidal anti-
inflammatory drugs who performed better on executive function tasks, however no
differences were observed with psychomotor speed (Hoth et al., 2008). These findings
suggest that ACE inhibitors may negatively influence performance on psychomotor speed
and nonsteroidal anti-inflammatory drugs positively influence performance on executive
function tasks in patients with HF and cardiovascular disease.
In contrast, a large Italian study revealed that cognitive function during hospitalisation, as
measured by the Hodkinson Abbreviated Mental Test (AMT) was higher in HF patients
treated with angiotensin converting enzyme (ACE) inhibitors compared to patients not treated
with this drug (Zuccalà et al., 2005). However, the relationship between cognition and ACE
inhibitors was not observed with inpatients that did not have HF. In this cohort, cognitive
impairment was more prevalent in patients with lower blood pressure. However, patients with
higher systolic blood pressure were more likely to be prescribed higher dosages of ACE
inhibitors, suggesting that that high systolic blood pressure influenced cognition rather than
the treatment itself (Zuccalà et al., 2005). Moreover, HF patients who started taking digoxin
in hospital showed improvement in cognitive function as measured by the Hodkinson AMT
compared to HF patients (≥ 65 years) who did not take this drug at discharge (23% vs 17%;
Laudisio et al., 2009). Finally, compared to normative data based on age and education.
Incalzi et al. (2003) found that patients (NYHA class II, III and IV) taking digoxin (68.8
verses 54.9%) and diuretics (68.2 verses 55.3%) were more likely to perform abnormally on
immediate word recall (Rey’s immediate recall) compared to patients not taking these drugs
(Incalzi et al., 2003). Interestingly, in this study diuretics and digoxin but not ACE inhibitor
Chapter 3: Cognitive impairment and mood in heart failure
33
use increased with increased disease severity, therefore it is possible that worse cognitive
performance was related to disease severity and not the medication.
To date there is mixed evidence for heart failure (HF) medications in particular angiotensin
converting enzyme (ACE) inhibitors, digoxin and diuretics to selectively improve cognitive
performance in older hospitalised HF patients (Hoth et al., 2008; Laudisio et al., 2009;
Zuccalà et al., 2005), although only a handful of studies have examined the effects of drugs
on patients with stable HF. It is therefore premature to conclude whether or not
pharmaceuticals contribute to cognitive impairments commonly seen in HF patients and
further trials need to examine this relationship. Interestingly, in a large study, Callahan,
Hendrie, and Tierney (1995) investigated the rate of cognitive impairment as measured using
a short Mental Status Questionnaire during routine oupatient visits in an eldely sample
requiring primary care. Patients with cognitive impairment were more likely to have
malnutrition, been prescribed aspirin and less likely to have taken anti-inflammatories two
years before the screening date. It is possible that the pharmcological mechanisms of these
drugs may prevent cognitive decline in patients. Only a few studies have explored the effects
of pharmaceutical interventions on cognition. To date there is some evidence indicating that
ACE inhibitors and digoxin to selectively improve cognitive performance in older
hospitalized HF patients (Laudisio et al., 2009; Zuccalà et al., 2005). Even with successful
conventional treatments for improving HF, there are no specific remedies designed to repair
or prevent cognitive deficits prevalent in HF patients.
3.7.2 Exercise programs
In a small study, Tanne and colleagues (2005) assessed the effects of an 18-week daily
aerobic exercise program (twice a week) on cognitive function in patients with stable HF
(NYHA class III). The exercise program consisted of a 15-minute warm up followed by 35
minutes on a treadmill, stair machine and bicycle (at 60-70% maximal heart rate). Five
patients who were unable to complete the exercise program were the controls. The aerobic
exercise program significantly improved patients (n=18; NYHA class III; mean age 63 years;
39-81 years; mean EF < 26%) performance times on the executive functioning tasks (Trail
Making-A and Trail Making-B, congruent Stroop word naming task). Although these effects
were possibly due to learning effects, the authors did not find similar improvements in the
control group (n=5; mean age 66 years; 58-77 years; mean EF < 23%). However, no changes
were seen on other cognitive measures (MMSE scores, verbal fluency). Additionally, no
significant changes due to exercise were observed in middle cerebral arterial (MCA) blood
flow velocity, which has been implicated in poor cognition (Tanne et al., 2005). Furthermore,
Chapter 3: Cognitive impairment and mood in heart failure
34
HF patients (aged 54.3±10.6 years; NHYA class II) who were able to walk long distances on
the 6-minute walk test had better cognitive performance (as measured by the CAMCOG;
Beer et al., 2009). Although the benefits of exercise are promising, elderly patient’s ability to
exercise may be limited due to concomitant diseases such as arthritis, poor vision and
hearing, which will affect patients’ ability to exercise (Schwarz, 2007).
3.7.3 Educational programs
Educational programs tailored to HF patients have proven to be promising for improving
patients understanding of treatments and in turn improving compliance. Educational program
guided by a nurse who outlined and explained treatment protocols to outpatients after
discharge, was effective in improving patients understanding of their treatments and
enhanced treatment compliance, however cognitive impairments still existed in these patients
(Pressler et al., 2011). Given that nurses and pharmacists do not usually provide ongoing
regular educational support and follow ups, educational programs utilizing computers at
home may also be promising. Pressler and colleagues (2011) showed that the effects of a
computerised educational program (Brain Fitness Program; PositScience) designed to
enhance learning through auditory and visual exercises, in addition to Health Education
Interventions involving regularly reading a magazine (Heart Insight), significantly improved
the number of words patients learned and recalled following a 12 week intervention (Pressler
et al., 2011). The computerised intervention but not the Health Education Intervention also
improved delayed memory performance following the 12-week intervention. It is possible
that due to poor cognitive capacities, patients did not understand or assimilate the information
provided to them during the education intervention (McLennan et al., 2006).
Despite promising findings for improving patient’s ability to learn and recall treatments, there
is no established universal treatment for detecting or improving cognitive dysfunctions in HF.
Authors have proposed that interventions are required to prevent and delay cognitive
impairments in HF (Pressler, Kim, et al., 2010). This may be achieved using a
multidisciplinary team to help detect early signs of memory loss and methods to help care for
these patients. Treatments that directly target mechanisms of cognitive impairment in these
patients need to be established and tested. Although there is no universal treatment for
improving cognitive function in HF patients, there is limited evidence suggesting that
exercise, educational programs and pharmaceutical treatment may assist. However, in order
to establish the most effective treatment for improving cognition in HF, a clear understanding
of which cognitive domains are impaired is imperative. In addition, targeting physiological
Chapter 3: Cognitive impairment and mood in heart failure
35
mechanisms associated with these impairments is vital when devising an appropriate
treatment intervention.
3.8 Summary
In summary, longitudinal studies have shown that overall cognitive scores improve (Almeida,
Beer, et al., 2012; Stanek 2009) and episodic memory declines (Hjelm et al., 2011) in older
heart failure (HF) patients without dementia. It seems that HF treatment improves patients’
short-term memory, working memory, speed of information processing and executive
function. Although comparing performance with those of age-matched controls, older HF
patients still exhibit impairments in attention, immediate and delayed memory recall,
visuospatial abilities and executive function. Possible interventions for improving cognitive
function in HF may include nurse led and computerised educational programs, exercise
programs and HF medications, however there are no established universal treatments for
improving cognitive function in HF patients. An understanding of the mechanisms for these
specific cognitive impairments HF is necessary in order to help devise a safe adjunctive
intervention to reduce or ameliorate further decline. Since HF is a complex condition,
addressing every potential mechanism is not feasible, therefore this thesis will focus on
vascular, inflammatory, oxidative stress and antioxidant mechanisms that may be associated
with cognitive impairments as assessed by non-invasive measurements and those that can be
easily used in a clinical setting.
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
36
CHAPTER 4 MECHANISMS ASSOCIATED WITH COGNITIVE IMPAIRMENT
AND MOOD IN HEART FAILURE
4.1 Introduction
Although cognitive impairment is prevalent in older HF patients, there are no proven
effective treatments to improve cognitive function in these patients. As outlined in the
previous chapter, it is clear that HF patients have significantly greater impairments in
cognitive processes compared to healthy controls (e.g. Beer et al., 2009; Sauvé et al., 2009;
Vogels, Oosterman, et al., 2007) and patients with other forms of cardiovascular disease
(Vogels et al., 2008). Cognitive domains primarily shown to be impaired include memory,
mental speed, attention, language and global cognitive scores. A relationship has been
purported between cognitive deficits and factors such as disease severity or medications,
however supportive evidence is inconclusive.
It is difficult to obtain an accurate account of the prevalence of cognitive impairments and the
domains effected. This is due to inconsistencies in trials, in particular with different studies
using different cognitive assessment batteries and cut off scores for cognitive impairments.
There is a poor understanding of the physiological mechanisms associated with cognitive
dysfunction in HF patients. Interventions addressing these mechanisms may help prevent or
ameliorate cognitive dysfunction in elderly HF patients.
This chapter will provide an overview of current theories related to possible mechanisms for
cognitive impairment in elderly HF. Firstly, the evidence concerning changes to vascular
function thought to relate to cognitive impairment in HF will be reviewed. Furthermore,
possible additional mechanisms including oxidative stress and inflammation, which are seen
in both HF and cognitive decline, will also be discussed. To provide an overview of the
vascular factors contributing to cognitive impairment in HF, the first section will address the
physiology of blood flow from the heart to the brain. Vascular factors described in this first
section will include the hearts functional abilities, haemodynamics, biomarkers and changes
in cerebral blood flow.
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
37
4.2 Vascular mechanisms
The function of the heart is to pump enough blood to provide tissues and organs in particular
the brain with a sufficient supply of nutrients, glucose and oxygen for adequate functioning.
The pathophysiological pathways involved in the progression of HF encompass a complex
model that involves the activation of neurohumoral mechanisms. It is thought that an “index
event” which can be sudden (e.g. myocardial infarction) or long standing pathology (e.g.
haemodynamic pressure and genetic cardiomyopathies) results in a loss of myocardial
functional force. Because of a decreased functional force, the heart is unable to pump
adequately and the resultant decrease in cardiac output leads to poor blood supply to organs
and tissues. With an aim to reach a level of homeostasis, the body has inbuilt protective
mechanisms, which work towards increasing cardiac output to a normal level (Libby, Bonow,
Mann, & Zipes, 2008) and in turn maintain perfusion to vital organs, and myocardial
hypertrophy to normalise heart wall stress (Rundqvist, Elam, Bergmann-Sverrisdottir,
Eisenhofer, & Friberg, 1997). During early stages of the disease, certain haemodynamic
compensatory mechanisms are activated in order to initially maintain a normal level of
cardiac output.
Vascular risk factors reducing carotid arterial and cardiac blood flow to the brain are believed
to relate to cognitive impairments (e.g. de la Torre, 2000). A theory presented by de la Torre
(2000) describes the role of critically attained threshold of cerebral hypoperfusion (CATCH)
in the development of AD. CATCH arises when additional vascular risk factors further
reduce cerebral blood flow over and above that seen in normal ageing resulting in a cascade
of events, including reduced energy metabolism and increased oxidative stress, leading to the
production of amyloid-β proteins, neurodegeneration and finally symptoms of dementia (de la
Torre, 2000).
Various authors studying this topic accept that vascular factors such as poor cerebral blood
flow, in particular to brain regions fundamental to cognitive processing and microemboli in
the brain are chief mechanisms involved in poor cognitive function in HF (Jesus et al., 2006).
Researchers have proposed that insufficient vascular function leads to reduced metabolism
and a deprived supply of vital nutrients and oxygen to the brain.
Researchers have proposed that low left ventricular ejection fraction (LVEF) results in
impaired cerebrovascular function and in turn poor cognitive function. LVEF is the
percentage of blood pumped out of the left ventricle. An early study by Zuccalà and
colleagues (1997) showed a non-linear correlation between global cognitive function
(MMSE) scores and ejection fraction in HF patients. In their study, Zuccalà and colleagues
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
38
(1997) demonstrated that greater decreases in visuospatial ability (Ravens matrix), overall
MMSE scores, and the attention subset of the MMSE related to ejection fraction values of
less than 30%. Ejection fraction has shown to not only predict global cognitive function but
also specific cognitive functions including motor speed and visuo-constructural performance
independent of beta-blockers (Gottesman et al., 2009), working memory (digit span),
attention, and executive function (Trail Making-B; Almeida & Tamai, 2001b).
Supporting these findings, other trials showed that low ejection fraction is related to poor
short-term, working and episodic memory, attention and executive function in patients with
decompensated and stable HF (Kindermann et al., 2012). Other authors however have failed
to find any correlations between ejection fraction and MMSE scores (Jesus et al., 2006),
memory or executive function (Wolfe et al., 2006). However, the sample size in the latter
study was small and echocardiography was conducted within 18-months prior to testing,
during which time the HF patient’s condition may have improved or worsened. There is some
evidence that structural changes in the heart as seen in left ventricular dysfunction causing
changes in ejection fraction may be a possible mechanism for worse performance on memory
in older HF patients, however more research is needed to confirm these suggestions.
Nutrient, glucose and oxygen supply to the brain is so important that during conditions where
blood flow is compromised, the brain has an in built mechanism to modify cerebral
microvascular circulation (or autoregulate) to achieve consistent blood flow in the brain
(Newell & Aaslid, 1992; Paulson, 2002). This dynamic process ensures that blood volume in
the brain remains consistent when there are changes in perfusion pressures (Newell & Aaslid,
1992). Researchers have suggested that cognitive impairments arise from functional and
pathological change in the entire cardiovascular system seen in older HF patients, including
changes to the heart, the central and peripheral vascular systems and cerebral vascular
changes (Vogels, Scheltens, et al., 2007). There is increasing evidence suggesting that
vascular factors contributing to ischemia and reduced cerebral blood flow are implicated in
cognitive impairments seen in HF patients (Georgiadis et al., 2000; Jesus et al., 2006; Vogels
et al., 2008).
4.3 Cerebral haemodynamic factors
There is evidence suggesting that cerebral hypoperfusion may be the mechanism underlying
cognitive impairments in heart failure (HF) patients. This is due to poor cerebral blood flow
in brain regions involved in cognitive function in patients with HF (Georgiadis et al., 2000).
Heart failure (HF) patients who have undergone heart transplants have shown significantly
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
39
improved cerebral blood flow following transplant (Deshields, McDonough, Mannen, &
Miller, 1996; Gruhn et al., 2001). Interestingly patients with heart transplants (Bornstein,
Starling, Myerowitz, & Haas, 1995), ventricular assist devices and other surgical devices (e.g.
pacemakers) have shown to improve cognitive function following procedure. These
aforementioned studies indicate that improvements in cognitive function are possible or that
cognitive impairments are reversible in HF patients (Taylor & Stott, 2002). Three months
following cardiac resynchronisation therapy through implantation of a biventricular device
(implantable cardioverter defibrillator), patients with moderate and severe HF (NYHA class
III and IV) were shown to improve performance on task measuring working memory,
attention and speed of information processing (Dixit et al., 2010). Additionally, quality of life
specific to cardiac symptoms improved in patients three months following implantation of the
device.
In recent years, an increasing number of researchers have provided further insight into the
cerebrovascular mechanisms associated with cognitive impairment in HF using advanced
imaging technologies. These studies have utilized advancements in Doppler and imaging
techniques to assess whether cerebral perfusion changes with cognitive decline. Exploring
changes in blood pressure, blood flow velocity and biomarkers known to influence the
vasculature can determine cerebrovascular function. Furthermore, with improvements in
imaging techniques, researchers have applied advanced techniques to assess whether blood
flow supplied to the periphery and in specific brain regions where cognitive processing
occurs are sufficient. Such useful imaging techniques include Transcranial Doppler (TCD)
and Magnetic Resonance Imaging (MRI) techniques.
4.3.1 Transcranial Doppler
There is growing interest in the relationship between changes in cerebral blood flow velocity
and the influence cognitive function in HF patients. Cerebral blood flow velocity in basal
cerebral arteries can be measured by means of a Transcranial Doppler (TCD) device. The
TCD is a non-invasive, hand held device used to measure velocity of blood flow using the
Doppler Effect. Blood vessels supplying blood to the brain include the common carotid
arteries, which branch off into the internal and external carotid arteries. The internal carotid
artery enters the skull via the carotid canal and the temporal bone. Further along its path, the
internal carotid artery penetrates the dura mater and branches into three branches, the anterior
cerebral artery (ACA), the posterior cerebral artery (PCA) and the middle cerebral artery
(MCA; Babikian & Wechsler, 1993). The ACA supplies blood to the anterior, superior and
medial sections of the frontal lobes and the medial area of the cerebral hemispheres. The PCA
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
40
supplies blood to the midbrain, thalamus and the inferior temporal lobe and medial section of
the occipital lobe. The MCA supplies blood to the basal ganglia and then splits into two or
three additional arteries, which collectively supply blood to the lateral hemisphere (superior
branch), temporal and inferior parietal lobes (inferior branch; Babikian & Wechsler, 1993).
The MCA is of particular interest in this study as it supplies almost 80% of blood to the
hemispheres and regions involved in cognitive processing. The temporal lobes are involved
in memory processes, in particular learning and retention (Newell & Aaslid, 1992), which
have shown to be impaired in HF patients. Additionally, the parietal lobes are important for
processing short-term memory and attention, which are cognitive factors impaired in elderly
HF patients. TCD has been utilised to assess whether associations exist between cognitive
function and blood flow velocities in common carotid and basal cerebral arteries providing
relevant brain regions with sufficient blood. Although carotid arterial blood flow in heart
failure patients has not been widely studied, cardiovascular risk factors general
cardiovascular disease risk scores have shown to predict reduced pulsatile blood flow
velocity in the common carotid arteries of healthy elderly individuals (Pase, Grima, Stough,
Scholey, & Pipingas, 2012).
4.4 Cerebral circulation and cognitive function
Few studies have explored the relationship between common carotid blood flow velocity and
cognitive function. Reduced common carotid arterial blood flow is associated with poor
global cognitive function in patients with mild-moderate carotid arterial disease (Fu, Miao,
Yan, & Zhong, 2012). The present study will expand on this preliminary evidence to explore
whether reduced blood flow in the common carotid artery is a possible mechanism for
cognitive impairment in HF patients. Research into the function of the cerebral arterial blood
flow velocity rather than common carotid blood flow and cognitive function has received
greater interest. This is possibly due to the anatomical position of the middle cerebral artery
being closer to regions involved in cognitive functioning. Jesus et al. (2006) recorded
cerebral blood flow velocity in younger adults with congestive HF (n=83; mean age 55±12
years). Reduced blood flow velocity in the right middle cerebral arterial was shown to relate
to poorer global cognitive scores as measured by the Mini Mental State Examination
(MMSE; mean = 23; range 7–30). However, since the researchers included patients with
stroke the middle cerebral blood flow changes observed may have been due to factors aside
from HF. Moreover, when corrected for stroke increased blood flow velocity in the right
anterior cerebral artery correlated with better global cognitive function scores (MMSE; Jesus
et al., 2006). These important findings indicate that increased cerebral blood flow is
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
41
associated with improved global cognition however, given that other cognitive domains are
also impaired in HF the effects of cerebral blood flow on these cognitive domains is also of
interest.
Expanding on the effects of cerebral blood flow velocities on global cognitive measures,
researchers conducted a series of experiments to ascertain whether similar blood flow
observations are seen with specific cognitive functions. In one study, Vogels et al. (2008)
explored such relationships in older age matched (≥ 50 years) outpatients with mild and
moderate HF (n=43; LVEF < 45%; NYHA class II and III; age 68.0±8.9 years),
cardiovascular disease but without evidence of HF (n = 33; age 67.8±9.7 years) and healthy
controls (n=22; LVEF > 55%; age 64.1±8.3years). Overall, group differences were observed
in memory, executive function, mental speed, attention and overall cognitive z scores. In this
trial, HF patients performed worse on these cognitive factors followed by the cardiovascular
disease group and healthy controls (Vogels et al., 2008). Compared with healthy controls
(56.1±10.9 cm/s), middle cerebral arterial blood flow velocity as measured by the TCD was
significantly slower in HF patients (47.3±10.7 cm/s) and cardiovascular disease patients
(49.8±11.2 cm/s; Vogels et al., 2008). No significant group differences were seen in mean
blood flow velocity in the middle cerebral artery between the HF and cardiovascular disease
patient groups. The authors therefore suggest that reduced cerebral blood flow in HF patients
may be due to risk factors shared by patients with cardiovascular disease, rather than due to
poor cardiac output seen in HF (Vogels et al., 2008). However, this theory needs to be further
explored. Despite group differences in cognitive function and middle cerebral arterial blood
flow velocities, unlike Jesus et al. (2006) who found relationships between cerebral blood
flow and global cognitive function, Vogels et al. (2008) did not find any relationship between
specific cognitive domains and cerebral blood flow velocity.
Since Vogels et al. (2008) recorded mean blood flow velocities from the right and left middle
cerebral arteries, any unilateral differences are therefore unknown. Additionally, total z
scores for the cognitive tests rather than individual test scores were used in the analysis.
Assessing relationships between cerebral blood flow velocities and specific
neuropsychological tests may have provided additional information that could have
potentially been lost when tests were combined as z scores. Furthermore, other possible
confounding variables such as medication, diagnosis and disease severity were not
considered. Finally, the study may have been underpowered by a small participant size.
The TCD system has also been used to measure cerebrovascular reactivity using a formula
incorporating middle cerebral arterial (MCA) blood flow velocity during normocapnea and
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
42
hypercapnea (with and without CO2 administration, respectively). In a small study Georgiadis
et al. (2000) compared cerebrovascular reactivity to CO2 (average between left and right
middle cerebral arteries) in HF patients with NYHA class II (59±11 years), III (61±11 years)
and IV (53±11 years) to that of age matched controls (57±9 years) recruited from a neurology
ward. It was revealed that controls had higher cerebrovascular reactivity compared to each of
the HF groups tested. Furthermore, patients with mild and moderate HF (NYHA functional
class II and III) showed higher cerebrovascular reactivity compared to patients with severe
HF (NYHA class IV). No differences in cerebrovascular reactivity were observed between
patients with mild and moderate heart failure. Furthermore, higher LVEF levels and lower
NYHA functional class, but not age related to greater cerebral vasoreactivity (Georgiadis et
al., 2000). Further, this study failed to find group differences in cerebrovascular reactivity in
patients receiving and not receiving beta-blockers, ACE inhibitors or nitrates indicating that
drugs do not influence cerebrovascular reactivity in HF patients.
Although improvements in executive function were seen in older patients with moderate HF
(NYHA class III) following an 18 week exercise program, no significant changes were
observed in middle cerebral arterial (MCA) blood flow velocity, which has been implicated
in poor cognition (Tanne et al., 2005). However, given that no changes were seen on global
cognition (MMSE scores), verbal fluency, and visual constructional abilities, visual
perceptual planning and short-term nonverbal memory (Rey-Osterrieth complex figure) it is
possible that poor middle cerebral arterial (MCA) blood flow velocity is not related to these
cognitive functions.
Studies using the TCD to measure cerebral blood flow have led authors to suggest that
mechanisms for cognitive impairment in HF are changes in haemodynamics, microemboli
(Jesus et al., 2006) and impaired cerebral autoregulation (Vogels et al., 2008). These authors
concluded that reduced cerebrovascular reserve capacity might contribute to poor cognitive
function in HF patients (Georgiadis et al., 2000).
4.4.1 Cerebral blood flow and mood
Few studies have examined physiological effects of mood in particular depressive symptoms
and anxiety in older heart failure patients. Using single-photon emission computed
tomography (SPECT), Alves et al. (2006) demonstrated slower cerebral blood flow in elderly
HF patients (NYHA II or III) with major depressive symptoms compared to patients that
were not depressed. In particular, in depressed HF patients, reduced blood flow was detected
in the medial temporal region (the left anterior parahippocampal gyrus and hippocampus) and
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
43
reduced blood flow was related to increased severity of depressive symptoms but not
cognitive dysfunction (Alves et al., 2006). There have been no studies that have explored the
associations between anxiety or depression with cerebral blood flow in the common carotid
and middle cerebral arteries using the TCD in elderly HF patients.
Blood flow measures in the common carotid arteries have revealed possible relationships
with cognitive functioning in healthy controls, however this thesis will expand on this new
evidence and explore whether any relationship exists in HF patients. The relationship
between MCA blood flow velocity and cognition in HF has been more widely researched but
with mixed results. Research described in this thesis will expand on previous studies and will
examine whether impairments in additional cognitive domains relate to reduced MCA blood
flow velocity in these patients.
4.4.2 Brain imaging studies
Since cognitive impairments may influence the patient’s ability to care for themselves, it has
been suggested that Magnetic Resonance Imaging (MRI) studies need to be undertaken to
obtain a better understanding of how areas of the brain involved in decision making are
affected in HF (Dickson, Tkacs, & Riegel, 2007). Utilizing single-photon emission computed
tomography (SPECT) analysis Alves, et al., (2005) revealed that compared to age matched
controls, elderly patients with mild and moderate HF (NYHA class II and III) displayed
bilateral reductions in regional cerebral blood flow of the posterior cortical brain regions. In
particular, poor blood flow in brain regions involved in memory processing including the
precuneus, cuneus, right lateral temporoparietal cortex and posterior cingulate gyrus was seen
in HF patients. Importantly, poor blood flow in the posterior cingulate cortex and precuneus,
which are brain regions related to cognitive functioning, suggests that poor cognitive function
in HF may be due to hypoperfusion in these brain regions (Alves et al., 2005). No significant
group differences in brain white matter hyperintensities as measured by MRI were seen.
Given that SPECT scanning in this study was conducted one week and two months following
cognitive assessments, the relationships found between cognitive tasks and brain imaging
may not accurately represent cognitive function at the time of assessment. Furthermore, a
lack of MRI findings may be due to small sample size and again the fact that the imaging
component of the study was conducted two months following cognitive assessments.
Cardiovascular symptoms as well as cognitive abilities in these patients may have changed
during this time and interpreting these findings should be done with caution.
Studies showing impairments in cognitive factors in HF patients have shown interesting
structural changes in these patients compared to controls, which may explain these
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
44
impairments. Almeida, Garrido, et al. (2012) for example, examined a cohort of HF patients
who scored significantly worse on psychomotor speed and immediate and delayed memory
tests compared to healthy controls. In this study, the HF group showed greater cerebral grey
matter loss in the frontal lobes, anterior cingulate, and temporal-parietal lobes compared to a
healthy control group and patients with ischemic heart disease (Almeida, Garrido, et al.,
2012). Supporting this, researchers suggest that regional grey matter loss seen in a younger
HF patient cohort may play a role in impaired cognitive function in these patients (Woo,
Macey, Fonarow, Hamilton, & Harper, 2001). On the contrary, another study failed to
demonstrate structural brain changes using MRI in HF patients (LVEF < 40%) who had
impairments in global cognition and memory, however failing to find relationships with
structural brain changes may have been due to methodological issues (Beer et al., 2009).
4.4.3 Summary
In summary, the mechanisms associated with cognitive impairment in heart failure (HF) are
poorly understood although it appears that cognitive impairment is underpinned by vascular
factors such as cerebral hypoperfusion. Additionally, mechanisms for cognitive impairment
in HF patients may include decreased ejection fraction due to impaired heart function.
Hypoperfusion due to emboli forming in the brain may reduce cognitive capacities. However,
smaller studies failed to find similar associations. Treatment with surgery or devices have
shown to improve working memory, attention, speed of information processing and quality of
life indicating that factors associated with poor heart function are implicated. Brain imaging
techniques have revealed that cerebral perfusion changes are related to cognitive impairments
in HF. Specifically, global cognitive function is related to reduced blood flow velocity in the
middle cerebral arteries (MCA). Although researchers have found impairments in memory,
executive function, attention and overall cognitive scores in HF patients, these cognitive
domains were not related to reduced MCA blood flow velocity.
4.5 Arterial stiffness
As mentioned, heart failure (HF) patients with cognitive impairments are more likely to have
low cerebrovascular reactivity, which may explain findings of increased white matter
hyperintensities and microemboli in these patients. Recent authors have proposed that these
brain changes leading to cognitive impairment relate to elevate arterial stiffness measures
(Hanon et al., 2005; Kearney-Schwartz et al., 2009; Mitchell et al., 2011). An understanding
of the circulatory system is required to understand how arterial stiffness is involved in the
pathogenesis of HF and cognitive dysfunction. The circulatory system needs to operate
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
45
optimally for sufficient blood flow to reach organs and tissues. This is to guarantee sufficient
nutrient and oxygen supply to the brain for adequate metabolic function. During healthy
conditions, the heart pumps with adequate force required to eject blood from the left ventricle
to circulate blood. The elastic smooth muscle walls of the arteries allow a continuous
pulsatile flow permitting a constant movement of blood throughout the circulatory system to
reach vital organs (Mann, 2008).
During long-term hardening of the arterial walls in the case of hypertension, arteriosclerosis
and ageing the arterial walls harden making blood flow through the arteries difficult. To
ensure that the body receives an adequate continuous flow of blood, the left ventricle pumps
with greater force to break through the increasing impedance in the arterial walls due to
stiffening. The pulsatile flow of blood pumped from the left ventricle creates an increase in
pulse pressure, which can cause injury to vessels and tissues. During normal vascular
functioning, compliant conduit vessels prevent damage from the increased pressure by
submitting a reflected wave back to the heart to augment diastolic pressure (Mitchell et al.,
2001). The reflective wave in turn minimises pressure on the left ventricle by augmenting
diastolic pressure and decreasing pulse pressure. Stiff arteries do not have the capacity to
minimise pulse pressure and as a result, there is an increase in left ventricular load (O'Rourke
& Safar, 2005)
Vessels in the brain however are vulnerable to the high-pressure fluctuations of an increased
pulse pressure. Due to high metabolic requirements and the need for a constant perfusion
through systole and diastole, arteries in the brain provide low resistance to blood flow
(O'Rourke & Safar, 2005). Furthermore, unlike other body tissues in which constricted
arteries protect downstream tissues, brain arteries remain dilated leading to high fluctuations
of pressure and flow, see O'Rourke and Safar (2005) for further reading. It has been
suggested that cerebral deterioration is due to microvascular damage caused by high pulse
pressure in vertebral and carotid arteries (O'Rourke & Safar, 2005). Microcirculatory
remodelling due to arterial stiffness is believed to cause microvascular ischemia in the brain
(Mitchell, 2008; Mitchell et al., 2001). Assessing the relationship between arterial stiffness
and cognitive function in heart failure is therefore important to gain an overview of the
mechanisms involved.
4.5.1 Arterial stiffness and heart failure
Added vascular pathologies seen in heart failure (HF) have also been associated with
cognitive impairments. Researchers have shown that arterial stiffness increases with age (e.g.
Elias et al., 2009), is increased in elderly patients with HF (Tartière, Logeart, Safar, & Cohen-
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
46
Solal, 2006) and is a risk factor for HF (Chae et al., 1999; Vaccarino, Holford, & Krumholz,
2000). Since a direct measure of arterial stiffness is in most cases not viable in research
settings, indirect measures including pulse wave velocity (PWV) and aortic pressure
augmentation (AIx) are studied using devices such as the SphygmoCor® Px. These
instruments use haemodynamic information through pulse waveforms to understand the
relationship between the heart and arterial system. Although PWV is the most robust
measurement of arterial stiffness measures, other non-invasive measures including
augmentation index (AIx) and pulse pressure (PP) are efficient measures that are suitable for
research and clinical settings. The SphygmoCor® Px uses a mathematical transfer function to
obtain the central (ascending) aortic pressure waveform from systolic and diastolic pressure
values of the brachial (as measured by a conventional cuff) and radial arteries. Augmentation
index (AIx) is an indirect measure of arterial stiffness defined by the difference between the
second (P2) and first (P1) systolic peaks of the central aortic waveform (augmentation
pressure), expressed as a percentage of the pulse pressure.
Elevated arterial stiffness is one of the factors known to be associated with the
pathophysiology of HF (Denardo, Nandyala, Freeman, Pierce, & Nichols, 2010). HF patients
(age > 40 years; 61±10), have greater central and peripheral pulse pressure and diastolic
pressures compared to patients without HF (age > 40 years; 57±10; Mitchell et al., 2001) and
greater brachial pulse pressures compared to patients with preserved LVEF (Tartière et al.,
2006). Based on their findings, Mitchell et al. (2001) explain that central conduits are
therefore likely to be more stiff and distal conduit vessels less stiff in HF patients. The
authors suggest that peripheral resistance has been a focus for vasodilator therapy, however
these results suggest that central changes in pressures are also important in HF patients.
Interestingly, drugs commonly taken by HF patients such as angiotensin converting enzyme
(ACE) inhibitors, angiotensin receptor blockers, calcium channel blockers and nitrates are
known to reduce arterial stiffness (O'Rourke & Safar, 2005). These drugs do this by
decreasing wave reflection leading to lowered central augmentation and pulse pressure
(O'Rourke & Safar, 2005). Heart failure (HF) patients with low LVEF (< 40%; NYHA class
2.6±0.9) are likely to be taking ACE inhibitors, angiotensin II receptor blockers and diuretics,
whereas those with preserved LVEF (≥ 40%; NYHA class 2.1±0.8) LVEF were more likely
to be taking calcium channel blockers (Tartière et al., 2006). Hypertensive medications
however do not seem to alter the relationship between pulse pressure and HF risk (Vaccarino
et al., 2000).
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
47
However, Scuteri et al. (2009) found that increased left ventricular mass index in older
individuals was associated with poor global cognitive function as measured by the MMSE,
independent of blood pressure or arterial stiffness as measured by pulse wave velocity
(Scuteri et al., 2009). Although the cohort tested did not have HF per-se, they did have
cardiovascular risk factors such as hypertension (and previous stroke, MI) and were taking
drugs commonly taken by HF patients such as anti-hypertensives, nitrates, statins, diuretics
and beta-blockers. In this study, however group differences were observed only in patients
taking anti-hypertensive drugs. This suggests that medications taken by HF patients may
influence arterial stiffness. In a cohort of patients attending a memory clinic, higher arterial
stiffness as measured by carotid-femoral pulse wave velocity was weakly related to poorer
global cognitive scores (as measured by the Mini Mental State Examination; MMSE) even
after controlling for nitrates, anti-hypertensive, demographics and cardiovascular risk factors
(Scuteri, Brancati, Gianni, Assisi, & Volpe, 2005).
Biomarkers related to vascular function are related to HF and arterial stiffness. During
neurohormonal activation, potent vasoconstrictors including endothelin-1 are released. The
role of endothelin-1 together with vasodilators (e.g. nitric oxide) is to maintain ideal vascular
tone and normal blood pressure. Increased levels of plasma endothelin-1 are associated with
poorer prognosis of HF patients (Pousset et al., 1997) and are seen predominantly in patients
with severe but not mild HF (Wei et al., 1994). Preliminary evidence proposes that an
additional function of endothelin-1 is to elevate pulse wave velocity and augmentation index
and decrease cardiac output (Vuurmans, Boer, & Koomans, 2003). Given that arterial
stiffness (see below), poor cardiac output and increased disease severity are perhaps related to
poor cognitive function, it is reasonable to investigate whether endothelin-1 also has a role in
cognitive dysfunction in HF patients.
4.5.2 Arterial stiffness and cognitive function
In recent years, a growing number of researchers have explored whether an association exists
between arterial stiffness and cognitive function. Evidence suggests there is a relationship
between vascular factors and cognitive dysfunction and dementia. Kearney-Schwartz et al.
(2009) reported that pulse wave velocity was associated with cognitive function in older men
(aged 60-85 years) but not women with hypertension and subjective memory impairment.
This suggests that the associations between arterial stiffness and cognition are strongly
influence by gender. Interestingly, compared to women, men performed significantly poorer
on overall memory scores and a greater number of men were taking angiotensin converting
enzyme (ACE) inhibitors (32% versus 15%) and anticoagulants (37% versus 19%). This may
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
48
suggest that the cohort tested overall showing gender differences may also be related to more
males taking drugs known to have vascular effects.
Researchers have observed a relationship between pulse wave characteristics and specific
cognitive domains. Pulse wave velocity (PWV) was shown to be significantly increased in
elderly participants (> 60years) with vascular dementia, Alzheimer’s dementia and in patients
with mild cognitive impairment (Hanon et al., 2005). Moreover, PWV was also related to
poorer global cognitive function (as measured by the MMSE) and overall cognitive scores
(Hanon et al., 2005). In community-dwelling older population (≥ 70 years) without
cardiovascular disease, higher brachial-ankle pulse wave velocities is linked to worse global
cognitive performance using the MMSE assessment tool (Fujiwara et al., 2005). Examining
baseline measures, older age and PWV combination related to worse performance on global
cognitive function, visual spatial organisation and memory, verbal episodic memory, and
scanning and tracking (Elias et al., 2009).
Longitudinal studies however have shown mixed results. Based on data from the large
Rotterdam study, Poels et al. (2007) showed that arterial stiffness as measured by carotid-
femoral PWV and carotid distensibility was significantly related to reduced global cognitive
scores (MMSE), poor performance on executive function as measured by the incongruent
Stroop test but not letter digit substitution. However, when adjusting for cardiovascular
factors, only small associations were found between PWV and poor executive function as
determined by the incongruent Stroop test (incongruent). These data suggest that in elderly
individuals, arterial stiffness was unrelated to cognitive decline over time or the risk of
developing dementia (Poels et al., 2007). Supporting this, Elias et al. (2009) failed to find
associations between PWV and working memory. This may have possibly been due to the
memory task being too simple to complete. In contrast, carotid-femoral pulse wave velocity
has shown to be the best predictor of longitudinal reductions (median =12 months) on MMSE
scores in older patients (79±6 years) with memory complaints, even after controlling for
demographics and cardiovascular risk factors (Scuteri et al., 2007).
Increased arterial stiffness (brachial pulse pressure; n=1749; 57.1±17.2 years and carotid-
femoral pulse wave velocity; n=582; 54.3±17.1 years) at baseline resulted in worse
performance on learning and cognitive screening tools over time (Waldstein et al., 2008) after
accounting for possible confounders including inflammation or psychological factors for
example anxiety which may have affect cognitive performance. However, attention,
psychomotor speed, executive function or language was not influenced by arterial stiffness
(Waldstein et al., 2008). On the contrary, in a middle age sample of healthy participants,
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
49
increased pulse pressure was weakly related to decreased performance on Quality of Episodic
Secondary Memory domain (Pase et al., 2010). Furthermore, in this study, elevated
augmentation index was related to slower memory retrieval times. Additionally pulse
pressure was an independent predictor of memory processes predicting 7% and 6% of the
variance in Quality of Episodic Memory and Speed of Memory performance, respectively.
Finally, augmentation index independently predicted 7% of the variation in Speed of Memory
(Pase et al., 2010). Ellins et al. (2008) demonstrated that carotid arterial stiffness 3 years post
baseline testing was related to high levels of systemic inflammation as measured by C-
reactive protein (CRP) in a cohort of middle-aged participants aged 45-59 years.
Interestingly, baseline levels of inflammatory markers interleukin-6 and tumour necrosis
factor alpha were not related to arterial stiffness (Ellins et al., 2008).
Recently, Mitchell et al. (2011) conducted a large study on elderly community dwelling
individuals (69-93 years) and revealed that arterial stiffness as measured by carotid-femoral
pulse wave velocity and carotid pulse pressure, were associated with lower memory and
executive function scores. Increased white matter hyperintensities and cerebral infarctions
possibly accounted for the variance between memory and carotid-femoral PWV.
Augmentation index however was not associated with any of the cognitive scores measured.
Mitchell et al. (2011) findings suggest that increased aortic stiffness is related to increases in
microvascular brain lesions and poor cognitive function via extreme flow pulsatility in the
brain. There has been great interest in the link between the arterial system and cognitive
impairment associated with ageing and in particular dementia of the vascular type. Arterial
stiffness is associated with and cognitive impairment, in particular, global cognitive function,
visual spatial and working memory. To date no study has investigated whether arterial
stiffness is a possible mechanism for cognitive impairments in HF.
An investigation of the association between arterial stiffness and cognitive impairment in HF,
in particular, global cognitive function, visual spatial and working memory may provide
insight into the possible mechanisms for cognitive decline in these patients.
4.5.3 Arterial stiffness and mood
The influence of arterial stiffness in mood has been explored in individuals with depression
and anxiety. In particular, a large study demonstrated that elevated depressive symptoms and
anxiety were related to early wave reflection due to arterial stiffness as measured by
augmentation index (Seldenrijk et al., 2011). In addition, increased augmentation index was
higher in individuals who had experienced longer periods of depressive symptoms and
anxiety. Data from the large Rotterdam population study indicated that elderly individuals
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
50
who are depressed are more likely to have increased arterial stiffness, as measured by
common carotid distensibility or carotid-femoral pulse wave velocity (Tiemeier, Breteler,
Van Popele, Hofman, & Witteman, 2003). Additionally, in a small study, patients with
general anxiety disorder as well as cardiovascular disease had greater levels of arterial
stiffness as measured by pulse wave velocity and augmentation index than controls
(Yeragani, Kumar, Bar, Chokka, & Tancer, 2007). It has been suggested that augmentation
index may therefore be a mechanism by which depression is linked to cardiovascular disease
risk (Seldenrijk et al., 2011) and vascular factors (Tiemeier et al., 2003). For this reason it is
also possible that arterial stiffness may be related to depressed mood and increased anxiety
levels in HF patient. However, no study has examined the association between depressive
symptoms and anxiety in HF patients.
4.5.4 Arterial stiffness and cerebral circulation
Recent evidence shows a link between arterial stiffness and cerebral blood flow. Kwater,
Gsowski, Gryglewska, Wizner, and Grodzicki (2009) found a relationship between peripheral
arterial stiffness and haemodynamic changes in the middle cerebral artery in individuals at
risk of cardiovascular disease. In particular, arterial stiffening (as measured by carotid-
femoral PWV and brachial pulse pressure) and age were positively related with pulsatility
and resistance indices of the middle cerebral artery flow, even when accounting for
covariates. No significant correlations were found between middle cerebral arterial indices,
blood pressures (systolic and diastolic) or mean arterial pressures. The authors however
suggested that aortic or carotid pulse pressure measurements would have provided more
accurate representations of how arterial stiffness is related to cerebral haemodynamics. More
recently, findings by Xu et al. (2012) suggest that increased systemic arterial stiffness effects
middle cerebral arterial blood flow in patients who were referred to a hypertension clinic and
not taking hypertensive medication. The authors found that this change in middle cerebral
arterial blood flow was due to elevated pulse pressure. Furthermore, white matter
hyperintensities as measured by magnetic resonance imaging was related to an increase in
carotid stiffens as measured by the augmentation index (Kearney-Schwartz et al., 2009).
These authors suggest that measurement of middle cerebral arterial blood flow will help
understand the effects of peripheral haemodynamic changes on central vascular changes.
Furthermore, in a cohort of patients with memory deficits, pulse wave velocity was
significantly higher in patients who also had cortical (brain) atrophy compared to those who
also had subcortical microvascular lesions (lacunes or white matter lesions) as detected by
computerised tomography (CT) scans (Scuteri et al., 2005).
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
51
4.5.5 Summary
In summary previous studies have demonstrated that indirect measures of arterial stiffness are
elevated in HF and in the elderly. Additionally arterial stiffness is related to poor global
cognition, learning, executive function and visuospatial abilities in elderly individuals.
Finally, impairments in speed of information processing and episodic memory in middle-aged
healthy cohort are related to increased arterial stiffness. The association between arterial
stiffness on cognitive function in elderly heart failure patients has not been examined.
4.6 Oxidative stress in heart failure and in cognitive impairment
In recent years, there has been growing interest in the role of oxidative stress and
inflammatory pathways on the natural aging process and the development of disease states.
Biological oxidative stress pathways were first purported by Harman in the 1950’s with his
“free radial theory of ageing” and the actual term “oxidative stress” was first defined by
Helmus Sies in 1985 (Sies, 1985). This theory proposes that highly reactive biomarkers
known as “free radicals” accumulate in the body over time as a result of normal metabolic
processes. This build-up of free radicals in turn causes cellular damage and cell death. This
next section will provide an overview of the various biomarkers involved in the physiological
processes related to the endogenous oxidative stress and inflammatory pathways, and how
they are implicated in natural healthy and diseased bodily functions in particular HF and
cognitive impairment.
4.6.1 The oxidative stress pathway
Mitochondria play a significant role in normal metabolic function by providing energy for
adequate cellular activity (Fernández-Checa et al., 1998; Valko et al., 2007). During normal
metabolism, numerous redox reactions take place whereby electrons are removed from
(oxidation) or added to (reduction) molecules to ensure cellular homeostasis. For example,
molecular oxygen is used to produce adenosine triphosphate (ATP) from adenosine
diphosphate (ADP) in a process known as oxidative phosphorylation. Molecules with
unpaired electrons are also known as oxidants or free radicals (Mak & Newton, 2001; Sies,
1997). As a result of these reactions and the reduction of oxygen, reactive oxygen species
(ROS) or “free radicals” including superoxide anions (O2-), hydrogen peroxide (H2O2), nitric
oxide (NO•) and hydroxyl radicals are formed (Thannickal & Fanburg, 2000; Figure 1). In
low concentrations, these ROS have protective physiological roles. For instance, superoxide
anions, hydroxyl radicals and hydrogen peroxide, respond to harmful processes such as
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Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
53
4.6.2 The role of antioxidants in the body
With the aim to reach cellular homeostasis, the body has inbuilt antioxidant protective
mechanisms to counteract the deleterious effects of oxidants. These defence mechanisms
encompass enzymatic and non-enzymatic molecules and pathways to protect and repair cells
damaged by free radicals (Sies, 1997; Valko et al., 2007). In one enzymatic antioxidant
pathway, superoxide dismutase initially speeds up the conversion of superoxide to hydrogen
peroxide (H2O2). Catalase and glutathione peroxidises then convert hydrogen peroxide to
water (Finkel & Holbrook, 2000). Through these pathways, the antioxidant enzymes prevent
oxidative damage by keeping reactive oxygen species levels to a minimum. Non-enzymatic
antioxidants including phenolic compounds, vitamin C (ascorbic acid), vitamin E (α-
tocopherol) and glutathione also play an important protective role. To prevent oxidants from
causing cellular damage, these non-enzymatic molecules convert them into non-radical end
products (Sies, 1997). The glutathione redox cycle for example intercepts the chain of
reactions that form reactive species by reducing hydrogen peroxide levels and in turn
reducing hydroxyl radical formation (Fernández-Checa et al., 1998). Alternatively, non-
enzymatic antioxidants relocate radicals to regions where their detrimental effects are less
harmful such as from a hydrophobic to aqueous phase (Sies, 1997). Coenzyme Q10 (CoQ10)
is a vitamin like substance found principally in the mitochondrial membranes and is involved
in metabolic functions including manufacturing adenosine triphosphate, protecting the
stability of cell membranes, regulating genes and enhancing the immune system (Boreková,
Hojerová, Koprda, & Bauerová, 2008). As an antioxidant CoQ10 has a vital role in protecting
DNA from free radical induced oxidative damage, and recycling and regenerating other non-
enzymatic antioxidants, ascorbic acid and α-tocopherol (Boreková et al., 2008). Rosenfeldt,
Hilton, Pepe, and Krum (2003) propose that CoQ10 decreases blood pressure by protecting
nitric oxide within the endothelium. ROS produced in the vasculature can reduce the amount
of nitric oxide available. Coenzyme Q10 ensures protection of nitric oxide indirectly by
scavenging reactive oxygen species, resulting in nitric oxide induced vasodilatation.
4.6.3 Oxidative stress in heart failure
Oxidative stress is known to play an important role in the pathogenesis of heart failure.
Oxidative stress causes impairments to myocardial function due to generalised and cardiac-
specific actions (Mak & Newton, 2001) causing oxidative damage to cellular proteins and
membranes leading to apoptosis (Grieve & Shah, 2003) and damage to DNA. Together these
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Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
55
4.6.4 Oxidative stress in cognitive impairment and mood
Interestingly elevated concentrations of oxidative stress markers and deficiencies in
antioxidants seen in HF patients are also observed in individuals with cognitive impairment.
Researchers have proposed that oxidative stress in the brain has a role in the development of
cognitive deficits in older individuals. Compared to other organs, the brain has a higher
metabolic rate and elevated oxygen consumption. Together these factors make the brain
highly susceptible to increased oxidative stress and lipid peroxidation (Gironi et al., 2011;
Mariani, Polidori, Cherubini, & Mecocci, 2005). In the normally aging brain, amyloid-β
plaques may play important roles in anti-oxidant defences and prevent neuronal dysfunction.
However, in AD and oxidative stress seen in ageing, the presence of additional sources of
oxidative stress increases the production of these amyloid-β plaques and in turn
neurodegeneration and dementia (Smith et al., 2002). Given that the oxidative stress
pathways are complex and involve various oxidative stress promoters, by-products and anti-
oxidants researchers suggest the need to explore multiple oxidative stress related biomarkers
in HF patients (e.g. Gironi et al., 2011).
Few researchers have examined the oxidative stress profile of elderly individuals with
memory impairments. Some authors demonstrated that older patients with mild cognitive
impairment have elevated concentrations of lipid peroxidation as measured by mild cognitive
impairment (Torres et al., 2011) and F2-isoprostanes (Praticò, Clark, Liun, Lee, &
Trojanowski, 2002) compared with healthy elderly controls. Interestingly a reduction in lipid
peroxidation and improvements in Quality of Working Memory was demonstrated following a
three month daily intervention of an antioxidant Pycnogenol®; Ryan et al., 2008).
Furthermore, Torres et al. (2011) demonstrated higher malondialdehyde (MDA) levels in
Alzheimer’s disease patients (66-90 years) compared to patients with mild cognitive
impairment patients (61-89 years). Additionally, patients with Alzheimer’s disease had
significantly higher antioxidant activity as measured by catalase and glutathione peroxidase
activity, compared with controls (62-83 years) and patients with mild cognitive impairment.
However, antioxidant activity does not appear to differ between patients with mild cognitive
impairment and healthy controls. In the study by Torres et al. (2011), controls had superior
global cognitive abilities as measured by the MMSE followed by patients with mild cognitive
impairment and Alzheimer’s disease. High levels of lipid peroxidation (MDA) and low
enzymatic antioxidant defences (as measured by glutathione reductase/glutathione peroxidase
ratio) were associated with poorer global cognitive function in Alzheimer’s disease patients
but not mild cognitive impairment patients or healthy controls. These findings indicate that
oxidative stress occurs during early stages of cognitive impairment. Moreover, an imbalance
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
56
exists between oxidative stress and antioxidant defences in patients with Alzheimer’s disease
and mild cognitive impairment (Torres et al., 2011). The authors suggest that in healthy
conditions and during early stages of cognitive impairment, the body adequately adapts to
oxidative stress changes. However, in severe cognitive decline as in Alzheimer’s disease, the
body progresses into a destructive phase, which in turn contributes to disease.
Contrary to previous findings (Praticò et al., 2002; Torres et al., 2011), Gironi et al. (2011)
found that elderly patients with memory deficits (65 – 90 years) defined as having either mild
cognitive impairment or dementia (Alzheimer’s disease, Parkinson’s disease with dementia,
dementia with Lewy bodies) showed lower concentrations of lipid peroxidation compared to
controls (Gironi et al., 2011). Patients with memory deficiencies had significantly lower
levels of the oxidative stress markers (malondialdehyde, glutathione, reduced glutathione)
and lower serum antioxidant power compared to controls. There were no significant
differences in Coenzyme Q10 (CoQ10) or reactive oxygen species as measured by
determinable reactive oxygen metabolites (DROMs). Additionally, oxidative stress and
antioxidant biomarkers however did not influence MMSE scores. Age and antioxidant power
predicted the risk of developing a memory deficit. Oxidative stress on the other hand as
measured by reduced glutathione and MDA predicted a reduced risk of developing memory
impairment. The authors suggest that reduced levels of oxidative stress markers may be a
result of over activation of the antioxidant system. In response to augmented oxidative stress,
there is an up regulation of the antioxidant system. As a result, oxidants and radical products
are cleared to prevent tissue damage (Gironi et al., 2011). Preliminary data revealed that
DROM levels are significantly increased in healthy ageing (Rosenfeldt et al., 2013). Older
healthy adults (> 60 years) have significantly higher levels of DROM compared to younger
healthy younger adults (18-30 years). Additionally, older healthy adults have lower plasma
levels of DROM compared to HF patients in the same age group. The current research will
expand on this data and explore whether DROM levels are associated with cognitive function
in healthy older HF patients.
The role of oxidative stress and antioxidants in cognitive functioning however is unclear.
Some authors theorise that with less antioxidant capacity compared to other organs, that the
brain is unable to combat the damaging effects of the oxidants. Neurodegenerative disease,
mild cognitive impairment and Alzheimer’s disease may occur because of persistent
oxidative damage and lack of antioxidant support (Mariani et al., 2005). However, others
suggest that during early stages, oxidants and radical products cleared by antioxidants to
prevent cognitive decline (e.g. Gironi et al., 2011). Although since an imbalance between
oxidative stress and antioxidant defences exist in HF and in some cases of cognitive decline,
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
57
it is reasonable to propose that similar associations may exist between oxidative stress and
cognitive impairments seen in HF patients.
It is well established that lower levels of the antioxidant and energy producer, coenzyme Q10
(CoQ10) are seen in the myocardium and plasma of HF patients and that blood levels
increase following supplementation (Folkers, Langsjoen, & Langsjoen, 1992; Keogh et al.,
2003). Numerous trials have examined the effects of CoQ10 supplementation on symptoms,
exercise capacity, recovery rates and biomedical status in HF. For example Keogh et al.
(2003) found that compared to controls taking placebo (n=17; 61±9years), patients with
dilated cardiomyopathy (n=18; 62±7years; not taking beta-blockers) improved NYHA
functional class by -0.5 class, exercise capacity following a three month 150mg/day CoQ10
intervention (Keogh et al., 2003). Additionally, an 8 week CoQ10 (300mg/day) intake was
positively correlated with improved vascular endothelial function as measured by flow
mediated dilatation in patients with ischaemic left ventricular systolic dysfunction (Dai et al.,
2011). Larger, long-term randomised studies have revealed that high daily intake of CoQ10
(2g/day) for 12 months reduced hospital stay in patients with moderate to severe HF (NYHA
class III and IV) by 33% (Morisco, Trimarco, & Condorelli, 1993). Additionally, reduced
complications associated with HF including pulmonary edema and cardiac asthma and
decreases in number of hospital readmissions due to worsening of the HF (23% vs 37%) were
seen in patients taking CoQ10. A meta-analysis examining eleven trials indicated that CoQ10
improved cardiac output and ejection fraction (3.7% absolute: relative improvement of
approximately 10%) and quality of life in HF patients suggesting that benefits from this
supplement is not only limited to cardiovascular related symptoms but extends to improve
feelings of wellbeing (Sander, Coleman, Patel, Kluger, & Michael White, 2006).
In the only study that examined the relationship between depression and oxidative stress in
HF patients, Kupper, Gidron, Winter, and Denollet (2009) failed to find an association
between serum levels of oxidative stress marker or an imbalance between oxidative stress and
antioxidant markers expressed as a ratio and depression in patients with chronic HF (aged <
80 years; LVEF ≤ 40%). Contrary to these findings, Michalakeas et al. (2011) demonstrated
that compared to non-depressed HF patients (≥ 18 years) those with depressive
symptomatology (Beck Depression Index > 10) had significantly higher levels of lipid
peroxidation as measured by malondialdehyde indicating higher oxidative stress levels.
Interestingly, following treatment with the SSRI sertraline, malondialdehyde levels decreased
in patients with depressive symptomatology. This reduction in oxidative stress in depressed
HF patients is possibly due to the antioxidant effects of sertraline (Michalakeas et al., 2011).
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
58
These trials only examined measures of oxidative stress and not inflammatory measures and
understanding the interaction between these are important when interpreting results from
studies that have examined depression in heart failure. Due to limited research it is unclear
whether oxidative stress is related to depressive symptoms especially in elderly patients with
heart failure, therefore more research is needed to clarify this relationship.
Recent evidence indicates that coenzyme Q10 (CoQ10) plasma levels are significantly lower
in patients with clinical depression compared to healthy controls (Maes et al., 2009). Here
just over half of depressed patients were deficient in CoQ10 levels. Despite a deficiency of
CoQ10 levels in depressed patients, CoQ10 levels were not associated with depressive
symptoms as measured by the Hamilton Depression Rating Scale. These observations
highlight the prospect of correcting deficient CoQ10 levels with an appropriate
supplementation to help reduce depressive symptoms by targeting oxidative stress and
inflammation that is seen in these patients (Maes et al., 2009). Additionally, CoQ10 was
shown to have neuroprotective effects related to cognition on Alzheimer’s disease model rats
and increased acetylcholinesterase (AChE) activity in the hippocampus and cerebral cortex
and decreased oxidative stress activity in the hippocampus (Ishrat et al., 2006). In addition,
some authors have examined the efficacy of CoQ10 in combination with other antioxidants,
anti-inflammatories and cofactors.
When Alzheimer’s disease patients with depression were encouraged to take CoQ10,
multivitamins, vitamin E, alpha-lipoic acid (ALA) and omega-3 PUFAs as adjuncts to their
regular treatments, improvements were seen on attention, delayed memory correct recall and
a decrease in delayed memory errors following 24 months treatment (Bragin et al., 2005).
Additionally depressive symptoms resolved following 6 months treatment, however with the
absence of a control group, no compliance monitoring and the addition of lifestyle
suggestions including dietary recommendations and physical exercises it is difficult to
ascertain which treatment/s influenced these positive results. These findings suggest that
reduced CoQ10 plasma levels may be related to decreased quality of life, mood and cognition
in HF. Whether there is a relationship between reduced CoQ10 on mood and cognition in
elderly HF patients, which are commonly seen in these patients, has not been explored.
4.7 Inflammation in heart failure and in cognitive impairment
4.7.1 The inflammatory pathway
There is widespread evidence indicating that inflammatory pathways and associated
biomarkers have a major role in the pathogenesis of chronic conditions such as HF,
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
59
depression and cognitive decline such as Alzheimer’s disease. The involvement of the
inflammatory response in the progression and pathogenesis of HF and cognitive impairment
has also received great interest. With the purpose of restoring a healthy equilibrium,
inflammatory and anti-inflammatory agents work with oxidants and antioxidants, to destroy
destructive agents in the body (Khaper et al., 2010). During any initial inflammatory
response, immune modulators such as Interleukin-6 (IL-6) and tumor necrosis factor–alpha
(TNF-α) trigger the formation of oxidants including superoxide anions and nitric oxide.
Furthermore, oxidative stress mechanisms play a role to assist with healing of damaged
tissue. For example, ROS triggers the release of inflammatory signalling molecules including
C-reactive protein (CRP) during the acute phase and IL-1, TNF-α during chronic phases of
injury (Khaper et al., 2010; Mann, 2008; Valko et al., 2007). Given the dynamic integrative
role between oxidative stress and inflammation in disease processes, we cannot explore these
pathways in isolation.
4.7.2 Inflammation and heart failure
HF is associated with increased pro inflammatory cytokines, activation of the complement
system, production of autoantibodies, over expression of histocompatibility complex class II
molecules and activation of vascular cellular adhesion molecules that perpetuate the
inflammatory state (Blum, 2009). In addition to the vasoconstrictors endothelins (e.g.
endothelin-1; ET-1), prostaglandins, nitric oxide, inflammatory mediators are also elevated in
heart failure (HF; Jackson, Gibbs, Davies, & Lip, 2000). Cytokines including TNF and IL-1
are pro-inflammatory mediators released from the myocardium in response to cardiac
damage. C-reactive protein (CRP), a measure of systemic inflammation is significantly
higher in patients with HF and is an indicator of disease severity (Xue, Feng, Wo, & Li,
2006). Anti-inflammatory cytokines levels (e.g. IL-10) are shown to be reduced in HF and
are inversely associated with the severity of the disease. It is the imbalance between the pro-
inflammatory and anti-inflammatory levels that are thought to be involved in the
pathogenesis of HF (Libby et al., 2008). The inflammatory response and resultant biomarkers
are believed to be a result of increased oxidative stress. Neurohormonal activation (increase
in AII, aldosterone, endothelin-1) and cytokines (TNF, IL-1) stimulate oxidative stress in the
heart, adding to the complexity of HF pathophysiology (Libby et al., 2008). Furthermore,
there is increasing evidence suggesting that vascular inflammation contributes to clinical
deterioration of clinical HF (Blum, 2009).
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
60
4.7.3 Inflammation and cognitive impairment
Interestingly a number of molecules associated with inflammation seen in HF are also
believed to be involved in the pathogenesis of Alzheimer’s disease (AD; e.g. Interleukin-1),
depression (e.g. high-sensitive C-reactive protein; hs-CRP) and impaired cognitive
performance in older adults (e.g. hs-CRP; Braunwald, 2008; Su, Huang, Chiu, & Shen, 2003;
Teunissen et al., 2003). This suggests that inflammatory factors may be related to
neuropsychological deficits seen in HF patients. Only two small studies to date have
examined the effects of inflammation on cognitive function in HF patients. In an early study
Said, Fouad, and Alvan (2007) observed that inflammatory markers TNF-α and IL-6 were
significantly higher in patients with moderate or severe HF (NYHA class III and IV)
compared to patients with no symptoms or mild HF (NYHA class I and II). When assessing
the entire HF population in this study, poor cognitive function evaluated by the Hodkinson
Abbreviated Mental Test (AMT) was strongly associated with high inflammatory markers
(TNF-α and IL-6). Additionally, IL-6 was the only variable shown to predict AMT scores.
Another recent study showed that in HF patients aged > 65 years (NYHA class I, III and III)
higher CRP and IL-6 levels were associated with lower global cognitive scores as measures
by the Montreal Cognitive Assessment battery (MoCA; Athilingam et al., 2012). However,
no associations were observed between tumour necrosis factor alpha and global cognitive
scores (Athilingam et al., 2012). Additionally, these studies measured global cognitive
function and a more detailed assessment would provide a better understanding of how
cognitive impairments are related to inflammatory markers (Athilingam et al., 2012).
However, in the absence of a control group, it is unknown whether the relationship between
cognition and inflammation was due to ageing. More recently, Kindermann et al. (2012)
assessed variance in cognitive function between decompensated HF patients with stable HF
and healthy controls. Using ANCOVA it was revealed that inflammation as measured by C-
reactive protein was significantly related to impaired memory, speed of information
processing and executive function. Another recent study showed that in HF patients aged >
65 years, higher CRP and IL-6 levels were associated with lower global cognitive scores as
measured by the MoCA (Athilingam et al., 2012). However, without a healthy control group
it is uncertain whether these relationships were due to the HF itself or aging.
4.7.4 Inflammation and mood
Few researchers have examined the link between these biomarkers on mood in heart failure
(HF). The prevalence of depression and anxiety among HF patients is high and these
conditions were shown to be independently associated with cognitive deterioration in HF
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
61
(Havranek, Ware, & Lowes, 1999). Interestingly mechanisms shown to be associated with
depression in HF may be similar to those associated with cognitive impairment in this patient
cohort. For instance, Andrei et al. (2007) found that high-sensitive C-reactive protein (hs-
CRP) levels were higher in elderly HF patients (LVEF < 0.40; NYHA class II or III) with
major depressive disorder (MDD) compared to patients without this psychiatric disorder.
Given that group differences were not observed with pro-inflammatory cytokine levels (TNF-
α or IL-6) the findings that systemic inflammation (hs-CRP) is an important marker for
depression in HF patients. However, in a small study the pro-inflammatory cytokines TNF-α,
but not IL-6 or IL-1, were higher in HF outpatients aged 18 years and over (age = 56±10
years; ejection fraction = 26±10%) with elevated levels of depression as determined by the
beck depression index (BDI; ≥ 10), compared to patients who scored low on the depression
scale (Ferketich, Ferguson, & Binkley, 2005). Supporting this, higher depression scores as
measured by the Hamilton depression scale related to the circulating inflammatory marker
IL-6 in elderly patients with decompensated HF (Guinjoan et al., 2009). Moreover,
significantly reduced systemic levels of IL-6 and TNF-α and a trend towards reduced CRP
levels were associated with higher levels of positive affect in HF patients as measured by the
Hospital Anxiety and Depression Scale (HADS; Brouwers et al., 2013). Given that
depression scores are related to circulating serum inflammation in HF patients with a
diagnosis of depression and in decompensated patients, an examination of whether
inflammatory markers are also related to depressed mood in elderly HF patients without
diagnosis of depression has not been examined.
Factors that reduce inflammation in heart failure (HF) patients may prove to be effective in
improving mood in these patients. Following 12 months omega-3 polyunsaturated fatty acid
(omega-3 PUFA) supplementation (month 1: 5g/day; 5 x EPA 850–882 mg and DHA as ethyl
esters in the average ratio of 0.9:1.5; months 2-12: 2g/day of omega-3 PUFAs), inflammatory
markers as measured by serum TNF-α, IL-1 and IL-6 levels had decreased in the treatment
group and increased in the placebo group. These findings indicated that omega-3 PUFA
showed anti-inflammatory effects in HF patients (Nodari et al., 2011). In addition, HF
patients taking omega-3 PUFA supplementation showed an improvement in left ventricular
ejection fraction, exercise capacity and NYHA functional class (from 1.88 to 0.33) following
12 month intervention, whereas the placebo group showed a decline in these factors (Nodari
et al., 2011). In addition, compared to controls, 1g daily omega-3 PUFA (EPA 850–882
mg/day DHA as ethyl esters in the average ratio of 1:1·2) mildly reduced all-cause mortality
(27% vs 29%) and admissions to hospital (NYHA class II to IV; 67±11 years for each group),
as a result of reduced cardiovascular events (57% vs 59%) in older HF patients (Tavazzi, et
Chapter 4: Mechanisms associated with cognitive impairment and mood in heart failure
62
al., 2008). In addition to improving cardiac symptoms associated with HF, omega-3 PUFA
levels may be linked with other concomitant conditions involving a heightened inflammatory
state seen in this syndrome.
There is strong evidence for the effective use of omega-3 polyunsaturated fatty acids (PUFA)
dietary intake for depression and some evidence supporting its utilisation in cognitive
functioning. Dietary PUFA intake in the form of fatty fish is inversely related to the risk of
cognitive impairment and Alzheimer’s disease (Kalmijn et al., 2004; Morris et al., 2003),
however supplementation studies have shown mixed results. Although evidence regarding
cognitive function is mixed, data from small studies have demonstrated the use of omega-3
PUFA as an effective treatment in depression. For example Su et al. (2003) showed that
compared to controls taking placebo (n=14), patients with major depression taking omega-3
PUFA supplement (EPA 4.40g/d; DHA 2.20g/d; n=14) for four weeks scored significantly
better on the Hamilton depression rating scale. A study using a lower EPA dose for 12 weeks
however (8g fish oil/day: EPA-0.6g & DHA-2.4g) did not support these results (Silvers,
Woolley, Hamilton, Watts, & Watson, 2005). Since the prevalence of depression and anxiety
among HF patients is high and changes in mood and quality of life have been correlated with
cognitive dysfunction in HF (Havranek et al., 1999), dietary omega-3 PUFA intake may also
be associated with mood factors in these patients. It is therefore possible that low omega-3
PUFA dietary intake will directly influence mood and cognition in HF by increasing
inflammation and indirectly through reducing cardiovascular symptoms associated with the
HF syndrome.
4.8 Summary
Heart failure, cognitive decline and mood disorders are associated with increased oxidative
stress, decrease antioxidant capacity and increased inflammation. However, the effects of
oxidative stress on cognitive function in HF have not been investigated. New evidence is
emerging suggesting that elevated levels of systemic inflammation may contribute to
cognitive impairment and may relate to depressed mood in HF patients.
Chapter 5: Rationale, research questions and hypotheses
63
CHAPTER 5 RATIONALE, RESEARCH QUESTIONS AND HYPOTHESES
5.1 Study Aims
The primary aim of the current thesis was to investigate the physiological mechanisms for
cognitive impairment in older heart failure (HF) patients. Disturbances in cognitive function
are prevalent in HF patients and contribute to negative health consequences in these patients.
In order to establish effective interventions that help preserve or improve cognitive function,
a clear understanding of the basic biological mechanisms associated with cognitive
impairment (CI) in particular attention, working memory, executive function and visual
spatial abilities using well-validated neuropsychological tests is required. This thesis will
focus on vascular, oxidative stress, antioxidant and inflammatory mechanisms implicated in
both HF and cognitive decline and to explore whether these mechanisms are associated with
cognitive function in HF. Findings from this study will provide a foundation for devising an
appropriate intervention which targets appropriate mechanisms which have been identified as
dysfunctional in these patients.
There are three major aims of this thesis. The first is to investigate additional cognitive
domains that may be impaired in HF, using a well-validated neuropsychological test battery
and after accounting for possible practice effects. Another aim is to examine whether
cognitive deficits are related to changes in mood. If cognitive deficits occur the second aim is
to investigate whether deficits in HF can be explained by decreased cerebral blood flow,
increased oxidative stress, reduced antioxidant capacity and increase in inflammation. The
third aim is to investigate whether depressive and anxiety symptoms in HF can be explained
by increased oxidative stress, reduced antioxidant capacity and increase in inflammation
compared to a healthy age matched control group.
5.2 Rationale
This thesis will specifically investigate whether impairments in attention, executive function,
working memory, episodic memory and visual spatial abilities exist in older patients with
mild, moderate and severe HF as diagnosed by the NYHA classifications after accounting for
practice effects. Moreover, this thesis will explore whether additional cognitive domains
including Speed of Memory, Power of Attention, Continuity of Attention, Quality of Episodic
Memory and Quality of Working Memory as measured by the Cognitive Drug Research® test
battery are impaired in older HF patients without dementia compared to an age matched
control group. Additionally, this thesis will select well-validated methods that can be easily
Chapter 5: Rationale, research questions and hypotheses
64
implemented in a clinical setting to detect biomarkers that may be related to cognitive
impairment.
Based on previous research, it is expected that older HF patients in this study will show
significantly poorer performance on attention, working memory, episodic memory tasks and
executive functioning (Beer, et al., 2009; Sauvé et al, 2009) and psychomotor function
(Almeida, Garrido, et al., 2012; Pressler, Subramanian, et al., 2010a) compared to age
matched healthy controls.
Performance on cognitive tests is often associated with depression and anxiety. This
investigation will therefore control for these mood parameters by excluding patients and
controls diagnosed with clinical depression and anxiety.
5.3 Methodological issues
5.3.1 Patient selection
Confounding factors are commonly seen in patients and the elderly, which may affect
participant scores on neuropsychological assessments. Factors such as premorbid IQ, age and
years of education and mood are known to influence cognitive performance in adults and not
all trials have controlled for these measures. Alosco, et al. (2012) showed that a higher score
on premorbid IQ as measured by the American National Adult Reading Test (AMNART)
was positively related to average composite scores on attention, executive function, memory
cognitive domains and language. This thesis will aim to compare cognitive functioning in HF
with those of a healthy control group matched for age and premorbid IQ functioning.
Concomitant conditions, such as fatigue and arthritis, which may negatively influence a
patient’s ability to complete tasks, need to also be considered. Administration time for some
of the cognitive test batteries in previous studies have lasted as long as 6 hours (Incalzi et al.,
2003; Vogels, Oosterman, van Harten, et al., 2007), which is less than optimal for patients
with HF who commonly experience fatigue. It has been previously reported that a
neuropsychological test battery taking on average 40 or 45 minutes to complete is well
tolerated and does not cause fatigue in patients with HF (Bauer & Pozehl, 2011; Bauer et al.,
2011). With the aim to obtain an accurate account of patients’ cognitive performance without
the influence of fatigue, this study will therefore incorporate a neuropsychological test battery
that takes approximately 45 minutes to complete.
Chapter 5: Rationale, research questions and hypotheses
65
5.3.2 Research environment
Research environments have not been consistent between studies that have explored cognitive
impairments in HF. Firstly, various professionals including nurses, neuropsychologists and
neurologists have administered cognitive tests. Additionally, testing environments between
studies have been inconsistent between studies with some groups being tested in the patients’
home, (Lavery et al., 2007) in a hospital bed as inpatients, others in controlled testing
environments located at medical centres and hospitals, whilst other patient have been tested
in their homes or in the researcher’s office (e.g. Suave, 2007). Neuropsychological testing
requires a suitable, consistent and controlled environment in order to minimise confounding
variables such as surrounding comfort, noise, temperature and lighting. With various testing
environments used between studies it is therefore difficult to ascertain whether the testing
environments have influenced patient’s mood or task performance. This thesis will conduct
neuropsychological testing in purpose built, environmentally controlled testing rooms.
5.3.3 Neuropsychological test batteries
Few studies have examined the validity and reliability of these cognitive assessments
specifically in a HF population (Bauer & Pozehl, 2011). Researchers have set out to
investigate the validity and reliability of a cognitive assessment battery in chronic HF patients
(Bauer & Pozehl, 2011; Bauer et al., 2011). A cognitive assessment battery entailing Trail
Making-A and Trail Making-B, RBANS and letter fluency was found to be a reliable and
valid test battery with good test-retest reliability for this patient group (Bauer et al., 2011).
Although adults aged 21 years and above were enrolled in this validity study the majority of
patients were elderly (95% aged > 50 years).
Since there is no universally exepted method for assessing cognitive function in HF research,
studies have used different screening tools to assess cognitive performance which vary in
reliability, validity and measure different outcomes. Some screening tools utilizing global
cognitive function (e.g. MMSE) use different cut off scores. For instance cut off scores for
cognitive impairment using the MMSE has varied between studies from 19-26 (McLennan et
al., 2006), < 24 (Cacciatore et al., 1998) and ≤ 24 (Debette et al., 2007). McLennan showed a
prevalence of CI as measured by MMSE score of ≤ 24 in 3.5% (out of n=200) of HF patients
at discharge (NYHA class II, III and IV) and when including probable MCI score of MMSE
≤ 26 this percentage raised to 13.5% (McLennan et al., 2006). Other measures however are
more comprehensive and assess various cognitive domains and therefore provide a better
assessement of which cognitive facets are effected. Additionally, when assessing cognitive
function in HF most studies have administered traditional cognitive assessments batteries
Chapter 5: Rationale, research questions and hypotheses
66
including paper pencil, computerised, verbal and non-verbal tests. These individual test
provide
Only a few studies however have provided a thorough cognitive assessment to provide an
evaluation of patient’s performance on each cognitive domain.
5.3.4 Practice effects
Another motivation for this thesis was to expand on the cognitive domains that may be
affected in these patients using a well-validated test battery. This study will use a
comprehensive computerised test battery to find examine whether additional domains are
impaired. Furthermore, no study to date has accounted for possible practice effects commonly
seen in neuropsychological testing. This is a possible confounding factor especially for
studies without a control group. It is not known in longitudinal studies whether improvements
in cognitive factors over time or whether it is the learning or practice effects. A training or
practice session on a day separate to the testing days would assist patients with task
familiarisation to minimise practice effects and patient anxiety, which has shown to influence
performance on cognitive tasks.
This thesis aimed to confirm whether these cognitive domains are still impaired when using a
research paradigm that controls for possible confounding factors including practice effects by
incorporating a training session on a day separate to baseline testing in order for participants
to become familiar with the neuropsychological tasks.
5.4 Summary of biological mechanisms
If the results show that patients perform significantly worse on these cognitive measures after
accounting for practice effects, this thesis will also investigate whether vascular, oxidative
stress, antioxidant and inflammatory biomarkers will be related to group differences in these
cognitive measures. Exploring the relationships between these biomarkers and cognitive
function may provide insight into possible mechanisms associated with cognitive
impairments in elderly heart failure patients.
5.4.1 Vascular
Only a few studies have explored the relationship between cerebral blood flow and cognitive
function in HF (Alves et al., 2005; Jesus et al., 2006; Vogels et al.,2008). There is evidence
that cognitive impairment in HF is underpinned by reduced cerebral blood flow (Alves et al.,
2005; Jesus et al., 2006). However there is limited evidence suppoting the role of cerebral
Chapter 5: Rationale, research questions and hypotheses
67
blood flow on performance on specific cognitive domains (Vogels et al.,2008). Therefore this
thesis will expand on previous studies and will explore whether cerebral blood flow measured
from the middle cerebral and common carotid arteries using the Transcranial Doppler (TCD)
is related cognitive impairment in HF patients compared to a control group. It is anticipated
that reduced cerebral blood flow will be related to decreased cognitive function in HF
patients.
An additional aim was to explore whether arterial stiffness is an additional vascular factor
associated with cognitive function (episodic memory, Speed of Memory, visuospatial ability
and executive function) in HF. Studies have shown relationships between reduced cerebral
blood flow velocities and global cognitive impairment in HF (Jesus et al., 2006; Vogels et al.,
2008) however, no study has explored whether arterial stiffness is related to patients impaired
cognitive performance. Additionally, another aim is to examine whether oxidative stress and
inflammation is related to arterial stiffness and cognitive function in HF.
Arterial stiffness is associated with aging (Elias et al., 2009) and is elevated in heart failure
(HF; Tartière et al., 2006) and cognitive impairments (Hanon et al., 2005; Kearney-Schwartz
et al., 2009; Scuteri et al., 2005). No study to date has assessed whether cognitive decline in
elderly HF patients is associated with arterial stiffness. Indirect measures of increased arterial
stiffness are associated with executive function and word fluency in elderly individuals, Poels
et al. (2007) although the association in HF patients has not been explored. Given that
pharmaceuticals commonly taken by HF patients and known to reduce arterial stiffness
(O'Rourke & Safar, 2005), it is therefore reasonable to explore whether there is a relationship
between arterial stiffness and cognitive function in HF patients. It is anticipated that arterial
stiffness will be related to cognitive function in elderly HF patients.
5.4.2 Oxidative stress and antioxidants
To date no study has investigated the effects of oxidative stress and antioxidant biomarkers
on working memory, episodic memory, executive function, attention and mood in older HF
patients compared to an age matched control group. Exploring whether a relationship exists
between oxidative stress, antioxidants and cognitive function may provide further insight into
the possible mechanisms for cognitive impairment in heart failure and in turn help devise
appropriate treatments to improve cognition in these patients. Given that oxidative stress
markers elevated in HF are also increased in memory impairments (Praticò et al., 2002;
Torres et al., 2011) an assessment of whether these biomarkers are related to cognitive
impairment in HF may provide an indication into suitable treatments for improving cognitive
function. Furthermore, the aim of this thesis is to explore whether reduced enzymatic
Chapter 5: Rationale, research questions and hypotheses
68
antioxidant (glutathione peroxidase) activity seen in older HF patients and in cognitive
impairment is a mechanism for cognitive impairment in elderly heart failure (Polidori et al.,
2004; Torres et al., 2011). Moreover, since coenzyme Q10 is known to be reduced in HF and
improvements in HF severity is observed following supplementation (Keogh et al., 2003),
examining whether a relationship exists between reduced plasma levels of coenzyme Q10 and
poor cognitive function in HF is another aim of the present thesis.
Given the complexity and the interrelatedness of the oxidative stress and antioxidant
pathways, the present study will examine multiple biomarkers rather than an individual
measure (Gironi et al., 2011). Examining a battery of oxidative stress and antioxidant
biomarkers will provide a better indication of how these biomarkers are related to each other
and to cognitive function. It is anticipated that oxidative stress and antioxidant measures will
be related to cognitive function in elderly HF patients.
5.4.3 Inflammation and omega-3 dietary intake
This thesis explored whether there is an association between systemic inflammation as
measured by high-sensitive C-reactive protein (hs-CRP) is in HF patients. Two studies to
date have explored the effects of inflammation on cognitive function in HF (Kindermann et
al., 2012; Said et al., 2007). Although, one study however failed to include a control group
(Said et al., 2007). This study will expand on these projects to examine whether cognitive
function is related to systemic inflammation in an elderly HF patient sample compared with a
control group matched for age and gender and a comprehensive cognitive test battery. It is
anticipated that cognitive function will be to inflammation as measured by hs-CRP in older
HF patients.
5.5 Mechanisms for changes in mood
Few studies have examined psychological factors including depression, anxiety, fatigue and
quality of life that could affect cognitive functioning in HF patients. Mood disturbances
including depression and anxiety are prevalent in HF patients (Cacciatore et al., 2008;
Stephens, 2008), however the mechanisms associated with these mood disturbances have not
been widely examined. Exploring the mechanisms for mood disturbances in elderly HF
patients will assist with formulating suitable interventions for improving mood in these
patients. Studies examining cerebral blood flow or arterial stiffness have not accounted for
confounders including cardiovascular disease risk factors such as inflammation or
psychological factors (e.g. depression or anxiety) which are known to influence cognitive
performance (Waldstein et al., 2008). It is unknown whether mood is realted to vascular,
Chapter 5: Rationale, research questions and hypotheses
69
oxidative stress or anitoxidant measures. However, there is some evidence to suggest that
inflammatory factors are involved with depressive symptoms in HF (Andrei, et al., 2007).
This thesis therefore aimed to assess whether oxidative stress, antioxidants, arterial stiffness
and cerebral blood flow are related to depression and anxiety in HF. Given that this is the first
study to explore whether arterial stiffness, cerebral blood flow, oxidative stress, antioxidant
capacity are related to mood disturbances in HF patients, this observation is purely
exploratory in nature and this study will explore whether a relationship between these
variables exist. Therefore a specific hypothesis regarding the direction of this relationship has
therefore not been made. It is anticipated that higher levels of systemic inflammation will be
related to depression in HF patients.
5.6 Hypotheses and research questions
Attention domains:
H1 HF patients will perform significantly worse on attention tasks as measured by
congruent Stroop task, Power of Attention and Continuity of Attention compared to the
control group.
H2 HF patients will perform significantly worse than controls on psychomotor function
(Trail Making-A).
Memory domains:
H3 HF patients will perform significantly worse than controls on Quality of Working
Memory, Quality of Episodic Memory and Speed of Memory tasks.
Executive function domains:
H4 HF patients will perform significantly worse on executive function as measured by
Trail Making-B, incongruent Stroop and Stroop effect tasks compared to the control group.
Mood:
H5 HF patients will score higher on depression, anxiety and fatigue measures and lower
on vigour as measured by the Profile of Mood States questionnaire than controls.
Vascular:
H6 HF patient group will have significantly lower cerebral blood flow velocity as
measured by common carotid and middle cerebral blood flow velocity compared to the
control group.
Chapter 5: Rationale, research questions and hypotheses
70
H7 HF patients will have increased arterial stiffness as measured by augmentation index
and central pulse pressures compared to the control group.
H8 HF patients will have higher plasma levels of the vasoconstrictor endothelin-1
compared to controls.
Oxidative stress and antioxidant biomarkers:
H9 HF patients will have significantly higher levels of oxidative stress as measure by
determinable reactive oxygen metabolites (DROMs) and lipid peroxides (F2-isoprostanes)
compared to the control group.
H10 HF patients will have significantly lower levels of plasma antioxidants as measured
by coenzyme Q10 and glutathione peroxidise compared to the control group.
H11 Patients will have significantly higher levels of inflammation as measured by hs-CRP
and omega-3 dietary PUFA intake compared to the healthy control group.
Relationships between cognitive measures and vascular markers:
H12 Reduced cerebral blood flow velocity as measured by common carotid and middle
cerebral arterial blood flow will be related to poorer performance on cognitive tests
measuring global cognition, attention, psychomotor speed, working memory, episodic
memory and executive function in HF patients.
H13 Arterial stiffness as measured by augmentation index and pulse pressure will be
related to cognitive tests measuring global cognition, attention, psychomotor speed, working
memory, episodic memory and executive function.
Relationships between cognitive measures, biomarkers and omega-3 dietary intake:
H14 Oxidative stress, antioxidants, inflammation and omega-3 dietary intake will be
related to cognitive tests measuring global cognition, attention, psychomotor speed, working
memory, episodic memory and executive function.
Relationships between mood and vascular measures:
Research question (R1) No specific hypotheses were made with relation to whether cerebral
blood flow or arterial stiffness have an effect on depression and anxiety in HF patients.
Therefore, the current investigation explored the research question “is there a relationship
between cerebral blood flow or arterial stiffness and depressive symptoms and anxiety “.
Chapter 5: Rationale, research questions and hypotheses
71
Relationships between mood and biomarkers:
Research question (R2) No specific hypotheses were made with relation to whether
oxidative stress, antioxidant measures or omega-3 dietary intake will have an effect on
depressive symptoms and anxiety in HF patients. Therefore the research question “is there a
relationship between oxidative stress, antioxidant measures and depressive symptoms and
anxiety” was explored in the present investigation.
Research question (R3) No specific hypotheses were made with relation to whether
inflammatory measures have an effect on anxiety in HF patients. Therefore the research
question “is there a relationship between inflammatory measures and anxiety in HF patients”
was explored in the present investigation.
H15 It was hypothesised that inflammation as measured by high-sensitive C-reactive
protein will be related to depression scores in HF patients.
Chapter 6: Methods
72
CHAPTER 6 METHODS
6.1 Introduction
This trial was an observational study designed to assess the mechanisms of cognitive deficit
in heart failure (HF) patients and in particular, whether, compared to a control group matched
on age and IQ, vascular, inflammatory and/or oxidative mechanisms are associated with
cognitive decline in HF. The trial received approval from the Alfred Hospital and Swinburne
University of Technology Human Research Ethics Committees (HREC; Appendix A). All
conditions concerning the ethics clearance were met and annual and final reports have been
submitted to the HRECs. After fulfilling the screening criteria, subjects took part in
neuropsychological testing to assess cognitive function, mood and quality of life as well as
biochemical tests to compare HF patients with a group of healthy controls matched on age
and IQ. This chapter will outline the materials used and recruitment process for the first
study.
6.2 Participants
The target population were males and females aged 60 years and above with an MMSE score
of ≥ 24 and estimated IQ of > 70 as measured by the Wechsler Abbreviated Scale of
Intelligence-Vocabulary subset (WASI-vocabulary). All participants were screened during an
interview by the researcher to ensure they had no existing or pre-existing neurological
conditions such as dementia, no history of psychiatric conditions (e.g. depression and
anxiety), no endocrine, gastrointestinal or bleeding disorders, no hearing impairments, not
taking psychoactive medication, no history of substance abuse, non-smoker and fluent in the
English language.
6.2.1 Heart failure patients
The patient group comprised of Heart Failure patients (n=46; NYHA class II, III and IV)
aged 60 years and over who were recruited from the Heart Centre, Heart Failure Clinic,
Alfred hospital, Prahran Melbourne. Prior to the patients regular outpatient appointment with
their cardiologist, the heart failure nurse and cardiologist initially assessed patient’s
suitability to participate in the study based on information recorded on the patients file such
as age and severity of disease. At their outpatient visit, the heart failure nurse or research
student asked potentially suitable patients if they were interested in taking part in the study.
The student investigator provided information regarding the study procedures, aims and
requirements to interested patients and in a quiet, private setting (consulting room in the
Chapter 6: Methods
73
Heart Centre, Heart Failure Clinic or in the waiting room) and conducted a screening
interview to assess patients’ eligibility to participant in the study. Eligible participants were
provided with a Participant Information and Consent Form (PICF; Appendix B) which further
explained the study. An appointment was then made for the initial testing session. Patients
who wanted more time to read the information sheet were followed-up with a phone call one
week later and if they were still interested in taking part in the study, an appointment was
made for the first testing session. Additional selection criteria for Heart Failure patients
included a current diagnosis of NYHA class II, III or IV, no heart failure due to thyroid
disease, and no stroke in the 6 months prior to enrolment or unstable angina.
6.2.2 Healthy control volunteers
Healthy participants were recruited and tested at the Centre for Human Psychopharmacology,
Swinburne University, Melbourne. Healthy volunteers were from the general public who
responded to local newspaper advertisements, word of mouth, emails and official flyers.
Individuals who responded to advertisements by way of phone call or email, were phoned by
one of the researchers who explained the study in detail, conducted screening interviews and
posted a copy of the information sheet so that participants could read the information sheet
before the first testing session. Additional selection criteria for the control sample included
unmedicated hypertension, no history of cardiovascular disease, no heart failure and
considered to be generally healthy.
6.3 Power analysis
The primary outcome was performance on the pencil-and-paper Trail Making-B task.
Previous work in this field using this outcome have found effect sizes consistent with a ‘large
effect' - defined by Cohen (1992) as at least .80 of a standard deviation. For example a
systematic review of cognitive impairment in heart failure by Vogels, Scheltens, et al. (2007)
reported effect sizes of up to 1.098 in the best controlled trials. According to Cohen (1992),
there is an 80% chance of detecting such an effect at the p < .05 level using a sample size of
26 per group in a between-subject study. Therefore, the sample size per group for this study
was at least 26 per group. Additionally, previous studies in the field have successfully
captured the effects of HF on Trail Making-A task with sample sizes between 40 and 140.
Using this information to calculate sample size and using a statistical power of 80% with an
alpha level of .05, to detect a 48% change in Trail Making-A a sample size of 12 per group
was required, and to detect a 30% change 24 per group would be required. The aim was
therefore to complete testing on at least 30 participants in each group. To account for a
Chapter 6: Methods
74
twenty per-cent participant dropout rate, the aim was to recruit at least 40 HF patients and 40
healthy male and female volunteers aged 60 years and above.
6.4 Materials
6.4.1 Case Report Form (CRF)
Participants completed a demographic questionnaire where data including age, sex, highest
level of school completion, ethnicity, five year medical history and current medications were
recorded on the Clinical reference form (CRF).
6.4.2 Cognitive measures
6.4.2.1 Cognitive Drug Research
Cognitive testing comprised of a battery of computerised and traditional paper-pencil tests.
There is evidence indicating that older HF patients show impairment in specific cognitive
domains including immediate and delayed recall (Almeida, Beer, et al., 2012), attention
(Sauvé et al., 2009) and visuospatial abilities (Lavery et al., 2007). Majority of these studies
have used age matched controls to compare cognitive scores with those of heart failure (HF)
patients (Beer et al., 2009; Sauvé et al., 2009) yet others compared results to normative data
(Bauer et al., 2011; Wolfe et al., 2006). The Cognitive Drug Research® (CDR) standardised
computerised test battery was employed in this study to measure participant’s performance on
memory and attention tasks. Previous studies have shown that scores on the well-validated
CDR test battery are correlated strongly with MMSE scores in older patients with dementia
(Simpson, Surmon, Wesnes, & Wilcock, 1991). The CDR test battery has been extensively
used to assess the effects of older age (Simpson et al., 1991), hypertension (Harrington,
Saxby, McKeith, Wesnes, & Ford, 2000), coronary artery bypass grafting (CABG; van den
Goor, Saxby, Tijssen, Wesnes, de Mol, & Nieuwland, 2008) and cardiac enterectomy on
cognitive function (Fearn et al., 2003). This is the first time CDR was administered to a HF
outpatient group. The CDR is has been shown to have good test retest reliability in an elderly
population (Simpson et al., 1991). The CDR test battery is comprised of ten individual task
variables outlined in Table 2. The CDR cognitive test battery was chosen for this study as
computerised tests provide a more accurate reaction time scores compared to paper pencil and
verbally administered tasks. Furthermore, the CDR test battery incorporates a training session
separate to the actual testing sessions to minimise practice effects. As no prior computer
skills were necessary, the training session provided an opportunity for participants to become
familiar with the two-button box response box, computer screen on which the tasks were
presented and to practice the reaction time tests using the button box (van den Goor et al.,
Chapter 6: Methods
75
2008). To date no study has eliminated practice effects when assessing cognitive function in a
HF cohort.
Table 2 Description of subsets from the Cognitive Drug Research (CDR) test battery and the
order in which the tasks were presented
CDR Task Task Description and assessment Measure
Immediate Word
Recall
A series of fifteen words are presented on the screen
one every 2 seconds. The participant is required to
remember word list and write down as many words
they can remember in 60 seconds
Number of
words
correctly
recalled.
Picture
Presentation
A series of twenty pictures are presented on the
screen at an interval of one every three seconds. The
participant is required to remember each picture in
detail.
No data
recorded
Simple reaction
time
The word “YES” appears on the screen for a period
of 200 msec at varying intervals ranging between 1
and 3.5 seconds. The participant then presses the
“YES” button as soon as they see the stimuli appear.
Mean RT
(msec)
Digit Vigilance
Task
A random digit appears on the right hand side of the
computer screen for the duration of the task. A series
of digits appear in the middle of the screen (150 per
minute) and each time the two digits match, the
participant presses the “YES” button a swiftly as
possible. Intensive vigilance, sustained
concentration, and ability to ignore distractions.
% accuracy,
RT (msec)
and number
of false
alarms.
Choice reaction
time
Either the words “YES” or “NO” appear randomly
on the screen. The participant is required to press the
corresponding button as soon as they see the word.
Speed and accuracy of stimulus discrimination
RT (msec)
and %
accuracy
Note: Reaction times (RT) were measured in milliseconds (msec; van den Goor et al., 2008).
Chapter 6: Methods
76
Table 2 cont’d... Description of subsets from the Cognitive Drug Research (CDR) test battery and the order in which the tasks were presented
CDR Task Task Description and assessment Measure
Spatial Working
Memory
A picture or a house with 9 windows initially appears
on the screen. Four of the windows are lit or
coloured white, the other windows are black, and the
participant is asked to remember the position of the
lit windows. The original house disappears and the
house then reappears 36 times with one of the nine
windows lit. The participant responds as quickly as
possible by pressing the “YES” and “NO” buttons if
the window was lit or was not lit in the original
house presentation, respectively.
Mean RT in
msec, and %
accuracy of
responses to
both original
and novel
(distractor)
stimuli.
Numeric Working
Memory
A sequence of five digits (0-9) appears on the screen
one at a time for the participant to remember. A
subsequent series of 30 random single digits appear
on the screen and the participant is required to
respond to each word by pressing the “YES” and
“NO” button as quickly as possible if that word was
presented or not presented in the original series,
respectively.
Mean RT
(msec), and
% accuracy
of responses
to both
original and
novel
(distractor)
stimuli.
Delayed Word
Recall
The participant is asked to write down as many
words that they can recall from the series of 15
words presented at the beginning of the test session.
Number of
words
correctly
recalled.
Note: Reaction times (RT) were measured in milliseconds (msec; van den Goor et al., 2008).
Chapter 6: Methods
77
Table 2 cont’d... Description of subsets from the Cognitive Drug Research (CDR) test battery
and the order in which the tasks were presented
CDR Task Task Description and assessment Measure
Delayed Word
Recognition
The 15 words from the Word recall task and 15
distractor words appear randomly on the screen
one at a time. For each word the participant
responds as quickly as possible by pressing the
YES” and “NO” button if the word was in the
original list or if it was not, respectively.
Ability to discriminate novel from previously
presented words and long-term verbal learning
capacity.
Mean RT (msec),
and % accuracy
of responses to
both original and
novel (distractor)
stimuli.
Delayed Picture
Recognition
As per the Delayed Word Recognition tasks with
pictures presented instead.
Ability to discriminate novel from previously
presented pictures.
Mean RT (msec),
and % accuracy
of responses to
both original and
novel (distractor)
stimuli
Note: Reaction times (RT) were measured in milliseconds (msec; van den Goor et al., 2008).
The scores on these individual task variables were later used to calculate the following five
cognitive domains or composite scores outlined in Table 3: Power of Attention, Continuity of
Attention, Speed of Memory, Quality of Working Memory and quality of episodic secondary
memory.
Chapter 6: Methods
78
Table 3 Composite scores for the five cognitive domains (van den Goor et al., 2008)
Domain Definition CDR subtest
Power of Attention* ability to focus ones attention
during a short period of time
when extreme concentration is
required.
sum of participant’s
response speed (msec) to
stimuli on simple and choice
reaction time and digit
vigilance tasks
Continuity of Attention the ability of focus attention
over a period of time when there
is a distraction and without
mistakes/error
average percentage accuracy
on choice reaction time and
digit vigilance accuracy
scores minus false alarms for
digit vigilance task.
Speed of Memory*
assessment of the time it takes to
decide whether information is
held in memory or information
retrieval time.
summation of speed of
responses (msec) on delayed
picture and word recognition
tasks, numeric working
memory and spatial memory
tasks.
Quality of Working Memory an assessment of the ability to
store, hold and manipulate
information
the mean percentage
accuracy scores from the
spatial and numeric working
memory tasks
Quality of Episodic Secondary
Memory
an assessment of the ability to
code and retrieve information
from episodic memory
combines performance
accuracy scores as a
percentage from delayed
word reconition, delayed
picture recognition,
immediate word recall and
delayed word recall tasks.
Note: *=higher scores on these cognitive domains indicate worse performance (van den Goor et al., 2008); msec=milliseconds.
Elderly individuals have been shown to adequately carry out computerised testing using the
CDR test battery (Simpson et al., 1991). To prevent learning effects parallel forms of the
tasks were presented at each session.
Computerised tasks were presented on a laptop and participants were asked to respond to
visual stimuli presented on the laptop screen by pressing either a “yes” or “no” button on a
Chapter 6: Methods
79
response box; except for the immediate and delayed word recall tasks where participants were
asked to write down words remembered from the word presentation task. Practice effects can
influence performance on cognitive tests (Lezak, 2012). To minimise practice effects and
anxiety, participants underwent a training day where they became familiar with the
equipment and computerised cognitive tasks. During the training sessions, the administrator
presented standardised instructions verbally to explain the CDR test battery and instructed
participants on how to use the response button box (Appendix C). Prior to each task the
administrator read out instructions on how to perform the task and if the participant did not
completely understand what was required of them, the instructions were repeated until the
participant clearly understood the task.
For the control group, the administrator read out task instructions once at the first training
session. During subsequent testing sessions, participants read abbreviated task instructions
displayed on the laptop screen and proceeded when ready. For the patient group, the
administrator read out instructions prior to every testing session during training and baseline
testing days.
Each task was initiated by the participant pressing the ‘yes’ button in the Control group and
by the administrator pressing the <Enter> key in the Patient group. A written log was kept
for each testing session recording problems occurred during the testing session that may
explain outliers and missing data. During the training sessions, patients and healthy
participants went through the cognitive test batteries twice and four times, respectively as per
the CDR training protocol.
Participants were seated at a desk and asked to position the button box in front of them in
such a way that it would be comfortable for them to rest their fingers gently on the response
buttons so that the speed of response was accurately measured. Participants were asked to use
their right hand to press the right “yes” button and their left hand to press the “no” response
button. Participants were informed that there would be a series of tasks (each a few minutes
long) and that it would take approximately 25 – 30 minutes to complete the whole session.
Technical support was received from CDR Ltd throughout the study.
6.4.2.2 Stroop word task
There is evidence in the literature for impairments on tests of selective attention in HF. The
Stroop word task (Stroop, 1935) is a commonly used measure of selective attention, cognitive
flexibility and response inhibition. As a measure of cognitive control, the Stroop task assesses
the ability to keep a goal in mind whilst suppressing a habitual response (Strauss, Sherman, &
Chapter 6: Methods
80
Spreen, 2006). Impaired performance on the Stroop task is associated with aging and is
shown to be influenced by years of education (Van Der Elst, Van Boxtel, Van Breukelen, &
Jolles, 2006). Elderly patients with HF have shown impairments in congruent, incongruent
(Vogels, Oosterman, et al., 2007) and Stroop interference scores compared to controls (Hoth
et al., 2008; Vogels, Oosterman, et al., 2007). In the current study a computerised version of
the Stroop task was employed where one of four stimulus words (BLUE, RED,
GREEN,YELLOW) were randomly presented on the computer screen on a black background
for 1.7 seconds with an inter stimulus interval (ISI) of 0.5 seconds (Pipingas et al., 2010). In
the congruent task the font colour of the stimulus word was the same as the stimulus word (i.e
the word BLUE was presented in the colour ‘blue’) and in the incongruent task the stimulus
word was presented in one of the other three colours (i.e. the word BLUE was presented in
the colour ‘yellow’). Participants responded by pressing the corresponding coloured button
on the button box in front of them for the colour of the word irrespective of the word
presented (Pipingas et al., 2010). The response scores were measured by percentage accuracy
and reaction time (ms). The Stroop interference score, a measure of executive function and
response inhibition, was determined by subtracting the mean congruent reaction time scores
from that of the incongruent mean reaction time. Instructions read out by the administrator
for the Stroop task are presented in Appendix D.
6.4.2.3 Trail Making Test (TMT)
The Trail Making Test (TMT; Reitan, 1958) has been frequently used to measure information
processing speed in older HF patients. The Trail Making Test is a reliable measure of
attention, visual perceptual and psychomotor speed, and mental flexibility (Giovagnoli et al.,
1996; Lezak, 2012; Strauss et al., 2006). The TMT is a paper pencil test consisting of two
parts. Part A (Trail Making-A) consists of 13 encircled numbers (1-13) randomly positioned
on a sheet of paper and the participant consecutively connects the numbers with a continuous
line, as quickly as possible. Similar to Trail Making-A, part B (Trail Making-B) involves 13
numbers (1-13) and 12 letters (A-K) encircled on a sheet of paper and participants join the
digits and letter by alternating between consecutive numbers and letters (i.e. 1 to A, 2 to B
etc). The time taken to complete each task is recorded in milliseconds as a measure of the
participants’ performance. Without stopping the stopwatch, the researcher stopped the
participant and notified them if they had made an error and instructed them to continue the
task from the previous number or letter correctly joined. Trail Making-B also assesses
executive function by measuring the ability to switch between stimuli. The standard
administration requires Trail Making-A to always be administered before Trail Making-B and
a short practice test prior to each part. The TMT takes 5-10 minutes to complete.
Chapter 6: Methods
81
Studies investigating speed of information processing using the Trail Making Test in HF
patients has yielded mixed results. There has been conflicting evidence for HF patient
performance on TMT. There is some evidence indicating no significant differences for
performance on Trail Making-A, however HF patients perform slower on Trail Making-B
(Almeida & Tamai, 2001b; Hoth et al., 2008). Table 4 provides a summary of the cognitive
domains measured by the Cognitive Drug Research® assessment battery and
neuropsychological tests that have previously shown to be sensitive to heart failure.
Table 4 Summary of neuropsychological tests
Cognitive Domain Task Source
Attention Congruent Stroop
Trail Making-A (psychomotor speed)
Power of Attention
Continuity of Attention
SNP
SNP
CDR
CDR
Working Memory Quality of Working Memory CDR
Episodic Memory Quality of Episodic Memory CDR
Executive function Trail Making-B SNP
Incongruent Stroop reaction time SNP
Stroop effect SNP
Note: CDR=Cognitive Drug Research test battery; SNP=neuropsychological measures sensitive to heart failure.
6.4.3 Screening Measures
6.4.3.1 Mini Mental State Examination (MMSE)
The Mini Mental State Examination (MMSE; Folstein et al., 1975) is a test widely utilised to
screen for mild to severe dementia based on an assessment of an individual’s orientation,
attention, immediate recall, short-term recall, language and the ability to follow simple verbal
and written commands. Previous studies employed the MMSE to assess cognitive impairment
in heart failure (HF) patients (Akomolafe, et al., 2005; Cacciatore et al., 1998; Cameron et al.,
2009; McLennan et al., 2006). Although HF patients show impairments on the MMSE
Chapter 6: Methods
82
compared to age matched controls, this measure is an assessment of global cognitive function
and does not evaluate performance on specific cognitive domains. In the present study, the
MMSE was used as a screening tool for dementia rather than an assessment of cognitive
function. The 30-item test involved verbally answering the examiners questions relating to
familiar items such as current location (i.e. building, floor), current season, and repeating and
later recalling words said by the examiner. The individual was asked to spell the word
“WORLD” backwards, write a random sentence on a piece of paper and copy a geometric
figure. The total number of correct responses is 30 with higher scores representing improved
global cognitive function. Scores of below 24 are suggestive of dementia, therefore only
participants who scored 24 or above were enrolled in the study. The MMSE took
approximately 10 minutes to complete.
6.4.3.2 Wechsler Abbreviated Scale of Intelligence Scales (WASI) Vocabulary subset
Factors such as intelligence are known to influence cognitive performance in adults.
Intelligence in HF patients has been shown to influence cognitive performance including
attention, executive function, memory cognitive domains and language (Alosco, Spitznagel,
Raz, et al., 2012). In order obtain an estimate of general premorbid intelligence (IQ) for the
experimental groups, participants completed the vocabulary subset of the Wechsler
Abbreviated Scale of Intelligence (WASI; Wechsler, 1999). The WASI Vocabulary subtest is
a well-accepted, short and reliable measure of estimated premorbid IQ in research and
clinical settings. This test is independent of confounding factors such as state of health. The
vocabulary subtest is a 42-item task individually administered and takes approximately 10
minutes to complete. The participant was asked to provide meanings of words that were
presented orally and visually. The administrator wrote down the participant’s answers and
queried the participant using the prompts in the WASI booklet if the answer was vague. The
task was discontinued if the participant had five consecutive scores of zero. The average
reliability coefficients calculated with an adult sample is 0.96 for the VIQ.
6.4.4 Mood and quality of life measures
6.4.4.1 Profile of Mood States (POMS)
It has been reported in the literature that HF patients experience increased levels of
depression, anxiety and fatigue (Almeida, Beer, et al., 2012; Evangelista et al., 2008; Fink et
al., 2012; Pressler, Subramanian, et al., 2010b; Stephen, 2008). The Profile of Mood States
(POMS; McNair, Lorr, & Droppleman, 1992) is a self-reported questionnaire designed to
measure six facets of mood: tension-anxiety; depression-dejection; anger-hostility; vigour-
Chapter 6: Methods
83
activity; fatigue-inertia and confusion-bewilderment (Appendix E). The POMS consists of a
list of 65 adjectives depicting mood and feelings and using a Likert-type scale ranging from
“not at all” to “extremely” the participant rates their mood responses in the preceding week
including the present day. The Total mood disturbance score is determined by adding the five
factor scores of Tension, Depression, Anxiety, Fatigue and Confusion and subtracting Vigour
from these scores. The internal consistency for each factor is highly satisfactory. The reported
Cronbach alpha values for males and females combined are between K-R20 = .86 - .95 for
each factor. Although HF patients have higher levels of depression and anxiety scores
compared to controls (Grubb et al., 2000), the severity of cognitive impairment in HF has not
shown to be effected by depression (Sauvé et al., 2009). Depression is known to correlate
with cognitive outcomes in elderly HF patients (Garcia et al., 2011; Incalzi et al., 2003;
Trojano et al., 2003). Yet other authors have shown depression to be independently realted to
patients cogntive function (Cacciatore et al., 1998; Garcia et al., 2012; Riegel & Weaver,
2009). The POMS questionnaire was therefore administered to control for mood, in particular
depression. Additionally, since inflammatory markers are higher in patients with depression
and heart failure patients with major depressive disorder (Andrei et al., 2007), relationships
between inflammation as measured by C-reactive protein (hs-CRP) and mood will be
explored examined in this study.
6.4.4.2 Short Form-36 Item (SF-36)
The influence of quality of life (QOL) on depression, disease severity has been investigated
in HF patients (Gottlieb et al., 2004). The Short Form-36 item (SF-36; McCallum, 1995) is a
widely used self-administered health survey containing 36 items measuring overall
wellbeing, and mental and physical health perception (Appendix F). The SF-36 measures
eight health dimensions: physical and social functioning, role limitations due to physical and
emotional problems, mental health energy/fatigue, bodily pain and general health perception.
The SF-36 has acceptable internal consistency and reliability with Cronbach’s alpha > 0.85
and reliability coefficients > 0.75 for each health dimension, except social functioning
(α=0.73, reliability=0.74; Brazier et al., 1992). In an elderly population, Cronbach’s alpha
was between .81-.94 and .8-.93 for each health dimension and summary scores in the 65-74
and 75-84 year age group, respectively (Gandek, Sinclair, Kosinski, & Ware, 2004). Internal
consistencies decline with increasing age and additional chronic illness.
Chapter 6: Methods
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6.4.4.3 Chalder fatigue scale
The Chalder fatigue scale (CFS; Chalder et al., 1993) is a widely used to measure the severity
of mental and physical fatigue. The CFS consists of 14 items each rated on a 4-point Likert
scale ranging from ‘better than usual’ to ‘much worse than usual’ and the items provide
mental and fatigue scores and a combined total fatigue score which is determined by adding
up all the items (Appendix G). The CFS has high level of internal reliability with a
Cronbach’s alpha for each item ranging between 0.88-0.90 (Chalder et al., 1993).
6.4.4.4 General Health Questionnaire
An evaluation of participant’s current mental health was established using the General Health
Questionnaire (GHQ-12; Goldberg, 1992). The GHQ-12 is a standard screening tool for
identifying psychological distress or minor (non-psychotic) psychiatric disorder in primary
medical care settings or among general medical outpatients (Goldberg, 1992) and elderly
heart failure patients (Johansson, Broström, Dahlström, & Alehagen, 2008). The respondents
answer questions on a 4-point Likert scale regarding the degree of happiness, depression and
anxiety symptoms, and sleep disturbance they experienced over the last four weeks
(Appendix H). The test has reported high internal consistency (Cronbach alpha = -.9), validity
(ROC curves = .88), overall sensitivity of 83.7% and specificity of 79.0% (Goldberg et al.,
1997).
6.4.4.5 Speilberger's State Trait Anxiety Inventory
Heart failure patients are known to have high levels of anxiety, which increases over time
(Almeida, Beer, et al., 2012). However, there is evidence that anxiety does not explain
performance on cognitive measures in HF patients (Pressler, Subramanian, et al., 2010b;
Sauvé et al., 2009). Yet it is always important to assess whether anxiety affects cognitive
performance. The Speilberger State-Trait Anxiety Inventory (STAI) was used to measure
participant’s state and trait anxiety symptoms (Speilberger, Gorsuch, & Lushene, 1970). The
STAI comprises two self-reported questionnaires used to measure and differentiate between
State (STAI-S) and Trait anxiety (STAI-T). The STAI-S consists of 20 statements reflecting
temporary anxiety at the time of the assessment. STAI-S Statements are rated on a 4-point
intensity scale ranging from not at all to very much so (Appendix I). STAI-T consists of 20
statements reflecting a person’s general anxiety symptoms seen as stable personality trait and
statements are rated on a 4-point intensity ranging from almost never to almost always. The
internal consistency for both STAI questionnaires is reasonable ranging from .83 to .92 in
males and females (Speilberger et al., 1970).
Chapter 6: Methods
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6.4.5 Oxidative stress, antioxidant, inflammatory and omega-3 samples
Blood samples were collected from participants by a registered nurse during baseline testing
sessions. 26ml of whole blood was obtained using a syringe by venipuncture from the
antecubital vein. High-sensitive C-reactive protein samples were collected in a 6 ml yellow
cap serum tube, stored at 4oC until transported to the pathology laboratory for analysis.
Remaining blood samples collected in 1 x 10ml heparin (for DROM, glutathione peroxidase
and CoQ10 assays) and 1 x 10 ml EDTA tube (for F2-isoprostane and endothelin-1 assays)
were kept on ice and centrifuged at 3000 g force for 10 minutes at 4oC within one hour of
blood collection. Plasma was extracted from centrifuged samples and stored in 2ml and 0.5ml
aliquot tubes at -80 oC and later shipped via courier on dry ice to respective pathology
laboratories for analysis. Prior to storage additional preparation was required for F2-
isoprostane and CoQ10 assays. The stored plasma intended for F2-isoprostane assays was
protected from oxidation with butylated hydroxy toluene (100µml/1ml). Since CoQ10 is
sensitive to light the vacutainer intended for CoQ10 analyses was covered with aluminium
foil to protect the sample from light exposure. Additional preparation was not required prior
to storing plasma for the other biological assays.
6.4.5.1 F2-isoprostanes
F2- isoprostanes are a series of chemically stable prostaglandin F2–like compounds that are
produced during the peroxidation of unsaturated fatty acids, mainly prostaglandins in
phospholipid membranes. Increases in F2-isoprostanes are measured not only in heart failure
(Polidori et al., 2004) but also in diseases associated with cognitive impairment including
Alzheimer’s dementia (Mariani et al., 2005). In vivo F2-isoprostanes can be measured
efficiently in plasma providing a reliable measure of lipid peroxidation and oxidative stress.
Plasma F2-isoprostane concentrations were quantified using a methodology employing a
combination of a) negative ionisation mass spectrometry (GC-ECNI-MS), the most reliable,
sensitive and specific method for measuring F2-isoprostanes, b) silica and c) reverse-phase
cartridges, and high-performance lipid chromatography (HPLC; Mori et al., 1999). This latest
and improved method begins with adding 8-iso- PGF2a-d4 (2 ng, internal standard) and 1 M
potassium hydroxide (KOH) in methanol (1 ml) to a glass tube containing 2 ml plasma. The
tube and its contents are subsequently flushed with N2, heated to 40oC for 30 minutes and
then cooled down. In order to precipitate or separate the proteins, the mixture was diluted
with methanol (1 ml) and centrifuged at 1500 g force, at 4oC for a further 10 minutes. The
supernatant was then diluted with 8ml of .1M phosphate buffer, which had a pH of four. The
acidity was then adjusted to a pH of three by using 2M hydrochloric acid and to remove
Chapter 6: Methods
86
protein precipitate, the mixture was centrifuged once more at 1500 g force, at 4oC for 10
minutes. The hydrolysate was then applied to a C18 Sep-Pak Cartridge and chromatographed
(Mori et al., 1999).
6.4.5.2 Determinable reactive oxygen metabolites (DROMs)
Hydroperoxides were quantified by measuring plasma determinable reactive oxygen
metabolite (DROM) levels. DROMs were measured in plasma using the free radical
analytical system (FRAS; Kanaoka, Inagaki, Hamanaka, Masaki, & Tanemoto, 2010). This
procedure involves mixing a small amount of plasma (10 µl) with an acidic buffer solution of
pH 4.8 in order to stabilise the hydrogen ion concentration. In this acidic medium, iron
released from the protein component of the plasma, operates as a catalyst to break down
hydroperoxides (ROOH) in the blood into hydroxyperoxyl (ROO+) and alkoxyl (RO+)
radicals. The solution was then transferred into a cuvette containing a colourless chromogen.
Here the radicals oxidize the colourless chromogen in the solution to become a pink-coloured
radical cation. The intensity of colour is directly proportional to the concentration of the
reactive oxygen metabolites (ROMs) which is expressed as Carratelli Units (1 CARR U =
.08mg hydrogen peroxide/dl; Pasquini, Luchetti, Marchetti, Cardini, & Iorio, 2008).
6.4.5.3 Coenzyme Q10
Coenzyme Q10 (CoQ10) is an antioxidant, cellular energiser, gene regulator and is involved
in the generation of ATP (Boreková et al., 2008). CoQ10 has shown to be neuroprotective in
animal models (Ishrat et al., 2006). HF patient have lower myocardial and blood CoQ10
levels and blood levels increase following supplementation (Folkers et al., 1992; Keogh et al.,
2003). Plasma CoQ10 levels will be correlated with cognitive measures. Total plasma CoQ10
was determined by Chromsystems Level I and level II (Cat. Number 0091). This technique
utilised a solid phase extraction method followed by ultraviolet detection on reverse-phase
high-performance liquid chromatography method (R P-HPLC; Barshop & Gangoiti, 2007).
Standards, internal standard and reagents were obtained from Chromsystems (GMBH).
6.4.5.4 Glutathione peroxidase
Levels of the enzyme antioxidant glutathione peroxidase have been shown to be reduced in
heart failure (Keith et al., 1998; Polidori et al., 2004). Glutathione peroxidase works jointly
with the other enzyme antioxidants superoxide dismutase (SOD) and catalase to reduce levels
of harmful reactive oxygen species (ROS). The Cayman Chemical Assay (Ann Harbor, MI)
kit was used to indirectly measure glutathione peroxidase activity by a coupled reaction with
glutathione reductase (Baud et al., 2004). The Cayman assay protocol involves stimulating
Chapter 6: Methods
87
reactions in the sample with Cumene hydroperoxide (20 µl). Oxidised glutathione, which is
produced as a result of the reduction of hydroperoxide by glutathione reductase, was recycled
to its reduced form by the oxidation of NADPH to NADP+. The decrease in the absorbance
(at 340 nm) is directly proportional to the GPx activity in the sample (Baud et al., 2004).
Glutathione reductase activity was quantified by the amount of enzyme triggering the
oxidation of one nmol of NADPH per minute and per milligram of protein in the sample
(Baud et al., 2004). The glutathione reductase activity was analysed by the laboratory team at
the Oxidative Stress Laboratory, Diabetic Complications Division, Baker IDI Heart and
Diabetes Institute, Melbourne, Australia.
6.4.5.5 High-sensitive C-reactive protein (hs-CRP)
Inflammatory markers observed in heart failure (HF) are also believed to be involved in the
pathogenesis of Alzheimer’s disease (AD; e.g. Interleukin-1), depression (e.g. high-sensitive
C-reactive protein; hs-CRP) and impaired cognitive performance in older adults (e.g. hs-
CRP; Braunwald, 2008; Su et al., 2003; Teunissen et al., 2003). Recent evidence suggests
that inflammation is related to memory, speed of information processing and executive
functioning (CRP; Kindermann et al., 2012). Preliminary evidence also suggests that
inflammation (TNF-α and IL-6) is related to global cognition with IL-6 predicting cognitive
scores (Said et al., 2007). In order to assess whether inflammation is related to cognitive
functioning in HF patients, serum high-sensitive C-reactive protein (hs-CRP) was assessed.
In order to quantify participant’s hs-CRP the Roche Diagnostics Tina-quant ® and Siemens
automated clinical chemistry analysers were used to assay patient samples at the Alfred
Hospital and Healthscope Pathology laboratories, respectively. This assay uses a particle-
enhanced immunotubdidmetric providing the desired high analytical sensitivity, reproducible,
accurate and valid results (Eda, Kaufmann, Roos, & Pohl, 1998; Price, Trull, Berry, &
Gorman, 1987). In short, an anti-CRP antibody-latex was added to the sample to trigger the
reaction. Anti-CRP antibodies that are coupled to the latex microparticles react with the
antigen in the sample to form an antigen/antibody complex. Once the red cells were clumped
together by the antibodies (agglutination) the sample was then measured turbidimetrically
(An instrument for measuring the loss in intensity of a light beam through a solution that
contains suspended particulate matter). Buffers and other materials required for this assay
were supplied by Roche diagnostics and Siemens.
Chapter 6: Methods
88
6.4.5.6 Polyunsaturated fatty acid questionnaire
The effects of omega-3 polyunsaturated fatty acid (omega-3 PUFA) dietary intake and
supplementation have been widely researched for the prevention and treatment of
cardiovascular disease and more recently in HF. Chronic administration of omega-3 PUFA in
the form of fish oils have shown to improve mortality, decrease hospital admissions (Tavazzi
et al., 2008), improve left ventricular ejection fraction (LVEF), NYHA class and exercise
capacity, and decrease serum inflammatory markers (TNF-α, IL-1 and IL-6) in HF patients
(Nodari et al., 2011). There is strong evidence for the effective use of omega-3 PUFA dietary
intake for concomitant conditions including depression and some evidence supporting its
utilization in cognition functioning. Dietary PUFA intake in the form of fatty fish has been
shown to be inversely related to the risk of cognitive impairment and Alzheimer’s disease
(Kalmijn et al., 2004; Morris et al., 2003), however supplementation studies have shown
mixed results. An assessment of participant’s dietary polyunsaturated fatty acid (EPA +
DHA) intake was determined by a short dietary questionnaire, the PolyUnsaturated Fatty
Acids Questionnaire (PUFAQ; Appendix J) devised by Rogers et al. (2008). This
questionnaire evaluates frequency of dietary fat intake over the last three months, including
dietary fat, including white fish and oily fish, and dietary supplements including fish oils.
Responses were coded whereby a score of one is equivalent to eating one serve of fish per
week.
6.4.6 Cardiovascular Measures
6.4.6.1 Endothelin-1 analysis
The effect of the vasoconstrictor endothelin-1 on cognitive function was assessed. An
increase in the vasoconstrictor endothelin-1 is seen in HF (Jackson et al., 2000) and is
associated with disease severity, and mortality in these patients (Wei et al., 1994).
Stimulation of the neurohormonal response activates endothelin-1 release and oxidative stress
in the heart (Pousset et al., 1997). Additionally, endothelin-1 activates inflammation (Sharma,
Coats, & Anker, 2000) and causes increase arterial stiffness (Vuurmans et al., 2003). Plasma
endothelin-1 samples were determined using the Enzo Life Science endothelin-1 Enzyme
Immunoassay (EIA) kit from Assay Designs (cat # ADI-900-020a). The extraction method of
the sample using this assay kit is comparable to that described in Rolinski, Sadri, Bogner, and
Goebel (1994). The endothelin-1 assay was conducted according to the manufacturer’s
protocol. Typical plasma levels of endothelin-1 in HF patients is approximately 33 pg/mL
(Kiowski et al., 1995).
Chapter 6: Methods
89
6.4.6.2 Transcranial Doppler (TCD) Ultrasonography
The middle cerebral artery supplies blood to the temporal and inferior parietal lobes, which
are brain regions related to memory. HF patients have slower blood flow velocities in the
middle cerebral artery compared to controls (Jesus et al., 2006). However, the relationship
between middle cerebral artery and cognitive function has shown mixed results. Common
carotid arterial and cerebral blood flow velocities (BFV) were determined by means of a
Transcranial Doppler (TCD; Compumedics DWL® Germany GmbH). The common carotid
and left middle cerebral arterial blood flow velocities were measured using a 4 and 2 MHz
DWL® transducer (probe) respectively. A small amount of ultrasound gel was applied to each
transducer to minimise noise and increase signal detection. Once blood flow through an
artery is detected, the ultrasound beam omitted from the transducer is reflected by the cells in
that blood vessel. Mean blood flow velocity is calculated by the following formula presented
in Figure 3 (Babikian & Wechsler, 1993).
MVPV EDV 2
3
Figure 3 Formula for calculating mean blood flow velocity. MV = mean velocity; PV= peak systolic blood flow velocity; EDV = end diastolic blood flow velocity
To obtain the common carotid blood flow velocity, the artery was first found by palpating the
neck with thumb or second fingers just above the clavicle and between the trachea and
sternocleidomastoid muscle. Once the artery was clearly detected the 4 MHz transducer was
applied to neck. Common carotid signals in healthy elderly individuals (≥ 60 years) have
normal velocities ranging from 19-21cm/sec (Hamada, Takita, Kawano, Noh-Tomi, &
Okayama, 1993; Scheel, Ruge, & Schöning, 2000). The left middle cerebral arterial blood
flow was detected via ‘acoustical windows’ or areas of the skull thin enough for the
transducer beam to pass through. These ‘windows’ are located in the temporal
region/zygomatic arch (Figure 4).
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6: Methods
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Chapter 6: Methods
91
collected and recorded straight on a laptop. The SphygmoCor® Px uses a mathematical
transfer function to obtain the central (ascending) aortic pressure waveform from systolic and
diastolic pressure values of the brachial (as measured by a conventional cuff) and radial
arteries.
Central pulse pressure (PP) is calculated by subtracting the central diastolic blood pressure
(DBP) from the central systolic (SBP) blood pressure (Figure 5).
CPP CSPB–CDBP
Figure 5 Formula for calculating central pulse pressure defined by subtracting the central diastolic blood pressure (CDBP) from the central systolic blood pressure (CSBP).
Augmentation index (AIx) is an indirect measure of arterial stiffness and was defined by the
difference between the second (P2) and first (P1) systolic peaks of the central aortic waveform
(augmentation pressure), expressed as a percentage of the PP (Figure 6 and Figure 7).
AIx AP x100PP
Figure 6 Augmentation index (AIx), defined as a percentage of augmentation pressure (AP) and pulse pressure (PP).
To ensure that the recordings were acceptable, recordings with an operator index of 80 and
above were used for analysis. The SphygmoCor® has been well-validated (Asmar et al., 1995;
Chen et al., 1997).
Figure 7 A central aortic pressure waveform. Augmentation index (AIx) as defined by the difference between the second (P2) and first (P1) systolic peaks of the central aortic waveform (augmentation pressure), and is expressed as a percentage of the pulse pressure (PP). TR=time to reflected wave. Adapted from Wilkinson et al., (2000).
Chapter 6: Methods
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A summary of the vascular, oxidative stress, antioxidant and inflammatory and omega-3
dietary measures is presented in Table 5.
Table 5 Summary of vascular, oxidative stress, antioxidant and inflammatory measures
Mechanism Measure
Vascular
Cerebral Blood flow velocity
Common Carotid blood flow velocity Transcranial Doppler
Middle cerebral arterial blood flow
velocity
Transcranial Doppler
Arterial stiffness
Augmentation Index SphygmoCor®
Central Pulse Pressure SphygmoCor®
Oxidative Stress
Hydroperoxides DROM
Lipid peroxidation F2-isoprostanes
Antioxidant
Non-enzymatic antioxidant Coenzyme Q10
Lipophilic enzymatic antioxidant Glutathione peroxidase
Inflammation and omega-3 intake
Systemic inflammation High-sensitive C-reactive protein
Omega-3 dietary intake PUFA questionnaire
Chapter 6: Methods
93
6.4.7 Study design
Participants were asked to attend two testing sessions. The first was a practice session to
become familiar with the cognitive tests, as well as gather information on their demographics,
medical history and provide informed consent.
The control group were asked to return to Swinburne University’s Centre for Human
Psychopharmacology within one month and patients would return during their next medical
appointment or another convenient day at The Alfred Hospital for their actual testing session.
The second testing session consisted of cognitive tests, cardiovascular measures including
blood flow and arterial stiffness, and a blood sample to measure inflammatory markers,
antioxidant status and oxidative stress.
Testing Session 1 – Screening, enrolment and practice session:
Participants were asked to attend two testing sessions. At the first testing session, participants
signed the Participant Information and Consent Form (PICF; Appendix B) after the student
researcher addressed any queries the participant had about the study. Additionally, a face-to-
face screening interview, including administration of the MMSE, brief medical history and
collection of demographic data was conducted to confirm volunteer’s eligibility.
During visit 1, participants completed the full battery of computerised cognitive tasks to
reduce practice effects. Following the Cognitive Drug Research (CDR) protocol, patients and
controls went through two and four training sessions, respectively. During the first training
session and prior to each CDR task the researcher verbally explained the task to the
participant and once the participant was confident on what was required of them the
researcher began the task. For the subsequent practice sessions, healthy controls read
instructions on the screen prior to each task and the researcher again verbally explained the
task to patients. Furthermore, the same procedure was administered to both groups for the
Stroop task whereby instructions were read out to participants prior to practice and once
participants were confident with the task, the researcher began the real test.
Chapter 6: Methods
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Testing Session 2 – Baseline data collection:
Participants were asked to return to the testing site for a second testing session, which was the
actual data collection session for the study. A brief medical update was obtained to ensure
participants were still eligible to take part in the study.
During the baseline visit, participants completed the following measures:
Mood Scales and quality of life measures
- Profile of Mood States (POMS)
- Chalder fatigue Scale
- Quality of life SF-36
- General health questionnaire (GHQ-12)
Cognitive Measures
- The CDR cognitive test battery
- Computerised Stroop task,
- Trail Making Tests A and B
Physiological measures
- Blood pressure
- Pulse rate
- Transcranial Doppler - carotid and middle cerebral arterial blood flow
- SphygmoCor® arterial stiffness.
Blood sample
- 10ml whole blood using a heparin vacutainer
- 10ml whole blood using a EDTA vacutainer
- 6-8ml whole blood using a serum tube
At the end of the study, a nurse took a blood sample. Within one hour of taking the blood, the
blood samples were centrifuged and plasma samples stored in a -80oC freezer. Additionally,
samples intended for hs-CRP analysis were collected by a courier and delivered to the
pathology laboratory for analysis on the same day. An outline of the timeline for each testing
day is presented in Table 6.
Chapter 6: Methods
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Table 6 Testing protocol timeline
Time elapsed (min)
Event
Training Day
0 5 15 20 (40*) 45 (90*) 55 (100*) 105 (150*) 115 (160*)
Participant Consent (5mins) Screening questions, medical and demographic questions (10mins) Mini Mental State Examination (5mins) CDR Cognitive testing - practice session test 1 (and 2 for controls) Premorbid IQ – WASI Vocabulary subset CDR cognitive testing - practice session test 2 (and 4 for controls) Stroop practice test Testing session complete
Baseline
0 5 15 60 70 75 85 95 105 115
Medical overview POMS, SF-36 CDR Cognitive testing Stroop task (congruent and incongruent) Trail Making-A and Trail Making-B STAI-S/T, Chalder fatigue scale, GHQ-12, PUFA questionnaires SphygmoCor® (arterial stiffness) Transcranial Doppler (middle cerebral and common carotid arterial blood flow velocity) Blood Samples Testing session complete
Note: *Time elapsed for control group; CDR=Cognitive Drug Research; WASI=Wechsler Abbreviated Scale of Intelligence; POMS= Profile of Mood States; STAI-S/T= Speilberger State-Trait Anxiety Inventory-state/trait; GHQ=General Health questionnaire; PUFA= Polyunsaturated fatty acid.
Chapter 6: Methods
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6.4.8 Experimental Design
6.4.8.1 Testing environment
Cognitive and cardiovascular assessments on the Patient group were performed at the Alfred
Hospital Heart Centre, Heart Failure Clinic in any one of three testing rooms available in the
Research Centre depending on availability. Healthy controls were tested at Swinburne
University’s Centre for Human Psychopharmacology in purpose built testing rooms.
Participants were examined by a trained researcher in a controlled environment in a quiet,
well-ventilated and well-lit room, free from distractions. Participants were seated at a table
with the CDR laptop screen placed in front of the participant in position that was at easy
reach and allowed ample space for the button box, arm movement, writing space and a
viewing distance of approximately 50 centimetres. Efforts were made to keep the testing
room conditions consistent with adequate lighting, comfortable room temperature and
minimal background noise.
6.4.8.2 Data safety and monitoring
Researcher was training on administering the questionnaires, cognitive and vascular measures
(BP, SphygmoCor® Px and TCS). The Doppler-Box™ is certified by CE and meets the
requirements of the 93/42/EEC – Annex II.3 Medical Device Directive (Clas IIb according to
rule 10. The Doppler-Box™ is designed to the standard EN60601-1, EN60601-1-1,
EN60601-1-2, EN^0601-1-4, ICE61157 and IEC60601-2-37 and complies with the
guidelines issued by the German Association of Medical Insurance Companies.
The SphygmoCor® is classified as Class IIa (Annex IX Rule 10) and is in conformity with the
Annex I essential requirements and provisions of the Council Directive 93/42/EEC Annex II.
CE 0120. The SphygmoCor® system is designed, tested and approved to the following
standards: IEC60601-1: EN60601-1: As/NZS 3200.1.0 Medical electrical equipment with
Amendments 1 & 2 Part 1: General requirements for safety (the international Electro-Medical
Safety Standard for Medical Equipment).
6.4.8.3 Equipment
The Cognitive Drug Research® (CDR) test battery was displayed on a Viglen Dossier CDP
laptop with a 12.1” TFT colour screen. Participants responded to stimuli presented on the
laptop screen by pressing ‘YES’ and ‘NO’ buttons on a two-button response box using the
index finger or thumb from each hand. The Stroop colour word task, Trail-Making Tests and
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97
WASI Vocabulary subset tests were administered according to the recommended standard
task instructions. The Stroop colour word task was presented on a 17-inch colour desktop
monitor using a DOS-based software package and participants responded by pressing one of
four coloured buttons on a button box using their fingers. Participants completed the Trail-
Making Test in pencil on photocopies of the task on an A4 sheet of paper and the
administrator using a stopwatch recorded the time taken to complete the tasks. The
administrator on standard response sheets recorded participant response to the WASI
Vocabulary subset verbatim. Mood and QOL questionnaires were designed using the
Teleform scanner software and presented on A4 sheets of paper and participants completed
the questionnaires using a ballpoint pen. Additionally, the Doppler-Box™ medical ultrasound
device (Compumedics DWL® Germany GmbH) and transducers (4MHz and 2MHz) were
used to measure MCA and CC blood flow velocities. SphygmoCor® Px (AtCor Medical,
Sydney, Australia) electronics module and tonometer was used to measure arterial stiffness
parameters. The SphygmoCor® and QL software were installed on separate laptops.
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CHAPTER 7 RESULTS - DEMOGRAPHIC CHARACTERISTICS FOR
EXPERIMENTAL GROUPS
7.1 Introduction
In this chapter, results are initially examined with respect to demographics (age, gender, and
estimated IQ). This will be followed by an overview of the patient group clinical
characteristics. Data was analysed using SPSS statistical package software (SPSS Version 20,
SPSS Inc, Chicago, IL). For all analyses, group differences were considered statistically
different if the p value was less than 0.05. Two tailed tests were used to determine whether
group differences were statistically different.
To determine whether the heart failure (HF) group demographic variables (age, premorbid IQ
and gender) differed from those of controls, differences were examined using the appropriate
parametric and non-parametric statistics. Independent or paired samples Student’s t-test was
conducted to examine group differences between normally distributed (parametric) numeric
and continuous variables. Non-parametric tests, e.g. Mann-Whitney U test were conducted
for continuous variables that were non- normally distributed. Chi squared tests were used to
examine group differences for categorical variables. Assumptions of normality were explored
examining skewness, kurtosis, normality plots in order to ascertain whether the distribution of
data for each variable was normally distributed.
Patients who failed to fulfil the selection criteria during the study period were excluded from
the analysis. From the time of enrolling in the study, five patients improved in their HF
symptoms and were classified as having NYHA class I at the time of the baseline data
collection period. These patients were therefore excluded in the statistical analysis.
7.2 Data screening
Prior to analyses, demographic variables (age, premorbid IQ, gender, vitals) were explored
for accuracy of data entry and missing values and fit between their distributions and the
assumptions of parametric and non-paramedic analysis. There were no missing values for
demographic, clinical characteristics and screening variables. There were no univariate
outliers. A full description of the data screening procedure is outlined in Appendix K. There
were no missing data, no cases were excluded due to violation of assumptions. There were 36
cases in the HF group (NYHA class II-IV) and 40 cases in the Control group.
Chapter 7: Results – demographic characteristics
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7.3 Demographic variables
To verify that the HF group did not differ from controls on demographic variables Mann-
Whitney U test, Student’s t-test and Chi squared test were conducted for age, premorbid IQ
and sex respectively. Demographic characteristics (age, gender and education), dementia
screening, premorbid IQ and vitals for HF and Control participants are presented in Table 7.
HF patients and controls were well matched for age (Z = -.480, p = .63). Although there were
more males (69%) than females (31%) in the HF group compared to controls (50% males)
this group difference was not significant (χ2 (1) = 2.97, p = .11; Table 7).
HF patients had significantly lower, premorbid IQ scores (t(74) = -2.15, p < .05) and MMSE
scores (Z = -3.10, p < .01) compared to controls. There was a significant group difference for
education with years of education significantly higher in controls than the HF group (χ2 (6) =
16.3, p = .02). No group differences were observed for general health questionnaire.
Examining the vital signs, the HF group had significantly lower systolic blood pressure (SBP;
p < .001) and diastolic blood pressure (DBP, p < .001) than controls.
Chapter 7: Results – demographic characteristics
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Table 7 Demographic characteristics (age, gender and education), dementia screening, general health questionnaire-12 item, premorbid IQ and vitals for HF and control participants. Data shown are mean and (SD) or sample size and percentages.
Heart failure Healthy control n=36 n=40
Characteristic n (%) M (SD) n (%) M (SD) Z/ χ2/t p
Age, years 68 (7) 67 (5) Z = -.48 .631
Gender χ2 (1) = 2.97 .105
Male 25 (69) 20 (50)
Female 11 (31) 20 (50) Education 4 (2) 5.8 (2) χ2 (6) = 16.3 .017
Year 7 3 (8) 1 (3) Year 8 6 (17) 2 (5) Year 9 5 (14) 0 (0) Year 10 4 (11) 7 (18) Year 11 4 (11) 1 (3) Year 12 6 (17) 9 (23) Year 12+ 8 (22) 20 (50)
Vital signs SBP mmHg 115 (17) 139 (16) Z = -5.39 <.001 DBP mmHg 66 (12) 80 (8) Z = -5.02 <.001 BMI 28 (4) 27 (4) t(73) = 1.40 .164 Heart Rate 66 (12) 65 (78) t(74) = .46 .647
Screening MMSE 28 (1) 29 (1) Z = -3.10 .002 WASI 60 (8) 64 (8) t(74) = -2.20 .035 GHQ-12 21 (3) 21 (2) t(66) = -.39 .697
Note: SBP=systolic blood pressure; DBP=diastolic blood pressure; BMI=body mass index; MMSE=Mini Mental State Examination; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; GHQ-12= general health questionnaire (12 item); mmHg=millimetres of mercury.
Clinical characteristics of the HF group are presented in Table 8. Since these clinical
characteristics are specific to the HF patient group and exclusions for the control group,
exploring statistical group differences was therefore not applicable.
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Table 8 Clinical characteristics for HF group - NYHA class, aetiology, common
comorbidities.
Clinical characteristic n (%)
NYHA Class
NYHA class II 30 (83)
NYHA class III 5 (14)
NYHA class IV 1 (3)
Cause of Heart Failure
Dilated cardiomyopathy 17 (47)
Ischemic cardiomyopathy 10 (28)
Diastolic Heart Failure 3 (8)
Hypertrophic cardiomyopathy 1 (3)
Pulmonary hypertension 1 (3)
Alcoholic cardiomyopathy 1 (3)
Amyloid 1 (3)
Valvular Heart Disease 1 (3)
Hypertrophic cardiomyopathy 1 (3)
Arrhythmias management of heart failure 1 (3)
Comorbidities
Atrial fibrillation 8 (22)
CABG 8 (22)
Pacemaker 7 (19)
Defibrillator 2 (5.6)
Note: NYHA=New York Heart Association; CABG=coronary artery bypass graft.
As displayed in Table 8 majority of patients were diagnosed with mild HF (NYHA class II;
83%) followed by moderate (NYHA class II; 14%) and severe HF (NYHA class IV; 3%).
The aetiology for HF was mainly dilated cardiomyopathy (47%), followed by Ischemic
cardiomyopathy (28%) and diastolic heart failure (8%). As expected, the main comorbidities
observed in the HF group were atrial fibrillation (22%) followed by coronary artery bypass
graft (CABG; 22%) and diabetes (11%). Furthermore, 19% (n=7) of patients had a
defibrillator and 5.6% (n=2) a pacemaker.
A list of the common medications taken by each experimental group is presented in Appendix
L. Majority of patients were taking diuretics (89%; n=32) and beta-blockers (81%; n=29). As
expected, more HF patients than controls were taking statins (64% vs 5%) and mild
anticoagulants (e.g. aspirin; 44% vs 10%). The aim of this investigation is not to explore
Chapter 7: Results – demographic characteristics
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whether drugs or comorbidities are related to cognitive impairment or mood in HF patients
and will therefore not be included as covariates in the ANCOVA models when exploring
group differences.
A detailed overview of comorbidities seen in experimental groups is displayed in Appendix
M. Additionally, a list of common pharmaceuticals including over the counter medicines and
natural supplements are displayed in Appendix N and Appendix O, respectively.
7.4 Quality of Life
Continuous Quality of Life (QOL) measures using the 36 Short Form (SF-36) measure were
examined in each experimental group to ascertain whether they met assumptions of
normality. Mann-Whitney U test was used to examine group differences on continuous QOL
variables that were non-normally distributed. The Student’s t-test was conducted to examine
group differences between normally distributed continuous variables.
Means and standard deviations for the quality of life (QOL) variables are presented in Table
9. Heart failure patients scored significantly lower on each of the SF-36 subtests except SF-
36-Health Transition, which did not show significant group differences (p > .05).
Experimental groups scored equally on Chalder fatigue scale-mental symptoms subscale and
a trend towards worse scores on the Chalder fatigue scale-physical symptoms subscale (p =
.057). Overall, HF patients had scored higher than controls on the Total Fatigue Scale as
measured by the sum of the Physical and Mental Fatigue subscales (p < .05).
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Table 9 Group differences for SF-36 subscales and Chalder fatigue scale
Heart Failure Controls
n = 36 n = 40
Quality of Life Variable n M SD n M SD z/t p
SF-36 subscale
Physical functioning 36 49.12 21.90 40 85.85 14.97 -6.24 <.001
Role limitn physical 36 53.24 25.22 40 87.86 16.51 -5.66 <.001
Role limitn emotional 36 76.62 26.94 40 93.96 10.33 -3.33 .001
Vitality 36 46.82 23.80 40 73.23 15.07 -4.89 <.001
Mental health 36 76.15 17.46 40 85.54 9.35 -2.69 .007
Social functioning 36 68.75 23.62 40 93.75 11.67 -4.91 <.001
Bodily pain 36 67.71 25.64 40 83.00 18.57 -2.63 .009
General health 36 43.13 19.95 40 75.95 14.39 -8.29 <.001
Health transition 36 49.31 22.75 40 45.63 17.80 0.79 .432
SS: Physical 36 36.37 13.43 40 62.41 8.68 -6.72 <.001
SS: Mental 36 67.08 19.92 40 86.62 8.13 -4.67 <.001
Chalder fatigue scale
Physical symptoms 35 9.37 3.08 40 7.97 2.33 -1.90 .057
Mental symptoms 35 6.35 1.91 40 5.95 0.96 -1.70 .089
Total Score 35 15.72 4.28 40 13.92 2.97 -2.12 .034
Note: SF-36=Short Form 36 item, higher score on SF-36 is better functioning; limitn=limitation; SS=summary score.
7.5 Summary
In the current thesis, the two experimental groups were well-matched for age and gender.
Group differences however were observed for premorbid IQ. The aim of this thesis was to
control for IQ as this variable can influence scores on cognitive measures. Since HF patients
scored significantly lower on premorbid IQ, this variable will be controlled for in subsequent
analyses exploring whether the two experimental groups differ significantly on cognitive
outcome measures. As expected groups differed on the Mini Mental State Examination
(MMSE) screening tool and although this tool was utilised for screening purposes to exclude
individuals with probable dementia, these findings suggest that the HF group had lower
global cognitive scores compared to controls, which is in line with previous research.
Subsequent analyses will explore whether biomarkers are associated with global cognitive
function.
The majority of patients were diagnosed with mild HF (NYHA class II; 83%) followed by
moderate (NYHA class II; 14%) and severe HF (NYHA class IV; 3%), therefore results from
Chapter 7: Results – demographic characteristics
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this thesis may apply mainly to patients with mild heart failure. Due to the unequal sample
size among NYHA classifications, an assessment of whether cognitive impairments found
differ across disease severity cannot be made. However, if warranted an exploratory analysis
was done between HF patients with mild (NYHA class II) and severe (NYHA class III and
IV) disease severity.
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CHAPTER 8 RESULTS – GROUP DIFFERENCES BETWEEN
COGNITIVE MEASURES, MOOD AND BIOMARKERS
8.1 Introduction
This chapter will provide an examination of whether the experimental groups differed on any
of the cognitive, mood and biomarker variables. For all analyses, group differences were
considered statistically different if the p value was less than 0.05. Two tailed tests were used
to determine whether group differences were statistically different. Due to the small samples
size of the current investigation and to prevent decreasing statistical power, Bonferroni
corrections were not applied to the analyses to ensure that moderate effect sizes were detected
and to account for possible type II error. The focus of this study was limited to cognitive
measures of secondary and episodic memory, attention, psychomotor speed and executive
function. Secondary measures included mood, quality of life, oxidative stress, antioxidant
levels, systemic inflammation, arterial stiffness and cerebral blood flow.
8.2 Cognitive tasks
8.2.1 Introduction
To test the hypotheses regarding whether groups would differ on attention, psychomotor
speed, secondary memory, episodic memory and executive function, a series of one way
analysis of covariance statistics were conducted to examine whether differences existed on
the cognitive measures after adjusting for premorbid IQ and other possible confounding
variables. In addition to premorbid IQ, mood variables that correlated significantly with the
cognitive dependent variables were also included in the ANCOVA model when assessing
whether group differences existed on the cognitive outcome measures.
8.2.2 Data Screening
To examine whether groups differed on the cognitive measures, data was initially explored
for accuracy of data entry and missing values, best model of fit and assumptions of normality
as required to conduct ANOVA statistics. Variables that violated the assumptions for
normality were transformed to create normally distributed variables. The transformed
variables were incorporated into the ANOVA analysis to explore the effects of cognitive and
mood variables between groups.
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106
Continuous Cognitive Drug Research® (CDR) individual task variables were examined in
each experimental group to ascertain whether they met assumptions of normality. The Mann-
Whitney U test was used to examine group differences on continuous CDR variables that
were non-normally distributed. The Student’s t-test was conducted to examine group
differences between normally distributed continuous variables. For a complete overview of
the data screening procedure, refer to Appendix P.
Non-normally distributed variables were transformed prior to running ANOVA analysis.
Outliers were removed following transformations except for Continuity of Attention and the
Mann Whitney U test was therefore conducted to explore group differences. Additionally,
due to major skewness of congruent Stroop percentage accuracy, incongruent Stroop
percentage accuracy and Continuity of Attention non-parametric analysis was conducted to
compare group differences for these variables.
For positively skewed variables Log transformations were chosen for congruent Stroop
reaction time, incongruent Stroop reaction time, Trail Making-A, Trail Making-B and Speed
of Memory variables. The square root transformation was selected for the Stroop effect
variable. For negatively skewed variables, cubed transformation was chosen for quality
working memory. Table 10 presents a summary of the transformations selected for non-
normally disturbed variables.
Table 10 Transformations selected for analysis for non-normally disturbed cognitive variables
Variable Chosen transformation
Congruent Stroop RT log10
Incongruent Stroop RT log10
Stroop effect square root
Trail Making-A log10
Trail Making-B log10
Quality of Working Memory cubed
Speed of Memory log10
All other outliers were removed following variable transformation except for Continuity of
Attention therefore non-parametric analysis was conducted to explore whether group
differences exist for this variable. Tests for homogeneity of variance revealed that group
Chapter 8: Results – group differences between cognitive measures, mood and biomarkers
107
variance did exist thereby rejecting the null hypothesis and the assumptions for ANOVA
were therefore met.
The first analysis explored main effects of group differences and after adjusting for
premorbid IQ as measured by the WASI Vocabulary subset. To examine whether groups
differed on cognitive measures, the main analysis initially explored group differences without
covariates and the second analysis was conducted including covariates.
8.2.3 Selecting covariates
With the attempt to minimise extraneous variables effecting cognitive performance, the
experimental design excluded participants with mood disorders including depression and
anxiety, participants with a premorbid intelligence quotient (IQ) score of less than 80 as
measured by the WASI Vocabulary subset and those with dementia. To adjust for extraneous
variables known to affect cognitive function, the main analysis explored whether differences
in cognitive function existed between the experimental groups after adjusting for additional
potential cofounding variables. Given that the control group scored significantly higher on
the premorbid IQ measure, WASI Vocabulary subset, compared to the HF group, group
differences for cognitive function were assessed after adjusting for the WASI Vocabulary
subset.
In order to reduce error variance, additional factors known to affect cognitive function were
included as covariates in the ANCOVA model. Due to limited research on the effects of
possible confounding factors on cognitive function, additional potential covariates were
selected based on the present data. Obtaining a large sample size on the patient group studied
in this investigation was difficult as patients were recruited from only one centre. Due to the
small sample size, additional potential covariates were chosen based on significant
relationships seen with outcome measures from the present data.
Univariate correlations between each of the cognitive measures and mood subscales were
conducted for the HF and control groups. Pearson’s correlation coefficients between
neuropsychological and mood variables for the HF and control groups are displayed in Table
11 and Table 12, respectively. Mood variables that correlated significantly with the cognitive
dependent variable were included as a covariate.
In the HF group Quality of Episodic Memory was significantly negatively correlated with
POMS-tension/anxiety, POMS-fatigue/inertia, POMS-confusion/bewilderment and POMS-
Total mood disturbance with the strongest correlation appeared with POMS-fatigue/inertia.
Given that each of the POMS variables were significantly correlated with each other, POMS-
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Total mood disturbance which represents a combined score of the mood variables was used
as a covariate when exploring group differences between Quality of Episodic Memory.
Additionally, POMS-vigour/activity was an additional covariate when exploring group
differences in Trail Making-A, as significant correlations were observed between these
variables. In the control group, there were no significant correlations between Quality of
Working Memory, Continuity of Attention and the mood variables.
Furthermore, multivariate regression analysis revealed no significant relationships between
the cognitive measures and POMS subscales in each experimental group. The only finding
was a trend for Power of Attention and POMS-fatigue/inertia in the HF group (p = .06) and
the control group (p = .07). However, since these relationships did not reach significance
POMS-fatigue/inertia was therefore not included as a covariate. Failing to find significant
relationships between cognitive and mood measures using multivariate analyses suggests that
no additional variables were suitable covariates for subsequent analyses.
Chapter 8: Results – group differences between cognitive measures, mood and biomarkers
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Table 11 Pearson’s correlation coefficients between neuropsychological and mood variables for the HF Group
ICS SE TM-A TM-B CDR- QESM
CDR- QWM
CDR- PA
CDR- SM
CDR- CA
POMS- T/A
POMS- D/D
POMS-A/H
POMS-V/A
POMS- F/I
POMS- C/B
POMS-TMD
CS .704*** .449** .283 .431* -.501** -.307 .677*** .571*** -.269 .180 .160 .009 .022 .105 .123 .066
ICS .945*** .394* .398* -.373* -.067 .495** .428* -.240 -.014 -.072 -.191 .197 .003 -.078 -.122
SE .386* .310 -.263 .065 .358* .311 -.182 -.076 -.134 -.233 .233 -.044 -.154 -.170
TM-A .504** -.482** -.031 .538** .216 .101 .239 .169 .280 .136 .270 .248 .162
TM-B -.475** -.094 .439** .347* -.120 .224 .101 .063 .002 .319 .116 .156
CDR-QESM .269 -.510** -.330* .023 -.410* -.279 -.238 .113 -.421* -.402* -.352*
CDR-QWM -.197 -.517** 409* .051 .221 .106 -.105 .192 .048 .152
CDR-PA .521** -.004 .168 .108 .137 .163 .053 -.002 .005
CDR-SM -.409* -.036 -.097 -.058 .206 -.267 -.205 -.179
CDR-CA -.028 .046 .194 -.262 .156 .132 .149
POMS-T/A .887*** .660*** -.630*** .730*** .827*** .899***
POMS-D/D .734*** -.647*** .740*** .740*** .922***
POMS-A/H -.469** .685*** .509** .765***
POMS-V/A -.611 -.629*** -.795
POMS-F/I .652*** .855***
POMS-C/B .824***
Note: CS=congruent Stroop; ICS=incongruent Stroop; SE=Stroop effect; TM-A=Trail Making-A; TM-B=Trail Making-B; CDR-QESM=Quality of Episodic Memory; CDR-QWM=Quality of Working Memory; CDR-PA=Power of Attention; CDR-SM=Speed of Memory; CDR-CA=Continuity of Attention; POMS-T/A=tension/anxiety; POMS-D/D=depression/dejection; POMS-A/H=anger/hostility; POMS-V/A=vigour/activity; POMS-F/I=fatigue/inertia; POMS-C/B=confusion/bewilderment; correlations significant at: * p < .05; ** p < .01; *** p < .001.
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Table 12 Pearson’s correlation coefficients between neuropsychological and mood variables for the control group
ICS SE TM-A TM-B QESM CDR- QWM
CDR- PA
CDR- SM
CDR- CA
POMS- T/A
POMS- D/D
POMS- A/H
POMS- V/A
POMS- F/I
POMS- C/B
POMS- TMD
CS .726*** .273 .391* .469** -.348* -.446** .674*** .616*** -.221 .016 -.055 -.089 -.093 -.161 .254 .050
ICS .856*** .392* .372* -.311 -.346* .561*** .564*** -.238 .093 .182 .163 -.108 .046 .049 .127
SE .274 .192 -.190 -.162 .276 .341* -.186 .131 .295 .302 -.075 .197 -.125 .138
TM-A .569** -.059 -.313* .269 .290 -.035 .059 -.055 -.018 -.465** .020 .023 .200
TM-B -.384* -729*** .336* .286 -.200 .101 -.007 -.036 -.307 .014 .244 .200
CDR-QESM
.494** -.220 -.315* .207 .089 .105 -.089 .019 .121 -.210 -.009
CDR-QWM -.288 -.191 .114 .033 .052 -.051 .161 .121 -.251 -.116
CDR-PA .678*** -.133 -.004 .045 -.088 -.098 -.133 .158 .054
CDR-SM -.005 -.020 -.015 .039 -.101 -.108 .031 .017
CDR-CA .088 -.089 -.093 -.147 -.043 -.233 -.028
POMS-T/A .673*** .364* -.425** .631*** .542*** .779***
POMS-D/D ¤ .527*** -.319* .683*** .461** .729***
POMS-A/H .001 .365* .178 .441**
POMS-V/A -.567 -.543*** -.764***
POMS-F/I .485** .830***
POMS-C/B .760
Note: CS=congruent Stroop; ICS=incongruent Stroop; SE=Stroop effect; TM-A=Trail Making-A; TM-B=Trail Making-B; CDR-QESM=Quality of Episodic Memory; CDR-QWM=Quality of Working Memory; CDR-PA=Power of Attention; CDR-SM=Speed of Memory; CDR-CA=Continuity of Attention; POMS-T/A=tension/anxiety; POMS-D/D=depression/dejection; POMS-A/H=anger/hostility; POMS-V/A=vigour/activity; POMS-F/I=fatigue/inertia; POMS-C/B=confusion/bewilderment; correlations significant at: * p < .05; ** p <.01; *** p < .001.
Chapter 8: Results – group differences between cognitive measures, mood and biomarkers
111
8.3 Results
8.3.1 Introduction
Analysis of covariance (ANCOVA) statistics was used to explore whether experimental
groups differed on cognitive measures after adjusting for WASI Vocabulary subset and other
possible confounding mood variables. The Mann Whitney U test was conducted for non-
normally distributed variables.
8.3.2 Attention domains
To test the hypothesis (H1) that heart failure (HF) patients will perform significantly worse on
attention tasks as measured by congruent Stroop task, Power of Attention and Continuity of
Attention compared to the control group, analysis of covariance and the Mann Whitney U
tests were conducted for normally and non-normally distributed variables, respectively.
Contrary to what was predicted, Mann Whitney U tests revealed that HF patients performed
as accurately as controls on the congruent Stroop (p = .670) and on the Continuity of
Attention cognitive domain (F(1,73) = 0.43, p >.05; Table 13).
Evaluating assumptions of normality of sampling distributions, linearity, homogeneity of
variance, homogeneity of regression and reliability of covariates were shown to be
acceptable. Without controlling for premorbid IQ (WASI Vocabulary), HF patients
performed significantly worse than controls on congruent Stroop (F(1,73) = 8.38, p =.01),
Power of Attention (F(1,73) = 10.46, p =.002) but similarly on Continuity of Attention
(F(1,73) = 0.43, p =.515.
As predicted, there was a significant weak effect of group on congruent Stroop task
performance, after controlling for premorbid IQ, F(1,26) = 6.7, p =.01, partial η2=.086.
Additionally, as predicted, after adjusting for premorbid IQ, there was a significant weak
main effect of group on Power of Attention cognitive domain (F(1,72) = 9.33, p < .01, partial
η2=.115; Table 13).
Chapter 8: Results – group differences between cognitive measures, mood and biomarkers
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Table 13 Means and standard deviations of attention, memory and executive function tasks for HF and controls after adjusting for premorbid IQ and mood covariates
Heart Failure Controls
Variables n M SD n M SD df F/Z p partial η2
Attention Tasks Congruent Stroop RT (ms)*
35 908.00 157.66 40 813.73 138.00 (1, 72) 6.79 .011 .086
Congruent Stroop %Acc
35 97.87 3.54 40 98.56 2.11 -.43 .670
Attention Domains Power of Attention (ms)*
36 1270.31 121.77 39 1191.52 87.63 (1, 72) 9.33 .003 .115
Continuity of Attention (ms)*
36 90.78 4.11 39 91.36 3.27 -.45 .651
Psychomotor Task Trail Making-A (ms)*
36 37.48 9.78 40 33.90 9.83 (1, 72) .49 .486 .007
Memory Domains Quality of Episodic Memory (ms)*
36 171.30 54.56 40 183.79 50.81 (1, 72) .43 .513 .016
Quality of Working Memory (ms)*
36 1.82 .19 40 1.78 .37 (1, 73) .20 .659 .003
Speed of Memory (ms)*
36 4353.22 889.69 40 4077.92 617.23 (1, 73) 1.67 .200 .022
Executive Function Incongruent Stroop RT (ms)*
35 1319.39 503.68 40 1057.75 291.25 (1, 72) 6.03 .016 .077
Incongruent Stroop %Acc
35 94.00 10.04 40 98.44 2.70
-1.98 .047
Stroop effect (ms)*
35 411.29 416.00 40 244.01 223.48 (1, 72) 2.74 .102 .037
Trail Making-B (ms)*
36 106.51 40.02 40 89.10 46.79 (1, 73) 3.94 .051 .051
Note: Group differences were obtained after adjusting for covariates (CV: WASI) except – Trail Making-A (CV: WASI, POMS-vigour/activity; CDR-Quality of Episodic Memory (CV: WASI, POMS-Total mood disturbance); RT=reaction time; %Acc=percentage accuracy; ms=milliseconds; *=higher scores indicate worse performance.
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8.3.3 Psychomotor function
To test the hypothesis (H2) that HF patients will perform significantly worse than controls on
psychomotor function (Trail Making-A) a between subjects (HF, controls) univariate analysis
of covariance (CV: WASI, POMS-V/A) was conducted. Without controlling for covariates
(IQ and POMS-vigour/activity), there were no significant differences between HF patients
and controls on psychomotor function as measured by Trail Making-A, F(1,74) = 3.57, p
=.06.
Contrary to expectation after controlling for premorbid IQ and POMS-vigour/activity, the
main effect of group on Trail Making-A task was not significant, F(1,72) = .491, p =.49,
partial η2=.007 (Table 13). The covariate POMS-vigour/activity had a significant weak effect
on Trail Making-A, F(1,72) = 4.25, p < .05, partial η2=.056.
8.3.4 Cognitive Drug Research task subsets
Mann-Whitney U test was carried out to examine group differences on continuous CDR
individual task variables that were non- normally distributed. The Student’s t-test was
conducted to examine group differences between normally distributed continuous variables.
Means and standard deviations for the CDR subtests for each experimental group are
presented in Table 14.
Heart failure (HF) patients recalled significantly fewer words in the immediate word recall
task compared to controls (Z = -2.15, p < .05). Furthermore, compared to controls, the HF
group’s reaction times were significantly slower for the Simple (t(73) = 3.70, p < .001) and
Digit Vigilance (t(73) = 2.09, p < .05) CDR individuals tasks. Reaction times during the
Word Recognition and Word Recognition New Stimuli tasks were significantly longer in the
HF group compared to controls, t(74)= 2.09, p < .05 and Z = -3.10, p < .01, respectively.
Almost significant findings were observed on Choice Reaction Time (Z = -1.93, p = .054)
and Word Recognition New stimuli percentage accuracy (Z = -1.93, p = .053).
Examining the individual cognitive tasks from which the CDR cognitive domains are
derived, these group differences in reaction times and digit vigilance tasks explains the Group
differences seen in the Power of Attention cognitive domain.
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Table 14 Group differences for Cognitive Drug Research subtests for HF and control group
Heart Failure Controls Cognitive Drug
Research Variable n M SD n M SD z/t p
IWR # correct 36 4.50 1.84 40 5.50 1.83 -2.15 .031 IWR % Acc 36 30.00 12.29 40 36.67 12.17 -2.15 .031 IWR # Errors 36 .39 .55 40 .40 .63 -.16 .871 SRT (ms) 36 315.06 42.33 39 283.93 29.92 3.70 <.001 DV RT (ms) 36 443.84 38.34 39 425.64 37.23 2.09 .041 DV %Acc 36 97.41 5.16 39 97.95 3.32 -.52 .602 DV # False Alarms 36 1.67 1.59 39 1.62 1.97 -.55 .583 CRT (ms) 36 511.10 65.88 39 481.95 45.13 -1.93 .054 CRT % Acc 36 97.22 2.84 39 97.79 2.14 -.67 .503 SWM RT (ms) 36 1050.56 318.22 40 974.18 159.33 -.56 .574 SWM Original RT (ms)
36 986.59 322.94 40 912.36 139.69 -.23 .819
SWM New RT (ms) 36 1098.02 325.18 40 1028.60 208.29 -.58 .560 SWM Original % Acc 36 92.88 10.89 40 94.06 12.01 -.78 .435 SWM New % Acc 36 94.58 7.11 40 93.38 14.38 -1.39 .166 NWM RT (ms) 36 887.81 227.43 40 874.16 210.16 -.28 .779 NWM Original RT (ms)
36 836.73 195.91 40 824.05 184.76 -.44 .662
NWM Original % Acc
36 95.00 6.83 40 93.50 8.78 -1.44 .150
NWM New RT (ms) 36 937.60 268.47 40 921.54 251.57 -.14 .892 NWM New % Acc 36 98.15 3.42 40 96.50 7.76 -.92 .356 DWR # correct 36 3.19 1.94 40 3.55 2.05 -.48 .632 DWR % Acc 36 21.30 12.93 40 23.67 13.67 -.48 .632 DWR # Errors 36 .64 .99 40 .53 .72 -.28 .777 WR RT (ms) 36 1094.58 224.07 40 977.00 227.37 2.27 .026 WR Original RT (ms) 36 1025.82 230.57 40 969.73 250.95 -1.31 .190 WR New RT (ms) 36 1159.52 272.82 40 990.59 245.52 -3.10 .002 WR Original % Acc 36 69.07 15.05 40 64.50 15.05 1.32 .190 WR New % Acc 36 84.44 12.75 40 90.00 9.90 -1.93 .053 PR New RT (ms) 36 1367.43 439.02 40 1301.93 284.19 -.10 .917 PR New % Acc 36 82.92 17.09 40 85.50 11.20 -.13 .895 PR Original RT (ms) 36 1288.12 489.78 40 1207.02 361.80 -.38 .700 PR Original % Acc 36 90.42 9.88 40 89.63 9.16 -.49 .627 PR RT (ms) 36 1320.28 439.87 40 1252.59 295.01 -.18 .860
Note: IWR=immediate word recall; SRT=simple reaction time; DV=digit vigilance; CRT=choice reaction time; SWM=spatial working memory; NWM=numeric working memory; DWR=delayed word recall; WR=word recognition; PR= picture recognition; # correct=number correct; % Acc=percentage accuracy; ms=milliseconds; RT=reaction time; items in bold represent variables that constitute the Power of Attention domain.
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8.3.5 Summary for attention and psychomotor function
In summary, the hypotheses that that patients would perform worse than controls on attention
tasks (H1) supported. However, contrary to expectation patients did not perform significantly
worse than controls on psychomotor function as measured by Trail Making-A (H2). As
predicted HF patients’ mean reaction time on the congruent Stroop RT task was significantly
slower than healthy controls after adjusting for WASI Vocabulary subset scores. However,
after adjusting for premorbid IQ and POMS-vigour/activity experimental groups did not
perform differently on the psychomotor task as measured by the Trail Making-A.
Furthermore, HF patients’ overall performance on the Power of Attention cognitive domain
was worse than controls after controlling for premorbid IQ (1270 ms versus 1192 ms).
Examining the individual CDR tasks defining the Power of Attention cognitive domain HF
patients displayed reduced simple (315ms versus 284ms), digit vigilance (444ms versus
426ms) and choice (511ms versus 482ms) reaction times. This indicates that HF patients have
an impaired ability to focus attention during a short period requiring extreme concentration.
Furthermore, it was hypothesised that patients will perform worse than controls on the
Continuity of Attention cognitive domains. After adjusting for WASI Vocabulary scores, this
investigation did not observe differences between experimental groups on the Continuity of
Attention cognitive domain performance. These results suggest that HF patients are impaired
on attention tasks however, they make a similar number of errors compared to controls when
focussing on a task over a prolonged period. Additionally, the results of the present study
suggest that HF patients are not impaired on their psychomotor abilities.
8.4 Memory tasks
8.4.1 Introduction
A series of one-way between-subject analysis of covariance statistics were performed to test
the hypothesis (H3) that HF patients will perform significantly worse than controls on Quality
of Working Memory, Quality of Episodic Memory, and Speed of Memory tasks. Evaluating
assumptions of normality of sampling distributions, linearity, homogeneity of variance,
homogeneity of regression and reliability of covariates were shown to be acceptable.
8.4.2 Results
Without adjusting for premorbid IQ and other covariates Quality of Working Memory
(F(1,74) = .003, p > .05), Quality of Episodic Memory (F(1,74) = 1.07, p > .05), Speed of
Memory (F(1,74) = 2.22, p > .05).
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Experimental groups did not differ on the Quality of Working Memory cognitive domain after
adjusting for premorbid IQ and other confounding factors (p > .05; Table 13). Although
patients recalled significantly fewer words in the immediate word recall task compared to
controls (p =.031), groups did not differ on the spatial working memory or numeric working
memory tasks (Table 14).
After adjusting for premorbid IQ and confounding factors, experimental groups did not differ
on the Quality of Episodic Memory (p > .05) or Speed of Memory cognitive domains (p > .05;
Table 13). Furthermore, there were no significant group differences in the number of words
recalled during the delayed word recall or number or images recognised during the picture
recognition task (Table 14).
This indicates that HF patients are not impaired compared with age matched controls on
working memory, episodic memory or in the time it takes to recall information from memory.
Additionally, these results suggest that HF patients may be impaired in their immediate
verbal memory recall ability although may not in spatial working memory or numeric
working memory (Table 14).
8.4.3 Summary for memory function
The hypothesis (H3) that HF patients will perform worse on the Quality of Working Memory
domain was not supported as this investigation found that after adjusting for IQ, the HF group
and controls performed equally on the Quality of Working Memory cognitive domain.
Examining the individual tasks from which this domain is derived, this investigation found
that HF patients and controls performed equally on spatial working memory tasks with
relation to reaction time, performance accuracy and error rates. This indicates that patients’
ability to store spatial visual information in working memory and the percentage accuracy is
the same as controls. Additionally, these findings suggest that patients’ ability to store
numeric information in working memory and the accuracy for this information is the same as
controls tasks with relation to reaction time, performance accuracy and error rates. Taken
together these findings suggest that when compared to age matched controls, HF patients are
not impaired in their visuo-spatial working memory and numeric working memory abilities.
After adjusting for possible confounding factors (premorbid IQ scores, POMS-Total mood
disturbance), this investigation did not observe differences between groups on the Quality of
Episodic Memory domain. These findings suggest that HF patients are not impaired in their
ability to recall verbal information correctly from short-term and episodic memory and visual
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information from episodic memory. Additionally, this suggests that patients are not impaired
in their ability to recall verbal and visual information correctly from episodic memory.
Examining the individual CDR tasks that reflect performance on Quality of Episodic
Memory, the hypothesis that HF patients would perform more poorly than controls on
episodic memory was supported. HF patients recalled fewer words than controls on the
immediate word recall task (M=5 verses M=6 out of a total of 15 words). However,
experimental groups did not differ in the number of words recalled in the delayed word recall
task.
8.5 Executive function domains
8.5.1 Introduction
To test the hypothesis (H4) that HF patients will perform significantly worse than controls on
executive function group differences on the Trail Making-B, incongruent Stroop and Stroop
effect tasks were explored using a one way ANOVA. This was followed by a between
subjects (HF, controls) univariate analysis of covariance (CV: WASI) to examine whether
experimental groups differed on measures of executive function after adjusting for IQ and
other possible confounding variables.
8.5.2 Results
The Mann Whitney U test indicated that HF patients were less accurate in their performance
on the incongruent Stroop task compared to controls (Z = -1.98, p < .05; Table 13). As
expected, a between subjects (HF, controls) univariate analysis of covariance (CV: WASI)
revealed that the covariate WASI Vocabulary subset was not significantly related to
incongruent Stroop, (F(1,72) = 2.77, p =.10). After controlling for IQ, there was a significant
weak effect of group on incongruent Stroop, F(1,72) = 6.03, p =.016, partial η2=.077.
Without adjusting for premorbid IQ HF patients performed significantly worse than controls
on Stroop effect (F(1,73) = 4.51, p =.037) and Trail Making-B (F(1,74) = 6.02, p =.016).
In the ANCOVA model the covariate WASI was not significantly related to Stroop effect
(F(1,72) = 3.172, p =.08). However, after controlling for IQ, there was no significant main
effect of group on Stroop effect F(1,72) = 2.74, p =.10, partial η2=.037.
In the ANCOVA model the covariate WASI was not significantly related to Trail Making-B,
F(1,73) =3.14, p =.08). Although, after controlling for WASI, there was an almost significant
group effect on Trail Making-B task performance, F(1,73) = 3.94, p =.051, partial η2=.051.
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8.5.3 Summary for executive function
Supporting the hypothesis HF patients demonstrated slower performance than controls on
executive function as measured by the Trail Making-B (107ms verses 89ms) task and
incongruent Stroop RT task (M=132 sec vs M=106 sec) even after controlling for premorbid
IQ. Additionally, HF patient’s accuracy on executive function performance as measured by
incongruent Stroop task was worse than that of controls (94% versus 98%). On the contrary,
after adjusting for WASI Vocabulary scores, there were no significant main effects for group
on executive function as measured by Stroop effect. This indicates that experimental groups
performed equally on Stroop effect measure.
8.6 Mood measures
8.6.1 Introduction
To explore the hypothesis (H5) that the HF group will score higher on the
depression/dejection, tension/anxiety, confusion/bewilderment, anger/hostility, fatigue/inertia
and lower on vigour/activity compared to controls, group differences were determined using a
one way ANOVA. Prior to analyses the mood variables were explored for accuracy of data
entry, missing values and best model of fit. The Profile of mood states (POMS) subscale and
Speilberger’s State Trait Anxiety Inventory (STAI) questionnaires were examined to
determine whether they met assumptions of normality for ANOVA.
These variables were also examined to determine whether they met assumptions of normality
for ANOVA. Appendix Q provides a detailed outline for the data exploration methods for
mood variables. The STAI-S, STAI-T and POMS subscales, except the vigour/activity subset,
were positively skewed. The log 10 transformation was used for all POMS subscales except
POMS-Total mood disturbance where the square root transformation was the best formula to
normalise the variable. All outliers were removed following variable transformation.
8.6.2 Results
Table 15 presents mean scores on the individual POMS subtests, STAI-State and STAI-Trait.
As expected an ANOVA revealed that HF patients scored significantly higher than controls
on the POMS-tension/anxiety, POMS-depression/dejection, POMS-anger/hostility, POMS-
fatigue/inertia, POMS-confusion/bewilderment subtests of the POMS questionnaire and
POMS-Total mood disturbance. Additionally, HF patients scored significantly lower than
controls on the POMS-vigour/activity subset. HF patients scored significantly higher than
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controls on the STAI-State (p < .01), however the results indicate that experimental groups
displayed equivalent scores on the STAI-Trait scale (Table 15).
Table 15 Analysis of variance, means and standard deviations for each experimental group for Profile of Mood States subscales and state trait anxiety inventory
Heart Failure Controls
Mood Variable n M SD n M SD df F p
POMS
Tension/anxiety 36 7.44 6.24 40 3.75 3.39 (1, 74) 9.69 .003 Depression/ dejection 36 7.14 9.37 40 2.05 2.70 (1, 74) 11.56 .001
Anger/hostility 36 5.06 4.97 40 2.48 2.95 (1, 74) 5.22 .025
Vigour/activity 36 15.50 5.67 40 20.38 6.20 (1, 74) 12.71 .001
Fatigue/inertia 36 9.42 7.18 40 4.15 4.06 (1, 74) 15.17 <.001 Confusion/ bewilderment 36 7.17 4.70 40 3.95 2.96 (1, 74) 12.11 .001 Total mood disturbance 36 20.72 32.84 40 -4.00 16.66 (1, 74) 18.04 <.001
STAI
State score 36 32.40 7.19 39 27.08 6.26 (1, 73) 13.12 .001
Trait score 36 32.90 8.43 39 30.56 7.13 (1, 73) 1.49 .226
Note: POMS=Profile of Mood States; STAI=State Trait Anxiety Inventory.
8.6.3 Summary of mood variables
The hypothesis that HF patients will display greater mood disturbances than controls was
supported. The findings from this study demonstrated that compared to controls, HF patients
had higher levels of tension/anxiety (7.44±6.24 vs 3.75±3.39), anger/hostility (5.06±4.95 vs
2.48±2.95), fatigue/inertia (M = 9.42±7.18 vs 4.15±4.06) and confusion/bewilderment
(7.17±4.70 vs 3.95±2.96) than controls. Additionally, HF patients scored higher on
depression/dejection (7.14±9.37 vs 2.05±2.70) and had less vigour/activity compared to
controls (15.50±5.67 vs 20.38±6.20). Finally, HF patients (20±32.84) had greater Total mood
disturbance compared to controls (-4.00±16.66).
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8.7 Vascular variables
8.7.1 Introduction
A series of one way ANOVA analyses were conducted to test the hypotheses related to
whether experimental groups differed on the vascular markers (H6 – H8).
8.7.2 Data Screening
To ensure the assumptions for ANOVA the Levene’s statistic confirmed no violation of
assumptions of homogeneity for each vascular variables except for endothelin-1 which had an
extreme outlier which was not removed following transformation, therefore this case was
removed for this variable before analysis. Additionally skewness and kurtosis statistics,
normality plots and bivariate scatter plots were examined for signs of violations of normality
and linearity assumptions. Apart from common carotid arterial blood flow velocity (BFV),
middle cerebral BFV and augmentation index which were normally distributed all other
vascular biomarkers were non-normally distributed and log transformations were used to
correct positive skewness were used in the ANOVA analyses. For a complete overview of the
data screening procedure refer to Appendix R.
8.7.3 Results
A one way ANOVA was conducted to test the hypothesis (H6) that HF patient group will
have significantly lower cerebral blood flow velocity as measured by common carotid and
middle cerebral blood flow velocity compared to the control group. Means and standard
deviations for vascular measures are displayed in Table 16. As expected, HF patient’s
common carotid (F(1,64) = 18, p < .01) and left middle cerebral arterial (F(1,53) = 5.07, p <
.05) blood flow velocities were significantly slower compared to controls (Table 16).
Additionally to test the hypothesis (H7) that HF patients will have increased arterial stiffness
as measured by augmentation index and central pulse pressures compared to controls, a one-
way ANOVA was conducted to examine group differences on the augmentation index and
central pulse pressure variables. In contrast to what was expected, experimental groups did
not differ on arterial stiffness as measured by augmentation index (F(1,57) = 1.20, p = .28).
Additionally, HF patients had significantly lower levels of arterial stiffness as measure by
central pulse pressure compared to controls (F(1,59) = 4.76, p < .05).
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Table 16 Means and standard deviations of the vascular measures for HF and control groups
Heart Failure Controls
Vascular variable n M SD n M SD df F p
Arterial Blood Flow Velocity (cm/s)
Common carotid 33 17.11 4.92 33 21.69 3.86 (1,64) 17.66 <.001
Middle cerebral 23 50.44 6.70 32 56.21 10.88 (1,53) 5.07 .028 Central Pressures (mmHg)
Central systolic BP 21 108.52 17.9 40 128.75 15.30 (1,59) 25.05 <.001
Central diastolic BP 21 68.24 12.91 40 81.12 7.53 (1,59) 21.01 <.001 Arterial Stiffness (mmHg)
Augmentation index*
19 21.79 11.15 40 24.60 8.16 (1,57) 1.20 .278
Central pulse pressure
21 40.29 13.33 40 47.63 14.91 (1,59) 4.76 .033
Vascular function Endothelin-1 (pg/mL)
34 24.94 11.39 22 27.77 6.56 (1, 54) 1.35 .235
Note: cm/s=centimetres per second; BP=blood pressure; mmHg=millimetres of mercury; pg/mL=pictograms per millilitre; *=corrected for heart rate.
The hypothesis (H8) that HF patients will have higher plasma levels of the vasoconstrictor
endothelin-1 compare to controls was not supported. An ANOVA showed that the
experimental groups did not differ on plasma endothelin-1 levels, (F(1,54) = 1.35, p > .05;
Table 16).
To explore whether elevated endothelin-1 levels in HF patients will be related to increased
inflammation, Pearson’s correlations were carried out between endothelin-1 and high-
sensitive C-reactive protein and each of the each of the arterial stiffness measures
(augmentation index, central pulse pressure). Pearson’s correlation coefficients for the HF
group are presented in Table 17.
Contrary to what was expected, as displayed in Table 17, endothelin-1 levels were not
significantly related to inflammation as measured by high-sensitive C-reactive protein or
arterial stiffness as measured by augmentation index, peripheral pulse pressure and central
pulse pressure.
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Table 17 Pearson’s correlations between endothelin-1, high-sensitive C-reactive protein and
each of the each of the arterial stiffness measures (augmentation index, central pulse
pressure) in the heart failure group.
AIx PPP CPP ET-1
hs-CRP .239 .429* .486* .275
AIx -439 .482 .193
PPP .950** .233
CPP .274
Note: hs-CRP=high-sensitive C-reactive protein; AIx=augmentation index; PPP= peripheral pulse pressure; CPP=central pulse pressure, ET-1=endothelin-1; correlations significant at: * p < .05; ** p < .01.
8.7.4 Summary
As predicted mean blood flow velocity in the left common carotid artery and left middle
cerebral artery was slower in patients compared to controls. However, the hypothesis that
patients will have increased arterial stiffness compared to controls, was partially supported
from the current findings. Only one of the two measures of arterial stiffness demonstrated
that patients had elevated arterial stiffness compared to controls. Central pulse pressure,
which is an indirect measure of arterial stiffness, was reduced in HF patients however there
were no differences observed on measures of augmentation index between experimental
groups.
8.8 Oxidative stress, antioxidant and inflammatory biomarkers
8.8.1 Introduction
A series of one way ANOVA analyses were conducted to test the hypotheses related to
whether experimental groups differed on the oxidative stress (DROM, F2-isoprostanes),
antioxidant (glutathione peroxidase, coenzyme Q10), inflammatory (hs-CRP) and omega-3
(PUFA; H9 - H11) measures. To ensure the assumptions for ANOVA the Levene’s statistic
confirmed no violation of assumptions of homogeneity for the oxidative stress, antioxidant or
inflammatory variables. Additionally skewness and kurtosis statistics, normality plots and
bivariate scatter plots were examined for signs of violations of normality and linearity
assumptions. The variables PUFA, F2-Isoprosane levels, glutathione peroxidase and
coenzyme Q10 were all normally distributed. To ensure the assumptions for ANOVA were
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met for hs-CRP, DROM and endothelin-1, which were non-normally distributed, the log
transformation of these variables were utilised in the analysis.
8.8.2 Results
Means and standard deviations for oxidative stress, antioxidant and inflammatory measures
are presented Table 18. A one way ANOVA was conducted to test the hypothesis that HF
patients will have significantly higher levels of oxidative stress as measure by determinable
reactive oxygen metabolites (DROMs) and lipid peroxides (F2-isoprostanes) compared to the
control group (H9). As predicted HF patients had significantly higher plasma levels of the
hydroperoxide measure, DROM compared to the control group, F(1, 55) = 28, p < .001.
However contrary to what was expected, the experimental groups had similar levels of lipid
peroxidation as measured by plasma F2-isoprostane levels, F(1,70) = 0.55, p = .46 (Table
18).
Table 18 Means and standard deviations of the oxidative stress, antioxidant and inflammatory measures for HF and control groups
Heart Failure Controls
Biomarker Variable n M SD n M SD df F p Oxidative Stress
DROM (Ucarr) 28 469.64 94.05 29 352.66 83.90 (1,55) 27.60 <.001
F2-isoprostanes (pmol/L)
36 1782.97 407.94 36 1712.31 403.68 (1,70) 0.55 .463
Antioxidant Glutathione peroxidase (nmol/min/ml
34 102.34 25.52 27 108.73 29.23 (1,59) 0.83 .367
CoQ10 (nmol/L) 35 795.17 376.87 37 1069.81 296.18 (1,70) 11.89 .001Inflammation and omega-3
hs-CRP (mg/L) 35 4.97 6.27 23 1.21 1.31 (1,56) 21.31 <.001
PUFA (units/day) 34 7.09 4.66 21 10.00 5.38 (1,53) 4.51 .038
Note: DROM=determinable reactive oxygen metabolites; Ucarr=Carratelli Units; pmol/L=picomole per litre; nmol/min/ml=nanomole per minute per mililitre; CoQ10=coenzyme Q10; nmol/L=nanomole per litre; hs-CRP=high-sensitive C-reactive protein; mg/L=milligrams per litre; PUFA=polyunsaturated fatty acid.
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It was hypothesised that HF patients will have significantly lower levels of plasma
antioxidants as measured by coenzyme Q10 (CoQ10) and glutathione peroxidise compared to
the control group (H10). A one way ANOVA revealed that this hypothesis (H10) was partially
supported. As predicted HF patients’ plasma CoQ10 levels were significantly lower than
controls, F(1,70) = 12, p < .01. However, contrary to what was anticipated, plasma levels of
the lipophilic antioxidant biomarker glutathione peroxidase was not significantly different
between experimental groups, F(1,59) = 0.83, p = .37 (Table 18).
It was predicted that HF patients will have significantly higher levels of inflammation as
measured by hs-CRP and dietary omega-3 PUFA intake compared to the healthy control
group (H11). As expected, HF patients had significantly higher levels of systemic
inflammation as measured by hs-CRP compared to controls F(1,56) = 21, p < .01.
Additionally, the hypothesis was further supported by the observation that HF patients
consumed significantly less dietary omega-3 polyunsaturated fatty acid units in the 3 months
prior to baseline testing compared to controls, F(1,53) = 4.51, p < .05. This suggests a lower
dietary intake of omega-3 polyunsaturated fatty acids in the heart failure group.
8.9 Relationships between the vascular, oxidative stress, antioxidant and
inflammatory biomarkers
8.9.1 Introduction
Correlation coefficients were examined to explore the interplay between the vascular (blood
flow velocity, arterial stiffness), oxidative stress (DROM, F2-isoprostanes), antioxidants
(CoQ10, GPx) and inflammatory biomarkers in each experimental group. Pearson’s
correlations were carried out between each of the oxidative stress, antioxidant, inflammatory
and vascular measures for each experimental group. Non-normally distributed variables were
transformed in order to meet the Pearson’s correlation assumptions of normality. Table 19
displays the correlations between the biomarker variables for the HF group. Additionally,
Table 20 displays the correlations between the biomarker variables for controls.
8.9.2 Oxidative stress measures and vascular, antioxidant and inflammatory markers
No significant correlations were observed between the oxidative stress measures and any of
the vascular, antioxidant or inflammatory measures in either experimental group. This
suggests that in the cohort tested in this investigation lipid hydroperoxides and F2-
isoprostanes are possibly not related to measures of arterial stiffness, cerebral blood flow or
systemic inflammation.
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8.9.3 Antioxidant and vascular measures
Furthermore, in the HF group, higher GPx activity was significantly moderately related to
reduced central pulse pressures, r(17) = -.517, p < .05. These observations were not seen in
controls. These results suggest that in the HF group, reduced antioxidant status as measured
by lower glutathione peroxidase activity relates to elevated arterial stiffness as measured by
central pulse pressure.
In the HF group, there was a significant, moderate, relationship between the antioxidant
CoQ10 and arterial stiffness as measured by central pulse pressure, r(18) = -.576, p < .01.
These results indicate that reduced antioxidant levels as measured by CoQ10 is associated
with elevated arterial stiffness as measured by central pulse pressure. In addition unlike the
control group, in the HF group, there was a significant, moderate, negative relationship
between the antioxidant CoQ10 and central systolic BP, r(18) = -.524, p < .05 these findings
indicate higher central systolic blood pressure is related to reduced antioxidant levels as
measured by CoQ10.
8.9.4 Antioxidant and inflammatory measures
The antioxidant glutathione peroxidase activity was significantly negatively correlated with
hs-CRP plasma levels, r(31) = -.534, p < .01 in the heart failure (HF) group (Table 19).
Glutathione peroxidase activity however was not significantly related to systemic
inflammation in controls (Table 20). These findings suggest that in the HF group, reduced
antioxidant status as measured by reduced glutathione peroxidase activity relates to raised
systemic inflammation as measured by hs-CRP. Additionally, there was a significant negative
trend between CoQ10 and hs-CRP in the HF group (r(34) = -.319, p = .066) but not in
controls (r(20) = -.074, p = .744).
8.9.5 Inflammatory and vascular measures
In the HF group, the inflammatory maker hs-CRP was significantly correlated with central
systolic BP, r(18) = .509, p < .05, this relationship was not significant in controls (p > .05).
Additionally, hs-CRP was significantly positively related to central pulse pressure in HF
patients, r(18) = .485, p < .05,but not in controls (p > .05). Taken together these finding
suggest an increase in systemic inflammation in HF patients relates to an increase in central
systolic blood pressure and arterial stiffness as determined by central pulse pressure.
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Note: DROM=determinable reactive oxygen metabolites; F2 Iso=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire; CC=common carotid arterial blood flow velocity; MCA=middle cerebral arterial blood flow velocity; PSBP=peripheral systolic blood pressure; PDBP=peripheral diastolic blood pressure; CSBP=central systolic blood pressure; CDBP=central diastolic blood pressure; AIx=augmentation index; PPP=peripheral pulse pressure; CPP=central pulse pressure; significant at: * p < .05; ** p <.01; *** p < .001; ¥=Trend.
Table 19 Correlations between oxidative stress, antioxidant, inflammatory and vascular measures for the HF group
Variable F2 Iso GPx CoQ10 hsCRP PUFA CC MCA PSBP PDBP CSBP CDBP AIx PPP CPP ET-1
DROM -.001 -.203 .001 .275 -.283 -.023 .153 .113 -.213 .198 -.241 .418 .326 .396 .155 F2 Iso -.290 .077 .148 .151 .184 .280 .250 .155 .405 .261 -.068 .140 .331 -.211 GPx .038 -.534** .076 .265 .233 -.184 .048 -.375 .038 -.249 -.286 -.517* -.013CoQ10 -.319¥ -.026 .213 -.276 -.299 -.128 -.524* -.233 -.019 -.277 -.576** -.246 hsCRP .003 -.306 -.115 .422* .103 .509* .139 .235 .428* .485* .239 PUFA .021 -.269 .258 .328 .056 .261 -.143 .058 -.096 -.191 CC .109 -.193 .009 -.215 -.098 .057 -.289 -.247 -.209 MCA .113 -.132 .066 -.247 -.038 .259 .491 -.039 PSBP .562*** .957*** .586** .305 .729*** .781*** -.008 PDBP .684** .993*** -.058 -.145 -.036 -.270 CSBP .721*** .470 .563** .686** .045 CDBP .094 -.103 .038 -.270 AIx .413 .464 .193 PPP .946*** .213CPP .274
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Table 20 Correlations between oxidative stress, antioxidant, inflammatory and vascular measures for the control group
Variable F2 Iso GPx CoQ10 hsCRP PUFA CC MCA PSBP PDBP CSBP PDBP AIx PPP CPP ET-1
DROM -.163 .100 -.114 .262 -.169 -.198 .276 -.067 .084 -.140 .101 -.087 -.105 -.184 -.069 F2 Iso .076 .186 -.062 -.219 -.044 .115 .007 -.080 -.104 -.058 -.225 .063 -.075 -.200GPx .278 .291 .113 -.253 .024 -.084 .138 -.182 .174 -.125 -.146 -.293 .218 CoQ10 -.074 .505* -.195 -.048 .028 .289 .110 .280 .033 -.104 -.053 .045 hsCRP -.227 .395 .006 -.260 -.171 -.246 -.179 .202 -.126 -.103 .482 PUFA -.560* -.402 -.392 .087 -.269 .091 .168 -.422 -.287 -.407CC -.022 -.022 -.113 -.025 -.140 -.139 .022 .081 .339MCA -.341 -.220 -.330 -.212 .007 -.264 -.275 .350PSBP .272 .943*** .282 .193 .871*** .806*** -.102PDBP .318* .994*** .076 -.226 -.225 -.244 CSBP .318* .420** .797*** .843*** -.047PDBP .073 -.207 -.224 -.260AIx .176 .415** .232 PPP .926*** .023
CPP .085
Note: DROM=determinable reactive oxygen metabolites; F2 Iso=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire; CC=common carotid arterial blood flow velocity; MCA=middle cerebral arterial blood flow velocity; PSBP=peripheral systolic blood pressure; PDBP=peripheral diastolic blood pressure; CSBP=central systolic blood pressure; CDBP=central diastolic blood pressure; AIx=augmentation index; PPP=peripheral pulse pressure; CPP=central pulse pressure; significant at: * p < .05; ** p <.01; *** p < .001.
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8.9.6 Summary
Taken together these findings suggest that patients have elevated levels of inflammation
compared to controls. Additionally, HF patients have higher levels of hydroperoxides as
measured by DROMs but equivalent levels of lipid peroxides as measured by F2-
isoprostanes. As anticipated, HF patient have lower levels of the antioxidant CoQ10 although
do not differ on levels of the lipophilic antioxidant biomarkers GPx compared to age matched
controls.
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CHAPTER 9 RELATIONSHIPS BETWEEN COGNITIVE MEASURES
AND BIOMARKERS
9.1 Introduction
This chapter will provide an examination of how the hypotheses (H12 - H13) pertaining to the
relationships between cognitive measures and vascular markers are examined. Specifically
results exploring the relationships between cognitive measures and blood flow velocities in
the common carotid and middle cerebral arteries (H12) and arterial stiffness as measured by
central pulse pressures and augmentation index (H13) will be presented. In order to test these
hypotheses and research questions, simple regression analyses were initially conducted for
each experimental group to explore whether biomarkers were related to cognitive
performance. These analyses were followed by one-way, between-subjects analysis of
covariance (ANCOVA) to examine how much of the variance between cognitive outcome
measures were accounted for by the physiological measures. Next, a series of multiple
regression analyses were performed to explore to what extent vascular measures and
biomarkers predicted performance on cognition in the HF group. For all analyses,
relationships between variables were considered to be statistically different if the p value was
less than 0.05 using two tailed tests. For all analyses relationships between variables were
considered to be statistically different if the p value was less than 0.05 using two tailed tests.
9.2 Examination of the relationships between cognitive and vascular measures
9.2.1 Introduction
To investigate whether blood flow velocity as measured by common carotid and middle
cerebral arterial blood flow and arterial stiffness were related to measures of attention,
memory or executive function in HF patients simple regression analyses were initially
conducted. The effects of the vascular biomarkers on cognitive outcome measures that were
significantly different between groups in the ANCOVA models presented in Chapter 8 will
be reported.
Pearson’s correlation coefficients between cognitive measures and vascular markers for each
experimental group are displayed in Table 21. MMSE scores were positively skewed and
none of the transformation variables adequately transformed the data, therefore Spearman’s
Rho statistic was used to correlate MMSE with biomarker variables.
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9.3 Relationships between common carotid and middle cerebral arterial blood flow
and cognitive function
9.3.1 Global cognition
It was hypothesised that reduced cerebral blood flow velocity as measured by common
carotid and middle cerebral arterial blood flow will be related to cognitive function in HF
patients (H12). Contrary to what was predicted, global cognitive scores, as measured by the
Mini Mental State Examination, was not significantly related to common carotid or middle
cerebral arterial blood flow velocities in either experimental group (Table 20).
9.3.2 Attention
Simple regressions were initially observed to examine the hypothesis that cerebral blood flow
velocity as measured by common carotid and middle cerebral blood flow will be related to
poorer performance on cognitive tests measuring attention, memory or executive function in
HF patients (H12).
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Table 21 Correlation matrix for neuropsychological domains and blood flow velocities,
arterial stiffness and vascular function
Variable Blood flow
Velocity Arterial Stiffness Vascular function
Heart Failure Group CC MCA AIx CPP ET-1
Mini Mental State Examination¥ .135 .260 .410 .116 -.128 Attention
Congruent Stroop -.414* .065 .108 .506* .253
Trail Making-A -.101 -.214 .171 .271 .081
Power of Attention -.379* .219 .130 .669** -.097
Continuity of Attention .146 -.074 -.471* -.287 -.026 Memory
Quality of Episodic Memory .377* .195 .114 -.214 .172
Quality of Working Memory -.073 .112 -.082 -.242 -.022
Speed of Memory -.362* -.018 .269 .401 -.078 Executive Function
Incongruent Stroop -.409* .188 .351 .502* -.151
Stroop effect -.353* .238 .471* .419 .090
Trail making-B -.084 -.055 .497* .552** -.255
Control Group CC MCA AIx CPP ET-1
Mini Mental State Examination¥ .126 .106 -.017 .097 -.374 Attention
Congruent Stroop -.097 -.072 -.069 .056 .091
Trail making-A .188 .818 .121 .175 .363
Power of Attention .057 -.244 -.057 .162 -.142
Continuity of Attention .040 .237 -.119 .020 .160 Memory
Quality of Episodic Memory .017 .186 .055 .190 -.176
Quality of Working Memory -.202 .113 -.072 -.048 -.310
Speed of Memory .087 -.309 .053 .274 .149 Executive Function
Incongruent Stroop -.231 -.163 .038 .367* .170
Stroop effect -.248 -.228 .091 .470** .171
Trail making-B .042 -.185 .256 .104 .212 Note: CC=common carotid blood flow velocity; MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP = central pulse pressure; ET-1=endothelin-1; ¥=Spearman’s correlations using untransformed data; * p < .05; ** p < .01.
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As presented in Table 21, in the HF group common carotid blood flow velocity was
negatively related to reduced performance on congruent Stroop task (r(31) = -.414, p < .05)
and Power of Attention cognitive domain (r(31) = -.379, p < .05). These observations were
not seen in controls. Scatterplots for the relationship between common carotid blood flow
velocity and Power of Attention for each group are presented in Figure 8. This result suggests
that poor attention abilities in HF patients may be related to reduced blood flow velocity in
the common carotid artery. Interestingly, middle cerebral arterial blood flow velocity was not
related to the attention measures in the HF group.
Figure 8 Scatter plots of Power of Attention and common carotid arterial blood flow velocity in the HF and control groups
Analysis of covariance statistics were conducted to explore the extent to which common
carotid blood flow velocity accounted for the group variance on congruent Stroop reaction
time and Power of Attention domain after adjusting for premorbid IQ.
In the first model, an ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI
Vocabulary subset, common carotid blood flow velocity] was conducted to assess whether HF
patients performed significantly worse on congruent Stroop reaction time after controlling for
common carotid blood flow velocity and premorbid IQ. The Levene’s test for homogeneity of
variance, normality of sampling distributions, homogeneity of regression and reliability of
covariates were satisfactory. Since there were no group by covariate interactions observed
therefore the interaction terms were removed from the model to obtain greater power to
detect the main effects. As displayed in Table 22 the premorbid IQ measure, WASI
Vocabulary subset, was not significantly related to congruent Stroop reaction time, F(1,62) =
r = -.379, p = .030 r = .057, p = .757
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.405, p > .05. However, the covariate common carotid blood flow velocity had a significant
weak effect across groups on the congruent Stroop task performance, F(1,62) = 5.1, p < .05,
partial η2=.076. There was no significant effect of group on congruent Stroop after
controlling for premorbid IQ and common carotid blood flow velocity (p = .093).
Table 22 The effect of group on congruent Stroop reaction time after adjusting for premorbid
IQ and common carotid blood flow velocity
Source SS df MS F P partial η2 Group .01 1 .01 2.91 .093 .045 WASI .00 1 .00 .41 .527 .006 CCA-BFV .02 1 .02 5.10 .027 .076 Error .29 62 .01 Total 564.99 66
Note: CCA-BFV=common carotid arterial blood flow velocity; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset.
In the second model, a between-subjects (group: HF, controls) ANCOVA (CV: WASI,
common carotid blood flow velocity) was conducted to examine whether common carotid
blood flow velocity accounted for some of the group variance seen in the Power of Attention
cognitive domain (Table 23). The Levene’s test for homogeneity of variance, normality of
sampling distributions, homogeneity of regression and reliability of covariates were
satisfactory. There were no significant interactions observed between the covariates and
group. The covariate WASI was not significantly related to Power of Attention, F(1, 61) =
.099, p > .05. There was a trend for a relationship between the covariate common carotid
blood flow velocity and Power of Attention, F(1, 61) = 3.43, p = .069, partial η2 = .053. There
was no significant main effect of group on Power of Attention after adjusting for premorbid
IQ and common carotid blood flow velocity (p = .06).
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Table 23 The effect of group on Power of Attention domain after adjusting for premorbid IQ
and common carotid blood flow velocity
Source SS df MS F p partial η2
Group 42029 1 42030 3.73 .058 .058
WASI 1112 1 1112 .10 .754 .002
CCA-BFV 38657 1 38658 3.43 .069 .053
Error 687317 61 11268
Total 98895723 65 Note: CCA-BFV=common carotid arterial blood flow velocity; WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset.
These results suggests that the group variance seen in attention as measured by congruent
Stroop and Power of Attention cognitive domains can be explained by reduced blood flow
speed in the common carotid artery. Middle cerebral blood flow velocity does not appear to
be related to attentional abilities in HF patients.
9.3.3 Memory
Although HF patients did not perform significantly different to controls on Quality of
Episodic Memory, Quality of Working Memory and Speed of Memory cognitive domains, the
hypothesis that cerebral blood flow velocity as measured by common carotid and middle
cerebral arterial blood flow will be related to attention, memory or executive function in HF
patients was explored (H12). Correlation coefficients were observed to explore whether
cerebral blood flow velocity measures related to Quality of Episodic Memory, Quality of
Working Memory and Speed of Memory domains (Table 21). In the HF group as expected
better scores on Quality of Episodic Memory cognitive domain was related to faster common
carotid blood flow velocity (r(31) = .377, p < .05). Additionally, in the HF group better
scores on the Speed of Memory cognitive domain was related to faster common carotid blood
flow velocity (r(31) = -.362, p < .05). These relationships were not significantly associated in
the control group. Middle cerebral arterial blood flow velocity was not related to Quality of
Episodic Memory, Quality of Working Memory or Speed of Memory domains in either
experimental group (p > .05).
There were no significant group differences observed between the HF and control group on
quality of episodic memory, Quality of Working Memory or Speed of Memory and cognitive
domains. Therefore, multiple regression analysis was not performed to explore the
relationship between these cognitive domains and vascular measures.
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9.3.4 Executive function
The hypothesis that cerebral blood flow velocity as measured by common carotid and middle
cerebral arterial blood flow will be related to reduced attention, memory or executive
function in HF patients was explored further (H12). Here, it was hypothesised that in the HF
group a relationship would be apparent between executive function and cerebral blood flow.
Correlations between incongruent Stroop reaction time and Trail Making-B executive
function measures, which were shown to be significantly different between groups and
cerebral blood flow were examined. As expected, in the HF group, reduced blood flow
velocity in the common carotid artery was related to worse performance on executive
function as measured by incongruent Stroop reaction time (r(31) = -.409, p < .05; Table 21).
These observations were not observed in controls (p > .05). Scatterplots for the relationship
between common carotid blood flow velocity and incongruent Stroop reaction time for each
group are presented in Figure 9. Contrary to expectation, no significant relationships were
observed between middle cerebral blood flow velocity and incongruent Stroop reaction time
in the experimental groups. Additionally, performances on Trail Making-B was not related to
blood flow velocity in the common carotid or middle cerebral arteries.
Figure 9 Scatter plots of common carotid blood flow velocity and incongruent Stroop in the HF and control groups
r = -.231, p = .196r = -.409, p = .018
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An analysis of covariance statistics were conducted to explore the extent to which common
carotid blood flow velocity explained the group differences seen in the executive function
measure incongruent Stroop reaction time task. Incongruent Stroop reaction time task was
the executive function variable which displayed significant group differences in which HF
patients performed significantly worse than control. Common carotid blood flow velocity was
significantly moderately negatively correlated with incongruent Stroop reaction time scores
in the HF group indicating that it is a suitable covariate to explore in the ANCOVA model.
A between-subjects (group: HF, controls) ANCOVA (CV: WASI, common carotid blood
flow velocity) was conducted to examine the effect of common carotid blood flow velocity on
the incongruent Stroop reaction time task performance between groups. The Levene’s test for
homogeneity of variance, normality of sampling distributions, homogeneity of regression and
reliability of covariates were satisfactory. Since there were no group by covariate interactions
observed the interaction terms were removed from the model to obtain greater power to
detect the main effects.
The ANCOVA model exploring the effect of group on incongruent Stroop reaction time
adjusting for premorbid IQ and common carotid blood flow velocity is presented in (Table
24). As presented in Table 24, the premorbid IQ covariate (WASI Vocabulary) was not
significantly related to incongruent Stroop reaction time, F(1,62) = 2.25, p > .05 However,
the covariate common carotid blood flow velocity has a significant weak effect on
incongruent Stroop, F(1,62) = 8.33, p < .01, partial η2=.118. After controlling for WASI
Vocabulary and common carotid blood flow velocity, the effect of group on incongruent
Stroop reaction time was no longer significant (p > .05).
Table 24 The effect of group on incongruent Stroop reaction time adjusting for premorbid IQ
and common carotid blood flow velocity
Source SS df MS F p partial η2 Group .04 1 .04 2.96 .091 .046 WASI .03 1 .03 2.25 .138 .035 CCA-BFV .11 1 .11 8.33 .005 .118 Error .81 62 .01 Total 611.90 66
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity.
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9.3.5 Summary
In summary faster blood flow velocity in the common carotid artery was related to better
performance on Quality of Episodic Memory and Speed of Memory cognitive domains in HF
patients. These results suggest that worse performance on the Quality of Episodic Memory
and Speed of Memory cognitive domains in HF patients relates to reduced cerebral blood flow
velocity in the common carotid artery but not in the middle cerebral artery. Furthermore, the
prediction that executive function will be related to cerebral blood flow was partially
supported. Improved performance on Incongruent Stroop but not Trail Making-B was related
to a reduced common carotid blood flow velocity. However, none of the executive function
measures were related to middle cerebral blood flow velocity in either experimental group.
9.4 Relationships between arterial stiffness and cognitive performance
9.4.1 Introduction
The next section will test the hypothesis that arterial stiffness as measured by augmentation
index and pulse pressure will be related to cognitive tests measuring attention, psychomotor
speed, working memory, episodic memory and executive function (H13). Correlation
coefficients for the relationship between cognitive and arterial stiffness measures are
presented in Table 21.
9.4.2 Global cognition
Global cognitive scores as measured but the MMSE was not significantly related to arterial
stiffness as measured by augmentation index or central pulse pressure in either experimental
group.
9.4.3 Attention
Correlation coefficients for the relationship between attention and arterial stiffness measures
are presented in Table 21. Arterial stiffness as measured by augmentation index was not
related to performance on congruent Stroop reaction time or Power of Attention cognitive
domain in either experimental group. Interestingly, arterial stiffness as measured by central
pulse pressure was positively, moderately related with congruent Stroop (r(18) = .506, p <
.05) and Power of Attention (r(18) = .669, p < .01) in the HF group. No relationships were
observed between arterial stiffness and measures of attention in the control group. This
suggests that reduced attention abilities in HF patients may be related to an increase in arterial
stiffness as measured by central pulse pressure.
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A series of between-subjects (group: HF, controls) ANCOVA (CV: WASI, central pulse
pressure) analyses were conducted to explore the extent to which central pulse pressure
explained the group differences seen in congruent Stroop reaction time and Power of
Attention cognitive domain. In the first model an ANCOVA [between-subjects factor: Group
(HF, controls); CV: WASI, CPP] examined the effect of IQ and central pulse pressure on
congruent Stroop.
The Levene’s test for homogeneity of variance, normality of sampling distributions and
reliability of covariates were satisfactory. A significant interaction was observed between
group and WASI (p = .042, partial η2 = .077), indicating that the direction of the regression
slope for WASI and congruent Stroop reaction time is different for each group. As presented
in Table 25, the ANCOVA model revealed that the covariates premorbid IQ and central pulse
pressure were not significantly related to congruent Stroop reaction time (p > .05). After
controlling for WASI and central pulse pressure, there was a significant main effect of group
on congruent Stroop reaction time (p = .013, partial η2 = .104).
Table 25 The effect of group on congruent Stroop reaction time after adjusting for premorbid IQ and central pulse pressure
Source SS df MS F p partial η2
Group .04 1 .04 6.53 .013 .104 WASI .00 1 .00 .20 .659 .004 CPP .02 1 .02 3.10 .084 .052 Error .33 56 .01 Total 512.43 60
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=common central pulse pressure.
In the second model an ANCOVA [between-subjects factor: Group (HF, controls); CV:
WASI, CPP] examined the effect of premorbid IQ and central pulse pressure on Power of
Attention cognitive domain. The Levene’s test for homogeneity of variance, normality of
sampling distributions, homogeneity of regression and reliability of covariates were
satisfactory. Since there were no group by covariate interactions observed the interaction
terms were removed from the model to obtain greater power to detect the main effects. As
presented in Table 26, the ANCOVA model revealed that the covariate WASI Vocabulary
subset was not significantly related to Power of Attention (p > .05). However, central pulse
pressure was significantly weakly related to Power of Attention across groups (p < .01,
Chapter 9: Results – relationships between cognitive measures and biomarkers
139
partial η2 = .157). After controlling for WASI and central pulse pressure, there was still a
significant main effect of group on Power of Attention (p < .01, partial η2 = .193).
Table 26 The effect of group on Power of Attention domain after adjusting for premorbid IQ and central pulse pressure
Source SS df MS F p partial η2
Group 143576 1 143576 13.38 .001 .193 WASI 265 1 265 .03 .876 .000 CPP 112273 1 112273 10.46 .002 .157 Error 601066 56 10733 Total 90228500 60
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=central pulse pressure.
9.4.4 Memory
To explore whether arterial stiffness was related to Quality of Episodic Memory, Quality of
Working Memory and Speed of Memory cognitive domains in the HF group Pearson’s
correlation coefficients were examined. Correlation coefficients for the relationship between
memory domains and arterial stiffness measures are presented in Table 21. As presented in
Table 21, arterial stiffness as measured by augmentation index and central pulse pressure,
was not significantly correlated with Quality of Episodic Memory, Quality of Working
Memory or Speed of Memory in either experimental group. No relationships were observed
between memory domains and arterial stiffness in the control group. This suggests that
arterial stiffness has not effect on Quality of Working Memory, Quality of Episodic Memory
or Speed of Memory in HF patients.
9.4.5 Executive function
To explore whether arterial stiffness was related to incongruent Stroop and Trail Making-B
tasks which were the executive measures shown to differ significantly between experimental
groups, Pearson’s correlation coefficients were examined. Correlation coefficients for the
relationship between incongruent Stroop, Trail Making-B and arterial stiffness measures are
presented in Table 21. As displayed in Table 21, worse performance on Trail Making-B in the
HF group was associated with increased arterial stiffness as measured by augmentation index
(r(17) = .497, p < .05) and central pulse pressure (r(19) = .552, p < .01). However, increased
Chapter 9: Results – relationships between cognitive measures and biomarkers
140
arterial stiffness as measured by central pulse pressure, which was significantly different
between groups was significantly correlated with slower incongruent Stroop reaction times in
both the HF (r(18) = .502, p < .05) and control (r(38) = .367, p < .05) groups.
These findings suggest that increased arterial stiffness as measured by central pulse pressure
is related to poorer executive function in HF.
An analysis of covariance statistics were conducted to further explore the extent to which
arterial stiffness as measured by central pulse pressure effects incongruent Stroop reaction
time after adjusting for IQ.
An ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CPP] revealed
that the covariates WASI and central pulse pressure were not significantly related to Trail
Making-B performance (Table 27). The effect of group on Trail Making-B remained
significantly different even after controlling for IQ and central pulse pressure, (p = .004,
partial η2 = .138). The Levene’s test for homogeneity of variance, normality of sampling
distributions, homogeneity of regression and reliability of covariates were satisfactory.
Table 27 The effect of group on Trail Making-B after adjusting for premorbid IQ and central pulse pressure
Source SS df MS F p partial η2
Group .29 1 .29 9.16 .004 .138 WASI .04 1 .04 1.38 .244 .024 CPP .11 1 .11 3.38 .071 .056 Error 1.78 57 .03 Total 235.44 61
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=central pulse pressure.
To further explore the effects of central pulse pressure on incongruent Stroop reaction time a
second ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CPP] was
conducted. The Levene’s test for homogeneity of variance, normality of sampling
distributions, homogeneity of regression and reliability of covariates were satisfactory. There
were no group by covariate interactions observed. As presented in Table 28 the ANCOVA
model revealed that the covariate IQ was not significantly related to incongruent Stroop
reaction time (p > .05). The covariate central pulse pressure however was significantly
Chapter 9: Results – relationships between cognitive measures and biomarkers
141
related to incongruent Stroop reaction time. After controlling for WASI and central pulse
pressure, there was a significant effect of group on incongruent Stroop reaction time (p =
.002, partial η2 = .165).
Table 28 The effect of group on incongruent Stroop reaction time after adjusting for
premorbid IQ and central pulse pressure
Source SS df MS F p partial η2
Group .14 1 .14 11.03 .002 .165 WASI .02 1 .02 1.18 .281 .021 CPP .14 1 .14 11.37 .001 .169 Error .69 56 .01 Total 555.24 60
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CPP=central pulse pressure.
9.4.6 Summary
Addressing the hypothesis that a relationship exists between arterial stiffness and cognitive
function it was revealed that arterial stiffness is related to executive function but not attention,
psychomotor speed, working memory or episodic memory. Increased augmentation index
related to slower performance on executive function as measured by Trail Making-B task in
the HF group but not controls. Furthermore, arterial stiffness as measured by central pulse
pressure was moderately related to worse performance on Trail Making-B in HF patients but
not in controls. Interestingly, higher central pulse pressure was associated with worse
performance on the incongruent Stroop reaction time task in both experimental groups.
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142
9.5 Relationships between cognitive performance and oxidative stress, antioxidant and
inflammatory markers
9.5.1 Introduction
The following section will test the hypothesis that oxidative stress, antioxidants,
inflammation and omega-3 dietary intake will be related to cognitive tests measuring
attention, psychomotor speed, working memory, episodic memory and executive function
(H14). Simple regressions using Pearson’s correlations for normally distributed and
transformed variables and spearman’s correlations for non-normally distributed variables
were conducted to initially explore whether relationships exist between cognitive measures
and biomarkers. Correlation coefficients between oxidative stress, antioxidants and
inflammation and cognitive outcome measures are presented in Table 29. Only relationships
between cognitive measure and biomarkers that displayed significant differences between
experimental groups are reported.
9.5.1.1 Global cognition in heart failure
Spearman’s correlation coefficient was used to detect whether relationships exist between
MMSE and oxidative stress. As displayed in Table 29, global cognition as measured by
MMSE was not significantly correlated with measures of oxidative stress (DROM, F2-
isoprostanes), antioxidants (glutathione peroxidase, CoQ10) or inflammation (hs-CRP) or
omega-3 dietary intake (PUFA). However, there was a trend for higher levels of
hydoperoxides as measured by DROM to be related to better scores on the MMSE in HF
(r(26) = .348, p = .069).
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143
Table 29 Correlation coefficients between oxidative stress, antioxidant and inflammatory markers with cognitive measures in each experimental group
Variable Oxidative Stress Antioxidant Inflammation and omega-3
Heart Failure Group DROM F2 GPx CoQ10 hsCRP PUFA
MMSE¥ .348 .195 .093 .058 -.066 .104
Attention Congruent Stroop .109 -.045 -.308 -.401* -.014 .063 Trail making-A -.044 -.136 .186 -.346* -.152 -.021 Power of Attention -.022 -.050 -.221 -.259 .012 .085
Continuity of Attention -.472* .060 .287 .214 -.035 .244
Memory Quality of Episodic Memory
-.025 .239 .083 .157 -.009 .203
Quality of Working Memory
-.146 .014 .104 .080 -.008 .019
Speed of Memory .282 -.067 -.205 .022 -.173 -.104 Executive Function
Incongruent Stroop .209 -.075 -.273 -.425* -.056 .047
Stroop effect .190 -.090 -.198 -.357* -.080 .057
Trail making-B .241 -.166 .139 -.305 -.193 -.097
Control Group DROM F2 GPx CoQ10 hsCRP PUFA
MMSE¥ .184 .131 -.282 -.147 .223 -.061
Attention Congruent Stroop -.235 -.249 .014 -.183 -.161 .024 Trail making-A -.013 .020 -.213 -.079 -.032 -.090 Power of Attention .031 -.238 -.195 -.158 .005 -.053
Continuity of Attention .432* .204 -.017 .000 .088 -.299
Memory Quality of Episodic Memory
.076 .196 -.169 .036 .125 -.307
Quality of Working Memory
.031 .287 .146 -.001 .233 -.031
Speed of Memory .001 -.132 -.176 -.086 .173 -.287 Executive Function
Incongruent Stroop -.063 -.322 .055 -.304 -.112 -.084 Stroop effect .067 -.252 .019 -.284 -.054 -.137 Trail making-B -.135 -.180 -.391* -.096 -.433* .279
Note: MMSE=Mini Mental State Examination; DROM=determinable reactive oxygen metabolites; F2=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzymeQ10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid; * p < .05; ** p <.01; *** p < .001.
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9.5.2 Relationship between oxidative stress and cognitive function
9.5.2.1 Attention
As revealed in the previous chapter, patients had significantly higher levels of
hydroperoxides as measured by plasma DROM levels. To explore whether relationships exist
between DROMs and attention as measured by congruent Stroop reaction time and Power of
Attention cognitive domain in the HF group Pearson’s correlation coefficients were observed.
As presented in Table 29, no significant correlations were observed between DROMs and
congruent Stroop reaction time or Power of Attention cognitive domain in either
experimental group.
9.5.2.2 Memory
There were no significant relationships between Quality of Episodic Memory, Quality of
Working Memory or Speed of Memory and oxidative stress in either experimental group
(Table 29).
9.5.2.3 Executive function
Although HF patients had significantly higher levels of hydroperoxides as measured by
determinable reactive oxygen metabolites (DROMs), oxidative stress was not significantly
related to incongruent Stroop or Trail Making-B tasks of executive function in either
experimental group (Table 29).
9.5.2.4 Summary
In summary these findings suggests that oxidative stress is not related to reduced global
cognition, poor attentional abilities, impairments in episodic or working memory or executive
function in HF.
9.5.3 Relationship between antioxidants and cognitive function
9.5.3.1 Attention
As shown in the previous chapter, patients had significantly lower plasma levels of the
antioxidant CoQ10 compared to controls. To explore whether CoQ10 is related to attention as
measured by congruent Stroop reaction time and Power of Attention cognitive domain in the
HF group Pearson’s correlation coefficients were observed. As presented in Table 29, lower
CoQ10 levels in the HF group was associated with slower reaction times on the congruent
Stroop task, r(32) = -.401, p < .05, however this relationship was not observed in controls,
Chapter 9: Results – relationships between cognitive measures and biomarkers
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r(35) = -.183, p = .28. Scatterplots for the relationship between CoQ10 and incongruent
Stroop for each group are presented in Figure 10. No significant correlations were observed
between CoQ10 and Power of Attention cognitive domain in either experimental group. This
suggests that reduced antioxidant levels as measured by CoQ10 are related to poor reaction
time in HF.
Figure 10 Scatter plots of coenzyme Q10 and congruent Stroop in the HF and control groups
Since CoQ10 was significantly correlated with congruent Stroop reaction time in HF, an
ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CoQ10] was
conducted to examine the effect of CoQ10 on congruent Stroop reaction time. The Levene’s
test for homogeneity of variance, normality of sampling distributions, homogeneity of
regression and reliability of covariates were satisfactory. As presented in Table 30, the
covariate WASI was not significantly related to congruent Stroop, F(1,67) = .014, p > .05,
partial η2 <.0001. The covariate CoQ10 was significantly related to congruent Stroop and had
a similar effect across groups, F(1,67) = 6.45, p < .05, partial η2 = .088. After adjusting for
WASI and CoQ10, the effect of group on congruent Stroop remained significant (p = .03).
Table 30 The effect of group on congruent Stroop after adjusting for premorbid IQ and CoQ10
Source SS df MS F p partial η2
Group .02 1 .02 4.95 .030 .069 WASI .00 1 .00 .01 .905 .000 CoQ10 .03 1 .03 6.45 .013 .088 Error .31 67 .00 Total 607.67 71
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CoQ10=coenzyme Q10.
r = -.401, p = .019 r = -.183, p = .278
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146
9.5.3.2 Memory
No significant relationships were observed between the cognitive domains Quality of
Episodic Memory, Quality of Working Memory or Speed of Memory and any of the
antioxidant biomarkers in either experimental group (Table 29).
9.5.3.3 Executive function
Pearson’s correlation coefficients were observed to explore whether CoQ10 was related to
executive function as measured by incongruent Stroop reaction time and Trail Making-B in
HF group (Table 29). Performance on incongruent Stroop reaction time task was significantly
related to lower plasma levels of the antioxidant CoQ10 in the HF group (r(32) = -.425, p <
.05) but not in controls (r(35) = -.304, p = .07). Scatterplots for the relationship between
CoQ10 and incongruent Stroop for each group are presented in Figure 11. CoQ10 was not
significantly related to Trail Making-B task performance in either experimental group.
Figure 11 Scatter plots of coenzyme Q10 and incongruent Stroop in the HF and control groups
Since CoQ10 was correlated with incongruent Stroop reaction time, CoQ10 was included as a
covariate in the model to examine whether it accounted for some of the variance in the IV.
An ANCOVA [between-subjects factor: Group (HF, controls); CV: WASI, CoQ10] was
conducted to examine whether CoQ10 accounted for some of the group variance seen in the
IV. The Levene’s test for homogeneity of variance, normality of sampling distributions,
homogeneity of regression and reliability of covariates were satisfactory. As presented in
Table 31, the covariate WASI was not significantly related to incongruent Stroop, F(1,67) =
r = -.304, p = .068r = -.425, p = .012
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.259, p > .05. However, CoQ10 had a significant similar effect across groups on incongruent
Stroop task performance (F(1,67) = 9.86, p < .01, partial η2 = .128). After adjusting for
CoQ10 and WASI, there was no effect of group on incongruent Stroop, F(1,67) = 2.66, p =
.11, partial η2=.038. This suggests that CoQ10 may influence performance on executive
function as measured by incongruent Stroop task.
Table 31 The effect of group on incongruent Stroop after adjusting for premorbid IQ and CoQ10
Source SS df MS F p partial η2 Group .04 1 .04 2.66 .107 .038 WASI .00 1 .00 .26 .613 .004 CoQ10 .14 1 .14 9.86 .003 .128 Error .94 67 .01 Total 660.15 71 Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CoQ10=coenzyme Q10.
9.5.3.4 Summary
In this investigation MMSE scores did not relate to any of the antioxidant biomarkers in
either of the experimental groups. Worse performance on attention and psychomotor function
as measured by congruent Stroop and Trail Making-A were related to reduced plasma CoQ10
levels in HF patients but not in controls. This suggests that lower antioxidants in the form of
CoQ10, which are deficit in HF, are related to poor performance on attention tasks and
psychomotor speed. In addition, these findings suggest that plasma CoQ10 levels are not
related to attention and psychomotor speed in older healthy populations. There were no
significant correlations observed between Power of Attention and antioxidant markers.
9.5.4 Relationship between inflammation and cognitive function
9.5.4.1 Attention
Although HF patients had significantly higher levels of inflammation as measured by hs-CRP
and had greater units of daily dietary polyunsaturated fatty acid consumption, were not
significantly related to specific attention tasks as measured by congruent Stroop reaction time
or Power of Attention cognitive domain in either experimental group Table 29.
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9.5.4.2 Memory
No significant relationships were observed between Quality of Episodic Memory, Quality of
Working Memory or Speed of Memory and inflammatory measures or omega-3 dietary intake
in either experimental group (Table 29).
9.5.4.3 Executive function
Although HF patients had significantly higher levels of inflammation as measured by hs-CRP
and dietary polyunsaturated fatty acid consumption, these inflammatory markers were not
significantly related to executive function as measured by incongruent Stroop and Trail
Making-B in the HF group (Table 29). Interestingly, in controls higher levels of inflammation
as measured by hs-CRP was related to better performance on Trail Making-B task r(21) =
-.433, p < .05.
9.5.4.4 Summary
In this investigation, systemic inflammation as measured and dietary polyunsaturated fatty
acid consumption did not relate significantly with measures of attention, Quality of Episodic
Memory, Quality of Working Memory, Speed of Memory or executive function in the heart
failure group.
9.6 Multiple regression analysis examining the effect of vascular, oxidative stress and
inflammatory predictors on cognitive function
9.6.1 Introduction
In the following section, multiple regression analyses were performed to further explore the
hypotheses (H13 H14) that vascular measures (H13), oxidative stress, antioxidants,
inflammation and omega-3 dietary intake (H14) will be related to cognitive tests measuring
attention, psychomotor speed, working memory, episodic memory and executive function.
Here multiple regressions were performed to explore the extent to which the vascular
measures and biomarkers predicted HF patients performance on cognitive measures, found to
be significant different between the two experimental groups. Cerebral blood flow, arterial
stiffness, oxidative stress, antioxidant and inflammatory measures were found to be
significantly related to measures of attention (congruent Stroop and Power of Attention) and
executive function (incongruent Stroop) were included as possible predictors in the multiple
regression models. Additionally, according to Cohen (1992) in order to achieve a large effect
size of .80 with two and three independent variables, a sample size of 30 and 34 in each
Chapter 9: Results – relationships between cognitive measures and biomarkers
149
group is required, respectively. Although the total sample size in each experimental group
was sufficient for multiple regression analysis involving a maximum of three independent
variables, there was some data missing for the biomarkers. Consequently, the sample size in
this investigation is insufficient for performing multiple regression analyses. Therefore,
interpretation of multiple regression results is only exploratory and need to be made with
caution.
9.6.2 Attention tasks
9.6.2.1 Hierarchical multiple regression analysis examining vascular and antioxidants
predictors on congruent Stroop performance
To investigate how well the common carotid blood flow velocity, central pulse pressure and
CoQ10 variables predict congruent Stroop reaction time in the HF group, after controlling for
IQ, a hierarchical linear regression was computed. As CoQ10 and central pulse pressures
were significantly related and in order to meet the assumptions for multiple regression, two
separate multiple regression analyses were conducted to explore these variables separately to
explore CoQ10 and central pulse pressure separately as possible predictors.
In the first multiple regression analysis, the extent to which the common carotid blood flow
velocity and central pulse pressure variables predict congruent Stroop reaction time in the HF
group, after controlling for IQ was computed. The assumptions of linearity, normally
distributed errors and uncorrelated errors were checked and the model was considered to be a
good fit. Examination of multivariate outliers using Mahalanobis distance, based on a critical
χ2 at α = .001 for three degrees of freedom is 16.266, indicated there were no apparent
outliers in the data (Tabachnick & Fidel, 2007).
The hierarchical multiple regression summary is presented in Table 32. In model 1, when IQ
was entered alone, it did not significantly predict performance on congruent Stroop reaction
time, F(1,17) = .02, p > .05. When common carotid blood flow velocity was entered into the
model it significantly improved the prediction of congruent Stroop reaction time, R2 change =
.22, F(1, 16) = 4.64, p < .05, accounting for 22.5% of the variance of congruent Stroop (R2 =
.225).
When the arterial stiffness measure central pulse pressure was added to the model, the entire
group of variables showed significant improvement in predicting congruent Stroop reaction
time, R2 change = .421, F(1,15) = 5.07, p < .05, accounting for 42.1% of the variance of
congruent Stroop (R2 = .421). The beta weights and significance values, presented in Table
Chapter 9: Results – relationships between cognitive measures and biomarkers
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32, reveal that central pulse pressure contributes significantly to predicting performance on
congruent Stroop task.
Table 32 Hierarchical multiple regression analysis summary predicting congruent Stroop reaction time from common carotid blood flow velocity and central pulse pressure after controlling for premorbid IQ in the HF group
Model summary
Predictors R R2 ∆ R2 ∆ F df p
Model 1 .03 .00 .00 .02 1, 17 .896
Model 2 .48 .23 .22 4.64 1, 16 .047
Model 3 .65 .42 .20 5.07 1, 15 .040 Coefficients
B SE Β t p Model 1
Constant 2.94 .15 20.17 .000 WASI .00 .00 .03 .13 .896
Model 2 Constant 3.13 .16 19.64 .000 WASI -.00 .00 -.05 -0.24 .811 CCA-BFV -.01 .00 -.48 -2.15 .047
Model 3 Constant 2.53 .30 8.33 .000 WASI 33 .00 .03 .16 .876 CC-BFV -.01 .00 -.36 -1.69 .112 CPP .33 .15 .47 2.25 .040
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CPP=central pulse pressure.
In the second multiple regression analysis, the extent to which the common carotid blood
flow velocity and coenzyme Q10 variables predict congruent Stroop reaction time in the HF
group, after controlling for IQ was computed. The assumptions of linearity, normally
distributed errors and uncorrelated errors were checked and the model was considered to be a
good fit. Examination of multivariate outliers using Mahalanobis distance, based on a critical
χ2 at α = .001 for three degrees of freedom is 16.266, there were no apparent outliers in the
data (Tabachnick & Fidel, 2007).
The hierarchical multiple regression summary is presented in Table 33. In model 1, when IQ
was entered alone, it did not significantly predict performance on congruent Stroop reaction
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time, F(1,30) = .04, p > .05. When common carotid blood flow velocity was entered into the
model it significantly improved the prediction of congruent Stroop reaction time, R2 change =
.17, F(1, 29) = 6.08, p = .020, accounting for 17.5% of the variance of congruent Stroop (R2
= .175).
When the antioxidant CoQ10 was added to the model, the entire group of variables showed
an almost significant improvement in predicting congruent Stroop reaction time, R2 change =
.101, F(1, 28) = 3.90, p = .058, accounting for 27.5% of the variance of congruent Stroop (R2
= .275). The beta weights and significance values, presented in Table 33, reveal that carotid
blood flow velocity contributes significantly and there is a trend for CoQ10 to contribute to
predicting performance on congruent Stroop task.
Table 33 Hierarchical multiple regression analysis summary predicting congruent Stroop reaction time from common carotid blood flow velocity and CoQ10 after controlling for premorbid IQ in the HF group
Model summary
Predictors R R2 ∆ R2 ∆ F df p
Model 1 .04 .00 .00 .04 1, 30 .834
Model 2 .42 .18 .17 6.08 1, 29 .020
Model 3 .53 .28 .10 3.90 1, 28 .058
Coefficients
B SE Β t p
Model 1
Constant 2.98 .10 29.43 .000
WASI -.00 .00 -.04 -.21 .834
Model 2
Constant 3.08 .10 29.82 .000
WASI -.00 .00 -.047 -0.28 .785
CCA-BFV -.01 .00 -.416 -2.47 .020
Model 3
Constant 3.06 .10 30.92 .000
WASI .00 .00 .05 0.29 .778
CC-BFV -.01 .00 -.34 -2.07 .048 CoQ10 -6.37E-005 .00 -.34 -1.97 .058
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CoQ10=coenzyme Q10.
Chapter 9: Results – relationships between cognitive measures and biomarkers
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9.6.2.2 Hierarchical multiple regression analysis examining the effect of vascular predictors
on Power of Attention
To investigate how well common carotid blood flow and arterial stiffness as measure by
central pulse pressure predict Power of Attention in the HF group, after controlling for IQ, a
hierarchical linear regression was computed. The assumptions of linearity, normally
distributed errors and uncorrelated errors were checked and the model was considered to be a
good fit. Examination of multivariate outliers using Mahalanobis distance, based on a critical
χ2 at α = .001 for three degrees of freedom is 16.266, revealed no apparent outliers in the data
(Tabachnick & Fidel, 2007). The hierarchical multiple regression analysis summary is
presented in Table 34. In model 1, when IQ was entered alone, it did not significantly predict
performance on Power of Attention cognitive domain, F(1,17) = .03, p > .05. When common
carotid blood flow velocity was entered into the model it did not significantly improve the
prediction of Power of Attention, R2 change = .18, F(1, 16) = 3.74, p > .05.
When the arterial stiffness measure central pulse pressure was added to the model, the entire
group of variables showed a significant improvement in predicting Power of Attention
performance, R2 change = .414, F(1, 15) = 16.82, p < .01, accounting for 63.1% of the
variance of Power of Attention performance (R2 = .631). The beta weights and significance
values, presented in Table 34, reveal that central pulse pressure significantly contributes to
predicting performance on Power of Attention cognitive domain.
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Table 34 Hierarchical multiple regression analysis summary predicting Power of Attention
from common carotid blood flow velocity and central pulse pressure after controlling for
premorbid IQ in the HF group
Model summary
Predictors R R2 ∆ R2 ∆ F df p
Model 1 .18 .03 -.03 .03 1, 17 .455
Model 2 .47 .22 .18 3.74 1, 16 .071
Model 3 .79 .63 .41 16.82 1, 15 .001
Coefficients
B SE β t p
Model 1
Constant 1096.03 235.25 4.66 .000
WASI 3.01 3.93 .18 .77 .455
Model 2
Constant 1380.18 236.15 5.25 .000
WASI 1.72 3.71 .10 .46 .650
CCA-BFV -12.53 6.48 -.44 -1.93 .071
Model 3
Constant -59.56 397.52 -.15 .883
WASI 3.80 2.68 .23 1.42 .176
CCA-BFV -7.06 4.78 -.25 -1.48 .161 CPP 780.88 190.38 .68 4.11 .001
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CPP=central pulse pressure.
9.6.2.3 Summary
In summary, common carotid blood flow velocity significantly predicted performance and
coenzyme Q10 showed a trend towards predicting performance on congruent Stroop task in
the HF group. The arterial stiffness measure central pulse pressure significantly improved the
prediction of Power of Attention performance when premorbid IQ and common carotid blood
flow velocity were included in the model. These results suggest that common carotid blood
flow velocity, central pulse pressure and possibly coenzyme Q10 significantly predict
attentional abilities in HF patients.
Chapter 9: Results – relationships between cognitive measures and biomarkers
154
9.6.3 Executive function
9.6.3.1 Hierarchical multiple regression analysis examining the effect of vascular and
antioxidant predictors on incongruent Stroop
To investigate how well the common carotid blood flow velocity, central pulse pressure and
CoQ10 variables predict incongruent Stroop reaction time in the HF group, after controlling
for IQ, a hierarchical linear regression was computed. Since CoQ10 and central pulse
pressures were significantly related to each other and in order to meet the assumptions for
multiple regression, two separate multiple regression analyses were conducted to explore if
these variables separately to explore CoQ10 and central pulse pressure separately as possible
predictors.
In the first multiple regression analysis, the extent to which the common carotid blood flow
velocity and central pulse pressure variables predict incongruent Stroop reaction time in the
HF group, after controlling for premorbid IQ was computed. The assumptions of linearity,
normally distributed errors and uncorrelated errors were checked and the model was
considered to be a good fit. Examination of multivariate outliers using Mahalanobis distance,
based on a critical χ2 at α = .001 for three degrees of freedom is 16.266, indicated there were
no apparent outliers in the data (Tabachnick & Fidel, 2007).
The hierarchical multiple regression analysis summary is presented in Table 35. In model 1,
when IQ was entered alone, it did not significantly predict performance on executive function
as measured by incongruent Stroop, F(1,17) = .12, p > .05. When common carotid blood flow
velocity was entered into the model it significantly improved the prediction of Incongruent
Stroop reaction time, R2 change = .33, F(1, 16) = 7.99, p < .05.
When the central pulse pressure was added to the model, the entire group of variables
showed significant improvement in predicting incongruent Stroop, R2 change = .18, F(1, 15)
= 5.74, p <.05, accounting for 52.1% of the variance of incongruent Stroop performance (R2 =
.521). The beta weights and significance values, presented in Table 35, reveal that carotid
blood flow velocity and central pulse pressure significantly contributes towards predicting
performance on incongruent Stroop performance in the HF group.
Chapter 9: Results – relationships between cognitive measures and biomarkers
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Table 35 Hierarchical multiple regression analysis summary predicting incongruent Stroop from common carotid blood flow velocity and central pulse pressure after controlling for premorbid IQ in the HF group
Model summary
Predictors R R2 ∆ R2 ∆ F df p
Model 1 .09 .01 -.01 .12 1, 17 .731
Model 2 .58 .34 .33 7.99 1, 16 .012
Model 3 .72 .52 .18 5.74 1, 15 .030
Coefficients
B SE β t p
Model 1
Constant 3.18 .23 14.00 .000
WASI .00 .00 -.09 -.35 .731
Model 2
Constant 3.55 .23 15.37 .000
WASI .00 .00 -.10 -.91 .373
CCA-BFV -.02 .01 -.58 -2.83 .012
Model 3
Constant 2.63 .43 6.10 .000
WASI .00 .00 -.11 -.57 .577
CC-BFV -.01 .01 -.46 -2.42 .029
CPP .50 .21 .45 2.40 .030
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CPP=central pulse pressure.
In the second multiple regression analysis, the extent to which the common carotid blood
flow velocity and CoQ10 variables predict incongruent Stroop reaction time in the HF group,
after controlling for IQ was computed. The assumptions of linearity, normally distributed
errors and uncorrelated errors were checked and the model was considered to be a good fit.
Examination of multivariate outliers using Mahalanobis distance, based on a critical χ2 at α =
.001 for three degrees of freedom is 16.266, indicated there were no apparent outliers in the
data (Tabachnick & Fidel, 2007).
The hierarchical multiple regression analysis summary is presented in Table 36. In model 1,
when IQ was entered alone, it did not significantly predict performance on executive function
as measured by incongruent Stroop, F(1,30) = .63, p > .05. When common carotid blood flow
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156
velocity was entered into the model it significantly improved the prediction of incongruent
Stroop reaction time, R2 change = .17, F(1, 29) = 6.14, p < .05.
When the CoQ10 was added to the model, the entire group of variables showed an almost
significant improvement in predicting incongruent Stroop, R2 change = .093, F(1, 28) = 3.64,
p = .067, with a trend accounting for 28.5% of the variance of incongruent Stroop
performance (R2 = .285). The beta weights and significance values, presented in Table 36,
reveal that carotid blood flow velocity significantly contributes towards and there is a trend
for CoQ10 (p = .067) to significantly contribute towards predicting performance on
incongruent Stroop task in the HF group.
Table 36 Hierarchical multiple regression analysis summary predicting incongruent Stroop from common carotid blood flow velocity and CoQ10 after controlling for premorbid IQ in the HF group
Model summary
Predictors R R2 ∆ R2 ∆ F df p
Model 1 .14a .02 -.02 .63 1, 30 .432
Model 2 .44b .19 .17 6.14 1, 29 .019
Model 3 .53c .29 .09 3.64 1, 28 .067
Coefficients
B SE β t p
Model 1
Constant 3.25 .20 16.16 .000
WASI .00 .00 .14 -.80 .432
Model 2
Constant 3.47 .21 16.87 .000
WASI .00 .00 -.152 -.91 .371
CCA-BFV -.01 .01 -.414 -2.48 .019
Model 3
Constant 3.43 .20 17.35 .000
WASI .00 .00 -.06 -.37 .717
CC-BFV -.01 .01 -.34 -2.09 .046 CoQ10 .00 .00 -.33 -1.91 .067
Note: WASI=Wechsler Abbreviated Scale of Intelligence Vocabulary subset; CCA-BFV=common carotid arterial blood flow velocity; CoQ10=coenzyme Q10.
Chapter 9: Results – relationships between cognitive measures and biomarkers
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9.6.3.2 Summary
In summary, common carotid arterial blood flow velocity and central pulse pressure
significantly predict HF patient’s performance on executive function tasks as measured by
incongruent Stroop. Additionally, when included in a model with premorbid IQ and common
carotid arterial blood flow velocity, there is a trend towards coenzyme Q10 to predict
performance on incongruent Stroop performance in the HF group.
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CHAPTER 10 RELATIONSHIPS BETWEEN MOOD AND BIOMARKERS
10.1 Introduction
This chapter will explore whether relationships between the Profile of Mood States (POMS)
subsets (tension/anxiety, depression/dejection) and biomarkers (vascular, oxidative stress,
antioxidants and inflammation) exist in heart failure (HF). Simple regression analyses were
initially conducted for each experimental group to explore whether biomarkers were related
to the Profile of Mood States subtests. Significant relationships were further explored by one-
way between-subjects analysis of covariance (ANCOVA) to examine how much of the
variance between the mood outcome measures were accounted for by the physiological
measure.
If warranted, a series of multiple regression analyses were then performed to explore to what
extent possible biomarkers predicted mood in the HF group. In order to have a large effect
size in a multiple regression analysis with two independent variables, a sample size of 30 is
required per group (Cohen, 1992). Additionally, in order to achieve a large effect size of .80
with three independent variables, a sample size of 34 in each group is required. Although the
total sample size in each group was sufficient for multiple regression analysis involving a
maximum of three independent variables, there was some data missing for the biomarkers.
Consequently, the sample size in this investigation is insufficient for performing multiple
regression analyses. Interpretation of results is therefore exploratory and need to be made
with caution. For all analyses, relationships between variables were considered to be
statistically different if the p value was less than 0.05 using two tailed tests.
10.2 Relationships between mood and vascular function
10.2.1 Introduction
There were no specific hypotheses generated with relation to whether cerebral blood flow or
arterial stiffness has an effect on depression and anxiety in HF patients. Therefore the current
investigation explored the research question “is there a relationship between cerebral blood
flow or arterial stiffness and depressive symptoms and anxiety (R1)“. To investigate whether
blood flow velocity (common carotid and middle cerebral arterial) and arterial stiffness
(central pulse pressure and augmentation index) were related to depression/dejection and
tension/anxiety measures in HF patients simple regression analyses were initially conducted
Chapter 10: Results – relationships between mood and biomarkers
159
on normally distributed and transformed variables. Pearson’s correlation coefficients for each
experimental group are displayed in Table 37.
Table 37 Correlation matrix for mood measures and blood flow velocities, arterial stiffness and vascular function
Variable Blood flow Velocity Arterial Stiffness Vascular function
Heart Failure Group CC MCA AIx CPP ET-1
Tension/Anxiety -.088 -.252 .306 -.057 -.357
Depression/Dejection -.197 -.227 .128 -.027 -.372
Control Group CC MCA AIx CPP ET-1
Tension/Anxiety .012 -.257 -.009 -.141 -.378
Depression/Dejection -.102 -.182 .080 .086 -.159
Note: CC=common carotid blood flow velocity; MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP=central pulse pressure; ET-1=endothelin-1.
10.2.2 Results
As indicated in Table 37, none of the POMS mood subsets were significantly related to
carotid blood flow blood flow velocity in either experimental group.
No significant correlations were observed between measures of arterial stiffness
(augmentation index and central pulse pressure) or vascular function (endothelin-1) and
tension/anxiety or depression/dejection measures in either of the experimental groups.
10.2.3 Summary
Decreased cerebral blood flow as common carotid or middle cerebral arterial blood flow
velocity does not appear to be related to the tension/anxiety or depression/dejection POMS
subscales in HF patients or age matched controls. In addition, arterial stiffness as measured
by augmentation index and central pulse pressure does not appear to be associated
tension/anxiety or depression/dejection measures in either of the experimental groups.
Chapter 10: Results – relationships between mood and biomarkers
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10.3 Relationships between mood and oxidative stress
10.3.1 Introduction
There were no specific hypotheses generated with relation to whether oxidative stress
(DROM, F2-isoprostanes) or antioxidant measures (CoQ10, glutathione peroxidase) have an
effect depressive symptoms and anxiety in HF patients. Therefore the research question “is
there a relationship between oxidative stress or antioxidant measures and depressive
symptoms and anxiety (R2)?” was explored in the present investigation. Biomarkers shown
to be significantly different between groups will be reported.
To investigate the whether the oxidative stress marker DROM was related to mood measures
in HF patients simple regression analyses were initially conducted on normally distributed
and transformed variables. Pearson’s correlation coefficients for each experimental group are
displayed in Table 38.
10.3.2 Results
The POMS tension/anxiety or depression/dejection subscales were not related to DROM in
the HF group. Although no significant group differences were seen in lipid peroxides as
measured by F2-isoprostanes, interestingly higher plasma F2-Isoprostane levels were
significantly associated with decreased scores on POMS-tension/anxiety (r(34) = -.355, p <
.05) and POMS- depression/dejection (r(34) = -.332, p < .05) in the HF group. These
observations were not observed in controls (p > .05).
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Table 38 Correlation matrix for mood measures and oxidative stress, antioxidant and
inflammatory biomarkers
Variable Oxidative Stress Antioxidant Inflammation and
omega-3
Heart Failure DROM F2 GPx CoQ10 hsCRP PUFA
Tension/Anxiety .147 -.355* .051 .062 .051 -.160
Depression/Dejection .108 -.332* -.003 .031 .166 -.125
Controls
Tension/Anxiety .136 -.129 -.040 -.334* .168 .306
Depression/Dejection .227 -.298 .167 -.169 .152 .308
Note: DROM=determinable reactive oxygen metabolites; F2=F2-isoprostanes; GPx=glutathione peroxidase; CoQ10=coenzyme Q10; hs-CRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid dietary consumption; * p < .05.
10.3.3 Summary
In the HF group higher scores on the tension/anxiety and depression/dejection subscales of
the POMS were moderately related to lower levels of lipid peroxidation as measure by F2-
isoprostanes in the HF group but not in controls.
10.4 Relationships between mood and antioxidants
10.4.1 Introduction
There were no specific hypotheses generated with relation to whether oxidative stress
(DROM, F2-isoprostanes) or antioxidant measures (CoQ10, glutathione peroxidase) are
associated with mood, in particular depressive symptoms and anxiety in HF patients.
Therefore the research question “is there a relationship between oxidative stress or
antioxidant measures and depressive symptoms and anxiety (R2)” was explored in the present
investigation were further explored. Biomarkers shown to be significantly different between
groups will be reported.
10.4.2 Results
To investigate the whether the antioxidant CoQ10 were related to mood measures in HF
patients simple regression analyses were initially conducted on normally distributed and
transformed variables. Pearson’s correlation coefficients for each experimental group are
displayed in Table 38. CoQ10 was not related to any of the mood measures in the HF group.
Chapter 10: Results – relationships between mood and biomarkers
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Interestingly, higher plasma CoQ10 levels were related to reduced scores on the POMS-
Tension/Anxiety subscale in controls (r(35) = -.334, p < .05).
10.4.3 Summary
Antioxidant levels were not related to mood measures in HF. However higher levels of the
antioxidant CoQ10 was related to lower levels of Tension/Anxiety in controls.
10.5 Relationships between mood, inflammation and dietary omega-3 intake
10.5.1 Introduction
There were no specific hypotheses generated with relation to whether inflammation (hs-CRP)
or dietary omega-3 PUFA intake is associated with mood measures in HF patient’s except
for depression where it was hypothesised that higher levels of inflammation as measured by
hs-CRP will be related to higher depression scores in HF.
10.5.2 Results
To explore whether inflammatory measures are related to mood in HF, Pearson’s correlation
coefficients were observed. Since no specific hypotheses were made with relation to whether
inflammatory measures have an effect on anxiety/tension HF patients, the research question
“is there a relationship between inflammatory measures and anxiety/tension, HF patients
(R3)” was explored in the present investigation. There were no significant relationships
between anxiety/tension or antioxidant markers as measured by hs-CRP and dietary PUFA
intake.
Additionally, it was hypothesised that inflammation as measured by high-sensitive C-reactive
protein (hs-CRP) will be related to depression scores in HF patients (H15). The hypothesis
that higher levels of inflammation as measured by hs-CRP will be related to higher
depression scores in HF was rejected as no significant relationships were observed, r(32) =
.166, p > .05.
10.5.3 Summary
Systemic inflammation as measured by circulating high-sensitive C-reactive protein levels
and increased dietary PUFA intake was not related to measures of tension/anxiety or
depression/dejection.
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CHAPTER 11 DISCUSSION
11.1 Introduction
This chapter will first provide a summary of the main findings of this thesis. This will be
followed by a summary of the comparison between the ways in which the different groups
performed on each individual cognitive domain tested, followed by results from the mood
and quality of life measures. The chapter will then provide a summary of the group
differences observed on oxidative stress, antioxidant, inflammatory and vascular measures
followed by a summary of which biomarkers relate to the cognitive domains tested in this
investigation. Following this a summary of the biomarkers found to relate to mood measures.
This chapter will then outline the limitations and strengths of this thesis followed by
suggestions for future research. The chapter concludes with limitations and strengths of this
study and possible future research to stem from this thesis.
11.2 Summary of the main findings
This thesis investigated whether there are additional cognitive functions impaired in HF other
than those already described. The additional cognitive domains examined were Speed of
Memory, Power of Attention, Continuity of Attention, quality of episodic working and Quality
of Working Memory and these were tested using the well-validated Cognitive Drug Research®
(CDR) computerised test battery.
Table 39 presents a summary of the cognitive performance in the heart failure group
compared with controls. Participants completed cognitive tests known to be sensitive in heart
failure including the Stroop word-naming task (congruent Stroop and incongruent Stroop)
and paper pencil tests (Trail Making-A and Trail Making-B). In addition, participants
completed the Cognitive Drug Research® computerised test battery to assess cognitive
domains that may be impaired in HF. As expected, HF patients showed impairments on tasks
measuring selective attention and executive functioning, cognitive flexibility and response
inhibition. However, the two groups displayed similar scores on Stroop effect. In addition,
patients were not impaired on traditional measures of psychomotor abilities (Trail Making-A),
however, they were impaired on traditional executive function measures (Trail Making-B).
The results also indicated that impairments in cognitive domains seen in HF patients are
restricted to the Power of Attention, as determined by the sum of reaction time scores on
CDR individual tasks measuring sustained attention from the simple, digit vigilance and
choice reaction times. This suggests that Power of Attention is an additional cognitive domain
impaired in heart failure.
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Table 39 Summary of the cognitive performance in the heart failure group compared with healthy controls
Variable Heart failure group
performance compared to controls
Cognitive measures
Attention Tasks
Congruent Stroop RT (ms) ↓ Congruent Stroop %Acc NS
Attention Domains
Power of Attention (ms) ↓ Continuity of Attention (ms) NS
Psychomotor Task
Trail Making-A (ms) NS Memory Domains
Quality of Episodic Memory (ms) NS Quality of Working Memory (ms) NS Speed of Memory (ms) NS
Executive Function
Incongruent Stroop RT (ms) ↓ Incongruent Stroop %Acc ↓ Stroop effect (ms) NS Trail Making-B (ms) ↓
Note: ↓=significantly reduced function compared to the control group; NS=no significant difference between the heart failure and control group; RT=reaction time; %Acc=percentage accuracy; ms=milliseconds.
Another aim was to examine whether reduced cognitive function in HF patients related to
decreased cerebral blood flow (common carotid arterial blood flow velocity, middle cerebral
arterial blood flow velocity) and elevated arterial stiffness (augmentation index, central pulse
pressures). A Transcranial Doppler instrument was used to record blood flow velocities from
participant’s common carotid and middle cerebral arteries. The results demonstrated that
compared to controls, blood flow velocity was reduced in the common carotid and middle
cerebral arteries of HF patients. The SphygmoCor® instrument was utilised to record indirect
measurements of arterial stiffness, augmentation index and central pulse pressures. The
results indicate that arterial stiffness as measured by augmentation index was not different
between experimental groups. However, central pulse pressure, an indirect measure of
arterial stiffness, was significantly lower in HF patients compared to controls.
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This investigation also explored other physiological mechanisms related to cognitive
impairment in older HF patients. In particular, the aim was to explore whether an increase in
oxidative stress (DROM, F2-isoprostanes), reduced antioxidants (CoQ10, glutathione
peroxidase) and an increase in systemic inflammation (hs-CRP) can explain cognitive deficits
seen in HF. As expected, patients had higher levels of oxidative stress than controls as
measured determinable reactive oxygen metabolites (DROM). However, experimental groups
had equivalent levels of lipid peroxidation as measured by F2-isoprostanes. Additionally,
compared to controls, HF patients had lower levels of the antioxidant and cellular energiser
CoQ10. Although plasma levels of the enzyme antioxidant glutathione peroxidase was
similar for the two experimental groups. A summary of the direction of the biomarker results
in the heart failure compared with the control group is presented in Table 40.
Table 40 Summary of the biomarker values in the heart failure group compared with healthy controls
Variable Heart failure group values compared to
controls
Blood Flow Velocity (cm/s)
Common carotid ↓
Middle cerebral ↓
Central Pressures (mmHg)
Augmentation index NS Central pulse pressure ↓
Vascular function
Endothelin-1 (pg/mL) NS Oxidative Stress
DROM (Ucarr) ↑ F2-isoprostanes (pmol/L) NS
Antioxidants
Glutathione peroxidase (nmol/min/ml) NS CoQ10 (nmol/L) ↓
Inflammation and omega-3
hs-CRP (mg/L) ↑ PUFA (units/day) ↓
Note: ↑=significantly higher levels in heart failure patients compared to controls; ↓=significantly lower levels in heart failure patients compared to controls; NS=no significant difference between the heart failure and control group; DROM=determinable reactive oxygen metabolites; Ucarr=Carratelli Units; pmol/L=picomole per litre; nmol/min/ml=nanomole per minute per mililitre; CoQ10=coenzyme Q10; nmol/L=nanomole per litre; hs-CRP=high-sensitive C-reactive protein; mg/L=milligrams per litre; PUFA=polyunsaturated fatty acid.
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The relationships between cognitive measures, mood and biomarker variables that were
significantly different between the two experimental groups is summarised for the heart
failure group in Table 41. Global cognitive function as determined by MMSE scores was not
associated with augmentation index or central pulse pressure in either experimental group. In
addition, MMSE scores did not relate to cerebral blood flow, arterial stiffness, lipid
peroxidation (F2-isoprostanes), antioxidants (glutathione peroxidase, CoQ10), inflammation
(hs-CRP) or dietary PUFA intake.
Slower reaction times on attention tasks and Power of Attention were related to reduced blood
flow velocity in the common carotid artery in HF patients. These associations however were
not significant in controls. An increase in central pulse pressure, another indirect measure for
arterial stiffness, related moderately to poorer reaction times and Power of Attention in the
HF group. These findings suggest that reduced attention abilities in HF patients relate to a
decrease in common carotid arterial blood flow velocity and an increase in arterial stiffness
as measured by central pulse pressure. Additionally, reduced performance on measures of
attention and psychomotor speed were significantly related to lower plasma antioxidant
CoQ10 levels in HF patients but not in controls.
In addition, common carotid arterial blood flow velocity and CoQ10 accounted for some of
the between group variance on tasks measuring reaction time on attention tasks. Furthermore,
blood flow velocity in the common carotid artery and arterial stiffness as measured by
central pulse pressure accounted for the variance between groups on the Power of Attention
cognitive domain.
These findings suggest that reduced blood flow velocity on the common carotid artery and
plasma CoQ10 levels are a possible oxidative stress marker related to attention and
psychomotor speed. Inflammatory markers did not relate to tasks measuring attention in HF
or controls.
Better scores on Quality of Episodic Memory and Speed of Memory cognitive domains related
to faster common carotid arterial blood flow velocities in HF patients. There were no
significant relationships between tasks measuring Quality of Episodic Memory or Speed of
Memory and oxidative stress, antioxidants or inflammation in either experimental group.
Worse performance on traditional measures of executive function (incongruent Stroop,
Stroop effect) was related to slower blood flow velocity in the common carotid artery and
increased arterial stiffness in the HF group but not in controls. Furthermore, blood flow
velocity in the common carotid artery and antioxidant levels (CoQ10) accounted for the
variance between groups on tasks measuring executive function and response inhibition.
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Table 41 Summary of the relationships between cognitive and mood measures with biomarkers in the heart failure group.
Cerebral blood flow
velocity
Arterial stiffness
Oxid stress
Anti-oxidant
Infla omega-
3
Variable CC MCA AIx CPP DROM CoQ10 hsCRP PUFA
Mini Mental State Examination
- - - - - - - -
Attention
Congruent Stroop -ve - - +ve - -ve - -
Trail Making-A - - - - - -ve - -
Power of Attention -ve - - +ve - - - - Continuity of Attention
- - -ve - -ve - - -
Memory
Quality of Episodic Memory
+ve - - - - - - -
Quality of Working Memory
- - - - - - - -
Speed of Memory -ve - - - - - - -
Executive Function
Incongruent Stroop -ve - - +ve - -ve - -
Stroop effect -ve - +ve - - -ve - -
Trail making-B - - +ve +ve - - - -
Mood POMS-depression/dejection
- - - - - - - -
POMS-tension/anxiety - - - - - - - - Note: +ve=significant positive correlation between variables; -ve= significant negative correlation between variables; Oxid=oxidative; Infla=inflammation; CC=common carotid arterial blood flow velocity MCA=middle cerebral arterial blood flow velocity; AIx=augmentation index; CPP=central pulse pressure; DROM=determinable reactive oxygen metabolites; CoQ10=coenzyme Q10; hsCRP=high-sensitive C-reactive protein; PUFA=polyunsaturated fatty acid questionnaire.
There was no evidence indicating an association between middle cerebral arterial blood flow
velocity speed and performance on attention, memory or executive function domains in HF.
Furthermore, higher levels of the oxidative stress measures in this investigation were not
related to global cognition, attention abilities, Quality of Episodic Memory or Quality of
Working Memory or executive function in HF.
A final aim was to explore whether vascular function, oxidative stress, reduced antioxidant
capacity or an increase in systemic inflammation relate to mood measures. Participants
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completed the Profile of Mood States (POMS) a self-report Questionnaire that measures six
facets of mood tension-anxiety; depression-dejection; anger-hostility; vigour-activity;
fatigue-inertia; and confusion-bewilderment. As expected, HF patients scored significantly
higher than controls on each of these mood dimensions except for vigour-activity where HF
patients scored significantly higher than controls.
Arterial stiffness does not appear to be associated with mood in HF patients or controls.
Additionally, greater lipid peroxidation levels in the body were related to lower levels of
tension/anxiety and depression/dejection in HF patients.
Summary of main findings:
Cognitive measures
1. HF patients are impaired on tasks measuring selective attention and executive
functioning, cognitive flexibility and response inhibition compared to age and sex-
matched controls
2. HF patients and controls displayed similar scores on Stroop effect.
3. HF patients were not impaired on executive function measures (Trail Making-B) but
not impaired on psychomotor abilities (Trail Making-A).
4. HF patients performed significantly worse than controls on sustained attention as
determined by the Power of Attention cognitive domain.
Vascular measures
5. Blood flow velocity in the common carotid and middle cerebral arteries was
significantly reduced in HF patients compared to controls.
6. Arterial stiffness as measured by augmentation index was not different between the
HF and control groups. However, central pulse pressure, an indirect measure of
arterial stiffness, was significantly lower in HF patients compared to controls.
Oxidative stress, antioxidant and inflammatory measures
7. HF patients had higher levels of oxidative stress than controls as measured
determinable reactive oxygen metabolites and lower levels of the antioxidant and
cellular energiser CoQ10. Although plasma levels of the enzyme antioxidant
glutathione peroxidase and lipid peroxidation as measured by F2-isoprostanes was
similar for experimental groups.
Chapter11: Discussion
169
Associations between cognitive measures and physiological makers
8. Global cognitive function as determined by MMSE scores was not associated with
arterial stiffness (augmentation index, central pulse pressure), cerebral blood flow
(common carotid and middle cerebral arterial blood flow velocity), oxidative stress
(DROMs, F2-isoprostanes), antioxidants (glutathione peroxidase, CoQ10),
inflammation (hs-CRP) or dietary PUFA intake in either experimental group.
9. In heart failure (HF) patient’s slower reaction times on attention tasks and Power of
Attention cognitive domain related to reduced blood flow velocity in the common
carotid artery.
10. An increase in central pulse pressure, an indirect measure for arterial stiffness, was
moderately related to poorer reaction times and Power of Attention in the HF group.
11. Reduced performance on measures of attention and psychomotor speed was
significantly correlated with lower plasma antioxidant CoQ10 levels in HF patients
but not in controls.
12. In the HF group, common carotid blood flow velocity and CoQ10 accounted for
some of the between group variance on tasks measuring reaction time on attention
tasks. Furthermore, blood flow velocity in the common carotid artery and arterial
stiffness as measured by central pulse pressure accounted for some of the variance
between groups on the Power of Attention cognitive domain.
13. Better scores on Quality of Episodic Memory and Speed of Memory cognitive
domains related to faster common carotid arterial blood flow velocities in the HF.
Associations between depression, anxiety and physiological makers
14. No significant relationships between tasks measuring Quality of Episodic Memory or
Speed of Memory and oxidative stress, antioxidants or inflammation were observed in
either experimental group.
15. Worse performance executive function related to slower blood flow velocity in the
common carotid artery and increased arterial stiffness in the HF group but not in
controls.
16. Blood flow velocity in the common carotid artery and antioxidant levels (CoQ10)
accounted for the variance between groups on tasks measuring executive function
and response inhibition.
17. Greater lipid peroxidation levels in the body related to lower levels of tension/anxiety
and depression/dejection in HF patients.
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11.3 Demographics and clinical characteristics
The experimental groups were well matched on demographic variables. In particular, the two
groups did not differ significantly on the demographic variables of age and sex. Patients
however scored significantly lower on the premorbid IQ test, Wechsler Abbreviated Scale of
Intelligence vocabulary subset, and had significantly less years of education compared to
controls. Therefore group difference on cognitive outcome measures after adjusting for
premorbid IQ were compared with controls in order to account for this possible confounding
factor. Premorbid IQ did not make a significant difference to the attention, psychomotor and
memory measures. The HF group demonstrated significantly worse performance compared to
controls on Stroop effect and Trail Making-B, however these group differences on the
executive function tasks disappeared after adjusting for premorbid IQ. In addition to
premorbid IQ, multivariate analyses for cognitive variables adjusted for mood variables that
significantly related to the cognitive outcome measure in the current data.
11.4 Summary of cognitive measures
The following sections discuss whether global cognitive function and scores on attention,
memory and executive function were significantly different between heart failure and control
groups. Additionally an examination of whether the HF and control groups differed on
overall scores on the Power of Attention, Continuity of Attention, Speed of Memory, Quality
of Episodic Memory and Quality of Working Memory cognitive domains will be discussed.
An outline of the overall summary of the cognitive results will be included in this section.
11.4.1 Global cognition and screening for dementia
As a screening tool, the MMSE was utilised as a quick measure to exclude individuals with
possible signs of dementia. Although all participants in each the HF and control groups
scored 25 or above on the MMSE, HF patients scored significantly lower on the global
cognitive measure than controls. These findings are in line with earlier studies that reported
impairment in MMSE in elderly patients with severe HF (Almeida & Tamai, 2001b)
compared to controls with no HF (Trojano et al., 2003), and those with mild and moderate
HF (Incalzi et al., 2003). However, the findings from this thesis did not support the results of
other studies, which failed to find significant group differences between HF patients
(62.9±14.6 years; NYHA class I – IV) and younger controls (53.3±17.2 years; Pressler et al
2010b). A decline in MMSE scores is possibly associated with increasing disease severity in
elderly patients (Incalzi., 2003). Some researchers have suggested that other tools are more
sensitive to changes in global cognitive function. The application of the Montreal Cognitive
Chapter11: Discussion
171
Assessment battery (MoCA) as a screening tool for cognitive dysfunction and overall
assessment of global cognitive function in HF has increased in recent years. The MoCA has
been shown to be better over the MMSE in detecting cognitive impairments in HF (e.g.
Cameron et al., 2009) and although the aim of this study was to utilise the MMSE purely as a
screening tool for dementia, future studies may also consider the MoCA.
Since global cognitive function provides an overall assessment of cognitive function, it does
not provide an assessment of which cognitive domains are impaired. Therefore, the aim of
this thesis was to examine the relationships between specific cognitive domains and
physiological mechanisms for cognitive decline in HF. In this investigation, MMSE scores
did not relate to any of the oxidative stress, antioxidant, inflammatory or vascular biomarkers
in either of the different groups. If significant relationships between specific cognitive facets
and biomarkers existed and the measure for cognitive function in this study was exclusively
global cognition, then this would have resulted in a Type I error.
Additionally, this investigation explored whether attention, Quality of Working Memory,
Quality of Episodic Memory and executive function are related to oxidative stress,
antioxidant capacity, inflammation and vascular measures. Moreover, the current
investigation explored whether additional cognitive domains that included Power of
Attention, Speed of Memory, Quality of Working Memory, Quality of Episodic Memory and
Continuity of Attention are impaired in HF patients and if so whether they are related to the
biomarkers tested.
11.4.2 Attention and psychomotor speed
The results from this investigation support the hypothesis that HF patients would perform
worse than controls on attention tasks as measured by congruent Stroop task, Power of
Attention and Continuity of Attention compared to controls (H1) was partially supported. As
predicted, after adjusting for WASI-vocabulary scores, HF patients’ mean reaction time on
the congruent Stroop task was significantly slower than healthy controls. However, the HF
patient’s (98%) were as accurate as controls (98%) on the congruent Stroop task
performance. These findings support previous studies, which indicated that HF patients
display reduced performance on attention tasks compared to healthy controls (Sauvé et al,
2009). However, unlike Sauvé et al (2009), who observed HF patients to have higher error
rates on attention tasks compared to controls, this study observed similar error rates on
congruent Stroop tasks between the two experimental groups. Power of Attention derived by
the average reaction times on the simple reaction time, digit vigilance and choice reaction
time individual CDR tasks was different between different groups. HF patients overall
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reaction time performance on the Power of Attention cognitive domain was 6% longer than
controls even after controlling for Premorbid IQ (1270 ms versus 1192 ms). Examining the
individual CDR tasks that define the Power of Attention domain, HF patients had
significantly longer reaction times than controls on the simple 9.8% longer (315ms versus
284ms), digit vigilance 4.5% longer (444ms versus 426ms) and choice 5.7% longer (511ms
versus 482ms) reaction time tasks. This indicates that HF patients have an impaired ability to
focus attention during a short period requiring extreme concentration. These findings support
previous research that found reduced ability to sustain attention in HF patients compared to
cardiovascular controls using the attention matrices tasks (Trojano et al., 2003). However,
summing multiple reaction time tasks enables a better representation of patients’ performance
on this cognitive domain. Adequate levels of attention and focus is required for information
to transfer into short and long-term memory.
The cognitive domain Continuity of Attention is determined by calculating the average
accuracy (%) scores on the choice reaction time and digit vigilance CDR tasks, subtracted by
the number of false alarms for digit vigilance task. This indicates that heart failure (HF)
patients and healthy controls make a similar number of errors when focussing on a task that
requires the ability to sustain attention over a prolonged period. As mentioned earlier, the HF
group and controls also had the same error rates for the congruent Stroop task. These findings
fail to support previous research that observed higher error rates on attention tasks in HF
patients compared to controls (Sauvé et al., 2009). Few studies however, have examined error
rates on measures of attention and vigilance in HF. Patients are required to have intense
concentration when listening and interpreting treatment advice given to them by their
physician and nurses. It seems likely that psychomotor control of elderly HF patients is intact.
However, these results suggest that patients may have an impaired ability to focus their
attention on tasks such as treatment advice given to them, which may in turn affect their
ability to remember these treatments.
The hypothesis that HF patients will perform significantly worse than controls on
psychomotor function (Trail Making-A; H2) was not supported. The results of this study
revealed that after adjusting for confounding variables premorbid IQ and POMS-
vigour/activity, the two groups performed equally on the on the psychomotor tasks (p = .49).
These findings support previous studies who also failed to find significant impairments on
psychomotor speed in elderly HF patients compared to patients without HF (Lavery et al.,
2007). However contrary to the present findings, other authors found impaired psychomotor
function in HF patients compared to healthy controls (Almeida, Garrido, et al., 2012;
Pressler, Subramanian, et al., 2010b), normative data (Bauer et al., 2011) and patients with a
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chronic condition other than HF (Pressler, Subramanian, et al., 2010b). These previous
studies differed to the present investigation in that they did not include a healthy age matched
control group (Bauer et al., 2011; Pressler, Subramanian, et al., 2010b) and included middle
aged as well as elderly participates (≥ 45 years; Almeida, Garrido, et al., 2012). Worse
performance on attention tasks and psychomotor speed increases with disease severity as
measured by NYHA class Bauer et al., (2011). Since NYHA class II was the predominant
disease classification for patients in this study, results suggest that elderly HF patients with
mild HF are impaired on attention domain but not psychomotor speed.
11.4.3 Quality of Episodic Memory, Quality of Working Memory and Speed of Memory
The results from the present study failed to support the hypothesis that HF patients will
perform significantly worse than controls on the Quality of Episodic Memory, Quality of
Working Memory and Speed of Memory cognitive domains (H3).
Quality of Episodic Memory:
Unexpectedly, after adjusting for possible confounding factors (premorbid IQ, POMS-Total
mood disturbance), this investigation did not observe differences between groups on the
Quality of Episodic Memory. These findings fail to support previous research demonstrating
worse performance on short-term, episodic (Hjelm et al., 2011) and visual memory (Brief
Visuospatial Memory; Sauvé et al., 2009) compared to controls without HF. However, in
support of previous trials, when examining the individual CDR tasks that reflect performance
on Quality of Episodic Memory, HF patients in the current study recalled less words than
controls on the immediate word recall task (M=5 verses M=6 out of a total of 15 words).
However, exploring additional individual CDR tasks that reflect performance on Quality of
Episodic Memory, HF patients in the current study recalled similar number of words as
controls on the delayed word recognition, picture recognition and immediate and delayed
word recall tasks. Supporting this, Lavery et al. (2007) did not show differences between an
elderly group of patients (≥ 65years) with (n=68; 78.8±7.2 years) and without HF (n=286;
77.2±6.5 years) on delayed recall, although similar to the current study worse performance on
visual immediate recall was reported in HF patients. Taken together, the results from the
present investigation suggest that patient’s resemble controls in terms of their ability to recall
verbal and pictorial information from episodic memory.
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Quality of Working Memory:
Surprisingly, after adjusting for possible confounding factors (premorbid IQ, POMS-Total
mood disturbance), this investigation did not observe differences between the HF and control
groups on the Quality of Episodic Memory. Examining the working memory tasks from
which the Quality of Working Memory cognitive domain is determined, revealed no
significant differences observed between the different groups on the spatial working memory
and numeric working memory CDR tasks. Findings from the present study fail support
previous researchers who found worse performance on working memory tasks compared to
age matched controls (Almeida, Garrido, et al., 2012; Beer et al., 2009; Hjelm et al., 2011;
Kindermann et al., 2012; Pressler, Subramanian, et al., 2010b; Sauvé et al., 2009). Previous
studies demonstrating impairments on working memory included HF patients with impaired
left ventricular ejection fraction (LVEF; e.g. Beer et al., 2009; Kindermann et al., 2012;
Sauvé et al., 2009). Additionally, the patient cohort in previous studies had greater HF
severity (e.g. Kindermann et al., 2012) and were older (> 80 years; Hjelm et al., 2011) than in
the present study. Since the current investigation did not record participants LVEF and were
classified predominantly as mild HF (NYHA class II), it is unknown how LVEF in the
current patient cohort compare to that of controls or participants in previous studies. Future
studies that replicate the present study may benefit from recording LVEF and sample a
greater number of patients with moderate and severe HF in order to examine whether disease
severity or LVEF are related to working memory abilities in HF.
Speed of Memory:
Refuting the hypothesis, patients did not exhibit worse performance in the Speed of Memory
domain compared to controls. Speed of Memory reflects the time it takes to recall information
from memory. Even after adjusting for possible confounding factors (premorbid IQ scores,
POMS-Total mood disturbance) no difference was observed between the groups on the Speed
of Memory. Calculating Speed of Memory involves averaging the speed of responses (msec)
on delayed picture and word recognition tasks, numeric working memory and spatial memory
tasks. When the individual CDR tasks that form the Speed of Memory cognitive domain, were
explored HF patients and controls performed similarly on the spatial, numeric and picture
recognition tasks.
However, patients required significantly longer to recognise words in the CDR delayed word
recognition task than controls, suggesting that patients are impaired in the time it takes to
recognise verbal information accurately from episodic memory, or to manipulate information
in working memory. HF patients and controls did not differ significantly in the CDR picture
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175
recognition task. This suggests that HF patients may be impaired in their ability to recognise
verbal information but not pictorial information stored in episodic memory. Comparing
results from the present study with other research is not possible since this is the first study to
measure Speed of Memory in heart failure patients. Taken together, findings from the current
investigation suggest that HF patients are similar to healthy age-matched controls in terms of
the Speed of Memory cognitive domain, although examining individual cognitive tasks their
ability to recall verbal information from episodic memory is impaired.
11.4.4 Executive Function
It was hypothesised that patients will perform worse than controls on tasks of executive
functioning as measured by incongruent Stroop, Stroop effect and Trail Making-B.
Supporting the hypothesis, HF patient were 16.8% slower at completing the Trail Making-B
task compared to controls (107 ms verses 89 ms). Additionally, HF patients’ mean reaction
time was 19.7% slower on the incongruent Stroop task (M=132 sec vs M=106 sec) even after
controlling for premorbid IQ. These results are consistent with previous findings
demonstrating that HF patients perform worse than healthy controls on the Trail Making-B
task (Lavery et al., 2007; Pressler, Subramanian, et al., 2010b), in patients diagnosed with a
condition other than HF (Pressler, Subramanian, et al., 2010b), and in patients with
cardiovascular disease (Hoth et al., 2008). However, the current results do not support
previous studies that showed no impairment in Trail Making-B task performance in HF
patients compared to normative data (Bauer et al., 2011). More recently, Bauer et al., (2011)
showed that increased disease severity as measured by NYHA functional class was
significantly related to poorer executive function performance (Trail Making-B). However,
due to the small sample size and unequal number of participants with mild, moderate and
severe HF classifications in the present investigation, the existence of any relationship
between executive function and disease severity could not be determined.
Few studies have reported data regarding HF on the Stroop effect (or Stroop interference).
The results of the current investigation indicate that after controlling for premorbid IQ, the
groups exhibited a similar Stroop effect. These findings support those of Kindermann et al.
(2012) who also did not observe group Stroop effect differences between patients with stable
HF and healthy controls. Similarly, studies using normative data also did not find
impairments in executive function in HF patients (Bauer et al., 2011). However, one study
that compared HF patients to a healthy control group did find impairments in the patient
group (Lavery et al., 2007). It is possible that in the current study, the computerised
congruent Stroop and incongruent Stroop tasks were not demanding enough to detect
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impairment. Taken together these findings suggest that a measure suitable for detecting
executive function impairments in HF is Trail Making-B task.
The next section discusses findings from the present investigation related to whether the heart
failure (HF) and control groups differed on cerebral blood flow, arterial stiffness, oxidative
stress, antioxidant and inflammatory biomarkers. Additionally, this section will cover an
overall summary of the relationships between each of the vascular, oxidative stress,
antioxidant and inflammatory variables.
11.5 Summary of vascular measures
11.5.1 Cerebral blood flow
The hypothesis that HF patient group would exhibit lower cerebral blood flow velocity as
measured by common carotid and middle cerebral arterial blood flow velocity compared to
the control group (H6 ) was supported. Mean blood flow velocity in the left common carotid
artery was significantly slower in patients (17±4.9 cm/s), compared to healthy controls
(22±3.9 cm/s). Furthermore, mean blood flow velocity in the left middle cerebral artery was
slower in the HF group (50±6.7 cm/s), compared to healthy controls (56±10.9 cm/s). These
findings corroborate those of Vogels et al. (2008), who demonstrated a similar reduction in
middle cerebral artery blood flow velocity as measured by Transcranial Doppler in HF
patients (47.3±10.7 cm/s) compared to healthy controls (56±10.9 cm/s). Carotid arterial
blood flow velocities in HF patients on the other hand have not been widely studied.
Cardiovascular risk factors have been shown to predict reduced pulsatile blood flow velocity
in both the common carotid and middle cerebral arteries of healthy elderly individuals (Pase
et al., 2012). Vogels and colleagues have suggested that reduced cerebral blood flow in HF
patients may be due to risk factors shared by patients with cardiovascular disease, rather than
poor cardiac output (Vogels et al., 2008). Given that HF patients display cardiovascular risk
factors, it is possible that slower blood flow in the common carotid and middle cerebral
arteries in HF is due to risk factors associated with cardiovascular disease. Results of the
current study may indicate that although both elderly and HF patients may possess
cardiovascular disease risk factors, greater cardiovascular disease risk factors in HF patients
may be related to reduced common carotid and middle cerebral arterial blood flow velocity
in HF patients.
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11.5.2 Arterial stiffness
The current investigation partially supported the hypothesis that HF patients would exhibit
increased arterial stiffness as measured by the augmentation index and central pulse
pressures compared to the control group (H7). Compared to controls, HF patients had reduced
arterial stiffness measures as determined by central pulse pressure but not on the
augmentation index. A reduction in central pulse pressure in the heart failure group is likely
due to lower cardiac contractility in heart failure patients. These findings do not support
previous observations demonstrating that HF patients have elevated central and peripheral
pulse pressures compared to patients without HF (Mitchell et al., 2001). However, these
findings do support recent trials that observed lower arterial stiffness measures in HF patients
with left ventricular systolic dysfunction compared with healthy controls (Denardo et al.,
2010) and those with preserved left ventricular ejection fraction (Tartière et al., 2006). In
these studies however, arterial stiffness was determined by different means, either indirectly
by recording augmentation index from the common carotid artery (Tartière et al., 2006) or
via carotid-femoral pulse wave velocity (Denardo et al., 2010). Also the central aortic
pressure wave has been determined from recordings of the radial artery in patients with
severe left ventricular dysfunction (Denardo et al., 2010). Although augmentation index is a
suitable measure in a clinical setting, based on findings from the current investigation it may
not be the optimal measure to use for detecting arterial stiffness in HF patients with mild
disease severity. Central pulse pressure may be a more sensitive measure to use in clinical
settings to detect arterial stiffness in HF patients. Interestingly, significant relationships were
not observed between cerebral blood flow and arterial stiffness measures. The present
findings suggest that HF patients in the present study may have elevated arterial stiffness as
an indication of increased peripheral resistance, however given that the current sample size
larger trials examining the validity of using augmentation index measurements with carotid-
femoral pulse wave velocity in heart failure patients are required.
11.5.3 Vasoconstriction: endothelin-1
In contrast to the prediction that HF patients will have higher plasma levels of the
vasoconstrictor endothelin-1 compared to controls (H8), heart failure (HF) patients and
controls had similar plasma endothelin-1 concentrations. These results are in contrast to
previous findings demonstrating elevated levels of the vasoconstrictor endothelin-1 in HF
compared to controls (Jackson et al., 2000). The findings from this study suggest that
vasoconstriction in HF patients was not significantly different to that of healthy age-matched
controls. Given that raised endothelin-1 levels are principally seen in patients with severe and
not mild HF (Wei et al., 1994), the present findings which involved predominantly mild heart
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178
failure patients, are therefore not surprising. Due to the low sample size and uneven
participant numbers classified with mild, moderate and severe HF there was inadequate
power to explore the difference in endothelin-1 across disease severity. Previous studies have
revealed that endothelin-1 relates to increased pulse wave velocities, augmentation index and
reduced cardiac output (Vuurmans et al., 2003). This was not observed in the present study
suggesting that increased arterial stiffness does not relate to endothelin-1 in patients with
mild heart failure.
11.6 Summary of oxidative stress measures
The current investigation partially supports the hypothesis that patients would exhibit
significantly higher levels of oxidative stress, as measured by determinable reactive oxygen
metabolites (DROMs) and lipid peroxides (F2-isoprostanes), compared to the control group
(H9). As expected, compared to controls HF patients had higher plasma levels of DROMs,
indicating higher levels of hydroperoxides in the patient group. These findings support
preliminary data indicating that higher plasma DROM concentrations are present in elderly
HF patients compared to healthy age matched controls, and that DROMs levels increased
with increasing age (Rosenfeldt et al., 2013). These results indicate that DROMs are an
additional oxidative stress marker impaired in HF.
Surprisingly, there were no group differences for lipid peroxidation as measured by plasma
F2-isoprostanes concentrations. These findings do not support earlier observations by
Polidori et al. (2004) who showed elevated F2-isoprostanes levels in older HF patients with
moderate to severe HF compared to age matched controls (Polidori et al., 2004). The absence
of significant group differences for F2-isoprostanes in the present study may be due to the
patient cohort diagnosed predominantly with mild heart failure and increased lipid peroxides
may be present in patients with greater disease severity. Results from the present
investigation suggest that patients with predominantly mild heart failure have high levels of
oxidative stress in the form of hydroperoxides but not lipid peroxides.
11.7 Summary of antioxidant measures
It was hypothesised that HF patients would have significantly lower levels of plasma
antioxidants as measured by Coenzyme Q10 (CoQ10) and glutathione peroxidise compared
to the control group (H10). As anticipated, plasma CoQ10 levels were significantly lower in
HF patients compared to age-matched controls, supporting previous investigations (Folkers et
al., 1992; Keogh et al., 2003). CoQ10 has multiple roles in the body including production of
energy in the form of adenosine triphosphate, enhancing the immune system and recycling
Chapter11: Discussion
179
other antioxidants including ascorbic acid and α-tocopherol (Boreková et al., 2008).
Furthermore, randomised trials have demonstrated that supplementation with CoQ10 results
in elevating previously deficient levels of CoQ10 in patients with HF (Folkers et al., 1992;
Keogh et al., 2003). Additionally, improvements in exercise capacity, decreases in disease
severity (Keogh et al., 2003) and reductions in hospital stay duration (Morisco et al., 1993)
have been observed in HF patients taking CoQ10 supplements.
Surprisingly, heart failure (HF) patients and healthy controls did not differ on observed levels
of the lipophilic antioxidant biomarker glutathione peroxidase. These findings are
inconsistent with previous studies that demonstrated reduced glutathione peroxidase activity
in HF patients compared to healthy controls (Keith et al., 1998; Polidori et al., 2004).
Previous studies have also shown that higher levels of the lipid peroxidation measure F2-
isoprostanes were associated with reduced circulating antioxidant biomarkers including
glutathione peroxidation in HF patients compared to controls (Polidori et al., 2004). Since the
present study also did not find that HF patients and controls differed on F2-isoprostanes, it is
possible that patients did not have high levels of oxidative stress and in turn reduction in
glutathione peroxidase activity. The HF cohort of the current study included only 5 patients
with moderate (NYHA class III) and one patient with severe (NYHA class IV) disease
severity with the remaining 30 patients (83%) classified mild HH (NYHA class II).
Therefore, due the HF group in the present study predominantly classified as mild HF, any
significant differences between oxidative stress and antioxidant biomarkers were not
detected. It is probable that higher levels of oxidative stress and reduced antioxidant
biomarkers would be observed in a sample with greater disease severity.
11.8 Summary of inflammation and dietary omega-3
As expected, HF patient’s systemic inflammation as measured by high-sensitive C-reactive
protein levels and dietary polyunsaturated fatty acid (PUFA) intake were significantly
elevated in HF patients compared to controls (H11). These findings support the well-
established evidence that CRP levels are elevated in HF patients and are a reliable biomarker
used in clinical practice as a reflection of disease severity (Xue et al., 2006). Moreover, HF
patients consumed less dietary omega-3 foods than controls three months prior to baseline
testing supporting the prediction that patients will have consumed higher dietary PUFA
compared to controls. These findings support previous work indicating that omega-3 PUFA
supplementation is related to decreased inflammatory markers in HF patients (Nodari et al.,
2011). Supplementing with omega-3 PUFAs in the form of fish oils has been shown to
decrease serum inflammatory markers (TNF-α, IL-1 and IL-6) in HF patients (Nodari et al.,
Chapter11: Discussion
180
2011), improve mortality, decrease hospital admissions (Tavazzi L et al., 2008), improve
NYHA class and improve exercise capacity in HF patients. Taken together these findings
suggest that HF patients have higher levels of inflammation compared to controls.
11.9 Summary of the relationships between vascular, oxidative stress and
inflammatory biomarkers
The following section discusses how the results from each of the vascular, oxidative stress,
antioxidant and inflammatory biomarkers examined relate to each other in HF patients and
controls. Considering how these biomarkers relate to each other in the current investigation
may help understand mechanisms underlying cognition impaired and mood in the following
sections.
11.9.1 Vascular measures and oxidative stress, inflammation and antioxidant biomarkers
The results of the current study suggest that cerebral blood flow velocity might not relate to
markers of oxidative stress, antioxidant and inflammatory markers in HF patients or healthy
controls, and similarly endothelin-1 did not relate to arterial stiffness or cerebral blood flow
measures. Previous researchers propose that a function of endothelin-1 is to elevate arterial
stiffness as measured by augmentation index and pulse wave velocity (Vuurmans et al.,
2003). Given that the different groups in the present study did not differ on the
vasoconstrictor endothelin-1 and augmentation index, it is possible that endothelin-1 levels in
the HF group did not influence elevation of arterial stiffness. Additionally, previous studies
have demonstrated that elevated endothelin-1 levels are associated with a poorer prognosis
for HF patients (Pousset et al., 1997). It is possible that elevated vasoconstriction as measured
by endothelin-1 level was not observed in the current patient cohort as majority of patients
did not have severe HF and perhaps their overall prognosis may have been more positive than
previous studies. Longitudinal studies are recommended to further explore this hypothesis.
Additionally, elevated endothelin-1 levels are seen predominantly in patients with severe but
not mild HF (Wei et al., 1994). Given that 80% of the patients in the current study were
NYHA class II it is possible that the present cohort were not severe enough to present with
vasoconstriction.
In the present study, augmentation index did not relate to antioxidant or oxidative stress
markers. However higher arterial stiffness as measured by central pulse pressure was
moderately related to lower antioxidant levels in HF patients (glutathione peroxidase and
coenzyme Q10). Additionally, the findings from the present study revealed that in HF
patients, elevated inflammation and reduced antioxidants were related to increased arterial
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181
stiffness as measured by central pulse pressure. Moreover, the level of high-sensitive C-
reactive protein (hs-CRP) was related to increased central pulse pressure in HF patients.
These findings are in line with previous studies which demonstrated that carotid arterial
stiffness was related to higher levels of systemic inflammation as measured by C-reactive
protein (CRP) but not the inflammatory markers Interkeukin-6 (IL-6) and tumor necrosis
factor-alpha (TNF-α) in a cohort of middle aged participants aged 45-59 years (Ellins et al.,
2008). The findings from the present study suggest that glutathione peroxidase activity and
coenzyme Q10 levels are associated with elevated arterial stiffness in HF patients.
11.9.2 Oxidative stress, inflammation and antioxidants
The current findings suggest that measures of oxidative stress (DROM; F2-isoprostanes) may
not be related to any of the vascular, antioxidant or inflammatory measures in either
experimental group. This is in contrast to an earlier study, which found that markers of
inflammation (tumour necrosis factor alpha), lipid peroxidation and decreased antioxidant
reserves are observed with increasing severity of oxidative stress (Keith et al., 1998). In the
present study, however levels of hydroperoxides as measured by F2-isoprostanes were not
elevated in HF patients and lipophilic antioxidants as determined by glutathione peroxidase
levels were not reduced in patients. This is in contrast to findings by Polidori et al. (2004)
who reported a negative relationship between F2-isoprostanes lipophilic antioxidant
biomarkers (e.g. glutathione peroxidase and super oxide dismutase). The current
investigation did not replicate this relationship between F2-isoprostanes and glutathione
peroxidase. In addition, levels of lipid peroxidation were higher in patients with greater
disease severity than those with less severe HF (Keith et al., 1998; Polidori et al., 2004). It is
therefore probable that high levels of lipid peroxidation were not observed in the current
investigation since the majority of patients had mild HF (Keith et al., 1998; Polidori et al.,
2004). Additionally this is possibly why there were no group differences on plasma
glutathione peroxidase activity. Since only five HF patients in the current were classified
with moderate HF (NHYA class III) and only one HF patient with severe HF (NYHA class
IV), there was insufficient power to assess whether biomarker levels varied as a function of
disease severity.
In the present study, enzymatic antioxidant levels as measured by glutathione peroxidise
activity were negatively related to hs c-reactive protein in HF patients. Additionally, in the
present study a trend towards an association between lower coenzyme CQ10 levels associated
and higher hs c-reactive protein in HF patients was observed. This suggests that reduced
antioxidant levels may be associated with elevated systemic inflammatory measures in older
HF patients. It is reasonable to suggest that reactive oxygen species, as measured by DROMs
Chapter11: Discussion
182
in the current study, trigger the release of inflammatory signalling molecules including c-
reactive protein, IL-1 and TNF-α, (Khaper et al., 2010; Mann, 2008; Valko et al., 2007).
However, given that no associations existed between the oxidative stress measures DROM
and F2-isoprostanes and any of the antioxidant biomarkers in either of the experimental
groups in the present investigation, further trials are required with larger sample sizes to
clarify these relationships. In particular, more elaborate statistical methods such as structural
equation modelling is recommended to better understand how each of these biomarkers are
interrelated in patients with HF.
11.10 Relationship between cerebral blood flow and cognitive function
11.10.1 Global cognition
The hypothesis that reduced cerebral blood flow velocity as measured by common carotid
and middle cerebral arterial blood flow will be related to poorer performance on cognitive
tests measuring global cognition, attention, psychomotor speed, working memory, episodic
memory and executive function in HF patients (H12) was partially supported. The outcomes
from the present study revealed that cerebral blood flow as measured by blood flow velocity
in the common carotid artery did not correlate with global cognitive scores. This finding
failed to support previous research that found that reduced common carotid arterial blood
flow was related to poorer global cognitive function in patients with mild-moderate carotid
arterial disease (Fu et al., 2012). The findings from the current investigation suggest that the
speed of blood flowing though the common carotid artery does not predict global cognitive
function in HF patients.
Further along the vascular path supplying blood to the cerebrum is the middle cerebral
artery. The hypothesis that higher MCA blood flow velocities will relate to better global
cognitive function was refuted by the results of this study. The findings support those of
Vogels et al. (2008) who also did not find a relationship between specific cognitive domains
and mean middle cerebral arterial blood flow velocity from both left and right sides. On the
contrary, findings from the present study fail to support those by Jesus et al. (2006) who
found that poor global cognition was related to decreased right middle cerebral arterial blood
flow in younger patients with HF. In the study by Jesus et al., (2006), HF patients with a
history of stroke, poor education and probable dementia (MMSE scores ranged from 3 to 30)
were recruited in the study. Pathological changes to the cerebrovascular system due to stroke
or dementia may have contributed to poor cerebral blood flow that study and not the HF
itself. Vogels et al. (2008) however failed to find a relationship between global cognitive
function and middle cerebral arterial blood flow velocity, however those authors included a
Chapter11: Discussion
183
more cognitively intact patient cohort with a mean MMSE score of 27.6 and excluded
patients with a history of stroke or prior diagnosis of dementia. A reason why the results from
the current investigation are more in line with Vogels et al. (2008) may be due to the adoption
of similar exclusion criteria. The current findings suggest that reduced blood supply to
temporal lobes and parietal lobes as measured by middle cerebral arterial blood flow
velocity does not affect global cognitive function in HF patients.
11.10.2 Attention
The prediction that cerebral blood flow would be related to attentional abilities and
psychomotor speed in HF patients (H12), was partially supported. In the present HF patient
cohort, slower reaction times on attention tasks (congruent Stroop reaction time) were
moderately associated with reduced blood flow velocity in the common carotid artery. These
findings suggest that slower cerebral blood flow is associated with lower attentional abilities
in elderly patients with HF. Contrary to expectation, psychomotor speed did not relate to
common carotid arterial blood flow velocity in either experimental group. These findings
suggest that attentional ability indicated by longer reaction times but not psychomotor speed
is associated with slower blood flow in the common carotid artery. Moreover, the present
investigation found that slower overall reaction times on the Power of Attention cognitive
domain were moderately associated with reduced common carotid arterial blood flow
velocity in HF patients but not in controls. These findings suggest that reduced ability to
sustain attention over a long period of time in HF patients may be explained by reduced
cerebral blood flow speed.
The prediction that middle cerebral arterial blood flow velocity would relate to attentional
abilities and psychomotor speed in HF patients (H12), was not supported. Contrary to
expectation, attentional abilities, psychomotor speed and Power of Attention domain were
unrelated to blood flow velocity the middle cerebral artery in both the HF and control
groups. These results suggest middle cerebral arterial blood flow velocity does not appear to
be related to attentional abilities in HF patients and controls. These findings support those of
Tanne et al. (2005) who found that improvements in attention (Stroop A, congruent Stroop)
and psychomotor speed (Trail Making-A) seen after an exercise program, were not due to
enhanced vasodilator reserve or middle cerebral arterial blood flow velocity in patients with
moderate HF. These findings did not support previous research where an association between
common carotid arterial blood flow velocity and global cognitive scores was observed (Fu et
al., 2012).
Chapter11: Discussion
184
Conversely, the present results support Vogels et al. (2008) who also failed to find an
association between mental speed and attention with mean cerebral blood flow velocities.
This is the first time that common carotid arterial blood flow velocity has been shown to
correlate with longer reaction times on attention tasks and a decreased ability to focus
attention over a long period in HF patients.
Multivariate analysis revealed that common carotid arterial blood flow velocity had a weak
effect across groups on attention as measured by congruent Stroop. Additionally, after
adjusting for premorbid IQ and common carotid arterial blood flow velocity, the main effect
of group on congruent Stroop was no longer significant. This suggests that after adjusting for
premorbid IQ, common carotid arterial blood flow velocity explained the difference between
groups on the attention task. Furthermore, after adjusting for premorbid IQ, there was a trend
between common carotid arterial blood flow velocities. After adjusting for premorbid IQ and
common carotid arterial blood flow, the main effects of group on Power of Attention
disappeared, suggesting that common carotid arterial blood flow velocity accounted for the
between group variance in the Power of Attention cognitive domain. These findings suggest
that the between group variance computed on attention might be explained by reduced
common carotid arterial blood flow velocity. Clinical applications may involve interventions
that can accelerate cerebral blood flow velocity to enhance HF patients’ ability to focus their
attention on information provided to them.
11.10.3 Memory
Cerebral blood flow velocity as measured by common carotid and middle cerebral arterial
blood flow was not related to Quality of Episodic Memory, Quality of Working Memory or
Speed of Memory domains in HF patients. While this investigation did not find that HF
patients were impaired on Quality of Episodic Memory, Quality of Working Memory or Speed
of Memory cognitive domains compared to healthy age matched controls, interpreting
relationships observed between these measures and vascular measures requires caution. The
results from the present study indicate that in HF patients, worse performance on Quality of
Episodic Memory was related to slower cerebral blood flow in the common carotid artery but
not the middle cerebral artery. Moreover, the current study revealed that in HF patients’
slower performance on Speed of Memory cognitive domain was related to reduced cerebral
blood flow in the left common carotid artery but not the left middle cerebral arteries. The
relationships observed between cerebral blood flow and Quality of Episodic Memory and
Speed of Memory domains were not present in controls. The present study failed to observe
relationships between cerebral blood flow and Quality of Working Memory domain in either
Chapter11: Discussion
185
experimental group. These results suggest that in HF patients, longer information retrieval
times from working memory and reduced accuracy in the ability to retrieve information from
episodic memory were related to reduced cerebral blood flow.
In the current study, relationships between middle cerebral arterial blood flow and cognitive
domains of Quality of Episodic Memory, Quality of Working Memory or Speed of Memory in
did not reach statistical significance in either HF patients or controls. These findings support
those of Vogels et al. (2008) who despite finding significant differences in cerebral blood
flow measures in HF patients and controls, there were no associations observed between
middle cerebral arterial blood flow speed and memory domains in HF patients (Vogels et al.,
2008).
11.10.4 Executive function
The prediction that executive function will be related to reduced cerebral blood flow (H12)
was partially supported. Improved performance on the incongruent Stroop but not the Trail
Making-B tasks, measuring executive function, was related to reduced common carotid
arterial blood flow velocity. However, none of the executive function measures were related
to middle cerebral arterial blood flow velocity in either group. These findings support those
of Vogels et al. (2008) who also failed to demonstrate a relationship between executive
function and MCA blood flow velocities in HF patients. Furthermore, supporting the current
findings, Tanne et al. (2005), did not to find significant changes in middle cerebral arterial
blood flow velocity in older patients with moderate HF (NYHA class III) who underwent an
exercise program, despite improvements seen in executive function following. The findings
from the present study indicate that reduced inhibitory control and divided attention in older
HF patients is related to slower cerebral blood flow velocities.
11.11 Relationship between arterial stiffness and cognitive function
The current investigation explored the hypothesis that arterial stiffness will be related to
cognitive tests measuring attention, psychomotor speed, working memory, episodic memory
and executive function (H13). Since previous studies have not explored the relationship
between arterial stiffness and cognitive function in HF patients, it is difficult to interpret the
findings of the current study.
11.11.1 Global cognition
In the present study, global cognitive function was not associated with measures of arterial
stiffness (augmentation index, central pulse pressure) in either of the experimental groups.
Chapter11: Discussion
186
These findings fail to support previous studies that have shown that arterial stiffness as
measured by pulse wave velocity is related to poorer MMSE scores in older individuals
without cardiovascular disease (Fujiwara et al., 2005), older patients with cognitive
impairments (Alzheimer’s Disease, Mild Cognitive Impairment; Hanon et al., 2005), patients
with cardiovascular disease risk factors (Scuteri et al., 2005) and elderly individuals free from
dementia (Poels et al., 2007). The findings from the current investigation suggest that
although HF patients without a history of dementia have significantly reduced global
cognitive scores compared to healthy controls, reduced cognitive function may not be due to
microvascular ischemia in the brain because of arterial stiffness (Mitchell et al., 2001). Future
studies utilizing larger samples sizes are required to substantiate these findings.
11.11.2 Attention and psychomotor function
Slower reaction times on the attention tasks (congruent Stroop and Power of Attention)
correlated strongly with increased central pulse pressures, an indirect measure of arterial
stiffness, in the HF group but not controls. However, there were no associations apparent
between augmentation index and attention or psychomotor speed in either of the experimental
groups, suggesting that less precise measures of arterial stiffness via pulse pressures rather
than augmentation index possibly are related to attention. This suggests that reduced attention
abilities in HF patients may be related to an increase in arterial stiffness as measured by
central pulse pressure, supporting previous studies that did not find that attention and
psychomotor speed is subjected to changes in arterial stiffness (Waldstein et al., 2008). On
the contrary, these findings do not support previous studies that found arterial stiffness as
measured by carotid-femoral pulse wave velocity is related to worse performance on
scanning and tracking tasks (Elias et al., 2009). Future studies using sophisticated and more
reliable measures of arterial stiffness, such as carotid-femoral pulse wave velocities, may
afford better insight into whether arterial stiffness in HF is related to the ability to focus
attention.
11.11.3 Memory
Refuting the hypothesis (H13), arterial stiffness measures (augmentation index, central pulse
pressures) were unrelated to Quality of Episodic Memory, Quality of Working Memory and
Speed of Memory in both experimental groups. Since there are no studies that have examined
arterial stiffness in HF patients, the results from this study cannot be compared directly with
results from previous trials. Although indirectly supporting the current results, longitudinal
trials also failed to find associations between arterial stiffness (PWV) and working memory
(Elias et al., 2009), cognitive decline or dementia risk (Poels et al., 2007) in elderly
Chapter11: Discussion
187
individuals. Conversely, it has been suggested that increased arterial stiffness is related to
lower memory scores in the elderly (Mitchell et al., 2011) and middle aged individuals (Pase
et al., 2010). Equally, the current findings indicate that reduced Quality of Episodic Memory,
Quality of Working Memory and Speed of Memory domains may not relate to arterial stiffness
and microvascular brain lesions caused by extreme pulsatile flow in the brain (Mitchell et al.,
2011) may not be a mechanism for memory impairment in HF patients. Additionally these
results suggest that since patients did not have elevated arterial stiffness, peripheral resistance
might not be a mechanism related to cognitive impairment in HF.
11.11.4 Executive function
Supporting the hypothesis (H13), the present investigation revealed that increased
augmentation index, an indirect assessment of arterial stiffness, was correlated with slower
performance on executive function as measured by Trail Making-B task in the HF group but
not controls. Furthermore, arterial stiffness as assessed by central pulse pressure moderately
related to worse performance on Trail Making-B in HF patients but not in controls.
Interestingly, elevated central pulse pressure was associated with worse performance on
incongruent Stroop in both experimental groups. These findings support previous work
showing that arterial stiffness measurements (common carotid-femoral-pulse wave velocity
and carotid pulse wave velocity) correlate with incongruent Stroop in elderly community
based individuals without dementia (Poels et al., 2007). These results also support previous
findings in which a significant association was observed between elevated pulse wave
velocity and worse performance on executive function as measured by the Stroop test in
elderly community based individuals without dementia (Poels et al., 2007). Similarly, other
studies found an association between executive function and arterial stiffness (CF-PWV,
carotid PP, PWV) in elderly individuals (Elias et al., 2009; Mitchell et al., 2011). On the
contrary, other researchers did not demonstrate a relationship between executive function and
arterial stiffness (Waldstein et al., 2008). The current findings suggest that elevated measures
of arterial stiffness are associated with reduced cognitive flexibility, divided attention ability,
and slower inhibitory responses in HF patients. Since these observations were also observed
in healthy controls, it is possible that the negative effects of arterial stiffness on executive
function are due to the natural ageing process and not unique to heart failure. It is possible
that microvascular lesions due to extreme pulsatile flow in the brain regions (Mitchell et al.,
2011) are a possible mechanism related to executive function impairments in HF patients.
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188
11.12 Relationship between oxidative stress and cognitive function
The hypothesis that oxidative stress, antioxidants, inflammation and omega-3 dietary intake
will be related to cognitive tests measuring global cognition, attention, psychomotor speed,
working memory, episodic memory and executive function (H14) was partially supported.
11.12.1 Global cognition
Using simple regression models, it was found that global cognition as measured by the
MMSE was not related to antioxidant capacity, oxidative stress, inflammation or omega-3
dietary intake in either the heart failure (HF) or control groups. These findings suggest that a
relationship does not exist between global cognitive function and the biomarkers. This
finding supports previous research reporting no association between oxidative stress and
antioxidant biomarkers on MMSE scores, in elderly patients with cognitive impairments such
as mild cognitive impairment or dementia (Gironi et al., 2011). The findings from the present
study support those of Torres et al. (2011) who demonstrated that higher levels of lipid
peroxidation (MDA) and low enzymatic antioxidant defences (as measured by glutathione
reductase/glutathione peroxidase ratio) are not related to poorer global cognitive function in
patients with MCI patients or healthy controls. The results from the present study suggest that
global cognitive function may not be related to increased oxidative stress markers and a result
of increased free radical production.
11.12.2 Attention
Despite significant increases in hydroperoxides as determined by increased DROM levels in
HF patients compared to controls, DROM concentrations were unrelated to measures of
attention and psychomotor function in either group. Furthermore, DROMs were not related to
Power of Attention, however a significant relationship was seen between DROM and
Continuity of Attention, although HF patients did not perform differently to controls on this
cognitive domain, suggesting that this relationship may be due to ageing. Additionally, F2-
isoprostanes, a measure of lipid peroxidation generated during the peroxidation of
unsaturated fatty acids (arachidonic acid) in phospholipid membranes (Mori et al., 1999;
Schwedhelm et al., 2008), were not related to measures of attention, psychomotor speed or
the domains of Power of Attention or Continuity of Attention in either experimental group.
The present results suggest that HF patient’s ability to attend to stimuli and perform tasks
requiring sustained and focussed attention, in addition to psychomotor speed may not be
related to increased oxidative stress markers because of increased free radical production.
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11.12.3 Memory
The findings from the present study did not reveal a relationship between Quality of Episodic
Memory, Quality of Working Memory or Speed of Memory and oxidative stress (DROMs and
F2-isoprostanes) in either experimental group. Although there were no experimental group
differences in the memory domains (Quality of Episodic Memory, Quality of Working
Memory and Speed of Memory), HF patients did indicate worse memory than controls as
measured by the delayed word recall task. Since patients who had reduced memory
performance compared to controls also had elevated oxidative stress markers, these findings
support studies that have shown lower levels of oxidative stress markers (MDA, glutathione,
reduced glutathione) in patients with memory deficits (Gironi et al., 2011). Few studies have
explored the association between oxidative stress and memory domains. Previous studies
have reported that older individuals with memory impairments exhibit elevated levels of lipid
peroxidation as measured by MDA (Gironi et al., 2011; Torres et al., 2011) and F2-
isoprostanes (Praticò et al., 2002) compared to healthy controls. Moreover, researchers have
revealed that older patients with memory impairments have a greater oxidative stress profile
(Praticò et al., 2002; Torres et al., 2011). For example, elevated lipid peroxidation levels as
determined by MDA are higher in patients with MCI compared to healthy elderly controls
(Praticò et al., 2002) and in AD patients compared to MCI patients (Torres et al., 2011).
Additionally, antioxidant administration in healthy older individuals improved memory and
reduced F2-isoprostanes following 3-month intervention suggesting a link between oxidative
stress and memory impairments (Ryan et al., 2008).
Since previous findings suggest that oxidative stress maybe related to memory impairments, a
failure to find a reduction in F2-isoprostane levels in the patient group is expected since the
HF patient group in did not exhibit memory impairments relative to the control group. The
results of the present study therefore suggest that HF patient’s ability to store information in
episodic memory and working memory, and psychomotor functions may not relate to
increased oxidative stress markers because of increased free radical production. A possible
future study may examine whether lipid peroxidation is elevated in patients with mild HF and
mild cognitive impairment. Alternatively, future studies may explore whether lipid
peroxidation in patients with moderate to severe HF are related to cognitive impairment, in
particular attention, Power of Attention and executive function.
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11.12.4 Executive function
The results from the present study partially supported the hypothesis that oxidative stress,
antioxidants, inflammation and omega-3 dietary intake will be related to cognitive tests
measuring global cognition, attention, psychomotor speed, working memory, episodic
memory and executive function (H14). The results from the present study failed to reveal
relationships between oxidative stress measures (DROMs and F2-isoprostanes) and
executive function tests in either the HF or control group. Few studies have explored the
association between oxidative stress and executive function. The results from the present
study suggest that HF patient’s ability to plan and capacity for executive control, may not be
affected by increased free radical production.
11.12.5 Summary
The results from the present investigation suggest that an increase in oxidative stress in the
form of lipid peroxidation and hydrogen peroxides commonly seen in HF may not affect HF
patients’ attentional abilities, memory domains or executive functions. Despite significantly
elevated levels of lipid hydroperoxides as measured by DROMs in the HF group compared to
healthy controls, hydroperoxides were not associated with cognitive impairments in HF
patients. Since the brain is highly susceptible to increased oxidative stress, lipid peroxidation
and potentially damaging end products (Gironi et al., 2011; Mariani et al., 2005) it is possible
that the brains of patients with predominantly mild HF observed in the present study are
protected from oxidative damage. Some authors suggest that during the early stages of
neurodegenerative disease, oxidants and radical products are cleared by antioxidants
preventing cognitive decline (e.g. Gironi et al., 2011). Since majority of patients in the
current study had mild HF, it is possible that in mild HF free radicals are removed by
antioxidants, protecting patients from cognitive decline. However, this hypothesis is based on
circulating oxidative stress markers, which do not represent oxidative stress in the brain.
Although the present study cannot establish the true extent of oxidative damage to the
myocardium and overall free radical damage in the HF patient group, the results suggest that
circulating oxidative stress measures F2-isoprostanes and DROMs do not relate to cognitive
function in mild HF patients. Since greater levels of oxidative stress are seen in patients with
severe heart failure compared to mild or moderate forms of the condition (Keith et al., 1998;
Polidori et al., 2004), future studies examining the effects of cognitive function in HF patients
with greater disease severity are required.
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11.13 Relationship between antioxidant measures and cognitive function
11.13.1 Global cognition
Further exploring the hypothesis that oxidative stress, antioxidants and inflammation are
related to cognitive function in HF (H14), global cognitive function, as measured by MMSE
scores were not associated with antioxidant biomarkers in the heart failure (HF) or control
groups. Since there have been no studies to date that have examined the effects of
antioxidants and global cognitive function in HF it is difficult to discuss the present results.
These findings suggest that global cognitive function may not relate to overall global
cognitive function in HF patients with mild disease severity.
11.13.2 Attention
Further exploring the hypothesis that there will be a relationship between oxidative stress,
antioxidants and inflammation and cognitive tests measuring attention, psychomotor speed
(H14), the present study did find an association between attention tasks and plasma
antioxidant concentrations. In particular, supporting the hypothesis, worse performance on
tasks measuring attention as measured by congruent Stroop and psychomotor function as
measured by the Trail Making-A task, were related to elevated plasma levels of the
antioxidant coenzyme Q10 (CoQ10) in HF patients. When incorporating premorbid IQ and
CoQ10 as covariates in the analysis of covariance model, CoQ10 explained approximately
9% of the variance on congruent Stroop performance. These observations were not observed
in age-matched controls. Moreover, no significant correlations between Power of Attention
and antioxidant markers were observed, suggesting that the attention domain in HF may not
be influenced by antioxidant status. Results from the present study suggest that worse simple
attentional but not sustained attentional processing in these patients may be due to reduced
CoQ10.
11.13.3 Memory
No significant relationships were observed between domains of Quality of Episodic Memory,
Quality of Working Memory or Speed of Memory and antioxidant measures in either
experimental group. Previous studies have shown that patients with memory impairments do
not present with lower coenzyme Q10 (CoQ10) levels compared to healthy controls (Gironi et
al., 2011). The results suggest that reduced antioxidant status does not have an effect on
memory domains assessing working memory, episodic memory or retrieval times to access
information from memory. Future studies examining whether domains of Quality of Episodic
Chapter11: Discussion
192
Memory, Quality of Working Memory or Speed of Memory in patients with moderate and
severe HF are required.
11.13.4 Executive function
In support of the hypothesis that there will be a relationship between antioxidant markers and
cognitive function (H14), in the present study, performance on two of the three executive
function measures correlated with antioxidant plasma levels. In particular, poorer
performance on executive function as measured by incongruent Stroop and Stroop effect was
moderately related to reduced plasma CoQ10 levels in HF patients but not in controls. In
addition, CoQ10 accounted for almost 13% of the variance across groups on executive
function as measured by incongruent Stroop.
Although no previous trial has examined the relationship between CoQ10 and executive
function, animal studies have demonstrated neuroprotective effects of CoQ10 on Alzheimer’s
disease model rats (Ishrat et al., 2006). It is reasonable to suggest that due to inadequate
CoQ10 levels removing free radicals in the brain, a build-up of oxidative stress that damages
the cerebral cortex may impact on HF patient’s executive functions. Findings from the
present investigation suggest that a reduction in plasma CoQ10 levels may be related to HF
patients’ reduced ability to perform higher cortical functions.
11.13.5 Summary
These results suggest that a reduction in coenzyme Q10 levels may relate to reduced
attentional abilities and poor executive function in older HF patients.
11.14 Relationship between inflammatory measures and cognitive function
11.14.1 Global cognition
Further exploring the hypothesis that inflammatory markers and dietary omega-3 intake will
be related to cognitive tests measuring global cognition, attention, psychomotor speed,
working memory, episodic memory and executive function (H14), the present study found that
global cognitive function as measured by MMSE scores was not related to systemic
inflammation as measured by high-sensitive C-reactive protein (hs-CRP). These results fail to
support previous findings demonstrating a negative relationship between inflammatory
measures and global cognition in HF patients (Said et al., 2007) and elderly patients (> 65
years; Athilingam et al., 2012). In particular a previous study found that elevated levels of the
inflammatory markers tumour necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) were
related to worse global cognitive scores in HF patients (Said et al., 2007). Additionally IL-6,
Chapter11: Discussion
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specifically predicted cognitive performance in HF patients (Said et al., 2007). Supporting
these previous findings, another study demonstrated that the pro-inflammatory cytokine IL-6
and systemic inflammation as measured by CRP were associated with global cognitive scores
as measures by the Montreal Cognitive Assessment battery (MoCA) in patients with NYHA
functional class I, II and III (Athilingam et al., 2012). It is possible that the reason why the
present study failed to find similar associations between global cognition and inflammation is
because the assessment tool used to measure cognitive function was different to those used in
previous studies. The present study used the MMSE whereas previous studies utilised the
MoCA (Athilingam et al., 2012) and the Hodkinson Abbreviated Mental Test (AMT; Said et
al., 2007). Additionally, studies that found an association between C-reactive protein and
global cognition included patients with moderate cognitive impairments (Athilingam et al.,
2012). The present study excluded patients with cognitive impairments, hence it is possible
that systemic inflammation may be related to cognition in patients with known cognitive
impairments. These aforementioned studies also failed to include a control group without HF.
Whether the relationships between inflammation and global cognitive function observed in
previous studies related to ageing or the heart failure itself is unknown.
11.14.2 Attention
Refuting the hypothesis (H14), the present study showed that inflammatory markers hs-CRP
did) not predict congruent Stroop mean reaction time, Trail Making-A task or Power of
Attention domain. These results contrast with the findings by Kindermann et al. (2012) who
demonstrated that systemic inflammation was related to poor performance on information
processing speed in patients with decompensated and stable heart failure and healthy controls.
Additionally, results of the present study did not reveal a relationship between dietary omega-
3 polyunsaturated fatty acid (PUFA) dietary intake and attention measures in HF patients.
Since previous studies have failed to examine the effects of dietary PUFA intake and
cognitive domains in HF include attentional abilities, the current results are difficult to
interpret. Earlier studies revealed that supplementing with omega-3 PUFA demonstrated anti-
inflammatory effects in HF patients by reducing TNF-α, IL-1 and IL-6 levels (Nodari et al.,
2011).
11.14.3 Memory
Refuting the hypothesis (H14), the present study indicated that the inflammatory marker hs-
CRP and dietary PUFA intake did not relate to domains of Quality of Episodic Memory,
Quality of Working Memory and Speed of Memory. These results fail to support findings by
Kindermann et al. (2012) who demonstrated that systemic inflammation was related to
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impaired memory in patients with decompensated and stable HF and healthy controls. One
explanation for this discrepancy is that the present investigation tested patients with stable HF
whereas Kindermann et al. (2012) included patients with decompensated HF patients who are
more likely to have memory impairments and increased inflammation. Additionally,
Kindermann et al. (2012) found that patients with stable heart failure are impaired in working
memory and episodic memory scores compared to controls. Since the present study failed to
find significant differences between domains of Quality of Episodic Memory, Quality of
Working Memory and Speed of Memory between the HF patients and controls it is possible
that inflammation may relate to memory in patients who are impaired in memory domains.
However, future studies are required to test this hypothesis. The results from the current
investigation suggest that inflammation may not be related to a reduction in memory
impairments in HF.
11.14.4 Executive function
The results in the present study showed that inflammatory markers hs-CRP and dietary PUFA
intake did not relate to Trail Making-B, incongruent Stroop or Stroop effect. These results fail
to support the findings of Kindermann et al. (2012) who demonstrated that systemic
inflammation was related to Stroop interference (Stroop effect) in patients with
decompensated HF, compensated HF and controls. Similar to Kindermann et al. (2012), the
present results showed that stable HF patients and controls performed similarly on the Stroop
effect measure for executive function. Further to this, Kindermann et al. (2012) showed that
patients with decompensated HF performed worse on executive function compared to
patients with stable HF and controls. One explanation for the discrepant results between these
studies is that the current investigation tested patients with stable HF whereas Kindermann et
al. (2012) included patients with decompensated HF patients who are more likely to have
memory impairments and increased inflammation. Results from the present study suggest that
increased systemic inflammation may not relate to executive control in HF patients.
However, future studies using a larger sample size to explore the relationships between
systemic inflammation and executive function in patients with stable HF are required.
11.15 Relationship between vascular, oxidative stress, antioxidant and inflammatory
measures and cognitive function
The following section discusses analysis of covariance models, which examined the extent to
which vascular, oxidative stress, antioxidant and anti-inflammatory measures explain the
variance in the attention, memory and executive function measures in HF patients and
controls. Multiple regression was used to explore which biomarkers were the best predictors
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195
of the cognitive outcome measures in the HF group. However, due to the small sample size,
interpretation of these results are made with caution.
As mentioned previously HF patients displayed impairments in selective attention as
determined by the congruent Stroop. Slower reaction times on selective attention tasks were
related to slower cerebral blood flow velocity as detected by blood flow speed through the
left common carotid artery. In addition, poorer selective attention in HF related to increased
arterial stiffness as measured by elevated central pulse pressures and lower levels of the
antioxidant and energy synthesiser coenzyme Q10.
Examining multivariate models, unlike the covariate premorbid IQ, which did not
significantly account for the variance in congruent Stroop reaction time, common carotid
arterial blood flow velocity predicted performance on attention tasks in HF patients as
determined by congruent Stroop reaction times. In addition, there was a trend for CoQ10 to
account for some of the variance in congruent Stroop reaction times. In a second multivariate
analysis, central pulse pressure was a moderate predictor of congruent Stroop, and when
combined with premorbid IQ and common carotid arterial blood flow velocity, the model
predicted 63% of the variance in attention in HF patients. Due to the low statistical power
because of the small sample size, interpretation of these finding are exploratory and larger
studies are required to confirm these findings. Preliminary results from the present
investigation raise the possibility that interventions that increase blood flow velocities in the
common carotid artery, treating CoQ10 deficiencies or increasing central pulse pressures
may help increase attention abilities in HF patients.
As mentioned previously, HF patients displayed an impaired ability to focus their attention as
determined by the Power of Attention cognitive domain. In HF patients, a reduced ability to
sustain attention on simple reaction time and vigilance tasks for prolonged periods were
related to vascular measures. In particular, the results from the present study indicate that
reduced performance on the Power of Attention cognitive domain is related to slower blood
flow speed through the common carotid artery. This suggests that HF patients’ reduced
ability to sustain attention for prolonged periods and during tasks requiring selective
attention, is explained by a reduced cerebral blood flow rate. Additionally, reduced ability to
be vigilant and sustain attention over a prolonged period in HF patients was related to
increased arterial stiffness as measured by central pulse pressures. When these variables were
included in a prediction model, after adjusting for premorbid IQ, there was a trend for
common carotid arterial blood flow velocity to relate moderately to Power of Attention in HF
patients. When the arterial stiffness measure central pulse pressure was added to the model, it
Chapter11: Discussion
196
was a significantly greater predictor for the Power of Attention cognitive domain significantly
contributing to an additional 41% (R2 change = .41) of the predictive value.
The results from the current investigation suggest that HF patients reduced ability to sustain
attention for prolonged periods and during tasks requiring selective attention, relates to a
reduced cerebral blood flow rate. It is possible that reduced simple attention abilities and
sustained attention in heart failure is related to cerebral deterioration due to microvascular
damage is caused by increased pulse pressure downstream in the vertebral and carotid arteries
(O'Rourke & Safar, 2005). Additionally, based on the findings from the present study it is
conceivable that microcirculatory remodelling due to arterial stiffness may cause
microvascular ischemia in the brain; in this case, affecting the brain regions associated with
attentional processing (Mitchell, 2008). Larger studies using direct measures of arterial
stiffness and such as carotid-femoral pulse wave velocity are required to explore this
hypothesis and to substantiate findings from the current investigation.
As mentioned previously, HF patients displayed impairments in executive functioning
compared to healthy controls. In particular, after adjusting for premorbid IQ, HF patients
demonstrated significant impairment in selective attention and response inhibition
(incongruent Stroop reaction time) and the ability to switch between stimuli (Trail Making-
B). Reduced selective attention and response inhibition in HF was related to vascular and
antioxidant measures. In particular, executive function as measured by incongruent Stroop
was associated with reduced blood flow velocity in the common carotid artery and higher
arterial stiffness as measured by central pulse pressure with medium effects sizes.
Furthermore, slower reaction times on the incongruent Stroop tasks indicating reduced
selective attentional abilities were moderately related to decreased antioxidant status and
energy production as detected by a reduced coenzyme Q10 levels.
Examining multivariate models, unlike the covariate premorbid IQ, which did not
significantly account for the variance in congruent Stroop reaction time, common carotid
arterial blood flow velocity significantly predicted performance on executive function in HF
patients as determined by incongruent Stroop reaction times. Furthermore, when the
covariate CoQ10 was added to the model, there was a trend towards CoQ10 explaining
further variance in incongruent Stroop performance.
Recent evidence suggests there is a relationship between peripheral arterial stiffness and
haemodynamic changes in the middle cerebral artery in individuals with risk for developing
cardiovascular disease (Kwater et al., 2009). Although the present study failed to find
associations between middle cerebral arterial blood flow velocity and arterial stiffness, given
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197
that HF patients share similar risk factors for cardiovascular disease it might be fruitful to
examine the relationships between arterial stiffness and cerebral blood flow in future studies
with a larger sample size.
Heart failure patients are impaired on tasks measuring selective attention, sustained attention,
executive functioning, cognitive flexibility and response inhibition compared to age and sex-
matched controls. These impairments may be related to vascular and antioxidant
physiological dysfunctions that are seen in heart failure patients specifically and not seen in
normal ageing.
That some biomarkers could explain group differences on some domain sub-tests and not
others suggest that these biomarkers assay the integrity (if indirectly) of brain regions that
may be critically engaged for performance of those particular sub-tests, although these same
brain regions may not be critical for performance of all domain sub-tests. That is, specific
tasks within specific cognitive domains may be influenced by different mechanisms. For
example, reduced common carotid arterial blood flow velocity and increased arterial stiffness
(central pulse pressures) may be associated with the congruent Stroop and Power of
Attention domain but not psychomotor speed or Continuity of Attention. Whereas reduced
psychomotor speed (Trail Making-A) and performance on the congruent Stroop task are
possibly related to reduced antioxidant levels. Collectively, impaired attentional abilities may
be associated with reduced blood flow, increased arterial stiffness and reduced antioxidant
capacity
11.16 Summary of the relationships between cognitive function and physiological
measures
The results from the present investigation suggest that HF patients are impaired on the Power
of Attention cognitive domain. Furthermore, determinable reactive oxygen metabolite
(DROM) is an additional oxidative stress measure elevated in elderly HF patients. Taken
together, the results from this study suggest that mechanisms related to cognitive function in
elderly HF patients, in particular simple attention, Power of Attention cognitive domain and
executive function abilities may involve central pulse pressure, common carotid arterial
blood flow velocities and possibly the level of coenzyme Q10. Interventions that improve
central pulse pressures, increase cerebral blood flow and increase circulating coenzyme Q10
levels may improve attention and executive function in elderly HF patients. Larger studies are
required to confirm these mechanisms and appropriate interventions need to be devised to
help improve clinical outcomes in HF patients.
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11.17 Mood measures
The hypothesis (H5) that HF patients will score higher on depression, anxiety and fatigue
measures, and lower on vigour as measured by the Profile of Mood States questionnaire than
controls was supported. The findings from this study demonstrated that HF patients had
approximately twice the levels of tension/anxiety (50%), anger/hostility (51%), fatigue/inertia
(56%) and confusion/bewilderment (45%) than controls. Additionally, there was a trend
towards a greater level of fatigue based on physical (p = .06) and mental symptoms (p = .09)
in HF patients. HF patients were significantly more fatigued overall as measured by the sum
of the physical and mental fatigue measures of the Chalder fatigue scale. Additionally, HF
patients were 71% more depressed and had 31% less vigour compared to controls. Finally,
HF patients had greater Total mood disturbance compared to controls (M=20 versus M= -4).
The findings from the present investigation support previous findings indicating that higher
levels of depression and anxiety in elderly HF patients compared to controls (Almeida, Beer,
et al., 2012; Pressler, Subramanian, et al., 2010b). The present results support previous
reports of a high prevalence of fatigue in elderly HF patients with preserved ejection fraction
(Stephen, 2008). Also, the results from the present study support previous studies that have
also indicated lower of vigour using the POMS questionnaire in female HF patients
(Riedinger et al., 2002).
11.18 Relationship between vascular measures, oxidative stress, antioxidant and
inflammatory measures on depression and anxiety
11.18.1 The relationship between cerebral blood flow and arterial stiffness with depression
and anxiety
No specific hypotheses were made with relation to whether cerebral blood flow or arterial
stiffness has an effect on mood, in particular anxiety and depression in HF patients.
Therefore, the current investigation explored the research question “Is there a relationship
between cerebral blood flow or arterial stiffness and depressive symptoms and anxiety (R1)?”
Despite scoring significantly higher on the POMS depression subset, POMS anxiety subset
and reduced cerebral blood flow in HF patients compared to controls, the results of the
present investigation did not demonstrate a relationship between depression symptoms and
cerebral blood flow in HF patients as measured by common carotid and middle cerebral
arterial blood flow velocities. These findings fail to support those of Alves et al. (2006) who
demonstrated slower cerebral blood flow in HF patients with major depressive symptoms as
measured by the SPECT imaging technique. However, the current findings cannot be directly
compared with those of Alves et al. (2006) as these researchers used a different imaging
Chapter11: Discussion
199
technique to measure blood flow and examined patients with major depressive symptoms. It
is possible that slower blood flow in the medial temporal regions (Alves et al., 2006) but not
in the common carotid or middle cerebral arteries as measured by Transcranial Doppler is
related to depression in elderly HF patients. Nonetheless, further investigation is required to
elucidate the relationships between cerebral blood flow and depressive symptoms and anxiety
in elderly HF patients. The current results suggest that cerebral blood flow speed is not
associated with depression or anxiety in elderly HF patients.
In addition, neither depression scores nor anxiety related to arterial stiffness as measured by
augmentation index or central pulse pressures in either experimental group. These results fail
to support previous findings indicating that early wave reflection as determined by
augmentation index was related to elevated depressive symptoms and anxiety in individuals
with cardiovascular disease (Seldenrijk et al., 2011). Furthermore, although elderly depressed
individuals were found to be more likely to have increased pulse wave velocities in a
previous trial (Tiemeier et al., 2003), the current investigation failed to find an association in
elderly heart failure patients and age-matched controls. Although it has been suggested that
augmentation index is a possible mechanism for linking depression to cardiovascular disease
risk (Seldenrijk et al., 2011), the current study does not support this notion in patients with
stable HF.
11.18.2 The relationship between oxidative stress and antioxidant measures with depression
and anxiety
No specific hypotheses were made in relation to whether oxidative stress or antioxidant
measures have an effect on depressive symptoms and anxiety in HF patients. Therefore, the
research question “Is there a relationship between oxidative stress or antioxidant measures
and depressive symptoms and anxiety? (R2)” was explored in the present investigation.
Exploring this research question, lower levels of lipid peroxidation as measured by F2-
isoprostanes were related to higher scores on the POMS subscale depression/dejection in the
HF group but not in controls. Other measures of oxidative stress (determinable reactive
oxygen metabolites; DROMs), however, were not associated with depression in HF patients.
The current study also showed that higher levels of anxiety/tension over the seven days
preceding baseline testing session were moderately related to lower levels of lipid
peroxidation as measured by F2-isoprostanes in the HF group but not in the controls. Other
measures of oxidative stress (determinable reactive oxygen metabolites; DROMs) were not
associated with anxiety measures in HF patients.
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The finding that increased lipid peroxidation but not DROMs were related to depression
scores partially support findings from previous research. Kupper et al. (2009) also failed to
find an association between serum levels of oxidative stress markers and depressive
symptoms in patients with chronic HF. Other researchers have shown that depressed HF
patients had higher levels of lipid peroxidation (MDA) compared to non-depressed patients
(Michalakeas et al., 2011). Although HF patients in the current investigation did not differ
significantly to controls on F2-isoprostane measures, interpretation of these findings are
therefore made with caution. However since previous studies have found a decrease in
oxidative stress markers in depressed HF patients following treatment with SSRIs which also
have antioxidant actions, it is possible that treating patients with a safe antioxidant will help
improve depressive symptoms. Future randomized, double-blind, placebo-controlled studies
are required. Given that the current investigation excluded patients with clinical depression, a
comparison of oxidative stress markers between depressed and non-depressed patients could
not be made. Further studies using a larger sample size, comparing lipid peroxidation and
reactive oxygen metabolite levels in patients with and without clinical depression will clarify
the role of oxidative stress in HF patients.
The findings from this study support the notion that plasma CoQ10 levels are lower in
patients with depression (Maes et al., 2009), and corroborate the finding of Maes et al (2009)
who did not observe a relationship between CoQ10 and depressive symptoms in HF patients.
However, given that the current study excluded patients with depression, a comparison in
CoQ10 levels between depressed and non-depressed patients could not be made. Taken
together, the present findings suggest that although HF patients have a deficiency in CoQ10
levels, these deficiencies are not related to depression or anxiety measures in these patients.
Moreover, other measures of antioxidants (glutathione peroxidase) were not found to be
associated with depression or anxiety measures in HF patients. The findings from the present
study suggest that lipid peroxides as measured by F2-isoprostanes but not the antioxidant
coenzyme Q10 are possibly related to higher levels of tension/anxiety and
depression/dejection in older HF patients. It is reasonable to suggest that factors that reduce
lipid peroxidation such as antioxidant treatment may decrease anxiety and depression in
elderly patients with HF. Future studies exploring the effects of an antioxidant treatment
shown to be effective in reducing lipid peroxidation levels may are required to examine the
effects of these antioxidants on mood in elderly patients with heart failure.
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11.18.3 The relationship between inflammatory measures and depression and anxiety
No specific hypotheses were made in relation to whether inflammatory measures have an
effect on anxiety in HF patients. Therefore, the research question “Is there a relationship
between inflammatory measures and anxiety in HF patients? (R3)” was explored in the
present investigation. The current investigation failed to find significant associations between
inflammatory markers and tension/anxiety measures in the HF patient group.
It was hypothesised that inflammation as measured by high-sensitive C-reactive protein
(hs-CRP) would be related to depression scores in HF patients (H15). Contrary to expectation,
inflammation as measured by hs-CRP and dietary levels of omega 3 essential fatty acid intake
were not associated with depression scores in HF patients.
Outcomes from the present study fail to support those of Andrei et al. (2007) who found that
hs-CRP levels were higher in elderly HF patients with major depressive disorder (MDD)
compared to patients without MDD. Although the cohort in the present study were free from
clinical depression or other psychiatric disorders, their levels of depression during the 7 days
preceding the baseline testing session was higher than controls. A reason why the present
study failed to find an association between depression in HF patients is because of small the
sample size in the present study. Additionally, the results from the current investigation
support those of Fink et al. (2012) who, despite demonstrating that younger heart failure
patients had higher levels of fatigue and depression than controls, also found a significant
association between fatigue and depression and inflammatory markers (TNF-α, IL-6, CRP or
IL-10), this finding was not replicated in the present study.
Additionally the findings of the present study fail to support previous work observing
elevated inflammatory markers that were related to higher depressive symptoms in HF
patients (Ferketich et al., 2005; Guinjoan et al., 2009). Contrary to the findings of the present
study, previous studies have shown that circulating levels of pro-inflammatory cytokines
TNF-α, but not IL-6 or IL-1, were higher in HF outpatients with elevated levels of depression
(Ferketich et al., 2005). Moreover, higher depression scores in elderly HF patients were
related to higher circulating inflammatory marker IL-6 in these patients (Guinjoan et al.,
2009). Patient cohorts in previous studies examining the relationships between inflammation
and mood in HF were generally younger (56±10 years; Ferketich et al., 2005), had
decompensated heart failure (Guinjoan et al., 2009) and inflammatory measures tested were
pro-inflammatory cytokines. The current study however enrolled an older cohort with stable
HF and measured systemic inflammation not cytokines. Findings from the current
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202
investigation suggest that systemic inflammation as measured by hs-CRP is not a possible
mechanism associated with depression or anxiety in elderly patients with stable HF.
11.19 Possible mechanisms for changes in mood in heart failure
It is clear from the results of the current investigation that HF patients have higher levels of
fatigue, depression, anxiety and reduced vigour. However, it is unclear from the present
investigation if physiological mechanisms are related to elevated levels of depressive
symptoms or anxiety in elderly HF patients. Cerebral blood flow, arterial stiffness, oxidative
stress, antioxidants and inflammatory makers did not relate to depressive symptoms or
anxiety in patients. However, further studies using larger sample sizes comparing these
physiological mechanisms in patients with and without clinical depression will clarify the
role of these physiological mechanisms in mood in HF patients.
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203
CHAPTER 12 STUDY LIMITATIONS, STRENGTHS
AND FUTURE DIRECTIONS
12.1 Study Limitations
There is a range of factors that limit the generalizability of the study findings.
12.1.1 Patient recruitment and sample size
The sample size for this thesis was moderately small. A low sample size reduces the
statistical power of the results therefore limiting the generalizability of findings. It took
approximately 24 months to recruit and test the HF patients for the study. Reaching a sample
size adequate for statistical power was not achievable during this time. The low sample size is
primarily due to challenges with recruiting from this population and further reducing
recruitment rates due to exclusion criteria. In this study, the purpose for excluding patients
with depression and anxiety and those taking anti-depressants was to control for possible
confounding factors known to influence cognitive performance. Since depression is common
in HF, excluding these patients further reduced the ability to recruit patients in a time
efficient manner.
In the present study, the NYHA classification was used as the HF diagnosis, which is an
objective measure of heart failure. Other markers for HF such as plasma brain neurotropic
factor (BNP) levels and left ventricular ejection fraction would have provided a more
accurate HF diagnosis and measure of disease severity. Patients aged 60 years and above
enrolled in this study may not have been able or want to commit the time to travel long
distances if they live far away to attend testing sessions, and/or sit through a 1.5 hour testing
session. The total testing time for each visit took 1.5 – 2 hours (0.45 to 1 hour for cognitive
testing, 0.5 hours for mood and quality of life questionnaires, 0.5 hours for vascular measures
and blood test) despite efforts made to keep the testing session time to a minimum. Patients
who lived far away or had multiple medical appointments and were interested in taking part
in the study preferred that the testing sessions coincide with other appointments at the Alfred
Hospital. However, the day would have been too exhausting and fatiguing for the patient and
some patients had medical appointments at the Alfred 6 or 12 months apart, which was too
long between the training day and baseline-testing visit. Furthermore, HF patients are more
likely to have comorbidities and as a result additional medical appointments, which may
reduce their willingness to commit to taking part in the trial.
Chapter12: Study limitations, strengths and future directions
204
12.1.2 Methodological issues
Due to the small sample size, there were constraints on statistical methods appropriate for
analysing the dataset. Analysis of covariance and regression were appropriate statistical
methods for the sample size. Although the sample size was not suitable to obtain high
statistical power, multiple regression used in this study was included for exploratory purposes
only. A more robust statistical analysis using structural equation modelling would have
provided a superior assessment to help ascertain whether a relationship exists between the
biomarkers, cognition, and mood, and if so, how these variables are linked and influence
other variables. A sample size of at least 100 in each experimental group is required for such
an analysis.
Due to the small sample size and unequal number of participants in each of the NYHA
classifications, there was insufficient statistical power to explore whether cognitive
dysfunction in this sample changed with increasing disease severity. Majority of the HF
patients had mild HF (NYHA class II), therefore the results of the current investigation may
not be applicable to patients with moderate or severe HF.
During the practice session, the CDR test battery was administered twice in patients and four
times in controls. Additionally, the researcher read out instructions to patients for each of the
two training session and the baseline session. The researcher read out task instructions only
once to controls at the first training session. During subsequent training sessions and the
baseline testing session control participants read instructions on the screen. Although there
was some variation in the administration of the CDR tasks, repeated measures analysis of
variance indicated that there were no group x time interactions for each of the CDR cognitive
domains assessed at the practice and baseline testing days.
Another limitation is the observational nature of the study design, which may contribute to
bias and inhibit generalisation of the research findings. However, the aim of the current study
was to acquire knowledge about the mechanisms associated with cognitive impairment in
heart failure and generate hypotheses for future experimental randomised controlled trials.
Although this study controlled for possible extraneous factors known to influence cognitive
function such as depression diagnosis, anxiety, psychiatric disorders, anti-depressant and
anti-psychotic medication and dementia, additional factors may have explained poorer
performance of cognitive function in HF patients. Poor sleep has been shown to effect
cognitive function and researchers have proposed that poor sleep negatively impacts on HF
patients’ cognitive function and self-care (Riegel & Weaver, 2009). A short sleep
questionnaire aimed to ascertain the quality of sleep the night before each testing session and
Chapter12: Study limitations, strengths and future directions
205
a typical night sleep may be useful in providing insight into whether patients sleep quality
differs to those of controls and if so whether inferior performance on cognitive tasks may
have been due to lack of sleep. Given that poor sleep results in daytime fatigue, it is
reasonable to suggest that the fatigue measures (POMS-fatigue subscale and the Chalder
fatigue questionnaire) administered in this study would have captured fatigued caused by
sleepiness.
Certain risk factors for HF are shared risk factors for diseases of cognitive impairment. This
study did not explore shared risk factors such as hypertension, atherosclerosis and history of
smoking as factors possibly contributing to poor cognitive performance in HF. Additionally,
this study failed to explore how these risk factors may have contributed to group variations of
oxidative stress, antioxidant, and inflammatory markers. Other shared risk factors may have
contributed to vascular changes such as in pulse pressures, augmentation index and cerebral
blood flow. For example, diabetes is linked to impaired cognitive function and increased
pulse pressure and arterial stiffness. A comprehensive assessment of participants’ medical
history may have provided useful data regarding how these risk factors contributed to
cognitive decline.
Other confounding variables not accounted for in this study included medications taken by
HF patients and controls. Unlike the healthy control group, HF patients take medications such
as digoxin (2009) and ACE-inhibitors (Zuccalà et al., 2005), which are known to influence
cognitive function. These are standard medications taken by patients and it would have been
unreasonable to exclude patients taking these medications.
12.1.3 Biological markers
In regard to measuring cerebral blood flow velocity, carbon dioxide challenge is a preferred
method of measurement. Due to financial limitations, equipment to undertake carbon dioxide
challenge was not available for the present investigation. Additionally, to avoid using
different devices at different testing sites the researcher transported the TCD and
SphygmoCor® devices to the two testing sessions. Transportation of the CO2 tank was not
practical for this work and measuring resting TCD measurements was better than not taking
these measurements at all.
Three different researchers administered the TCD and SphygmoCor® for the controls and
only one for the HF group. This raises the possibility for inconsistent measurement error
between researchers. Additionally, patients tended to be more agitated and tired than controls,
and their agitation made it difficult for them to sit still which is required in order to obtain a
clear adequate signal especially for the SphygmoCor® measurement, resulting in some
Chapter12: Study limitations, strengths and future directions
206
missing data. It is worthwhile considering testing patients in a supine rather than an upright
position in future studies when taking vascular measurements, as this would have made
patients more relaxed and prevented missing data. Additionally, it was difficult to obtain a
SphygmoCor® measurement in patients with atrial fibrillation and irregular heart rate leading
to additional missing data.
High-sensitive C-reactive protein (hs-CRP) blood samples were analysed at two different
pathology laboratories and this may have led to increased unreliability. Further, although hs-
CRP is a viable measure of systemic inflammation, an assessment of interleukins (IL-6 and
IL-10) would have provided a better assessment of inflammation. Furthermore, examining
fatty acid by means of omega-3 index in fasting blood samples would have been a preferred
assessment of omega-3 in the blood rather than a questionnaire to determine dietary intake.
Additionally, future studies exploring other measures of oxidative stress (e.g. urinary F2-
Isoprostanes, malondialdehyde, glutathione or reduced glutathione) and antioxidants (e.g.
superoxide dismutase, vitamins C, E and A, α-carotene and β-carotene) would enhance the
understanding of the oxidative stress and antioxidant profiles associated with cognitive
function in heart failure patients.
12.2 Study strengths
Despite the limitations discussed above, the present investigation had various strengths.
Firstly, the present study compared cognitive performance and mood measures in HF patients
to those of a control group matched for age and gender. The results from the present study
therefore reflect impairment of cognitive performance, in particular attentional processing
and executive function and mood deficiencies in HF patients themselves that are independent
of the cognitive decline seen in the normal aging process.
Secondly, including the CDR test battery enabled assessment of additional cognitive domains
that may be vulnerable in HF and other domains not detected in previous studies. This study
revealed that Power of Attention is an additional cognitive domain impaired in HF patients.
Although patient recruitment was challenging due to the reasons outlined above, involvement
of the cardiologists and nurses at the Alfred Heart Centre, Heart Failure Clinic, aided
recruitment. Cardiologists at the Alfred Heart centre, Heart Failure Clinic, would
occasionally introduce a potentially suitable patient to the researcher after their appointment.
Further, two cardiologists were co-investigators in the study. These factors may have
Chapter12: Study limitations, strengths and future directions
207
provided confidence in patients to communicate with the unfamiliar researcher and obtain
information about the study, thereby boosting recruitment.
Testing rooms were on the same level as the Alfred Heart Centre, Heart Failure Clinic where
patients have their regular cardiology appointments. The testing environment was therefore
familiar to the patients and the same receptionists who greet patients at their medical
appointments greeted them when they attended their testing sessions and paged the researcher
informing them that the patient had arrived and the researcher was able to meet them
immediately. It is reasonable to suggest that the familiar environment minimised patients’
anxiety levels and they felt comfortable during the testing session.
An additional strength of this study was the inclusion of multiple oxidative stress and
antioxidant biomarkers to explore their relationship with cognitive function. Given the
complexity of the oxidative stress and antioxidant biochemical pathways, examining multiple
rather than single biomarkers has provided an indication of which biomarkers are specifically
relevant. If this study only examined one biomarker, an incomplete view of the relationship
between these biomarkers and cognitive function would have been achieved. Owing to the
complexity and the interrelatedness of the oxidative stress and antioxidant pathways, some
authors have referred to the importance of examining multiple rather than single biomarkers
(e.g. Gironi et al., 2011). Examining a battery of oxidative stress and antioxidant biomarkers
will provide a superior overview of how these biomarkers are related to each other, and
moreover with cognitive function. Since no study to date has explored the effects of oxidative
stress and antioxidant biomarkers on cognitive function in HF this component of the study
was novel.
The present study incorporated a practice session. A practice session on a day separate to
baseline testing day enabled participants to become familiar with the cognitive tasks and
minimise factors including anxiety and training effects, shown to influence performance on
neuropsychological testing. The data collected at baseline therefore was a more accurate
representation of participant’s actual cognitive performance. The Cognitive Drug Research®
(CDR) test battery incorporates a training session separate to the actual testing sessions to
minimise practice effects.
The present study utilised the CDR, which is a comprehensive cognitive assessment battery
specific to detecting cognitive changes in ageing. The CDR was used to ascertain whether
additional cognitive domains are impaired in HF patients.
Furthermore, in the present study, patients classified as NYHA class I were excluded.
Patients with NYHA class I are asymptomatic and without any physical limitations; since
Chapter12: Study limitations, strengths and future directions
208
their symptoms for HF are well controlled, they do not present with clinical symptoms of HF.
Previous studies that have examined cognitive function HF have included patients without
symptoms of HF (NYHA class I; e.g. Almeida, Beer, et al., 2012; Pressler, Subramanian, et
al., 2010b; Riegel et al., 2002). It is possible that cognitive dysfunction may not be apparent
in patients with NYHA class I and as a result, a true account of cognitive function from
patients with mild moderate or severe heart failure also included in the study may be masked.
However, although patients with NYHA class I are asymptomatic and their condition is well
controlled, they may be taking common HF medications which may affect cognitive function
in these patients. Future studies need to ascertain whether HF pharmaceuticals taken over
time have detrimental effects on patients cognitive function and mood and whether these
pharmaceuticals influence vascular measures such as arterial stiffness and cerebral blood
flow, as well as oxidative stress, antioxidant capacity and inflammation.
12.3 Directions for future research
A key finding in the present investigation was that older patients with heart failure have
impairments in the Power of Attention cognitive domain indicating that patients are deficient
in their ability to sustain their attention for a prolonged period. Additional studies utilizing a
larger sample size are required to confirm these findings.
This study also indicated that HF patients are impaired in attention and executive function
and that cerebral blood flow, arterial stiffness and antioxidant levels may be related to these
cognitive processes in HF patients. Future studies employing a larger sample size, preferably
in a multicentre trial, will help substantiate these findings. Furthermore, a larger sample size
of at least 100 HF patients will provide greater power to incorporate robust multiple
regression models and to confirm possible biomarker predictors for cognitive impairment. A
larger sample size will also permit application of other statistical methods such as structural
equation modelling, which will provide further information regarding how predictor variables
are related to each other and whether they directly or indirectly relate to cognitive function, in
particular attentional and executive functions.
Additionally, future studies incorporating a longitudinal study design may be able to profile
HF patients in relation to biochemical, psychosocial, mood, lifestyle and cardiovascular risk
factors that may help predict worsening of cognitive function over time. Establishing a profile
of HF patients who are at risk of cognitive decline, may provide information about which
preventive interventions may be suitable and then later tested. Longitudinal studies may also
provide an understanding of how cognitive function in HF changes in relation to disease
Chapter12: Study limitations, strengths and future directions
209
severity, and whether vascular and biochemical biomarkers are related to any changes in
cognitive function over time.
Future studies combining neuropsychological assessments, neuroimaging such as functional
magnetic resonance imaging, and the biomarkers examined in the present study, arterial
stiffness, oxidative stress, antioxidant capacities and inflammation, will provide a better
understanding of the neural mechanisms underpinning cognitive dysfunction and associated
mechanisms. Brain imaging studies will also help detect brain regions affected by blood flow,
oxidative stress and whether these brain regions are related to attentional and executive
function processes. The present study examined augmentation index and pulse pressure as
indirect measures of arterial stiffness. Future studies employing carotid-femoral PWV, a
direct measure of arterial stiffness, will provide additional information regarding how this
measure is related to cognitive function and whether it is related to other biomarkers
including oxidative stress, antioxidant capacity and inflammation.
Additionally, larger studies are required to explore how vascular and antioxidant mechanisms
are associated with cognitive impairment across disease severity. Studies with larger sample
size and with equivalent numbers of participant in NYHA class II, III and IV will help
understand how these cognitive domains, in particular Power of Attention, and related
biological markers change with increasing disease severity.
Other factors known to affect cognitive function including comorbidities, sleep and
pharmaceutical drugs commonly taken by HF patients, and how vascular, oxidative stress,
antioxidant and inflammatory measures are related to these factors can be tested in future
studies. For example, future studies may incorporate the comorbidity index to get an
overview of how comorbidities effect cognitive performance in patients and a sleep
questionnaire such as the Pittsburgh Sleep Quality Index for an overview of whether sleep in
an additional factor effecting cognitive function and whether biomarkers are related to poor
sleep quality.
The present study excluded individuals with dementia, depression, anxiety and other
psychological conditions to control for factors known to affect cognitive performance. Since
depression is prevalent in HF, future studies are required to examine the physiological
mechanisms, in particular cerebral blood flow, oxidative stress, antioxidants and
inflammatory markers, in HF patients with and without clinical depression. Additionally, an
examination of which biological mechanisms are related to cognitive function in patients with
mild cognitive impairment and dementia will provide a better understanding of cognitive
impairment in these patients.
Chapter12: Study limitations, strengths and future directions
210
It is still unknown whether medications commonly taken by HF patients such as ACE-
inhibitors, digoxin and diuretics influence cognitive function in HF patients. Therefore,
longitudinal studies aimed at monitoring cognitive function and modifications to
pharmaceutical treatments and dosages may help ascertain whether pharmaceutical drugs
affect cognitive function in HF patients.
Findings from the present study suggest that reduced cerebral blood flow, increased arterial
stiffness as measured by central pulse pressure and low plasma coenzymeQ10 levels are
related to attention and executive function in elderly HF patients. New evidence has emerged
demonstrating an association between cerebrovascular impairment and obstructive sleep
apnea syndrome in AD patients (Buratti et al., 2014). Future studies assessing whether a
relationship between cerebrovascular impairment and sleep dysfunction in heart failure
patients is a possible mechanisms for cognitive impairment are required.
Future studies examining interventions using safe and effective treatments aimed at
addressing each of the reported mechanisms (reduced blood flow, arterial stiffness and low
antioxidants) rather than a single pathophysiological pathway shown to be associated with
cognitive impairments in HF are required. For instance, cognitive enhancers known to
improve blood flow, reduce arterial stiffness and/or increase levels of coenzyme Q10 in heart
failure patients are likely interventions to improve cognitive function in HF patients.
Such interventions may include the Ayurvedic herb Bacopa monnieri, which has been shown
to improve attention and cognitive processing following 12 weeks daily intervention in older
individuals (Peth-Nui et al., 2012). Mechanisms by which Bacopa monnieri improves
cognitive function may be due to increasing antioxidant levels in the prefrontal cortex and
hippocampus (Stough et al., 2008), and reduce amyloid-β peptides as demonstrated in animal
studies (Holcomb et al., 2006). Additionally, polyphenols present in a flavonol-rich cocoa
drink (990mg/day) for example increase blood flow velocity in the middle cerebral artery and
decrease lipid peroxidation in adults (Desideri et al., 2012). Future randomised, double blind,
placebo-controlled studies exploring the effects of interventions such as Bacopa monnieri and
flavonols on cognitive function in HF are required.
In addition, coenzyme Q10 supplementation has been previously shown to have positive
effects on symptoms related to heart failure (Keogh et al., 2003) and animal studies have
revealed possible neuroprotective effects related to cognition (Ishrat et al., 2006).
Furthermore, CoQ10 supplementation in HF patients has been shown to improve plasma
levels of CoQ10 (Folkers et al., 1992; Keogh et al., 2003), improve NYHA functional class
by -.05 (Keogh et al., 2003), and improve ejection fraction, cardiac output and quality of life
Chapter12: Study limitations, strengths and future directions
211
(Sander et al., 2006). These aforementioned trials have shown that CoQ10 is tolerated well
and produces no known side effects in cardiac patients taking their regular pharmaceutical
treatments. Given that CoQ10 is beneficial for cardiac symptoms and wellbeing in HF, is an
antioxidant and has neuroprotective effects in areas of the brain related to memory, it is
reasonable to suggest that CoQ10 may be important in reducing cognitive deficits in HF
patients.
Even though some studies have examined the effects of oxidative stress and anti-
inflammatory treatments (e.g. CoQ10, n-3 PUFA) on cardiovascular factors and quality of
life in cardiac conditions, no study to date has tested whether these interventions positively
influence cognition, mood or patient self-care in HF. It is clear that multiple etiologies
contribute to cognitive impairment in HF, hence treatment of this condition must therefore
target more than one pathophysiological pathway aimed at improving vascular function,
cardiac symptoms, cerebral blood flow, and reducing inflammation and oxidative stress.
CoQ10 and n-3 PUFAs have been administered in cardiac patients without any side effects or
interactions with other medical treatments and have been well tolerated. In combination,
these products have anti-inflammatory, antioxidant and vascular benefits making them ideal
treatments for addressing each of the mechanisms also associated cognitive impairment in
these HF patients.
Future studies examining the effects of CoQ10 and omega-3 PUFAs employing a
randomized, parallel groups, multi-centre, double blind, placebo controlled design is
supported. The effects of these interventions following a minimum of three months and
ideally 12 months on neuropsychological measures (attention, executive function, episodic
and working memory), cardiovascular factors (e.g. NYHA class, LVEF), oxidative stress
(F2-isoprostanes, DROMs), antioxidant levels (CoQ10, glutathione peroxidase) and
inflammation (hs-CRP, TNF-α, IL-1 and IL-6) compared to baseline, will be an important
study aimed towards a possible intervention for improving cognitive function and mood in
HF patients.
References
212
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Appendices
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List of Appendices
Appendix A Human Research Ethics Committee approval i
Appendix B Participant Information and Consent Form for the Heart Failure Group iv
Appendix C Cognitive Drug Research task instructions xi
Appendix D Computerised congruent and incongruent Stroop task instructions xv
Appendix E The Profile of Mood States Questionnaire xvi
Appendix F The Short Form (36 Item) quality of Life Questionnaire xvii
Appendix G The Chalder Fatigue Scale xx
Appendix H The General Health Questionnaire (12 Item) xxi
Appendix I Speilberger’s State Trait Anxiety Inventory xxii
Appendix J Polyunsaturated Fatty Acid Questionnaire xxiv
Appendix K Data screening: demographic variables xxv
Appendix L Frequency for common medications taken by heart failure patients and
controls presented as n (%) xxvi
Appendix M Frequency table for comorbidities in HF and Control groups xxvii
Appendix N Common pharmaceuticals including over the counter medicines and
supplements taken by participants xxviii
Appendix O Common natural medicines and supplements taken by participants xxix
Appendix P Data screening for baseline Stroop word colour tasks, Trail making-A,
Trail making-B and the five cognitive drug research factors xxx
Appendix Q Data exploration for mood variables to test assumptions for analysis of
variance xxxi
Appendix R Data exploration for vascular, oxidative stress, antioxidant and
inflammatory variables to test assumptions for ANOVA xxxii
Appendix
x A Human RResearch Eth
hics Commit
ttee approvaal
AAppendices
i
Appendix
Appendix
x A cont’d…
x A cont’d…
Human Res
Human Res
search Ethic
search Ethic
cs Committee
cs Committee
e approval
e approval
AAppendices
ii
Appendix
x A cont’d… Human Res
search Ethic
cs Committee
e approval
AAppendices
iii
Appen
ndix B Particcipant Inform
mation and C
Consent For
rm for the H
A
Heart Failure
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iv
e Group
Appendix
x B Participaant Informat
tion and Con
nsent Form ffor the Hear
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rt Failure Gr
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roup
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x B cont’d…PParticipant IInformation
n and Consennt Form for
A
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Failure
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A
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Failure
AppendixGroup
AppendixGroup
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x B cont’d…P
Participant I
Participant I
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Failure
AppendixGroup
x B cont’d…PParticipant IInformation
n and Consennt Form for
A
the Heart F
Appendices
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Failure
Appendix
x C Cognitivee Drug Rese
earch task in
structions
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Appendix
x C cont’d…Cognitive Drug Researc
ch task instruuctions
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x C cont’d…Cognitive Drug Researc
ch task instruuctions
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Appendix
x C cont’d…Cognitive Drug Researc
ch task instruuctions
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Appendix D Computerised congruent and incongruent Stroop task instructions In this task you will need to respond to a series of coloured words.
A series of words will appear on the screen one at a time. These will be the words BLUE, RED, GREEN, and YELLOW. The words will be presented in matching colours, BLUE, RED, GREEN, and YELLOW.
You need to respond to each word by pressing the corresponding coloured button as quickly as possible.
Since you will need to use all four coloured buttons for this task, you might want to re-position the button box so you can press all four buttons easily.
We want you to make accuracy your priority but also try to be quick to respond.
We will do a short practice first. Any questions?
[Run practice task]
How was that? Any questions?
[Run full task]
How was that? Any problems?
Stroop Incongruent task
The next task will test your ability to ignore a distraction.
A series of words will appear on the screen one at a time in the same manner as in the previous task. Again, these will be the words BLUE, RED, GREEN, and YELLOW. The words will be presented in different colours, either BLUE, RED, GREEN, and YELLOW. However, this time the colour will not match the written word.
Your job is to ignore the written word and focus on the colour that it is presented in. Respond by pressing the corresponding button.
Again, we want you to make accuracy your priority but also try to be quick to respond.
We will do a short practice first. Any questions?
[Run practice task]
How was that? Any questions?
[Run full task]
How was that? Any problems?
Appendix
x E The Proffile of Mood
States Ques
stionnaire
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Appendix
1.1
x F The Shorrt Form (36 Item) quality
ty of Life Quuestionnaire
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Appendixx F cont’d…The Short FForm (36 Item
m) quality off Life Questi
A
ionnaire
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Appendixx F cont’d…The Short FForm (36 Item
m) quality off Life Questi
A
ionnaire
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Appendix
x G The Chalder Fatigue
e Scale
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Appendix
x H The Genneral Health
Questionna
ire (12 Item))
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Appendix
x I Speilbergger’s State Tr
rait Anxiety
Inventory
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Appendix
x I cont’d Speilberger’s S
State Trait A
Anxiety Invenntory
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Appendix
x J Polyunsaaturated Fatty
ty Acid Ques
stionnaire
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xxiv
Appendices
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Appendix K Data screening: demographic variables
i) Missing Values and Univariate outliers
Missing values for demographic (age, sex, education, handedness and estimated IQ), clinical considerations (NHYA class, diagnosis or no heart failure) and screening criteria (MMSE < 24) were explored using Missing Value Analysis (MVA) in SPSS. There were no missing values for demographic, clinical characteristics and screening variables. To detect univariate outliers, standardised Z scores were calculated for each demographic measure for the two experimental groups. None of the standardised scores for age, MMSE and WASI Vocabulary subset (premorbid IQ) for each group exceeded the α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007).
ii) Normality
Examining skewness and kurtosis, inspection of probability plots of residuals. Age and MMSE did not meet the assumptions of normality and non-parametric tests were therefore used to assess group differences for these variables. WASI Vocabulary subset (premorbid IQ). Basal metabolic index (BMI), and GHQ-12 and heart rate were normally distributed in each group therefore the Student’s t-test was conducted to explore group difference for these variables.
Appendices
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Appendix L Frequency for common medications taken by heart failure patients and controls presented as n (%)
Heart failure Healthy control
n = 36 n = 40
Diuretic 32 (89) 0
Aldostrone antagonist 14 (39) 1 (2.5)
B-blocker 29 (81) 1 (2.5)
Warfarin 19 (53) 0
Digoxin 13 (36) 0
Statins 23 (64) 2 (5)
Potassium chloride 11 (31) 0
ACEI (antihypertensive) angiotensin II blocker 21 (58) 4 (10) Angiotensin II receptor antagonist 15 (42) 1 (3) CARTIA, (anticoagulant) 2 (5.6) 0 other anticoagulant 1 (2.8) 0 Calcium channel blocker (anti-hypertensive) 4 (11) 0 Antiarrhythmic (e.g. amiodarone) 11 (31) 0 Statins (hypolipid Agents) 23 (64) 2 (5) Aspirin (anticoagulant) 16 (45) 4 (10) Alpha blockers 3 (8.3) 0 Slow K (KCl-potassium supplement) 11 (31) 0 Plavix - (platelet aggregation inhibitor) 2 (6) 0 Imidure/nitrate 1 (3) 0 Cardiotonic agent (e.g. Coloran) 1 (3) 0
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Appendix M Frequency table for comorbidities in HF and Control groups
displayed as n (%)
Heart failure Healthy control
n = 36 n = 40
High blood pressure - slight 5 (14) 2 (5)
High blood pressure - controlled 6 (17) 6 (15)
Cholesterol 11 (31) 0
Respiratory 3 (8.3) 4 (10)
stomach/intestinal 5 (13.9) 4 (10)
Liver problems 0 1 (2.5)
Kidney/urinary 1 (2.8) 2 (5)
Diabetes 4 (11.1) 0
Anaemia 0 0
Hemochromatosis 0 1 (2.5)
Epilepsy/fitting 1 (2.8) 0
Cancer/remission/past 3 (8.3) 4 (10)
Cancer current 1 (2.8) 0
Skin disorders 1 (2.8) 1 (2.5)
Anxiety/depression 0 2 (5)
Arthritis 10 (27.8) 1 (2.5)
Gout 6 (16.7) 0
Oedema 1 (2.8) 0
Shortness of Breath 2 (5.6) 0
Hyperthyroidism 2 (5.6) 0
Hypothyroidism 1 (2.8) 0
Depression, not diagnosed 1 (2.8) 0
Sleep apnoea 1 (2.8) 1 (2.5)
Poor Sleep 1 (2.8) 0
High alcohol use in past 1 (2.8) 0
Osteoporosis 2 (5.6) 0
Vertigo 1 (2.8) 0
Other 7 (19.4) 0
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Appendix N Common pharmaceuticals including over the counter medicines and supplements taken by participants displayed as n (%)
Heart failure Healthy control
n = 36 n = 40
Viagra 1 (2.8) 0 Vasodilator 3 (8.3) 0 Glucocorticoid 4 (11.1) 0 Corticosteroid 1 (2.8) 0 Gout treatment 5 (13.9) 0 Insulin 2 (5.6) 0 Cholesterol lowering 6 (16.7) 2 (5) Fluticasone/salmeterol inhaler/sibicord 2 (5.6) 0 COX 2 inhibitor (anti-inflammatory) 0 1 (2.5) Proton pump inhibitor (e.g. Somac) 7 (19) 2 (5) Thyroxine 3 (8.3) 0 Panadol/Paracetamol 1 (2.8) 1 (2.5) Analgesic 2 (5.6) 0 Hormone replacement therapy 1 (2.8) 0 Panadol - Osteo 3 (8.3) 1 (2.5) Alendronate 0 1 (2.5) Antibacterial 1 (2.8) 0 Imodium 0 1 (2.5) Anti-hypertensive 3 (8.3) 2 (5) Antiasthma 0 1 (2.5) Antacid 3 (8.3) 1 (2.5) Asthma puffer (as needed) 1 (2.8) 2 (5) Immune-suppressant 1 (2.8) 0 Antibiotic 1 (2.8) 1 (2.5) Anti-inflammatories 1 (2.8) 0 Cholesterol lowering 2 (5.6) 3 (7.5) Keppra (grand mal epilepsy) 1 (2.8) 0 Antidepressant (Tricyclic) 1 (2.8) 0 Antidepressant (SSRI) 1 (2.8) 0 Benzodiazapine (anti-anxiety) 1 (2.8) 0
Appendices
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Appendix O Common natural medicines and supplements taken by participants displayed
as n (%)
Heart failure Healthy control
n = 36 n = 40
Lipic Acid 0 1 (2.5) Folic Acid 0 1 (2.5) Arginine 0 1 (2.5) CoQ10 current 2 (5.6) 3 (7.5) Lysine 0 1 (2.5) Fosamax D 0 4 (10) Cranberry 0 1 (2.5) Selenium 0 1 (2.5) Tumeric powder 0 1 (2.5) Royal jelly 0 1 (2.5) Aloe Vera 0 1 (2.5) Zinc 0 1 (2.5) Saw Palmeto 0 0 Glucosamine 2 (5.6) 9 (22.5) Iron supplement 2 (5.6) 1 (2.5) Fish Oil 2 (5.6) 14 (35) Vitamin D 1 (2.8) 5 (12.5) Vitamin B complex 0 3 (7.5) Vitamin B12 shots (every 2 months) 1 (2.8) 2 (5) Multivitamin 1 (2.8) 5 (12.5) Vitamin E 1 (2.8) 2 (5) Spirulina 0 1 (2.5) Vitamin C 0 5 (12.5) Thiamine 1 (2.8) 0 Magnesium 5 (13.9) 4 (10) Calcium/Caltrate 4 (11.1) 10 (25) Garlic 0 1 (2.5)
Appendices
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Appendix P Data screening for baseline Stroop word colour tasks, Trail making-A, Trail making-B and the five Cognitive Drug Research factors
i) Missing Values
The variables were assessed separately for each of the experimental groups. For the HF group, Missing value analysis (MVA) revealed one missing case for each of the Stroop variables (congruent Stroop, incongruent Stroop and Stroop effect) and no missing values for Trail Making-A, Trail Making-B or CDR tasks. The missing case for the Stroop variables was due to the button box not working for the participant on the data collection day and this case was therefore deleted from the analysis exploring Stroop colour task variables.
For the Control group, there were no missing values for the Stroop tasks, Trail Making-A or Trail Making-B. However, there were 13 missing CDR task variables and two for each of the CDR factor scores. Since these missing variables were all from the same case, this participant was included when analysing the other three CDR factor scores.
ii) Univariate outliers
Standardised Z scores for each demographic measure for the two experimental groups determined univariate outliers. The α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007) was used to determine outliers for every cognitive variable in each experimental group. Standardised z scores detected an outlier for each of the following variables in the HF group: congruent Stroop percentage accuracy, incongruent Stroop percentage accuracy and Stroop effect variables. Additionally, in the control group one case was an outlier for the Stroop effect variable. Finally, there was one outlier in the HF group for the time it took to complete the Trail Making-A task. In the HF group, there was one outlier for the Continuity of Attention CDR domain. In the control group, there was one outlier for each the Quality of Working Memory and Continuity of Attention. Transforming these variables removed these outliers (see below).
iii) Normality
The distributions of the cognitive variables were examined for skewness and kurtosis and Q-Q and P-P plots for normality. Slight kurtosis and positive skewness was observed for congruent Stroop, incongruent Stroop, Stroop effect, Trail Making-A and Trail Making-B variables. These tests for normality indicated that Stroop, Trail Making-A and Trail Making-B variables violated the ANOVA assumptions of variable normality. These variables were therefore transformed prior to running ANCOVA analyses. Congruent Stroop percentage accuracy and Incongruent Stroop percentage accuracy scores were heavily negatively skewed and non-parametric analysis was therefore conducted to determine whether group differences existed for these variables. Quality of Episodic Memory and Power of Attention cognitive domains were normally distributed. Kurtosis and slight negative skewness was observed for Quality of Working Memory and Continuity of Attention. Kurtosis and slight positive skewness was observed for the Speed of Memory variable. Transformations were conducted for Quality of Working Memory and Power of Attention. None of the formulas suitable for negatively skewed distributed variables were successful in transforming Continuity of Attention and non-parametric tests were therefore conducted to examine group differences.
Appendices
xxxi
Appendix Q Data exploration for mood variables to test assumptions for analysis of variance
i) Missing Values
The variables were assessed separately for each of the experimental groups. Missing value analysis (MVA) revealed no missing cases for the Profile of Mood States (POMS) and Short Form-36 (SF-36) subscales and STAI-S and STAI-T variables in the HF group. There was one missing case for each the Chalder fatigue scale-physical symptoms, Chalder fatigue scale-mental symptoms symptoms and Chalder fatigue scale-total in the HF group. There were no missing cases for each of the POMS mood domains, SF-36 and Chalder fatigue scale variables in the control group. There was one missing case for each the STAI-S and STAI-S variables in the control group.
ii) Univariate outliers
Standardized z scores based on untransformed data were calculated to examine univariate outliers in each cognitive variable for each experimental group. There was one outlier for Chalder fatigue scale-mental symptoms in the HF group. In the control group there was one outlier for each the POMS-fatigue/inertia, SF 36- physical functioning, SF-36 role limitations due to emotional problems and SF-36 vitality subsets and Chalder fatigue scale-total score.
iii) Normality
The STAI-S, STAI-T and POMS subscales, except POMS-vigour/activity subset, were positively skewed. The log 10 transformation was used for all variables expect POMS-Total mood disturbance where the square root transformation was the best formula to normalise the variable. All outliers were removed following variable transformation.
iv) Homogeneity of variance
Levene’s test for equality of variance on transformed POMS variable, STAI-S and STAI-T establishes no significant differences in the variance of the POMS subscales. These analyses indicate that POMS and STAI meet the assumption of ANOVA.
Appendices
xxxii
Appendix R Data exploration for vascular, oxidative stress, antioxidant and inflammatory variables to test assumptions for ANOVA
i) Missing Values
The variables were assessed separately for each of the experimental groups. For the HF group, Missing value analysis (MVA) revealed missing cases were reported for each of the common carotid arterial blood flow (n=4), middle cerebral arterial blood flow (n=13), central pulse pressure (n=17) and augmentation index (n=19). These missing cases were due to inability in obtaining suitable signals for recording. In the HF group there were no missing cases for F2-isoporstanes in the HF group, however missing cases were observed for determinable reactive oxygen metabolite (DROM; n=9), glutathione peroxidase activity (n=2) and coenzyme Q10 (n=1), PUFA questionnaire (n=4) and high-sensitive C-reactive protein (n=1). For the control group, there were missing cases for common carotid (n=7) and middle cerebral arterial (n=8) blood flow velocities, PUFA questionnaire (n=19), high-sensitive C-reactive protein (n=17), DROM (n=11), glutathione peroxidase activity (n=13) and coenzyme Q10 (n=3). Missing cases for blood measures in both the HF and control group were due to insufficient volumes of blood obtained as a result of difficult bleed. There were no missing values in the control group for augmentation index and central pulse pressure.
ii) Univariate outliers
Standardised Z scores were calculated to examine outliers in each biomarker variable for each experimental group. Standardised z scores for one HF patient case for high-sensitive C-reactive protein exceeded the α = .001 criterion of 3.29 for two tailed test (Tabachnick & Fidel, 2007). For the control group there was one outlier for central pulse pressure.
iii) Normality
The common carotid and middle cerebral blood flow velocities, augmentation index, PUFA questionnaire, F2-isoprostanes, CoQ10 and glutathione peroxidase activity variables were normally distributed. Other variables were positively skewed and log transformations were chosen to normalise these variables (high-sensitive C-reactive protein, DROM, endothelin-1). There was one outlier for endothelin-1, which was removed before analyses. All other outliers were removed following variable transformation.
iv) Homogeneity of variance
Levene’s test for equality of variance on transformed variables, except endothelin-1 indicated that these variables met the assumption of ANOVA.