Biomarkers of Ceramide Accumulation and Decline in Verbal Memory in

Coronary Artery Disease

by

Parco Chan, B.Sc.

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Department of Pharmacology and Toxicology

University of Toronto

© Copyright by Parco Chan 2018

ii

Biomarkers of Ceramide Accumulation and Decline in Verbal Memory in

Coronary Artery Disease

Parco Chan

Master of Science

Department of Pharmacology and Toxicology

University of Toronto

2018

Abstract

Background: Biomarkers in cognitively vulnerable populations like those with coronary artery

disease (CAD) may inform earlier intervention in vascular neurodegeneration. Circulating

ceramide C18:0 (CerC18:0) is associated with cognitive changes in early neurodegeneration and

CAD progression.

Objective: To investigate whether plasma CerC18:0 accumulation is associated with longitudinal

declines in verbal memory in CAD.

Methods: In addition to CerC18:0, we assessed its relative abundance to its precursors as ratios: to

monohexosylceramide C18:0 (CerC18:0/MHxCer18:0), to sphingomyelin C18:0

(CerC18:0/SM18:0), and to sphingosine-1-phosphate (CerC18:0/S1P). In 60 CAD participants, we

evaluated associations between baseline CerC18:0 and its ratios to changes in verbal memory,

adjusted for potential confounders.

Results: Increased baseline CerC18:0 (b[SE]=-0.91[0.30], p=0.003), CerC18:0/MHxCerC18:0

[(b[SE]=-0.90[0.40], p=0.03)], CerC18:0/SM18:0 (b[SE]=-1.11[0.36], p=0.004) correlated with

steeper declines in verbal memory.

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Conclusions: Aberrant CerC18:0 metabolism may be an early neurobiological change in vascular

neurodegeneration. Future studies should measure enzymatic activity responsible for conversion of

precursors into CerC18:0.

iv

Acknowledgements

First and foremost, I would like to thank my supervisor and advisor, Drs. Krista Lanctôt and

Nathan Herrmann, for their invaluable mentorship and support throughout the past 5 years. I

developed as an aspiring scientist in this laboratory as long as I have, because of the collaborative

learning environment that you both nurture and facilitate. You are both inspiring scientists. I

appreciate your emphasis on critical thinking and ongoing self-development more and more as I

take on other opportunities. As selfless teachers, I truly appreciate your openness in sharing

guidance. For this, I am grateful and aspire to incorporate these qualities in my future endeavours.

To my collaborators, Drs. Michelle Mielke, Norman Haughey, and Alexander Kiss, thank you for

your support. Without you, this project would not have been possible. In particular, I would like to

thank Dr. Paul Oh and the staff at Toronto Rehabilitation Institute for their crucial assistance in

recruitment and data collection. Of course, I would also like to extend my gratitude to the study

participants, whose enthusiasm about clinical research made this project particularly enjoyable.

To everyone in the Neuropsychopharmacology Research Group, thank you for making these years

memorable. I still can’t believe that I got to be part of an amazing group that bonds outside of

work after spending 8 hours a day with each other! Mahwesh, Myuri, Ivonne, and Abby – thank

you all for being such supportive mentors. Your patience and generosity never ceases to impress

me and I am confident that you all will find happiness, fulfilment, and success in your future

endeavours.

Lastly, I would like to thank you my family and friends for their constant support over the past

years. From enduring rehearsals of my presentations to listening to my passionate (often one-

sided) discussions of my research, you all have been integral in this journey and its successes.

v

Table of Contents

Acknowledgements ...................................................................................................................... iv

List of Tables .............................................................................................................................. viii

List of Figures .............................................................................................................................. ix

List of Abbreviations ..................................................................................................................... x

Chapter 1: Introduction ................................................................................................................ 1

1.1. Statement of Problem ........................................................................................................ 1

1.2. Study Purpose and Objectives .......................................................................................... 2

1.3. Hypotheses and Corresponding Rationale ........................................................................ 2

1.3.1. Primary Hypothesis ................................................................................................... 2

1.3.2. Secondary Hypothesis ............................................................................................... 3

1.3.3. Tertiary Hypothesis ................................................................................................... 5

1.3.4. Exploratory Hypothesis ............................................................................................. 5

1.4. Review of literature ........................................................................................................... 6

1.4.1. Coronary artery disease ............................................................................................. 6

1.4.2. Coronary artery disease and cognitive decline .......................................................... 7

1.4.3. Potential pharmacotherapy for vascular cognitive impairment in CAD ................... 8

1.4.4. Biomarkers of vascular cognitive change in CAD .................................................. 11

1.4.5. Ceramides as a potential mediator between CAD and cognitive decline ................ 12

1.4.6. Ceramides in CAD and neurodegeneration ............................................................. 13

Chapter 2: Materials and Methods ............................................................................................ 16

2.1. Study design .................................................................................................................... 16

2.2. Study participants ............................................................................................................ 16

2.2.1. Eligibility criteria ..................................................................................................... 16

2.3. Demographics and medical history ................................................................................. 18

2.4. Neurocognitive battery and assessment of depressive symptoms .................................. 18

2.4.1. Calculation of composite Z-scores .......................................................................... 19

2.4.2. Possible vascular cognitive impairment, no dementia ............................................. 20

vi

2.5. Biochemical assays ......................................................................................................... 20

2.5.1. Blood collection ....................................................................................................... 20

2.5.2. Measurement of plasma sphingolipids .................................................................... 20

2.6. Selection of covariates .................................................................................................... 21

2.6.1. Age ........................................................................................................................... 21

2.6.2. Body-mass index ..................................................................................................... 22

2.6.3. Years of education ................................................................................................... 22

2.7. Statistical analyses .......................................................................................................... 22

2.7.1. Data pre-processing ................................................................................................. 22

2.7.2. Characterization of study participants and comparison of sub-cohorts ................... 23

2.7.3. Longitudinal analyses .............................................................................................. 23

2.8. Calculation of sample size .............................................................................................. 24

Chapter 3: Results ....................................................................................................................... 26

3.1. Recruitment of study participants ................................................................................... 26

3.2. Differences between study participants who returned and those who did not ................ 27

3.3. Associations between sociodemographic and clinical characteristics and change in verbal

memory performance in all study participants ........................................................................... 28

3.4. Associations between sociodemographic and clinical characteristics and change in verbal

memory performance in study participants with possible VCIND ............................................ 30

3.5. Sociodemographic and clinical differences between those with VCIND and those with no

possible VCIND ......................................................................................................................... 31

3.6. Trajectory of verbal memory performance throughout the study ................................... 32

3.7. Covariate multi-collinearity and normalization of plasma sphingolipid concentrations.33

3.8. Primary: Associations between baseline CerC18:0 and change in verbal memory

performance ............................................................................................................................... 38

3.9. Secondary: Associations between CerC18:0/MHxCerC18:0 ratio and change in verbal

memory ...................................................................................................................................... 38

3.10. Tertiary: Associations between CerC18:0/SM18:0 ratio and change in verbal

memory……………………………………………………………………………………….. 39

3.11. Exploratory analyses: Differences in associations between those with possible VCIND

and those with no possible VCIND ........................................................................................... 40

3.11.1. Association between CerC18:0 and change in verbal memory performance .......... 40

vii

3.11.2. Association between CerC18:0/MHxCerC18:0 and change in verbal memory

performance ............................................................................................................................ 41

3.11.3. Association between CerC18:0/SM18:0 and change in verbal memory

performance…………………………………………………………………………………42

3.11.4. Association between CerC18:0/S1P and change in verbal memory

performance………………………………………………………………………………....42

3.12. Summary of Results .................................................................................................... 43

Chapter 4: Discussion and Recommendations for Future Studies ......................................... 44

4.1. Study Findings and Interpretations ................................................................................. 44

4.1.1. Characterization of study participants and comparison of sub-cohorts ................... 44

4.1.2. Primary Findings ..................................................................................................... 46

4.1.3. Secondary Findings ................................................................................................. 46

4.1.4. Tertiary Findings ..................................................................................................... 48

4.1.5. Exploratory Hypothesis ........................................................................................... 49

4.2. Pathological mechanisms of ceramides in neurodegeneration ....................................... 50

4.3. Strengths ......................................................................................................................... 52

4.4. Limitations ...................................................................................................................... 52

4.4.1. Methodological limitations with recommendations for future studies .................... 52

4.4.2. Mechanistic Limitations .......................................................................................... 53

4.5. Implications on drug development .................................................................................. 53

4.6. Conclusions ..................................................................................................................... 55

References ..................................................................................................................................... 56

Appendix 1: Approval from Research Ethics Board................................................................ 68

Appendix 2: Informed Consent Form ....................................................................................... 70

viii

List of Tables

Table 1: Comparison of study participants who returned and those who did not ……………...28

Table 2: Associations with change in verbal memory in overall population……………………30

Table 3: Associations with change in verbal memory in those with possible VCIND…………31

Table 4: Comparison of those with possible VCIND and those without ………………………32

Table 5: Change in verbal memory performance over course of the study……………………..33

Table 6: Multi-collinearity statistics of covariates……………………………………………...33

Table 7: Raw and log-transformed sphingolipid concentrations……………………………......34

Table 8: Association between CerC18:0 and change in verbal memory in overall population…38

Table 9: Association between CerC18:0/MHxCerC18:0 and change in verbal memory in overall

population……………………………………………………………………………………….39

Table 10: Association between CerC18:0/SM18:0 and change in verbal memory in overall

population……………………………………………………………………………………….39

Table 11: Association between CerC18:0/S1P and change in verbal memory in overall

population……………………………………………………………………………………….40

Table 12: Association between CerC18:0 and change in verbal memory in terms of possible

VCIND…………………………………………………………………………………………..41

Table 13: Association between CerC18:0/MHxCerC18:0 and change in verbal memory in terms

of possible VCIND……………………………………………………………………………...41

Table 14: Association between CerC18:0/SM18:0 and change in verbal memory in terms of

possible VCIND…………………………………………………………………………………42

Table 15: Association between CerC18:0/S1P and change in verbal memory in terms of possible

VCIND …...…………………………………………………………………………………......43

Table 16: Summary of Results………………………………………………………………….43

ix

List of Figures

Figure 1: Ceramides biosynthesis pathways……………………………………………………...4

Figure 2: Flow of participant recruitment……………………………………………………….27

Figure 3: Trajectory of verbal memory performance throughout study………………………...32

Figure 4: Normal Q-Q plot of log-transformed CerC18:0………………………………………34

Figure 5: Normal Q-Q plot of log-transformed SM18:0………………………………………..35

Figure 6: Normal Q-Q plot of log-transformed MHxCerC18:0………………………………...35

Figure 7: Normal Q-Q plot of log-transformed S1P…………………………………………….36

Figure 8: Normal Q-Q plot of log-transformed CerC18:0/MHxCerC18:0……………………..36

Figure 9: Normal Q-Q plot of log-transformed CerC18:0/SM18:0……………………………..37

Figure 10: Normal Q-Q plot of log-transformed CerC81:0/S1P………………………………..37

x

List of Abbreviations

AChEI Acetylcholinesterase inhibitors

AD Alzheimer’s Disease

APOE4 Apolipoprotein E4

ASM Acid sphingomyelinase

BBB Blood-brain-barrier

BEST Biomarkers, EndpointS, and other Tools

BMI Body-mass index

BVMT-R Brief Visuospatial Memory Test-Revised

df Degrees of freedom

C18:0 Acyl chain length of 18 carbons

CABG Coronary artery bypass grafting

CAD Coronary artery disease

Cer Ceramides

CRP C reactive protein

CSF Cerebrospinal fluid

CVLT-II California Verbal Learning Test – 2nd

edition

DSM-IV Diagnostic and Statistical manual – Version 4

EDTA Ethylenediaminetetraacetic acid

FIASMA Functional inhibitors of acid sphingomyelinase

HbA1C Haemoglobin A1C

HDL High-density lipoproteins

HIV Human immunodeficiency virus

IL-6 Interleukin-6

LDL Low-density lipoproteins

MCI Mild cognitive impairment

MDD Major depressive disorder

MI Myocardial infarction

MHxCer Monohexosylceramides

NSM Neutral sphingomyelinase

NINDS-CSN National Institute of Neurological Disorders

and Stroke-Canadian Stroke Network

PD Parkinson’s Disease

PTCA Percutaneous transluminal coronary

angioplasty

S1P Sphingosine-1-phosphate

SD Standard deviation

SE Standard error

SM Sphingomyelin

sMMSE Standardized mini mental state examination

SCID Structured clinical interview for depression

TNF-α Tumour necrosis factor alpha

TRI Toronto Rehabilitation Institute

xi

VCI Vascular cognitive impairment

VCIND Vascular cognitive impairment, no dementia

VD Vascular dementia

VIF Variance inflation factor

1

Chapter 1: Introduction

1.1. Statement of Problem

Half of Canadians with vascular cognitive impairment, no dementia (VCIND) progress to

dementia within 5 years (Wentzel et al., 2001), which results in high institutionalization rates

and poor quality of life. Brain damage in those with dementia may be too extensive to be

reversed by therapeutic intervention, implicating that intervention at earlier pre-symptomatic

stages of dementia may improve clinical outcomes (Becker, Greig, & Giacobini, 2008). In

addition, given that there are no recommended drugs to prevent this progressive vascular

neurodegeneration, early identification and active control of cardiovascular risk factors are

currently considered the only ways to modify the course of this disease (Rockwood, 2002;

Ikeda, 2003).

As a result, there is significant interest in the development of biological markers or biomarkers

to detect disease signatures in cognitively vulnerable populations. One such population is

patients with coronary artery disease (CAD) as those with CAD experience increased brain

atrophy (Koschack & Irle, 2005; Barekatain et al., 2014), increased white matter lesions

(Debette et al., 2007), and an increased risk of memory impairment (Vinkers et al., 2005).

Consequently, this clinical population is approximately 2 times more likely to develop incipient

neurodegenerative disorders such as VCIND (Roberts et al., 2010), which may progress to

dementia (van Oijen et al., 2007). Because of this increased risk of neurodegeneration, those

with CAD are an ideal prodromal population to identify biomarkers of early cognitive changes

and identify potential therapeutic targets, which may improve our understanding of the

2

relationship between CAD and neurodegeneration (Buratti et al., 2015).

1.2. Study Purpose and Objectives

The purpose of the present study is to evaluate the association between baseline concentration of

ceramides and verbal memory performance over time in a CAD population. This information

will help us determine whether ceramides are a clinically important correlate of cognitive

decline in a population that is at-risk of developing vascular dementia (VD) in the future.

Furthermore, this research may inform us whether there are relevant targets in the biosynthesis

pathways of sphingolipids for drug discovery initiatives.

1.3. Hypotheses and Corresponding Rationale

1.3.1. Primary Hypothesis

Primary hypothesis: Greater plasma concentrations of ceramides with an acyl chain length of 18

carbons (CerC18:0) at baseline will predict greater decline in verbal memory performance over

time in CAD participants.

Rationale: Aberrant CerC18:0 metabolism is consistently associated with both cardiovascular

and neurodegenerative diseases. Plasma levels of CerC18:0 are elevated in coronary plaques, a

characteristic pathology of CAD (Uchida et al., 2017). Evidence from numerous studies also

implicates CerC18:0 in the biochemical profile of various neurodegenerative diseases. In adults

at-risk of Alzheimer’s disease (AD), cerebrospinal fluid (CSF) CerC18:0 was associated with

higher CSF β-amyloid and higher CSF tau levels, which are hallmark pathologic markers of AD

(Mielke et al., 2014). Notably, these associations were stronger among individuals aged 54 years

and older, an age range during which the prevalence of CAD increases. In a cognitively

3

asymptomatic population, higher levels of serum CerC18:0 were associated with an increased

risk of incident memory impairment over 9 years of follow-up (Mielke, Bandaru, Haughey, et

al., 2010). In combination with its biological role as a pro-apoptotic signalling molecule (Obeid,

Linardic, Karolak, & Hannun, 1993), CerC18:0 may be involved in the etiopathology of CAD

and neurodegeneration. Collectively, this evidence coupled with the abundance of CerC18:0 in

brain tissue (Filippov et al., 2012) demonstrates the potential and feasibility of CerC18:0 as a

biomarker of the pre-symptomatic phases of cognitive impairment due to vascular causes.

Compelling evidence suggests that changes in verbal memory may be one of the earliest events

in cognitive decline caused by vascular pathology. A previous study elucidated the

neuropsychological profiles of patient populations along the spectrum of vascular cognitive

impairment (VCI): healthy elders, those at-risk of cerebrovascular disease, those with VCIND,

and those with VD. Those with VCIND had significantly worse verbal memory compared to the

healthy participants, while performing within normative ranges in other cognitive domains

(Garrett et al., 2004). Moreover, our laboratory previously reported that plasma CerC18:0

concentrations significantly correlated with verbal memory performance over 6 months of

cardiac rehabilitation (CR) (Saleem et al., 2017). However, it remains to be seen whether this

association can be observed over a longer period of time in which cognitive deterioration may

become more apparent.

1.3.2. Secondary Hypothesis

Secondary hypothesis: Greater plasma ratios of CerC18:0 relative to monohexosylceramide

C18:0 (CerC18:0/MHxCerC18:0) will predict greater decline in verbal memory performance

over time in CAD participants.

4

Rationale: Given that the majority of sphingolipids are synthesized within the body (Lee et al.,

2004; Jana & Pahan, 2007), CerC18:0 abnormalities are likely the result of enzymatic

imbalances, which can be represented empirically by ratios. Ratios of sphingolipids have been

reported to be stronger predictors of cognitive decline than individual sphingolipids (Mielke et

al., 2011), but this has not been investigated in the context of VCI.

Briefly, ceramides are generated through four biosynthesis pathways (Kitatani, Idkowiak-

Baldys, & Hannun, 2008) (Figure 1). The salvage pathway involves the catabolism of

monohexosylceramides (MHxCer) such as galactosylceramides and glucosylceramides to

generate ceramides. In the catabolic pathway, sphingomyelinase breaks sphingomyelin (SM)

down into ceramides. The de novo pathway synthesizes ceramides from palmitoyl coenzyme A

and serine. Ceramides may also be formed from sphingosine-1-phosphate (S1P) through the

recycling pathway. This relationship should be particularly apparent with MHxCerC18:0 as the

pre-cursor as the salvage pathway accounts for 50-90% of sphingolipid generation (Kitatani et

al., 2008); thus dysregulation of this pathway may have greater cognitive impact.

5

Figure 1: The four primary biochemical pathways through which ceramides are synthesized.

1.3.3. Tertiary Hypothesis

Tertiary hypotheses: Greater plasma ratios of CerC18:0 relative to sphingomyelin C18:0

(CerC18:0/SM18:0) and sphingosine-1-phosphate (CerC18:0/S1P) will predict greater decline in

verbal memory performance over time in CAD participants.

Rationale: Elevated peripheral inflammation may induce CerC18:0 biosynthesis through the

catabolic pathway (Zeidan & Hannun, 2010). Since levels of pro-inflammatory cytokines are

frequently observed to be elevated in those with CAD, a significant association between

CerC18:0/SM18:0 and verbal memory performance may suggest the importance of peripheral

inflammation in perpetuating vascular cognitive decline.

S1P has been touted to be neuroprotective in AD (Couttas et al., 2014; Ceccom et al., 2014),

while involvement of CerC18:0 in pro-apoptotic pathways suggests that it contributes to

neurodegeneration. Collectively, the antagonizing biochemical functions of these sphingolipids

suggest that a ratio of CerC18:0/S1P may be sensitive to changes in verbal memory performance

in CAD participants.

1.3.4. Exploratory Hypothesis

Exploratory Hypothesis: Inverse associations between baseline CerC18:0 ratios and change in

verbal memory performance will be significantly different between those with possible VCIND

and those without possible VCIND.

6

Rationale: These changes may be particularly important in those with VCIND as this is an

intermediate stage between normal cognition and VCI. Moreover, therapeutic intervention or

CR may be more beneficial in early prodromal states as a more preventative approach (Intzandt

et al., 2015). Our exploratory hypothesis focuses on study participants with possible VCIND,

because the neuropathology may be too advanced by the time the stricter neurocognitive criteria

of VCIND is met (Gorelick et al., 2011). Moreover, evidence shows that cognitive decline is

more rapid following onset of detectable deficits (Stern et al., 1994).

1.4. Review of literature

1.4.1. Coronary artery disease

CAD is a leading contributor to global mortality as it is globally responsible for an estimated

32% of deaths (Mortality & Causes of Death, 2015). Characterized by significant narrowing of

one or more major coronary arteries (defined as ≥50% of diameter), CAD substantially reduces

the flow of oxygen-rich blood to the heart. These blockages, otherwise known as coronary

stenosis, are formed through the process of atherosclerosis, in which leukocytes, lipid deposits,

and collagen-rich extracellular matrix form plaques, ultimately thickening the arterial wall. In

more severe cases, CAD can precipitate myocardial infarction (MI), angina, and even death

(Nabel & Braunwald, 2012).

Lifestyle choices and vascular risk factors such as smoking, hyperlipidemia, diabetes,

hypertension, obesity, and peripheral inflammation contribute to the incidence and progression

of CAD (Jousilahti, Vartiainen, Tuomilehto, & Puska, 1999; Canto et al., 2011; Rimm et al.,

1995). Fortunately, pharmacological interventions (e.g. statins) and non-pharmacological

interventions (i.e. dietary modifications, physical exercise during CR) are effective in managing

CAD progression (Task Force et al., 2013; Heran et al., 2011). However, in CAD patients with

7

severe arterial occlusions or those at high-risk of MI, revascularization procedures such as

coronary artery bypass grafting (CABG) or percutaneous transluminal coronary angioplasty

(PTCA) may be required.

1.4.2. Coronary artery disease and cognitive decline

Epidemiologically, CAD has long been associated with increased likelihood of developing

dementia in later life. In a longitudinal study, a history of CAD in cognitively normal elderly

adults was associated with significantly greater rates of cognitive decline (Zheng et al., 2012). A

recent systematic meta-analysis of 24,801 persons across 10 prospective studies quantified this

association, reporting that those with CAD have 45% increased odds of developing cognitive

impairment or dementia at follow-up (Deckers et al., 2017). An autopsy study elucidated this

relationship at the pathological level. The authors reported that extent of CAD correlated

significantly with AD-related neuropathology like neurofibrillary tangles (Beeri et al., 2006).

However, most studies do not distinguish between cognitive decline secondary to vascular risk

factors or AD-related neurodegeneration. While their perpetuating interactions are well-

recognized, it is important to make a distinction between these different etiologies as it may

improve our ability to accurately predict the prognosis of a person with dementia and provide

unique targets for therapeutic interventions. In response to this need, the concept of VCI has

emerged.

However, it is clinically difficult to study the development of both diseases as they rarely

emerge concurrently in the same patient. This is likely because VCI follows a chronic disease

model (White et al., 2002; Roman et al., 2004). Briefly, a disease that follows this progression

model begins latently, presenting with molecular damage and no changes in functions or

behaviour. This is followed by a prodromal stage with greater molecular damage and mild

8

changes in function. Ultimately, the syndrome progresses to dementia, which is typically

irreversible (Katzman, 1976). Since CAD is generally diagnosed before onset of dementia,

many patients may experience profound molecular-level changes and express mild cognitive

impairment attributable to these cardiovascular risk factors, but do not satisfy

neuropsychological criteria for dementia. This is otherwise known as VCIND.

Specifically, compelling evidence suggests that changes in verbal memory may be one of the

earliest events in cognitive decline caused by vascular pathology. A previous study elucidated

the neuropsychological profiles of patient populations along the spectrum of cognitive decline

due to cardiac risks: healthy elders, those at-risk of cerebrovascular disease, those with VCIND,

and those with VD. Those with VCIND had significantly worse verbal memory compared to the

healthy participants, while performing within normative ranges in other cognitive domains

(Garrett et al., 2004). As such, this study will investigate longitudinal changes in verbal memory

in CAD participants.

1.4.3. Potential pharmacotherapy for vascular cognitive impairment in CAD

No pharmacotherapy has been successfully developed for VCI. Acetylcholinesterase inhibitors

(AChEI) originally indicated for AD, have been proposed to slow progression from VCI to

dementia, based on findings that demonstrate cerebrovascular damage affects cholinergic tracts

(Moretti et al., 2008; Humpel, 2011). However, a meta-analysis of randomized controlled trials

reported that AChEIs only improve cognition by half of the magnitude observed in previous AD

trials (Russ & Morling, 2012)and is further obscured by an increased incidence of adverse

events (Rojas-Fernandez, 2013). Memantine, an N-methyl-D-aspartate antagonist, is another

drug that is approved for moderate-severe AD. Like the AChEIs, memantine demonstrated

9

minimal, clinically insignificant improvement in cognition scores in those with VCI (McShane,

Areosa Sastre, & Minakaran, 2006).

It comes with little surprise when clinical consensus from the fourth Canadian Consensus

Conference on the Diagnosis and Treatment of Dementia recommended against using AChEIs

in VCI patients, unless there is comorbid AD (Gauthier et al., 2012). In contrast, the American

Heart Association and Canadian Stroke Best Practices recommend that AChEIs could be

considered for use in those with VCI in light of limited therapeutic options, but notes that this is

only supported by intermediate grade evidence (Eskes et al., 2015; Gorelick et al., 2011).

Other therapies have also been investigated in the context of VCI. The potential of a serotonin

5-HT2 receptor antagonist, naftidrofuryl, was evaluated, because of its ability to increase

availability of oxygen and energetic mediators in cerebral tissue through vasodilation (James,

Newbury, & Woolard, 1978). Despite its theoretical benefits, naftidrofuryl has yet to be shown

to be an effective treatment in clinical trials (Lu, Song, Hao, Wu, & McCleery, 2011). Another

class of drugs, calcium channel blockers, have been investigated as a potential treatment of VCI,

because their ability to restrict calcium influx into neural cells decreases likelihood of

excitotoxicity, which is hypothesized to contribute to neuropathologies like AD (Harkany et al.,

2000). For instance, nomidipine crosses the blood-brain-barrier (BBB) and decreases calcium

ion influx into cells; thus, modulating synaptic excitability and neurotransmission. Clinically,

however, its use is controversial as its short-term cognitive benefits do not justify its long-term

use in dementia patients (Lopez-Arrieta & Birks, 2002). Similar results were observed with

nilvadipine, leading to conclusions stating that there is insufficient clinical trial evidence to

support its use (Hanyu et al., 2007). Likewise, other calcium channel blockers like amlodipine

10

have been investigated in this regard, but its inability to cross the BBB prevents further

investigation (Paris et al., 2011).

In those with mild cognitive impairment, current evidence suggests that peripheral management

of cardiovascular risk factors may be effective prophylaxis (Langa & Levine, 2014). Peripheral

endothelial dysfunction is a primary concern, because it may reflect impairment in cerebral

blood perfusion and contribute to the pathogenesis of AD and VD (Zuliani et al., 2008). As

such, existing cardiovascular medications such as β-blockers and angiotensin-converting-

enzyme inhibitors that improve endothelial function may potentially be effective in improving

endothelial function (Peller et al., 2015).

The failure to develop effective treatments for VCI is likely multi-factorial. First, the clinical

concept of VCI is in its infancy; it has only been recently recognized as a syndrome that can be

caused by multiple cerebrovascular diseases. As such, therapies may need to be designed to

target specific etiologic mechanisms (Smith et al., 2017). This stagnancy in drug development is

compounded by a lack of reliable biomarkers. Currently, neuroimaging biomarkers are preferred

to detect cerebrovascular damage, but they are expensive, unable to detect early microstructural

cerebrovascular damage (Baykara et al., 2016), and ultimately may not correlate with cognition

as in the case of white matter hyperintensities (Schmidt et al., 2012). An alternative

physiological medium is blood. Although blood-based biomarkers may be difficult to develop

due to the uncertain link between peripheral markers and central cognitive processes, a reliable

blood-based biomarker would cheap, non-invasive, and more easily incorporated into clinical

care for those in the early stages of VCI (The Ronald and Nancy Reagan Research Institute of

the Alzheimer's Association and the National Institute on Aging Working Group," 1998).

Furthermore, considering that dementia symptoms may not become clinically evident until

11

neuropathology becomes irreversible, regulatory agencies like the Food and Drug

Administration are starting to accept the role of biomarkers as surrogate clinical trial outcomes

in drug development for dementia (The Food and Drug Administration, 2018).

1.4.4. Biomarkers of vascular cognitive change in CAD

Since VCI does not become clinically apparent until later stages despite early molecular

damage, prognostic biomarkers that are indicative of pathologic biological processes may help

to predict clinical progression as suggested by the Biomarkers, EndpointS, and other Tools

(BEST) guidelines (Group, 2016). However, identifying robust biomarkers of VCI is difficult

due to its heterogeneous nature as well as the presence of vascular risk factors in patients

presenting with AD-like characteristics. As a result, there is no standardized blood-based

biomarker in the literature ready for clinical use.

The BBB is a selectively permeable barrier that separates the brain’s circulation from the rest of

the body’s. BBB dysfunction may contribute to the pathogenesis of VCI as its integrity is clearly

compromised in animal models that exhibit symptoms of VD like rats with chronic carotid

hypoperfusion (Ueno, Tomimoto, Akiguchi, Wakita, & Sakamoto, 2002; Ueno et al., 2016).

This observation has been extrapolated to humans using the CSF/serum albumin quotient, which

is indicative of clinical BBB leakage because liver-made albumin does not normally appear in

the CSF. Thus, a greater quotient indicates more severe BBB compromise. Indeed, in a meta-

analysis of clinical studies, the CSF/serum albumin quotient is particularly high in those with

VCI, compared to AD (Farrall & Wardlaw, 2009). However, CSF/serum albumin quotient is

elevated in both VCI and AD compared to control, making differentiation in the presence of AD

difficult.

12

This increased BBB permeability allows the measurement of blood-based biomarkers to be

reflective of their levels in the brain. In particular, peripheral inflammatory markers have been

extensively studied as it is a key contributor to the progression of cardiovascular and

cerebrovascular disease (Rosenberg, 2009). In the vasculature, interleukin-6 (IL-6) is secreted as

a pro-inflammatory cytokine, which in turn, triggers the production of C reactive protein (CRP)

from the liver (Yoshida et al., 2010). Elevated levels of CRP and IL-6 are both associated with

increased risk of incident VD (Ravaglia et al., 2007; Engelhart et al., 2004). In a neuroimaging

study, CRP is found to be associated with decline in white matter microstructural integrity

(Wersching et al., 2010). Moreover, sulfatide, a prominent glycophospholipid in

oligodendrocyte-produced myelin sheaths, has been reported to be severely elevated in the CSF

of those with VCI-SSVD in relation to controls and those with AD, suggesting white matter

degradation (Fredman et al., 1992).

In 2006, the US National Institute for Neurological Disorders and Stroke and the Canadian

Stroke Network convened and harmonized expert recommendations for biomarkers in VCI

(Hachinski et al., 2006). While multiple biomarkers for VCI have been proposed, they

concluded that none were ready for standardization and widespread clinical application.

1.4.5. Ceramides as a potential mediator between CAD and cognitive decline

Sphingolipids are characterized by an 18-carbon amino-alcohol backbone, known as

sphingosine. Modification of this fundamental structure results in various sub-classes of

sphingolipids. Sphingomyelins are produced by the attachment of a phosphocholine headgroup

and are the most abundant sphingolipids in humans (Gault, Obeid, & Hannun, 2010). A more

complex sphingolipid sub-class is the glycosphingolipids, which can be further sub-divided into

galactosphingolipids and glucosphingolipids depending on the sugar residue attached to the

13

head group. Ceramides have a sphingosine base attached to a fatty acid of varying chain length

via an amide linkage. As ceramides are our sub-class of interest due to their role in apoptotic

signalling, the rest of this biochemical review will focus on them.

Ceramides are generated through four main biosynthesis pathways (Kitatani et al., 2008)

(Figure 1). The salvage pathway involves the catabolism of monohexosylceramides such as

galactosylceramides and glucosylceramides to generate ceramides. In the catabolic pathway,

sphingomyelinase breaks down sphingomyelin into ceramides. The de novo pathway

synthesizes ceramides from palmitoyl coenzyme A and serine. Ceramides may also be formed

from sphingosine through the recycling pathway.

Initially thought to be an inert component of cellular membranes, sphingolipids are now widely

recognized to be involved in essential cellular signalling processes. For instance, the hydrolysis

of sphingomyelins by sphingomyelinases results in the production of ceramides, which function

as second messengers to modulate pro-apoptotic pathways (Hannun & Bell, 1989) and govern

cellular responses to stress (Hannun, 1996). Given their involvement in fundamental pathways

that determine cellular fate, dysregulation of ceramide metabolism has been implicated in the

pathophysiology of various diseases including diabetes (Galadari, Rahman, Pallichankandy,

Galadari, & Thayyullathil, 2013), cancer (Morad & Cabot, 2013), atherosclerosis (Bismuth, Lin,

Yao, & Chen, 2008), and neurodegenerative disorders (Mencarelli & Martinez-Martinez, 2013).

1.4.6. Ceramides in CAD and neurodegeneration

Although the biochemical mechanisms by which aberrant sphingolipid metabolism can cause

structural brain lesions and precipitate cognitive decline in CAD are unclear, their presence as a

frequently observed pathophysiological feature in both CAD and cognitive decline suggests that

14

they may be a potential mediator between the two diseases. Ubiquitous in membranes,

ceramides have been found to independently escalate the progression of both diseases, but

whether they are a mediator of one to another is a question that has yet to be answered.

Ceramides propagate worsening of atherosclerosis. For instance, in those with CAD, ceramides

levels were found to be higher in those with unstable coronary plaques relative to those with

stable angina (Uchida et al., 2017). During plaque formation, low-density lipoproteins (LDL)

transport sphingomyelins to the arterial wall, where sphingomyelinases cleave them to yield

ceramides. This accumulation of ceramides causes LDL aggregation, which induces foam cell

formation and consequently, atherogenic formation (Xu & Tabas, 1991). This mechanism is

sustained by chronic inflammation that is often observed in those with CAD (Hansson, 2005), to

which tumour necrosis factor alpha (TNF-α) is a prominent contributor (Hannun, 1996). Of

interest, a specific ceramide species with an acyl chain length of 18 carbons (CerC18:0) is

associated with significantly increased risk of cardiovascular death in stable CAD patients and

in those with acute coronary syndromes (Laaksonen et al., 2016).

Evidence from numerous studies implicates the role of CerC18:0 in the profile of biochemical

perturbations in various neurodegenerative states. In adults genetically at-risk of AD, CerC18:0

in cerebrospinal fluid was associated with build-up of β-amyloid and tau proteins (Mielke et al.,

2014). Notably, these associations were stronger in individuals aged 54 years and older, an age

range that is similar to that of individuals with CAD. In subjects infected with the human

immunodeficiency virus (HIV), CerC18:0 in cerebrospinal fluid was a strong predictor of

performance in multiple cognitive domains (Mielke, Bandaru, McArthur, Chu, & Haughey,

2010). Even in a cognitively asymptomatic population, higher levels of CerC18:0 predicted an

increased risk of incident verbal memory impairment (defined as <1.5 standard deviation (SD)

15

below standard norms) over 9 years of follow-up (Mielke, Bandaru, Haughey, et al., 2010).

These sphingolipid abnormalities in neurodegenerative processes are likely the result of

enzymatic imbalance as the majority of sphingolipids are synthesized within the body (Lee et

al., 2004; Jana & Pahan, 2007).

16

Chapter 2: Materials and Methods

2.1. Study design

This observational, longitudinal study investigated associations between plasma sphingolipid

concentrations at baseline and verbal memory performance over time. This study was approved

by research ethics boards at both the Sunnybrook Health Sciences Centre and Toronto

Rehabilitation Institute (TRI) in the University Health Network. The results from this

observational study are reported in accordance to the STrengthening the Reporting of

OBservational studies in Epidemiology (STROBE) guidelines (von Elm et al., 2007).

2.2. Study participants

All study participants were recruited from a CR program at the Rumsey Centre of University

Health Network - TRI and provided written, informed consent to participate before study

enrolment and assessment for inclusion and exclusion criteria.

2.2.1. Eligibility criteria

Patients were included into the study if they met all of the following inclusion criteria:

Between age 45-80

Spoke and understood English

o To complete assessments

Diagnosis of CAD based on at least one of the following:

o Coronary angiographic evidence of ≥50% blockage in ≥1 major coronary artery

o Previous hospitalization for acute myocardial infarction

o Had undergone a prior revascularization procedure (PTCA or CABG)

17

o Previous diagnosis of ischemic heart disease

Stable CAD defined as no hospitalizations for cardiac events such as an acute MI,

unstable angina or coronary revascularization procedure within 4 weeks of baseline

assessment

Statin medication use

o Because statins may lower plasma sphingolipid concentrations (Bergheanu et al.,

2008; Tarasov et al., 2014).

Conversely, patients were excluded from the study if they met any of the following criteria that

could affect interpretation of outcomes:

Significant acute medical illness (including active cancer, anemia, autoimmune

condition, drug overdose, hypothyroidism, sepsis, severely impaired kidney, liver, or

lung function, uncontrolled diabetes mellitus)

A history of a neurodegenerative disease (dementia, Parkinson’s, Huntington’s chorea)

Co-morbid psychiatric diagnosis of conditions that may impair cognitive function (e.g.

schizophrenia, bipolar disorder)

Surgery planned within 12 months

Significant cognitive impairment

o The Standardized Mini Mental Status Examination (sMMSE) was used to screen

for this and consequently, those with a sMMSE score <24 were excluded

(Molloy & Standish, 1997).

18

2.3. Demographics and medical history

Demographic information, comprehensive medical history, co-morbidities, concomitant

medications, and vascular risk factors (hypertension, smoking, hypercholesterolemia, obesity,

and diabetes) were collected from participant interviews. Anthropometric data such as height,

weight, body-mass index (BMI) were obtained from patient charts at TRI or assessed by trained

researchers. VO2peak (an indicator of cardiopulmonary fitness) was collected from electronic

medical records from TRI. Lipid profiles including total cholesterol, triglycerides, low-density

lipoproteins (LDL), high-density lipoproteins (HDL) were measured using standard clinical

assays.

2.4. Neurocognitive battery and assessment of depressive symptoms

Study participants were assessed for their cognitive performance at a baseline visit (in the

beginning of CR), 3-month visit, 6-month visit, and post 12-month visit. The time interval

between the baseline and post 12-month visit was variable; this will be addressed in Section 2.8.

A 30-min standardized battery of tests was used to assess cognitive performance due to vascular

causes, as recommended by the National Institute of Neurological Disorders and Stroke-

Canadian Stroke Network (NINDS-CSN) (Hachinski et al., 2006). A trained researcher

administered the battery at a standardized time (0930 hr ±30 min) and participants refrained

from eating or drinking any caffeine-containing beverages for at least 4 h before testing. As the

primary outcome, verbal memory was assessed using the California Verbal Learning Test 2nd

Edition (CVLT-II). The CVLT-II yields multiple measures of verbal memory function,

including verbal learning (recall of a word list over 5 learning trials), short-delay free recall

(recall of a word list after an interfering list), and long-delay free recall (recall of a word list

after 20 min) (Hachinski et al., 2006). Its use in this population is appropriate, because it is able

19

to distinguish elderly adults with MCI from cognitively normal elderly individuals as it assesses

learning across multiple trials (Rabin et al., 2009).

VCIND patients also have deficits in other cognitive domains along a continuum of severity.

Measures of executive function included the Trail-Making Test Part B (Gaudino, Geisler, &

Squires, 1995) and Stroop Color-Word Interference Test. Performance in visuospatial memory

was assessed using the Brief Visuospatial Test-Revised (BVMT-R), which presents a measure

of visual learning and delayed recall (Benedict, 1996). The domain of processing speed was

assessed using the Trail-Making Test Part A (Gaudino et al., 1995) and the Digit Symbol-

Coding task, a measure of complex attention and psychomotor speed from the Wechsler Adult

Intelligence Scale 3rd Edition (Joy, Kaplan, & Fein, 2004)

The Structured Clinical Interview for Depression (SCID) was used to diagnose depression at

baseline. It was administered by a trained researcher under the supervision of the study

psychiatrist, to determine if participants met the Diagnostic and Statistical Manual, Version 4

(DSM-IV) criteria for minor or major depressive disorder.

2.4.1. Calculation of composite Z-scores

Z-scores were determined from age, gender and education-matched normative scores (Delis,

2000). Z-scores from tests that involve the same cognitive domain were summed into a

composite Z-score to provide a more stable representation of performance in that particular

domain and to avoid multiple comparisons (Weuve et al., 2004; Harrison et al., 2007). For

verbal memory, three Z-scores from the CVLT-II: learning, short-delay free recall, and long-

delay free recall were summed into a composite score. For executive function, the Z-scores from

the Trails-Making Test Part B and Stroop Colour-Word Interference Test were summed into a

20

composite score. For visuospatial memory, the Z-scores from visuospatial learning and delayed

recall from the BVMT-R were summed. For processing speed, the Z-scores from Trails-Making

Test A and the Digit Symbol-Coding task were summed. Once the component scores were

summed together, the composite scores were standardized according to the following equation:

2.4.2. Possible vascular cognitive impairment, no dementia

In this study, participants with possible VCIND were defined as those with composite Z-scores

of ≤1 in either the verbal memory or executive function domain, as described previously

(Suridjan et al., 2017).

2.5. Biochemical assays

2.5.1. Blood collection

On the same day as cognitive testing during the baseline visit, a trained clinician or researcher

drew blood from fasting participants at 0900 h ± 1 h, in order to control for diurnal and dietary

influences on concentration of ceramides and other sphingolipids. The blood was drawn into

EDTA-containing vacutainer tubes and then centrifuged at 1000 g for 10 min at 4°C. The

resultant plasma fraction was immediately isolated, aliquoted, and stored at –80°C until time of

measurement.

2.5.2. Measurement of plasma sphingolipids

Sphingolipids were isolated from the plasma fraction using high-performance liquid

chromatography coupled electrospray ionization tandem mass spectrometry. Briefly, high

performance liquid chromatography (PerkinElmer, MA, USA) with a reverse phase C18 column

21

(Phenomenex, Torrance, CA, USA) was used for temporal resolution. The eluted sample was

then injected into the ion source for detection and quantification of ceramides and

sphingomyelins (m/z 264.4, 266.4 for ceramides and 184.4 for sphingomyelins respectively).

Data were collected and processed by Analyst 1.4.2 software package and MultiQuant software

(AB Sciex Inc, Thornhill, Ontario, Canada). Ceramide concentrations were initially presented in

counts per second (cps), but were converted to ng/ml by applying a standard curve. These eight-

point calibration curves (0.1–1000 ng/mL) were constructed by plotting area under the curve for

different sphingolipids for each calibration standard normalized to the internal standard. The

final sphingolipid concentrations (ng/mL) were determined by fitting the identified species to

standard curves based on acyl chain length. All samples were measured while blinded to the

clinical characteristics of participants.

2.6. Selection of covariates

Potential confounders that were adjusted for in multivariate analyses were chosen a priori,

because the relationship between ceramides and cognition has been explored previously in the

literature. Potential multi-collinearity of these covariates was assessed using variance inflation

factor (VIF) and tolerance statistics: covariates with a VIF ≥ 2.5 and tolerance ≤ 0.4 were

considered collinear.

2.6.1. Age

Increasing age is correlated with ceramide concentrations (Mielke et al., 2015). Previous work

by our group also reported mean age to be associated with baseline ceramide levels (Saleem et

al., 2013).

22

2.6.2. Body-mass index

Higher BMI has been shown to be associated with increasing ceramide concentrations (Mielke

et al., 2015). Notably, concentrations of sphingomyelins and ceramides were found to be

increased in adults who were obese, defined by BMI (Hanamatsu et al., 2014). Furthermore,

another study reported higher BMI to be correlated with worse verbal memory performance (De

Wit et al., 2017).

2.6.3. Years of education

In epidemiologic studies, lower level of education has been demonstrated to be a risk factor for

accelerated memory decline, independent of education bias in measurement of verbal memory

(Schmand et al., 1997). A pivotal investigation by Mortimer and Borenstein postulated that

attained education may even bolster one’s cognitive reserve and off-set the presence of

dementia-related pathophysiology (Mortimer, Borenstein, Gosche, & Snowdon, 2005). Years of

education are associated with better verbal memory performance (De Wit et al., 2017); thus it is

pertinent for us to adjust for this confounder. Indeed, this was found to be true as more years of

education at baseline visit was significantly associated with milder decline in verbal memory

(Table 2).

2.7. Statistical analyses

2.7.1. Data pre-processing

Ratios of CerC18:0 relative to its precursors were used to explore accumulation of CerC18:0

(Figure 1). Ratios of CerC18:0/SM18:0, CerC18:0/MHxC18:0, CerC18:0/S1P were calculated

using raw sphingolipid concentrations. The resulting CerC18:0 ratios and CerC18:0 were then

log-transformed to obtain a normal distribution prior to analyses and presented as Q-Q plots.

23

Only sphingolipid ratios with identical acyl chain lengths were included, because bi-directional

conversions between ceramides and its precursors primarily modify the head group and not the

acyl chain length (Mielke, Bandaru, McArthur, et al., 2010).

2.7.2. Characterization of study participants and comparison of sub-cohorts

T-tests and χ2 tests were conducted on SPSS software (IBM, Chicago, Illinois) to determine if

there were any baseline sociodemographic or clinical difference between those who returned for

the post 12-month visit and those who did not. Results were presented as corresponding test

values, degrees of freedom, and p-values. Similar univariate comparisons were performed to

determine whether there were any baseline sociodemographic or clinical differences between

those with possible VCIND and those without.

To characterize our participant population, mixed regression models were used to evaluate

bivariate relationships between sociodemographic features at baseline and change in verbal

memory. These analyses were conducted in two populations: the overall study population and

those with possible VCIND.

2.7.3. Longitudinal analyses

For the primary, secondary, and tertiary hypotheses, mixed regression models were used to

evaluate the longitudinal relationship between baseline CerC18:0 concentrations, baseline ratios

of CerC18:0 to its precursors, and changes in verbal memory over time. For the exploratory

hypotheses, mixed regression models from the main analyses were analyzed with possible

VCIND and CerC18:0 biomarker interaction as a covariate. This was to evaluate whether

associations between baseline sphingolipid concentrations and change in verbal memory were

significantly different between those with possible VCIND and those without. These analyses

24

were only conducted with the baseline CerC18:0 ratios, aligning with the primary, secondary,

and tertiary hypotheses.

Mixed regression model analyses were conducted using the PROC MIXED procedure in SAS

University Edition statistical software (SAS Institute Inc., North Carolina, USA) with a two-

tailed significance level of p≤0.05. However, a two-tailed significance level is p≤0.025 is used

in tertiary analyses after applying Bonferroni’s correction to account for multiple comparisons.

As discussed in Section 2.7, age, BMI, and years of education were included as potential

confounders a priori based on previous literature, while the number of days since baseline was

added as a fixed effect to build a spatial power covariance structure. Moreover, in the

exploratory analysis, substantially smaller sample sizes of the sub-groups does not allow for the

same number of covariates while maintaining adequate power. As such, the covariate with the

highest p-value was omitted from the model in a backward manner.

The spatial power covariance structure was selected in order to adjust for the time intervals

between visits, which were unique to each participant. In general, in this structure, adjacent

times have the highest correlation, while increasing distance between time points results in a

systematically decreasing correlation (Science). Results were presented as b, which quantifies

the SD change in verbal memory Z-score per 1 unit increase in the independent variable and

standard error (SE).

2.8. Calculation of sample size

Given that validated calculations of sample size for mixed models are limited (Guo, Logan,

Glueck, & Muller, 2013), sample size calculation for our mixed model analysis was based on

that of a general linear multivariate model (Helms, 1992). The sample size was calculated using

25

IBM SPSS SamplePower 3.0 (Chicago, IL, USA). Assuming a mean SD of 0.60 in log ceramide

concentrations based on previous findings in MCI (Mielke et al, 2010) and a mean SD of 3.41 in

verbal memory domain Z-score (our data in 115 undergoing CR showed a mean CVLT-II

composite Z-score of 1.50±3.41), a sample size of 58 provided 80% power for a detection of a

change in slope (b) of 2 units with a two-sided α of 0.05. This allows for us to conservatively

adjust for up to 3 confounding covariates, in addition to the predictor of interest and days since

baseline in our spatial covariance structure.

26

Chapter 3: Results

3.1. Recruitment of study participants

Between February 2012 and November 2015, 1563 patients at TRI were screened for evidence

of CAD before starting the CR program. Of the 933 who had angiographic evidence of CAD,

555 agreed to be contacted for research. After explaining the study and asking for informed

consent, 300 agreed to participate and gave written informed consent. From those who agreed to

participate, 159 were excluded based on the exclusion criteria. In addition, 21 participants were

not assessed due to various reasons including, but not limited to: missed study visits, withdrawal

of consent, vacation, and conflicts with work schedule.

In total, 120 participants enrolled into this study and completed the baseline visit. By the end of

the 6-month visit, 101 participants remained in the study. Between August 2016 and September

2017, these 101 participants were contacted to return for a post 12-month visit. Of those 101, 60

participants completed the extension visit. 41 were not assessed due to various reasons

including, but not limited to: vacation, conflict with work schedule, concurrent health issues,

lack of transport to study site, lack of interest, and loss to follow up. In summary, a flow

diagram illustrating the flow of participant recruitment is presented in Figure 2, in accordance

to the CONsolidated Standards Of Reporting Trials (CONSORT) guidance (Schulz, Altman,

Moher, & Group, 2010).

27

Figure 2: Flow of participant recruitment. Abbreviations: CR, CR; CAD, coronary artery

disease.

3.2. Differences between study participants who returned and those who did not

A significant portion (40.6%) of study participants did not return for the post 12-month visit

after completing the 6-month visit. As such, an evaluation of sociodemographic and clinical

differences between those who returned and those who did not was conducted in order to assess

potential selection bias. A greater proportion of the non-returning cohort had the presence of an

28

apolipoprotein E4 (APOE4) allele. There were no other significant inter-group differences in

terms of the characteristics (Table 1).

Table 1: Comparison of clinical and sociodemographic characteristics between study

participants who returned for the post 12-month visit (n=60) and those who did not (n=41).

Mean (SD) or N (%)

Characteristic Returned for post-

12 month visit

(n=60)

Did not return for

post-12 month visit

(n=41)

Test values (df), p-

value

Sociodemographics

Age (years) 64.5 (6.0) 64.7 (6.6) 0.14 (99), 0.89

Female 10 (16.7) 6 (14.6) 0.08 (2), 0.96

BMI 29.4 (5.6) 28.8 (4.6) -0.52 (99), 0.60

Education (years) 16.6 (3.2) 15.9 (3.1) -1.17 (99), 0.25

Married 49 (81.7) 32 (78.0) 1.83 (2), 0.40

Has a smoking history 32 (53.3) 28 (68.3) 4.51 (2), 0.11

Has a APOE4 allele 11 (18.3) 12 (29.3) 8.17 (2), 0.02*

CAD Severity

Cumulative stenosis (%) 152.28 (71.36) 141.97 (60.46) -0.68 (79), 0.50

Fitness Parameters

VO2peak (ml/kg/min) 20.67 (5.76) 21.71 (5.66) 0.88 (98), 0.38

Comorbidities

Depressed 8 (13.3) 5 (12.2) 2.67 (2), 0.26

Diabetic 10 (16.7) 6 (14.6) 0.08 (2), 0.96

Cognitive Domain Z-Scores

Verbal memory (SD) 0.13 (0.86) 0.04 (1.06) -0.44 (98), 0.66

Visuospatial memory (SD) 0.01 (0.89) -0.06 (1.06) -0.39 (99), 0.70

Processing speed (SD) 0.19 (0.98) -0.11 (0.90) -1.53 (99), 0.13

Executive function (SD) -0.01 (1.04) 0.08 (0.83) 0.45 (98), 0.66 Abbreviations: APOE4, apolipoprotein E4; BMI, body-mass index; CAD, coronary artery disease; df, degrees of

freedom; SD, standard deviation.

3.3. Associations between sociodemographic and clinical characteristics and change in

verbal memory performance in all study participants

In total, 60 study participants returned for the post 12-month visit; thus, completing all four

assessments. Study participants were followed a median of 3.5 years from their baseline visit

29

(inter-quartile range: 1.1 years). The majority of participants were male (n=49, 83.1%) and

Caucasian (n=47, 79.7%) with an average age of 65±6 years.

In bivariate longitudinal associations, greater years of education (b[SE]=0.05[0.02], p=0.04) and

greater VO2peak at baseline (b[SE]=0.03[0.01], p=0.01) was associated with a less decline in

verbal memory. Conversely, having depression (b[SE]=-0.54[0.24], p=0.03) was associated with

steeper decline in verbal memory performance over time. There were no other significant

associations between participant characteristics and verbal memory (Table 2).

30

Table 2: Baseline clinical and demographic characteristics in association with verbal memory

Z-score over a median of 3.53 years in all study participants (n=60)

Characteristic Mean (SD) or

N(%)

Association with verbal memory (b [SE], p-

value)

Sociodemographics

Age (years) 64.5 (6.0) 0.004288 [0.01], 0.74

Female 10 (16.7) -0.24 [0.21], 0.26

BMI 29.4 (5.6) -0.01 [0.01], 0.49

Education (years) 16.6 (3.2) 0.05 [0.02], 0.04*

Married 49 (81.7) -0.04 [0.20], 0.83

Has a smoking history 32 (53.3) 0.05 [0.15], 0.76

Has a APOE4 allele 11 (18.3) -0.05 [0.20], 0.81

CAD Severity

Cumulative stenosis (%) 152.28 (71.36) 0.002 [0.001], 0.05

Lipid Profile and HbA1c levels

LDL (mmol/L) 1.59 (0.63) -0.07 [0.12], 0.59

HDL (mmol/L) 1.28 (0.34) -0.16 [0.23], 0.48

Cholesterol (mmol/L) 3.45 (0.83) -0.13 [0.09], 0.17

Triglycerides (mmol/L) 1.23 (0.62) -0.23 [0.12], 0.07

HbA1c (mmol/mol) 0.06 (0.01) -17.13 [11.77], 0.15

Fitness Parameter

VO2peak (ml/kg/min) 20.67 (5.76) 0.03 [0.01], 0.01*

Comorbidities

Depressed 8 (13.3) -0.54 [0.24], 0.03*

Diabetic 10 (16.7) -0.05 [0.21], 0.81 Abbreviations: APOE4, apolipoprotein E4; BMI, body-mass index; CAD, coronary artery disease; df, degrees of

freedom; Hb1AC, hemoglobin A1C; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard

deviation.

3.4. Associations between sociodemographic and clinical characteristics and change in

verbal memory performance in study participants with possible VCIND

In total, 14 (23.3%) study participants had possible VCIND at baseline. In those with possible

VCIND, having diabetes at baseline was associated with less decline in verbal memory

performance (b[SE]=0.68[0.24], p=0.01). There were no other significant associations between

participant characteristics and verbal memory (Table 3).

31

Table 3: Baseline clinical and demographic characteristics in association with verbal memory z-

score over a median of 3.96 years in participants with possible VCIND (n=14).

Characteristic Mean (SD) or N (%) Association with verbal memory (b [SE], p-value)

Sociodemographics

Age 62.5 (5.6) 0.02[0.02], 0.44

Female 3 (21.4) 0.23[0.32], 0.49

BMI 29.0 (3.5) 0.0008[0.0009], 0.38

Years of education 16.1 (3.6) -0.02[0.04], 0.59

Married 13 (92.9) -0.58[0.48], 0.25

Has a smoking history 7 (50.0) -0.12[0.26], 0.64

APOE4 allele 2 (14.2) -0.39[0.35], 0.29

CAD Severity

Cumulative stenosis 117.8 (65.4) -0.003[0.002], 0.29

Lipid Profile and Hb1AC LDL (mmol/L) 1.7 (0.9) -0.07[0.21], 0.74

HDL (mmol/L) 1.3 (0.3) -0.05[0.30], 0.86

Cholesterol (mmol/L) 3.6(1.1) 0.08[0.14], 0.60

Triglycerides (mmol/L) 1.4(0.7) 0.06[0.17], 0.71

Hemoglobin A1C 0.06(0.01) -20[27.26], 0.46

Fitness Parameters

Max VO2 18.2(5.1) -0.03[0.02], 0.32

Comorbidities

Depressed 3(21.4) -0.45[0.32], 0.18

Diabetic 5(35.7) 0.68[0.24], 0.01*

Abbreviations: APOE4, apolipoprotein E4; BMI, body-mass index; CAD, coronary artery disease; df, degrees of

freedom; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation.

3.5. Sociodemographic and clinical differences between those with possible VCIND and

those with no possible VCIND

A greater proportion of those with possible VCIND were diabetic at baseline (χ2(df)=4.77(1),

p=0.03), compared to those with no possible VCIND. In terms of performance on the cognitive

battery, those with possible VCIND performed significantly worse in all cognitive domains.

There were no other significant sociodemographic or clinical differences between these two sub-

groups (Table 4).

32

Table 4: Comparison of baseline clinical and demographic characteristics between those with

possible VCIND and those with no possible VCIND.

Mean (SD) or N (%)

Characteristic Possible VCIND

(n=14)

No possible VCIND

(n=46)

Test Value (df), p-value

Sociodemographics

Age 62.5 (5.8) 65.1 (6.0) 1.45 (58), 0.15

Female 3 (21.4) 7 (15.2) 0.30 (1), 0.59

BMI 29.0 (3.7) 29.5 (6.0) 0.27 (58), 0.79

Years of education 16.1 (3.7) 16.8 (3.1) 0.64 (58), 0.53

Married 13 (92.9) 36 (78.3) 1.53 (1), 0.22

Has a smoking history 7 (50.0) 21 (54.3) 0.08 (1), 0.78

Has a APOE4 allele 2 (14.3) 9 (19.6) 0.20 (1), 0.66

CAD Severity

Cumulative stenosis 117.8 (69.0) 161.6 (70.0) 1.76 (45), 0.09

Fitness Parameter

Max VO2 (ml/kg/min) 18.20 (5.31) 21.43 (5.73) 1.87 (58), 0.07

Comorbidities

Depressed 3 (21.4) 5 (10.9) 1.04 (1), 0.31

Diabetic 5 (35.7) 5 (10.9) 4.77 (1), 0.03*

Cognitive Domain Z-Scores

Verbal memory (SD) -0.67 (0.92) 0.35 (0.70) 4.34 (57), 0.00*

Visuospatial memory (SD) -0.58 (1.02) 0.19 (0.78) 3.04 (58), 0.00*

Processing speed (SD) -0.31 (0.92) 0.34 (0.96) 2.21 (58), 0.03*

Executive function (SD) -1.16 (0.80) 0.34 (0.83) 5.98 (58), 0.00* Abbreviations: APOE4, apolipoprotein E4; BMI, body-mass index; CAD, coronary artery disease; df, degrees of

freedom; SD, standard deviation; VCIND, vascular cognitive impairment, no dementia.

3.6. Trajectory of verbal memory performance throughout the study

Across all study participants, verbal memory performance did not change significantly over time

(b=-0.0001, p=0.32). This was consistent even when separate mixed regression models were

conducted for those with possible VCIND (b[SE]=0.00024[0.00020], p=0.24) and those with no

possible VCIND (b[SE]=0.00005[0.0001], p=0.70).

33

Table 5: Composite Z-scores of verbal memory performance in standard deviations over the

course of the study.

Cohort Baseline 3-month visit 6-month visit Post 12-month visit

Overall 0.11 -0.03 0.06 0.02

Possible VCIND -0.67 -0.59 -0.55 -0.79

No possible VCIND 0.33 0.14 0.26 0.25 Abbreviations: VCIND, vascular cognitive impairment, no dementia.

Figure 3: Trajectory of average composite Z-score in verbal memory domain in participant

cohorts.

3.7. Covariate multi-collinearity and normalization of plasma sphingolipid

concentrations

The covariates were not collinear as seen in Table 6. Therefore, they were included in all

subsequent multivariate models.

Table 6: Multi-collinearity statistics of a priori covariates in mixed regression models

Tolerance VIF

Age 0.98 1.02

BMI 0.95 1.05

Years of education 0.98 1.03 Abbreviations: BMI, body-mass index; VIF, variance inflation factor.

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

Visit 1 Visit 2 Visit 3 Visit 4

Co

mp

osi

te Z

-sco

re (

S.D

.)

All

VCIND

NoVCIND

34

Raw and log-transformed concentrations of sphingolipids are presented below (Table 7).

CerC18:0, SM18:0, MHxCerC18:0, and S1P plasma concentrations were log-transformed to

obtain a normal distribution for mixed regression analyses, so that their concentrations on a

notionally common scale. The normal distributions of the log-transformed sphingolipid

concentrations are presented in Figures 4 to 7. CerC18:0 ratios were calculated using the log-

transformed sphingolipid ratios.

Table 7: Overview of plasma sphingolipid concentrations at baseline and their log-transformed

values.

Species (ng/mL) Mean (S.D.) Range (min.-max.)

CerC18:0 7.60 (5.14) 1.45-30.89

SM18:0 123505 (27762) 65300-205000

MHxCerC18:0 8.33 (4.66) 1.84-28.86

S1P 197.70 (178.16) 16.32-878.95

Log CerC18:0 0.80 (0.27) 0.16-1.49

Log SM18:0 5.08 (0.10) 4.81-5.31

Log MHxCerC18:0 0.87 (0.22) 0.27-1.46

Log S1P 2.13 (0.39) 1.21-2.94 Abbreviations: C18, acyl chain of 18 carbons; Cer, ceramide; MHxCer, monohexosylceramide; S1P, sphingosine-

1-phosphate; SD, standard deviation; SM, sphingomyelin; VCIND, vascular cognitive impairment, no dementia.

Figure 4: Normal Q-Q plot of log-transformed CerC18:0

35

Figure 5: Normal Q-Q plot of log-transformed SM18:0

Figure 6: Normal Q-Q plot of log-transformed MHxCerC18:0

36

Figure 7: Normal Q-Q plot of log-transformed S1P

Figure 8: Normal Q-Q plot of log-transformed CerC18:0/MHxCerC18:0

37

Figure 9: Normal Q-Q plot of log-transformed CerC18:0/SM18:0

Figure 10: Normal Q-Q plot of log-transformed CerC18:0/S1P

38

3.8. Primary: Associations between baseline CerC18:0 and change in verbal memory

performance

Longitudinally, each log-unit increase in baseline plasma CerC18:0 was significantly associated

with a 0.91 SD decline in verbal memory performance (b[SE]=-0.91[0.30], p=0.003) after

adjustment for age, BMI, years of education, and days since baseline visit by including them as

fixed effects (Table 8).

Table 8: Association between plasma CerC18:0 at baseline and change in verbal memory

performance over the course of the study in participants with CAD (n=60), adjusted for age,

BMI, years of education, and days since baseline.

Variable Estimate (b) [95% CI] SE df t-value Significance

(p-value)

Intercept -0.95 [-3.09, 1.19] 1.07 55 -0.89 0.38

Days since baseline -0.0002 [-0.0004, 0.00008] 0.0001 177 -1.29 0.20

Age 0.02 [-0.01, 0.05] 0.01 55 1.45 0.15

BMI -0.01 [-0.03, 0.02] 0.01 55 -0.36 0.72

Years of education 0.04 [-0.00555, 0.09] 0.02 55 1.77 0.08

CerC18:0 -0.91 [-1.51, 0.32] 0.30 55 -3.08 0.003* Abbreviations: BMI, body-mass index; C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer,

ceramide; CI, confidence interval; df, degrees of freedom; SE, standard error.

3.9. Secondary: Associations between CerC18:0/MHxCerC18:0 ratio and change in

verbal memory

Baseline plasma MHxCerC18:0 concentration was not associated with change in verbal memory

over time (b[SE]=-0.46[0.38], p=0.22), after adjusting for age, BMI, years of education and

days since baseline visit. In contrast, baseline plasma CerC18:0/MHxCerC18:0 was significantly

associated with change in verbal memory performance (b[SE]=-0.90[0.40], p=0.03), after

adjusting for age, BMI, years of education and days since baseline visit. Each log-unit increase

in the ratio was associated with a 0.90 SD decrease in verbal memory performance (Table 9).

39

Table 9: Association between plasma CerC18:0/MHxCerC18:0 at baseline and change in verbal

memory performance over the course of the study in participants with CAD (n=60), adjusted for

age, BMI, years of education, and days since baseline.

Variable Estimate (b) [95% CI] SE df t-value Significance

(p-value)

Intercept -1.29 [-3.53, 0.94] 1.12 55 -1.16 0.25

Days since baseline -0.0002 [-0.0004, 0.0001] 0.0001 177 -1.27 0.20

Age 0.01 [-0.02, 0.04] 0.01 55 0.78 0.44

BMI -0.004 [-0.03, 0.02] 0.01 55 -0.30 0.76

Years of education 0.05 [0.002, 0.10] 0.02 55 2.10 0.04*

CerC18:0/MHxCerC18:0 -0.90 [-1.70, -0.09] 0.40 55 -2.23 0.03* Abbreviations: BMI, body-mass index; C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer,

ceramide; CI, confidence interval; df, degrees of freedom; MHxCer, monohexosylceramide; SE, standard error.

3.10. Tertiary: Associations between CerC18:0/SM18:0 ratio and change in verbal

memory

Plasma concentrations of SM18:0 were not associated with change in verbal memory over time

(b[SE]=-0.90[0.84], p=0.29), after adjusting for age, BMI, years of education, and days since

baseline. However, baseline CerC18:0/SM18:0 was significantly associated with change in

verbal memory performance (b[SE]=-1.11[0.36], p=0.004) in a mixed regression model adjusted

for age, BMI, years of education, and days since baseline. Each log-unit increase in the

CerC18:0/SM18:0 ratio correlated with a 1.11 SD decline in verbal memory performance

(Table 10).

Table 10: Association between plasma CerC18:0/SM18:0 at baseline and change in verbal

memory performance over the course of the study in participants with CAD, adjusted for age,

BMI, years of education, and days since baseline.

Variable Estimate (b) [95% CI] SE df t-value Significance

(p-value)

Intercept -6.30 [-10.54, -2.06] 2.11 55 -2.98 0.004

Days since

baseline

-0.00016 [-0.0004, 0.0009] 0.0001 177 -1.27 0.20

Age 0.02 [-0.01, 0.05] 0.01 55 1.41 0.16

BMI -0.01 [-0.04, 0.02] 0.01 55 -0.62 0.54

Years of

education

0.04 [-0.01, 0.09] 0.02 55 1.75 0.09

CerC18:0/SM18:0 -1.11 [-1.84, -0.38] 0.36 55 -3.05 0.004* Abbreviations: BMI, body-mass index; C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer,

ceramide; CI, confidence interval; df, degrees of freedom; SE, standard error; SM, sphingomyelin.

40

Plasma concentrations of S1P were not associated with decrease in verbal memory performance

(b[SE]=-0.41[0.20], p=0.05) over time, after adjusting for age, BMI, years of education, and

days since baseline. Likewise, CerC18:0/S1P was not associated with change in verbal memory

over time, after adjustment for the same covariates (b[SE]=0.01[0.21], p=0.97; Table 11).

Table 11: Association between plasma CerC18:0/S1P at baseline and change in verbal memory

performance over the course of the study in participants with CAD, adjusted for age, BMI, years

of education, and days since baseline.

Variable Estimate (b) [95% CI] SE df t-value Significance

(p-value)

Intercept -1.04 [-3.41, 1.32] 1.18 52 -0.88 0.38

Days since

baseline

-0.0002 [-0.0004, 0.0001] 0.0001 168 -1.13 0.26

Age 0.01 [-0.02, 0.03] 0.01 52 0.52 0.60

BMI -0.001 [-0.03, 0.03] 0.01 52 -0.10 0.92

Years of

education

0.05 [-0.001, 0.10] 0.02 52 1.98 0.05

CerC18:0/S1P 0.01 [-0.41, 0.42] 0.21 52 0.04 0.97 Abbreviations: BMI, body-mass index; C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer,

ceramide; CI, confidence interval; df, degrees of freedom; S1P, sphingosine-1-phosphate; SE, standard error.

3.11. Exploratory analyses: Differences in associations between those with possible

VCIND and those with no possible VCIND

Analyses were repeated with an interaction term of possible VCIND and baseline CerC18:0

ratio in mixed models.

3.11.1. Association between CerC18:0 and change in verbal memory performance

Although presence of VCIND was significantly associated with decline in verbal memory, it did

not significantly affect the association between baseline CerC18:0 and change in verbal memory

performance (b[SE]=0.10[0.56], p=0.86), after adjusting for age, years of education, days since

baseline, possible VCIND, and baseline CerC18:0 (Table 12).

41

Table 12: Association between a possible VCIND and CerC18:0 interaction at baseline and

change in verbal memory performance in CAD participants (n=60), adjusted for age, years of

education, days since baseline, possible VCIND, and baseline CerC18:0.

Variable Estimate (b) [95% CI] SE df t-value p-value

Intercept -0.03 [-1.64, 1.58] 0.80 54 -0.04 0.97

Days since baseline -0.0001 [-0.0003, 0.0001] 0.0001 177 -1.14 0.26

Age 0.01 [-0.02, 0.03] 0.01 54 0.48 0.63

Years of education 0.04 [-0.005, 0.08] 0.02 54 1.77 0.08

Possible VCIND -1.00 [-1.94, -0.06] 0.47 54 -2.13 0.04*

CerC18:0 0.10 [-1.01, 1.21] 0.56 54 0.18 0.86

VCIND*CerC18:0 0.10 [-1.01, 1.21] 0.56 54 0.18 0.86 Abbreviations: C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer, ceramide; CI,

confidence interval; df, degrees of freedom; MHxCer, monohexosylceramide; SE, standard error; VCIND, vascular

cognitive impairment, no dementia.

3.11.2. Association between CerC18:0/MHxCerC18:0 and change in verbal memory

performance

Both possible VCIND and CerC18:0/MHxCerC18:0 are significantly associated with change in

verbal memory performance. However, the presence of VCIND did not significantly affect the

association between baseline CerC18:0/ MHxCerC18:0 and change in verbal memory

performance (b[SE]=1.08[0.74], p=0.15), after adjusting for age, years of education, days since

baseline, possible VCIND, and baseline CerC18:0/MHxCerC18:0 (Table 13).

Table 13: Association between a possible VCIND and CerC18:0/MHxCerC18:0 ratio

interaction at baseline and change in verbal memory performance in CAD participants (n=60),

adjusted for age, years of education, days since baseline, possible VCIND, and

CerC18:0/MHxCerC18:0.

Variable Estimate (b) [95% CI] SE df t-value p-value

Intercept -0.34 [-2.01, 1.33] 0.83 54 -0.41 0.69

Days since baseline -0.0001 [-0.0003, 0.00009] 0.0001 177 -1.16 0.25

Age -0.002 [-0.02, 0.02] 0.01 54 -0.15 0.88

Years of education 0.04 [0.001, 0.08] 0.02 54 2.08 0.04

Possible VCIND -0.86 [-1.20, -0.53] 0.17 54 -5.19 <.0001*

CerC18:0/MHxCerC18:0 -1.10 [-1.92, -0.27] 0.41 54 -2.67 0.01*

VCIND*CerC18:0/MHxCerC18:0 1.08 [-0.39, 2.56] 0.74 54 1.47 0.15 Abbreviations: C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer, ceramide; CI,

confidence interval; df, degrees of freedom; MHxCer, monohexosylceramide; SE, standard error; VCIND, vascular

cognitive impairment, no dementia.

42

3.11.3. Association between CerC18:0/SM18:0 and change in verbal memory performance

Both possible VCIND and CerC18:0/SM18:0 were not significantly associated with change in

verbal memory performance. Accordingly, the association between baseline CerC18:0/SM18:0

and change in verbal memory performance was not statistically significant between those with

possible VCIND and those without (b[SE]=-0.28[0.67], p=0.68), after adjusting for age, years of

education, days since baseline, possible VCIND, and baseline CerC18:0/SM18:0 (Table 14).

Table 14: Association between a possible VCIND and CerC18:0/SM18:0 ratio interaction and

change in verbal memory performance over the course of the study in CAD participants (n=60),

adjusted for age, years of education, days since baseline, possible VCIND, and

CerC18:0/SM18:0.

Variable Estimate (b) [95%

CI]

SE df t-value Significance

(p-value)

Intercept -3.51 [-7.69, 0.67] 2.08 54 -1.68 0.10

Days since baseline -0.0001 [-0.0003,

0.0001]

0.0001 177 -1.10 0.27

Age 0.0003 [-0.02, 0.03] 0.01 54 0.28 0.78

Years of education 0.04 [-0.004, 0.08] 0.02 54 1.82 0.07

Possible VCIND -2.08 [-7.80, 3.65] 2.86 54 -0.73 0.47

CerC18:0/SM18:0 -0.70 [-1.46, 0.06] 0.38 54 -1.84 0.07

VCIND*CerC18:0/SM18:0 -0.28 [-1.62, 1.06] 0.67 54 -0.41 0.68 Abbreviations: C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer, ceramide; CI,

confidence interval; df, degrees of freedom; SE, standard error; SM, sphingomyelin; VCIND, vascular cognitive

impairment, no dementia.

3.11.4. Association between CerC18:0/S1P and change in verbal memory performance

Both possible VCIND and CerC18:0/S1P were not significantly associated with change in

verbal memory performance. Similarly, the presence of VCIND did not significantly affect the

association between baseline CerC18:0/S1P and change in verbal memory performance

(b[SE]=0.65[0.55], p=0.25), after adjusting for age, years of education, days since baseline,

possible VCIND, and baseline CerC18:0/S1P (Table 15).

43

Table 15: Association between plasma CerC18:0/S1P concentrations at baseline and change in

verbal memory performance over the course of the study in CAD participants (n=60), adjusted

for age, years of education, days since baseline, possible VCIND, and CerC18:0/S1P.

Variable Estimate (b) [95% CI] SE df t-value Significance

(p-value)

Intercept -0.15 [-2.08, 1.79] 0.96 51 -0.15 0.88

Days since baseline -0.0001 [-0.0003, 0.0001] 0.0001 168 -0.95 0.34

Age -0.005 [-0.03, 0.02] 0.01 51 -0.44 0.66

Years of education 0.04 [-0.0008, 0.08] 0.02 51 1.97 0.05

VCIND -0.02 [-1.30, 1.26] 0.64 51 -0.03 0.98

CerC18:0/S1P -0.12 [-0.51, 0.26] 0.19 51 -0.64 0.52

VCIND*CerC18:0/S1P 0.68 [-0.20, 1.55] 0.44 51 1.55 0.13 Abbreviations: C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer, ceramide; CI,

confidence interval; df, degrees of freedom; S1P, sphingosine-1-phosphate; SE, standard error; VCIND, vascular

cognitive impairment, no dementia.

3.12. Summary of Results

Overall, higher plasma CerC18:0 concentrations as well as increased baseline ratios of

CerC18:0/MHxCerC18:0 and CerC18:0/SM18:0 were significantly correlated with steeper

decline in verbal memory performance over time. However, in exploratory analyses, none of

these associations were mediated by the presence of VCIND. A summary of the results is

presented in Table 16.

Table 16: Associations between baseline CerC18:0 biomarkers and change in verbal memory in

the overall CAD population (n=60), adjusted for age, BMI, and years of education.

b [SE], p-value Without interaction With interaction*

CerC18:0 -0.91 [0.30], 0.003* 0.10 [0.56], 0.86

CerC18:0/MHxCerC18:0 -0.90 [0.40], 0.03* 1.08 [0.74], 0.15

CerC18:0/SM18:0 -1.11 [0.36], 0.004* -0.28 [0.67], 0.68

CerC18:0/S1P 0.01 [0.21], 0.97 0.68 [0.44], 0.13 *this column presents results of an interaction term of baseline CerC18:0 biomarker and possible VCIND Abbreviations: C18:0, acyl chain length of 18 carbons; CAD, coronary artery disease; Cer, ceramide; MHxCer,

monohexosylceramide; S1P, sphingosine-1-phosphate; SM, sphingomyelin; SE, standard error; VCIND, vascular

cognitive impairment, no dementia.

44

Chapter 4: Discussion and Recommendations for Future Studies

4.1. Study Findings and Interpretations

The present findings demonstrate a relationship between markers of aberrant CerC18:0

metabolism and cognitive decline, corroborating our previous finding that baseline plasma

CerC18:0 is a robust marker of verbal memory performance in a cohort of CAD patients

(Saleem et al., 2017). We investigated longitudinal relationships between the equilibrium of

baseline CerC18:0 biosynthesis and change in verbal memory performance. In both the

catabolic and salvage pathways, an increased baseline ratio of CerC18:0 to its respective

precursor was significantly correlated with worse verbal memory performance over time.

However, in exploratory analyses of these CerC18:0 ratios, none of these associations appeared

to be mediated by the presence of VCIND. Collectively, our results show that ratios of

CerC18:0 to its certain precursors may be sensitive marker of cognitive change in the early,

prodromal stages of VCI.

4.1.1. Characterization of study participants and comparison of sub-cohorts

Considering the high rate of attrition in participant retention between the 6-month visit and post

12-month visit, a comparison of sociodemographic and clinical characteristics was warranted

between study participants who returned for a post 12-month visit and those who did not. We

found that a greater portion of those that did not return had an APOE4 allele. Given that

presence of an APOE4 allele is associated with worse cognitive function (Weir, 1990), which in

turn is associated with increased risk of dropping out of CR, this suggests that those who did not

return would have worse cognitive performance. However, we did not find any significant

differences in cognitive performance between those who returned and those who didn’t.

45

In the overall study population, greater years of education attained and greater VO2peak at

baseline was associated with a smaller decline in verbal memory. This was expected as

education is a well-characterized predictor of verbal memory performance (Schmand et al.,

1997; Mortimer et al., 2005). To account for its confounding effect, years of education were

included as a priori covariate in subsequent analyses. The significant relationship between

VO2peak and verbal memory performance is not unexpected either. Previous studies conducted by

our group and others have found cardiopulmonary fitness to be predictive of verbal memory,

alluding to the importance of physical exercise in modulating cognitive performance in older

adults (Zhu et al., 2014; Saleem et al., 2017; Hayes, Forman, & Verfaellie, 2016). However, due

to limited sample size, our models were not adequately powered to adjust for VO2peak.

In those with possible VCIND, the presence of diabetes mellitus at baseline was significantly

associated with less decline in verbal memory performance, which is contrary to expectations

(Kelly et al., 2016; Yau, Kluger, Borod, & Convit, 2014). A plausible explanation may be found

in the insulin-sensitizing medications that diabetic patients take chronically. For instance,

duration of metformin use was inversely correlated with risk of cognitive impairment in the

Singapore Longitudinal Aging Study (Ng et al., 2014). Findings reported by Abbatecola and her

colleagues corroborate this hypothesis as duration of metformin/rosiglitazone use stabilized

cognitive decline in individuals with diabetes mellitus and mild cognitive impairment

(Abbatecola et al., 2010). However, given our limited sample size (n=14), the significance of

this association is likely by chance. Conversely, when comparing cross-sectional profiles of

those with possible VCIND and those with no possible VCIND, a greater portion of those with

possible VCIND also had diabetes mellitus compared those with no possible VCIND. As

discussed, this aligns with evidence in the literature as those with diabetes mellitus may

46

experience rates of cognitive decline that are up to 2 times faster compared to those without

(Biessels, Staekenborg, Brunner, Brayne, & Scheltens, 2006). The chronic hyperglycemia and

reactive hyperinsulinemia associated with diabetes mellitus may cause the accumulation of

oxidative stress products and resultant microvascular damage (Kalmijn, Feskens, Launer,

Stijnen, & Kromhout, 1995; Gispen & Biessels, 2000).

4.1.2. Primary Findings

In our primary findings, baseline CerC18:0 concentrations were associated with change in

verbal memory over a median of 3.5 years. This finding corroborates our previous report that

CerC18:0 is associated with general cognitive decline over 6 months (Saleem et al., 2017),

providing valuable evidence that CerC18:0 may be a robust biomarker of verbal memory change

in those with CAD. As such, we postulate that inter-participant differences in CerC18:0

concentrations are associated with rate of cognitive change over time. Moreover, given that

sphingolipids ratios may be more predictive of cognitive decline than individual sphingolipids

(Mielke et al., 2011), CerC18:0 ratios were used as preliminary markers of shifts in enzymatic

activity.

4.1.3. Secondary Findings

We report a novel inverse association between baseline CerC18:0/MHxCer18:0 ratio and

change in verbal memory performance over time. In the salvage pathway, galactocerebrosidase

and glucosylcerebrosidase catalyze the conversion from monohexosylceramides into ceramides

(Figure 1). Given that the salvage pathway accounts for 50-90% of sphingolipid generation

(Kitatani et al., 2008), dysregulation of these enzymes may have significant clinical impact. This

is no more evident than in individuals with Krabbe disease; a disease characterized by infantile-

47

onset neurologic deterioration due to deficient galactocerebrosidase activity, ranging from 5-

10% of normal levels (Suzuki, 2003). Interestingly, in adults diagnosed with late-onset Krabbe

disease, a case series demonstrated a clinical profile similar to that of cerebrovascular disease,

reporting progressive cognitive impairment and white matter hyperintensities not unlike those

associated with VCI (Malandrini et al., 2013). Furthermore, galactocerebrosidase gene

expression increases with severity of cognitive impairment in temporal regions; functional brain

regions that are involved in tasks involving verbal memory (Katsel, Li, & Haroutunian, 2007;

Swardfager et al., 2018).

As monohexosylceramides encompass both glucosylceramides and galactosylceramides, the

ratio of C18:0/MHxCer18:0 may also reflect glucerebrosidase activity. In Parkinson’s disease

(PD) patients with cognitive impairment, plasma levels of CerC18:0 were higher than those in

PD patients without cognitive impairment. The authors suggest that this may be a result of a

mutation in the glucerebrosidase gene, which is a common genetic risk factor for sporadic PD

(Mielke et al., 2013). This was inconclusive, because genotyping was not done in that study.

However, evidence from another study supports this hypothesis, demonstrating that

glucerebrosidase mutations in PD patients are associated with more severe impairment in

executive function (Mata et al., 2016). Furthermore, expression of UDP-glucose ceramide

glucosyltransferase, an enzyme that catalyzes the synthesis of glycosphingolipids, was

decreased in all stages of cognitive decline along the AD spectrum (Katsel et al., 2007).

As evident in sporadic PD and Krabbe disease, severe dysregulation of the salvage pathway has

significant clinical consequences, but the extent of perturbation in those pathologies likely

48

exceeds that observed in our study population. Despite this, our findings suggest that chronic,

mild dysregulation may have a cumulative, clinical impact.

4.1.4. Tertiary Findings

In our tertiary hypotheses, an increased ratio of CerC18:0/SM18:0 suggests increased activity of

sphingomyelinase in the catabolic pathway, which catabolizes SM18:0 into CerC18:0. In

support of this, plasma levels of secretory acid sphingomyelinase and ceramides have been

found to be elevated in CAD patients (Pan et al., 2014). This increase in ceramides causes

aggregation of LDL, which leads to formation of atherosclerotic plaques (Tabas, Williams, &

Boren, 2007). However, the role of sphingomyelinase in cognition remains less clear. A study

investigating gene expression in the brain, found that sphingomyelinase gene expression did not

differ between cohorts along the cognitive impairment spectrum (Katsel et al., 2007). In

contrast, a recent paper reported that an induced deficiency of neutral sphingomyelinase-2

improved cognition in an animal model of AD (Dinkins et al., 2016). As such, the influence of

sphingomyelinase activity on cognitive performance in CAD patients remains unclear and

warrants further investigation.

Surprisingly, elevated plasma S1P was associated with worse verbal memory performance over

time. This was surprising, because numerous studies report S1P to be neuroprotective,

particularly in AD pathology (Couttas et al., 2014; Ceccom et al., 2014). While there is evidence

to suggest a neuroprotective role of S1P, other studies report that accumulation of S1P can be

neurotoxic (Hagen et al., 2009; Mitroi et al., 2016). Alternatively, the elevation of S1P, driven

by upregulation of sphingosine kinase, may be a compensatory mechanism to certain stimuli.

This has been suggested to be a physiological response to inflammatory cytokines like TNF-α

49

and interleukins (Spiegel & Milstien, 2003), which may be a potential mechanism through

which ceramides is related to neurodegeneration. However, this has yet to be explored in clinical

studies. Overall, these discrepancies may implicate the importance of S1P balance as suggested

by Karunakaran and colleagues (Karunakaran & van Echten-Deckert, 2017). An alternative

explanation may be that peripheral S1P levels in our participants may be more representative of

CAD pathology than neurodegenerative change, due to its proximity to the anatomical location

of CAD. In coronary arteries, S1P signalling induces adhesion molecule expression and

subsequent atherogenesis (Xia et al., 1998), implicating its involvement in CAD pathogenesis.

Furthermore, peripheral levels of S1P are elevated in CAD patients and predict occurrence and

severity of coronary stenosis (Deutschman et al., 2003; Sattler et al., 2010). Taken together, we

propose that greater CAD severity due to elevated peripheral S1P levels is associated with more

severe cognitive impairment.

4.1.5. Exploratory Hypothesis

Despite evidence that suggest cognition in those with VCIND declines at a greater rate than

those without VCIND (Stern et al., 1994), associations between CerC18:0 ratios and verbal

memory did not remain significant in those with VCIND at baseline. The reason for this is

unclear, but it is likely be attributed to our limited sample size as only 14 participants had

possible VCIND at baseline. As such, stratification by neuropsychological criteria warrants

further investigation in a larger, more balanced analysis of patients with possible VCIND and

those with no possible VCIND.

Although our findings were statistically significant, the inconsistency in our findings brings

attention to a prevailing need to integrate multiple biomarkers into predictive biomarker models,

50

if prognostic biomarkers are to be clinically useful, rather than investigating the predictive

ability of a single biomarker. More advanced statistical methods like partial least discriminant

analysis and random forest analysis are becoming more accessible for the general scientific

community and is the logical progression in computational methods to discover and validate

biomarker models such as in recent efforts to validate blood-based amyloid markers for AD

(Nakamura et al., 2018).

4.2. Pathological mechanisms of ceramides in neurodegeneration

Ceramides contribute to progressive and irreversible cognitive deterioration by inducing the

premature death of neurons and oligodendrocytes in multiple ways (Mencarelli & Martinez-

Martinez, 2013). These terminally differentiated neural cells are particularly vulnerable to

damage due to their low antioxidative capacity; thus they are not easily replaced (Thorburne &

Juurlink, 1996; Juurlink, Thorburne, & Hertz, 1998). One extensively-studied mechanism is

through the excessive apoptosis: increased level of ceramides induces release of TNF-α, which

triggers cell death (Obeid et al., 1993), while their depletion hinders apoptotic processes (Bose

et al., 1995). They can directly promote apoptosis by increasing permeability of mitochondrial

membranes (Falluel-Morel et al., 2004), which subsequently releases pro-apoptotic factors such

as cytochrome C and oxidative stress markers (Stoica, Movsesyan, Lea, & Faden, 2003; France-

Lanord, Brugg, Michel, Agid, & Ruberg, 1997). Alternatively, ceramide may indirectly induce

apoptosis by amplifying apoptotic signals. Ceramide can cluster key death signalling molecules

(e.g. CD95 or TNF-α receptors) through spatial reorganization of the plasma membrane and

formation of lipid microdomains (Gulbins, 2003).

51

While ceramides induces apoptosis, it is also known to inhibit pro-survival pathways such as the

Akt pathway, which supplies cells with glucose and essential nutrients. In an aforementioned

study, ceramides decreased the viability of neural cells by inhibiting the pro-survival PI3-K/Akt

pathway through activation of protein phosphatase 2A (Scarlatti et al., 2004). Interestingly, the

addition of S1P directly counteracts ceramide-induced apoptosis by decreasing oxidative stress

and upregulation of the anti-apoptotic protein, Bcl-2 (Czubowicz & Strosznajder, 2014). As

such, the balance of ceramide and S1P is proposed to be an important determinant of cellular

fate (Cuvillier et al., 1996).

CerC18:0 may induce senescence. For instance, CerC18:0 downregulates the expression of

telomerase, which usually elongates the end of existing chromosomes to prevent senescence.

Telomerase is constitutively expressed in the hippocampus, a brain region which is frequently

associated with verbal memory performance (Bonner-Jackson, Mahmoud, Miller, & Banks,

2015). Moreover, the hippocampus is continuously supplied with neural stem and progenitor

cells. As such, accumulation of CerC18:0 may increase neuronal vulnerability in the brain.

In considering these detrimental effects, ceramides have been suggested as a novel therapeutic

target (Canals, Perry, Jenkins, & Hannun, 2011). However, it is important to be mindful that

ceramides are also crucial in the proper functioning of the central nervous system (Furuya,

Mitoma, Makino, & Hirabayashi, 1998). Therefore, an emphasis on maintaining sphingolipid

equilibrium rather than decreasing levels indiscriminately is necessary. Further investigation on

the role of these catalyzing enzymes is warranted as their activity is most proximal to this

balance. Overall, a plethora of evidence and the role of ceramides in multiple biochemical

pathways suggest that there are likely multiple mechanisms by which this occurs.

52

4.3. Strengths

This study has numerous strengths. The varying time intervals between study visits were

adjusted for using a spatial covariance structure in mixed regression models. This ensures a

more precise adjustment as opposed to assuming that inter-visit time intervals are equal between

individuals. In addition, we also investigated the possible perturbations of three CerC18:0

biosynthesis pathways to ensure a comprehensive analysis of CerC18:0 metabolism. Although

metabolites specific for the de novo pathway were not assessed, ceramide synthesis through this

pathway accounts for only a minority of brain sphingolipids. Furthermore, we adjusted for

pertinent covariates, particularly years of education. Our findings remained significant after

adjustment for education, demonstrating robustness.

4.4. Limitations

4.4.1. Methodological limitations with recommendations for future studies

Our main analysis did not adjust for cardiopulmonary fitness, because our sample size did not

permit the addition of another covariate into the model. Although exercise in CR has been

shown to improve certain cognitive domains (Gunstad et al., 2005), the effect of aerobic

exercise on cognition is inconsistent (Young, Angevaren, Rusted, & Tabet, 2015). Nonetheless,

future studies should adjust for baseline cardiopulmonary fitness if sample size is adequate.

Secondly, the independent influence of CAD on plasma ceramides levels could not be

elucidated as we did not have a healthy control group. However, this population of CAD

participants with perturbed lipid metabolism and normal cognitive function is the ideal

population to investigate our hypotheses as they are representative of the latent, pre-

symptomatic stage of VCI. Our analyses should be investigated in a larger population in

validation studies. Another limitation is the technical inability of mass spectroscopy to isolate

53

glucosylceramides from galactosylceramides (Savica et al., 2016). As such, more extensive

techniques should be used to separate them in future studies to see if there are clinically

impactful differences between these two related isomers.

4.4.2. Mechanistic Limitations

A key limitation in the interpretation of our findings is that peripheral levels of ceramides may

not be indicative of its levels in the brain, the area in which its downstream effects are most

relevant. Synthetic ceramides (e.g. CerC2:0 and CerC6:0) can cross the BBB (de la Monte et al.,

2010), but the BBB permeability of long-chained ceramides like CerC18:0 has yet to be

investigated. In light of this, matched CSF and serum sphingolipid concentrations have been

found to be correlated in patients with HIV (Bandaru et al., 2009). Ultimately, even if peripheral

concentrations of long-chained ceramides do not correlate with those in the central nervous

system, they may still be indicative of pathophysiological changes in the brain. Since ceramides

function as secondary messengers, their downstream effectors (e.g. TNF-α) may cross the BBB

and elicit neurotoxic effects (de la Monte, 2012). Lastly, the use of CerC18:0 ratios in this study

are exploratory and do not directly support the discussed implications on enzymatic activity.

Future investigations should consider the use of biochemical assays such as those to measure

sphingomyelinase (Taki & Chatterjee, 1995), galactocerebrosidase (Martino et al., 2009), and

ceramide synthase activity (Kim, Qiao, Toop, Morris, & Don, 2012) as a more accurate and

direct measurement of enzymatic activity.

4.5. Implications on drug development

Considering the established role of ceramides in apoptosis and cellular survival, the present

findings invite further research into whether these sphingolipid pathways should be considered

54

for drug development in vascular neurodegeneration. Although ceramides are a direct inducer of

these pathologic effects, it is a poor drug target given its unpredictability as a protein (Makley &

Gestwicki, 2013). Instead, targeting the enzymes that catalyze its synthesis may be a more

feasible strategy due to their specificity of action, crucially neutral and acidic

sphingomyelinases.

Functional inhibitors of acid sphingomyelinase (FIASMAs) block ASM activity by preventing it

from attaching to inner lysosomal membranes and results in its proteolytic inactivation. Most

FIASMAs such as desipramine, dextromethorphan, and maprotiline are already approved for

clinical use. However, given that these FIASMAs are part of many other drug classes, their

effects on their originally intended drug targets need to be minimized (Kornhuber et al., 2010).

The abundance of neutral sphingomyelinase (NSM) in the hippocampus (Hofmann, Tomiuk,

Wolff, & Stoffel, 2000) and its effect on nerve growth factor-induced neurite outgrowth,

synaptogenesis (Brann et al., 1999), and synaptic plasticity (Wheeler et al., 2009) due to

downstream ceramide production has inspired efforts to modulate NSM activity in order

preserve neuronal function. Recent high-throughput screening at Johns Hopkins found that

cambinol decreased TNF-induced and ILB-induced ceramide production; thus, it may have

neuroprotective properties (Figuera-Losada et al., 2015). However, caution should be exercised,

because the detrimental effect of NSM modulation has also been reported. The same group at

Johns Hopkins reported that inhibition of NSM-2 disrupted spatial memory performance in rats

through an AMPA receptor-mediated mechanism (Tabatadze et al., 2010). While these results

demonstrate the importance of sphingolipid balance in memory function, these discrepant

findings highlight a need for better characterization of these enzymes and their cognitive impact.

55

4.6. Conclusions

In summary, higher baseline CerC18:0 concentrations, higher CerC18:0/MHxCerC18:0, and

higher CerC18:0/SM18:0 ratios were significantly associated with steeper decline in verbal

memory performance in CAD participants. These ratios may indicate perturbations within the

catabolic pathway and salvage pathway of ceramides biosynthesis, respectively. However, when

we compared these relationships between those with possible VCIND and those who without,

they were not significantly different between these two groups. This is likely due our limited

sample size. Taken together, our present findings suggest that altered sphingolipid metabolism

may be among the earliest neurobiological changes associated with vascular neurodegeneration

and invite further research to determine whether its metabolic pathways may be a clinically

relevant target for drug discovery. Future studies should measure the enzymes responsible for

conversion of sphingolipid precursors into CerC18:0 to directly assess enzymatic activity.

56

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Appendix 1: Approval from Research Ethics Board

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Appendix 2: Informed Consent Form

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April 1, 2016 Supported by a grant from the Canadian Institute of Health Research

Sunnybrook Health University of Toronto Toronto Rehabilitation Institute

Sciences Centre Faculty of Medicine Cardiac Rehabilitation Program 2075 Bayview Ave. Suite FG05 1 King’s College Circle, 347 Rumsey Road,

Toronto, Ontario, M4N 3M5 Toronto, Ontario, M5S 1A8 Toronto, Ontario, M4G 1R7

The Heart-Mind Connection: Evaluating the Association between

Ceramides and Cognitive Decline in Coronary Artery Disease

Subject Information and Consent

Investigators:

Dr. K.L. Lanctôt, PhD Dr. P. Oh, MD FRCPC Dr. N. Herrmann, MD FRCPC

Sunnybrook Health Sciences Centre

Toronto Rehabilitation Institute

Sunnybrook Health Sciences Centre

INFORMED CONSENT:

You are being invited to participate in a research study conducted at the Toronto Rehabilitation

Institute and Sunnybrook Health Sciences Centre under the supervision of the above

investigators. A research study is a way of gathering information on a treatment, procedure or

medical device or to answer a question about something that is not well understood.

Participation is completely voluntary and you are free to withdraw from the study at any time. A

description of this study follows. This form explains the purpose of this research study, provides information about the study

procedures, possible risks and benefits, and the rights of participants. Please read this form

carefully and ask any questions you may have. Please ask the study staff or one of the

investigators to clarify anything you do not understand or would like to know more about. Make

sure all your questions are answered to your satisfaction before deciding whether to participate

in this research study. INTRODUCTION

You are being asked to consider participating in this study because you have coronary artery

disease (CAD) and because you are taking part in the Toronto Rehabilitation Institute’s

Cardiac Rehabilitation Program. As a greater proportion of Canadians reach older ages, there

is a need to maintain cognitive function later in life. The knowledge from this study will help us

to better understand memory decline in patients with coronary artery disease.

72

WHY IS THIS STUDY BEING DONE?

The purpose of this study is to investigate how certain substances in the blood can affect thinking. It has recently been discovered that certain byproducts of fat breakdown involved in the development of CAD, called ceramides, can harm brain cells. This study is being conducted to determine if there is a relationship between the levels of ceramides in the blood

and memory decline. In addition, relationships between ceramides and other aspects of brain

function, such as thinking speed and the ability to plan and sort information will be explored.

WHAT WILL HAPPEN DURING THIS STUDY?

If you choose to participate in this study, we will notify your TRI physician and your TRI-

Cardiac rehab team of your involvement. This study will not interfere with any of the usual care

received in rehab or from your family physician.

Baseline Visit: If you agree to participate in this study, we would ask to review information that you have provided to the rehab team including demographic data (age, gender and diagnoses), what medications you are using, and the results of your exercise tests in the past year. If you agree to participate, you will be asked to undergo an assessment with a trained researcher that will take about 2 hours. This will include assessments of memory and thinking speed, and a screening interview for depression or substance abuse. We are assessing depressive symptoms as it is not uncommon for CAD rehab patients to show signs of depression. You will be asked to complete a few simple depression questionnaires assessing your mood and anxiety. For the cognitive scales you will be asked to complete a few verbal and visual tasks and reproduce a few simple shapes on paper. With your permission, we would notify your Toronto Rehabilitation team if the results of this interview suggest you might benefit from the resources that are already in place to assist subjects showing signs of depression or cognitive impairment. These resources include the opportunity to make appointments with a psychologist on staff at the Toronto Rehab. At this baseline visit approximately 2½ tablespoons of blood will be drawn.

If the results from the interview or blood sample show clinical abnormalities, with your

permission, we will contact your physician at TRI.

Visit 2 (3 months), Visit 3 (6 months) and Visit 4 (post 12 months):

After the initial baseline visit, you would return for 3 in-clinic visits, each lasting approximately 2 hours. Visits 2 and 3 will take place 3 months and 6 months after your initial visit. Visit 4 will

take place at least 12 months after your initial visit. At each visit you will be asked to complete a number of paper and pencil assessment questionnaires. If you choose to participate in this research study, it will be necessary to collect some fasting blood samples for analysis at Visits

2 and 3. At visits 2 and 3 approximately 2½ tablespoons of blood will be drawn. All blood samples will be identified by a unique number only (not your name). All samples will be

analyzed for only these markers needed for the study and then destroyed once the assay is complete.

HOW MANY PEOPLE WILL TAKE PART IN THE STUDY?

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April 1, 2016

It is anticipated that about 129 people recruited from the Toronto Rehabilitation Institute will

participate in the study conducted with Sunnybrook Health Science Centre. The length of this

study for participants is 5 years. The entire study is expected to take about 5 years to complete

and the results should be known in 5½ years.

WHAT ARE THE RESPONSIBILITIES OF STUDY PARTICIPANTS?

If you decide to participate in this study you will be asked to do the following:

Attend 4 visits at Sunnybrook Health Sciences Centre (2075 Bayview Avenue, Room EG04).

Each visit will last approximately 2 hours. You will be asked to complete a number of

questionnaires, as well as give a blood sample at visits 1, 2 and 3.

WHAT ARE THE RISKS OR HARMS OF PARTICIPATING IN THIS STUDY?

There are no medical risks to you from participating in this study, as this is an observational study and does not involve a medical intervention but taking part in this study may make you feel uncomfortable. Blood draw: As with any blood test, you may experience slight discomfort or bruising. Cognitive testing: You may experience mental stress as a result of memory or timed tasks.

WHAT ARE THE BENEFITS OF PARTICIPATING IN THIS STUDY?

You may or may not benefit directly from participation in this study. Your participation may or

may not help other people with coronary artery disease in the future. Knowledge gained from

this study may be helpful to subjects in the future in the management of depressive symptoms

or cognitive changes resulting from heart disease. As mentioned, the results may suggest that

you would benefit from existing Toronto Rehabilitation Institute resources. The study results

will be published, and if you wish, we will be happy to forward to you a copy of any

publication(s) that may arise from this work.

CAN PARTICIPATION IN THIS STUDY END EARLY?

You can choose to end your participation at any time. If you withdraw voluntarily from the

study, the information about you that was collected before you left the study will still be used.

No new information about you will be collected without your permission.

WHAT ARE THE COSTS OF PARTICIPATING IN THIS STUDY?

Participation in this study will not involve any additional costs to you.

ARE STUDY PARTICIPANTS PAID TO PARTICIPATE IN THISSTUDY?

You will not be paid to participate in this study. However you will be reimbursed $23.00 for

parking expenses each time you visit Sunnybrook for the purposes of this study.

DO THE INVESTIGATORS HAVE ANY CONFLICTS OF INTEREST?

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April 1, 2016

There are no conflicts of interest to declare related to this study.

WHAT ARE THE RIGHTS OF PARTICIPANTS IN A RESEARCH STUDY?

All participants in a research study have the following rights:

1. You have the right to have this form and all information concerning this study explained to

you and if you wish translated into your preferred language. 2. Participating in this study is your choice (voluntary). You have the right to choose not to

participate, or to stop participating in this study at any time without having to provide a reason.

If you choose to withdraw, your choice will not have any effect on your current or future

medical treatment. 3. You have the right to receive all significant information that could help you make a decision

about participating in this study. You also have the right to ask questions about this study and your rights as a research participant, and to have them answered to your satisfaction, before you make any decision. You also have the right to ask questions and to receive answers

throughout this study. If you have any questions about this study you may contact the person in charge of this study (Principal Investigator) Dr. Lanctôt, Department of Psychiatry at 416-480

6100 x2241. If you have questions about your rights as a research participant or any ethical issues related to this study that you wish to discuss with someone not directly involved with the study, you may call Dr. Philip C. Hébert, Chair of the Sunnybrook Research Ethics Board at

(416) 480-4276. 4. You have the right to have any information about you and your health that is collected, used

or disclosed for this research study to be handled in a confidential manner.

If you decide to participate in this study, the investigator(s) and study staff will look at your personal health information and collect only the information they need for this study. “Personal health information” is health information about you that could identify you because it includes information such as your;

• name, • address, • telephone number, • date of birth, • new and existing medical records, or • the types, dates and results of various tests and procedures.

The following people may come to the hospital to look at your personal health information to

check that the information collected for the study is correct and to make sure the study

followed the required laws and guidelines:

Representatives of the Sunnybrook Research Ethics Board, a group of people who oversee

the ethical conduct of research studies at Sunnybrook.

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April 1, 2016

Access to your personal health information will take place under the supervision of the

Principal Investigator. In addition, any study data about you that is sent outside of the

hospital will have a code and will not contain your name or address, or any information that

directly identifies you. “Study data" is information about you that is collected for the research

study, but that does not directly identify you. Study data that is sent outside of the hospital

will be used for the research purposes explained in this consent form.

The investigator(s), study staff and the other people listed above will keep the information

they see or receive about you confidential, to the extent permitted by applicable laws. Even

though the risk of identifying you from the study data is very small, it can never be

completely eliminated.

When the results of this study are published, your identity will not be disclosed. The

Principal Investigator will keep any personal information about you in a secure and

confidential location for 25 years and then destroyed as required by Sunnybrook policy.

5. By signing this consent form, you do not give up any of your legal rights. 6. You have the right to receive a copy of this signed and dated informed consent form before

participating in this study. You have the right to be told about any new information that might

reasonably affect your willingness to continue to participate in this study as soon as the

information becomes available to the study staff. 7. You have the right to access, review and request changes to your personal health

information. 8. You have the right to be informed of the results of this study once the entire study is

complete.

Contacts:

If you have any questions about this study or for more information you may contact the Study Co-ordinator, Janelle Bradley (416-480-6100 x3185), Dr. Krista Lanctôt (416-480-6100 x2241) or Dr. Paul Oh (416-597-3422 x5263). Should you have any questions about your rights as a research subject, you may

contact the Vice Chair of the UHN Rehabilitation Medicine and Sciences Research

Ethics Board at (416) 597-3422 x3081 or the Sunnybrook Health Sciences Centre

Research Ethics Board at (416) 480-4276. DOCUMENTATION OF INFORMED CONSENT

Full Study Title: The Heart-Mind Connection: Evaluating the Association between

Ceramides and Cognitive Decline in Coronary Artery Disease

5 Ceramides and Cognitive Decline in CAD

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April 1, 2016

Name of Participant: ________________________________________

Participant/Substitute decision-maker By signing this form, I confirm that: • This research study has been fully explained to me and all of my questions answered to my satisfaction

• I understand the requirements of participating in this research study • I have been informed of the risks and benefits, if any, of participating in this research study • I have been informed of any alternatives to participating in this research study • I have been informed of the rights of research participants • I have read each page of this form • I authorize access to my personal health information, medical record and research study data as explained in this form

• I have agreed to participate in this study or agree to allow the person I am responsible for to

participate in this study

Name of participant/Substitute

Signature

Date decision-maker (print)

_______________________ ______________________ ___________________

Person obtaining consent By signing this form, I confirm that: • This study and its purpose has been explained to the participant named above • All questions asked by the participant have been answered • I will give a copy of this signed and dated document to the participant

Name of Person obtaining

Signature

Date

consent (print)

_______________________ ______________________ ___________________

Statement of Investigator I acknowledge my responsibility for the care and well being of the above participant, to respect

the rights and wishes of the participant as described in this informed consent document, and to

conduct this study according to all applicable laws, regulations and guidelines relating to the

ethical and legal conduct of research.

Name of Investigator (print) Signature Date

_______________________ ______________________ ___________________

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Neuropsychopharmacology

The Heart-Mind Connection: Evaluating the Association between Ceramides and

Cognitive Decline in Coronary Artery Disease Patient Number: ____________ Date: ______________ Visit Number: _________

Informed Consent Checklist/Note

Consent version # / date:

Consent signed and dated by subject prior to any study related procedures: YES NO

Date/Time signed:

Was a copy of the consent given to the subject? YES NO

Did subject demonstrate comprehension of consent form contents? YES NO

Comments:

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______________________________________________________________________________ ______________________________________________________________________________

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Consent obtained by: