popeye or pinocchio? the relationship between muscle mass...

Popeye or Pinocchio? The relationship

between muscle mass and grip strength

in an elderly population by measuring

muscle mass and testing grip strength

Internship Scientific Research - Master’s programme in Medicine - University of Groningen

Author: P. Kooijman

Student number: 1974378

Time Period: January 11th

, 2016 – June 6th

, 2016

Location: Divisions of Geriatric Medicine and Radiology at Gelre Hospital,

Apeldoorn

Facultary supervisor: Dr. B.C. van Munster, physician and geriatric specialist at Gelre

Hospital, Apeldoorn and University Medical Center Groningen.

Postdoc.

Second supervisor: E.R.J. Bruns, MD PHD at Academic Medical Center (AMC) in

Amsterdam, Gelre Hospitals Apeldoorn.

Internship Scientific Research P. Kooijman

2

Abstract

Background. The frailty syndrome is considered to be the most problematic expression of

ageing. One of the underlying causes is a gradual decline in muscle mass and muscle strength.

In the screening for patients at risk, standardization of the measurement instruments of loss of

muscle is essential.

Objectives. This study aimed to measure muscle mass and muscle strength in elderly in order

to correlate existing measurement instrument tools to each other. The second objective is to

measure and correlate muscle density and muscle strength in elderly.

Design and setting. This prospective observational study included patients of 70 years and

above visiting the outpatient clinic of the Gelre Hospital in Apeldoorn to undergo a Computed

Tomography (CT) scan of the abdomen (January-April 2016). Muscle mass was calculated by

Total Psoas Area (TPA). Muscle strength was measured by hand grip strength (GS). In pa-

tients with identical amount of contrast fluid, muscle density was measured using the Houns-

field Unit Average Calculation (HUAC).

Results. A total of 175 patients (median age 76 years) was included. After correction for con-

founding factors (age, gender, and frail state) GS and TPA remained significantly correlated

(R²=0.43, P=0.001). GS and HUAC were not significantly correlated (P=0.46 for men and

P=0.39 for women). Influence of contrast fluid on HUAC was observed.

Conclusion. Although muscle strength (GS) and muscle mass (TPA) are correlated, they each

symbolize different aspects of a patient’s physical condition. Muscle density (HUAC) is not

correlated with muscle strength.

Words: 241


3

Samenvatting

Achtergrond. Kwetsbaarheid wordt gezien als de meest problematische uiting van ouder

worden. Een van de onderliggende oorzaken is een geleidelijke afname van spiermassa en

spierkracht. Het standaardiseren van de instrumenten deze afname meten is essentieel in het

screenen voor risicopatiënten.

Doel. Deze studie beoogde het correleren van de bestaande meetinstrumenten door het meten

van spiermassa en spierkracht bij ouderen. De tweede doelstelling is het meten en correleren

van spierdichtheid en spierkracht bij ouderen.

Opzet en methode. In deze prospectieve observationele studie werden patiënten van 70 jaar

of ouder geincludeerd die de polikliniek van het Gelre Ziekenhuis in Apeldoorn bezochten

(januari-april 2016) om een Computed Tomography (CT) scan van de buik te ondergaan.

Spiermassa werd berekend middels de Total Psoas Area (TPA). Spierkracht werd gemeten

met de hand grijpkracht (GS). Bij patiënten die eenzelfde hoeveelheid contrastvloeistof ont-

vingen werd spierdichtheid gemeten middels de Hounsfield Unit Average Calculation (HU-

AC).

Resultaten. In totaal werden 175 patiënten geincludeerd (leeftijd: mediaan 76 jaar). Na cor-

rectie van confounders (leeftijd, geslacht en kwetsbaarheid) bleven GS en TPA significant

gecorreleerd (R² = 0.43, P = 0.001). GS en HUAC waren niet significant gecorreleerd (P =

0.46 voor mannen, P = 0.39 voor vrouwen). Er werd invloed van contrastvloeistof op HUAC

waargenomen.

Conclusie. Spierkracht (GS) en spiermassa (TPA) zijn significant gecorreleerd. Wel symboli-

seren zij verschillende aspecten van de fysieke toestand van de patiënt. Spierdichtheid (HU-

AC) is niet gecorreleerd met spierkracht.

Woorden: 230


4

Table of Contents

Page

Abstract ................................................................................................................................... 2

Samenvatting .......................................................................................................................... 3

Table of Contents .................................................................................................................... 4

Introduction ............................................................................................................................ 5

Ageing population ............................................................................................................... 5

Frailty .................................................................................................................................. 5

Sarcopenia ........................................................................................................................... 5

Rationale ............................................................................................................................. 5

Aim ..................................................................................................................................... 7

Main question ......................................................................................................................... 7

Sub-question ....................................................................................................................... 7

Material and Methods ............................................................................................................. 8

Study design ........................................................................................................................ 8

Procedure ............................................................................................................................ 8

Missing values .................................................................................................................. 12

Statistical analysis ............................................................................................................. 12

Results .................................................................................................................................. 13

Participants ........................................................................................................................ 13

Descriptive data ................................................................................................................ 14

Outcome data .................................................................................................................... 15

Main results ....................................................................................................................... 16

Other analyses ................................................................................................................... 17

Discussion ............................................................................................................................. 19

Key results ........................................................................................................................ 19

Interpretation of results ..................................................................................................... 19

Suggestions for future research ......................................................................................... 21

Implementation in clinical practice ................................................................................... 21

Conclusion ........................................................................................................................ 21

Literature .............................................................................................................................. 23

Appendices ........................................................................................................................... 25

Appendix I – Case Report Form (CRF) ............................................................................ 25


5

Introduction

“... old men have grey beards, that their faces are wrinkled, their eyes purging thick amber

and plum-tree gum and that they have a plentiful lack of wit, together with most weak hams”

~Shakespeare in Hamlet, act II, scene II~

Ageing population

Ageing has been a matter of concern for centuries. Not only in poetry as indicated by Shake-

speare’s quote above, but in health care. As the ageing population is expanding, knowledge

about the condition of this population is becoming increasingly relevant. Exponential growth

of the ageing population is expected in the coming decades. The elderly population will be

covering more than a quartile of the total population in 2040(1).

In addition, hospitals will increasingly be burdened with the need to deal with the care, treat-

ment and related costs of these senior patients. Because of the higher incidence of comorbid-

ities and reduced functional reserves in this population, all forms of treatment impose a sig-

nificant challenge to our health care system(2).

Frailty

PubMed’s Mesh term defines aged as a person over 65 years old, but this group holds a wide

variety in alterations of physiologic function, associated comorbidities and life expectancy

(2). Within this heterogenic elderly population from very active to very disabled patients,

overall resilience as a condition might be more specific than age to predict a potential group at

risk.

Frailty can be described as an age-related syndrome. It is a state of decreased resilience and

increased vulnerability to poor resolution towards stressors. It increases the risk of adverse

health outcomes such as morbidity, disability and even mortality(3)(4)(5). The syndrome is

considered to be the most problematic expression of ageing(3).

Clinically, frail people tend to walk more slowly, are weaker and tire more easily(6). This

phenotype shows substantial overlap with that of sarcopenia(7).

Sarcopenia

Sarcopenia is a syndrome characterized by a progressive and generalized decline in muscle

mass and muscle strength. The substantial toll of sarcopenia is measured in terms of morbid-

ity, disability, high costs of health care and even mortality(4)(8)(9). Estimates based on the

World Health organisation population data give an indication about the width of the problem,

as they suggest that more than 50 million people are affected by sarcopenia today. In addition,

more than 200 million people over the next 40 years will be affected by sarcopenia(4). Given

the clinical consequences and the expected growth in prevalence, the related costs are ex-

pected to grow in the future. Thus, sarcopenia could justly be considered as a public health

problem.

Rationale

In order to keep the population ageing with a high quality of life, prevention prevails over

treatment. In consequence, syndromes like frailty and sarcopenia has gained the researchers’

interests in the last decades(10). It is critical to develop and validate measurement instruments

for these syndromes.


6

An essential element of both frailty and sarcopenia is muscle tissue. The European Working

Group on Sarcopenia in Older People has described both muscle mass as muscle function to

be important in the diagnosis of sarcopenia.

Currently, a wide variety of instruments to measure these parameters is available(4). Most are

independently validated, but not calibrated. This study aimed to calibrate two easily measura-

ble, yet precise and objective parameters: muscle mass using Computed Tomography (CT)

and muscle strength using a dynamometer.

Muscle mass

The gold standard for measurement of the muscle mass is by use of CT imaging(4). To meas-

ure the cross-sectional muscle area accurately, Image-analyses software can be used. How-

ever, this software is usually costly and the used images do not form part of routine radiologi-

cal reporting. Previous studies show that CT based calculations are representable as well(11).

The measurement of a muscle on a single abdominal image correlates with the total body

skeletal muscle(12)(13).

A recently developed method to measure the density of the muscle as a measure for sarco-

penia is the Hounsfield Unit Average Calculation (HUAC) (14). The HUAC expresses the

average muscle density corrected for surface area. Measurements are performed on the same

level on Computed Tomography scans as the psoas area measurement.

Muscle strength

One of the existing measures of strength is assessing hand grip strength by using a dyna-

mometer. This simple tool correlates with physical function and can be regarded reliable

marker of frailty and sarcopenia in the elderly(4)(15)(16)(17).


7

Aim

This study aimed to measure both muscle mass and muscle function in a group of older pa-

tients in order to correlate existing measurement instrument tools to each other.

Firstly, this correlation will contribute to the existing knowledge of the unclear relationship of

muscle mass and muscle strength.(18) In addition, this correlation will simplify the assess-

ment of sarcopenia in the elderly, since assessing the muscle strength with a dynamometer is

easier than assessing the muscle mass using CT imaging. In this way, sarcopenia could be

assessed repeatedly without necessary availability of CT images (e.g. in the preoperative out-

patient clinic or even the general practitioner’s office). Finally, identification of the decline in

muscle mass and function will contribute to the ease to sequel the effect of training, for exam-

ple in preoperative training programs. We hypothesized that muscle mass, measured as the muscle area of the psoas muscle at the

level of the third lumbar vertebra (L3), correlates with hand grip strength measured using a

dynamometer.

Main question

Does muscle mass, measured as the muscle area of the psoas muscle on the level of the third

lumbar vertebra (L3), correlate with hand grip strength measured using a dynamometer?

Sub-question

Does muscle density, measured as the HUAC of the psoas muscle on the level of the third

lumbar vertebra (L3), correlate with hand grip strength measured using a dynamometer?


8

Material and Methods

Study design

The study can be regarded as a cross-sectional observational study.

Medical Ethical Committee The Medical Ethical Commission of the Academic Medical Centre (AMC) in Amsterdam

assessed that this study is not bound to the Wet Medisch-Wetenschappelijk Onderzoek met

Mensen (WMO), considering the fact that this study is not regarded as burdensome for partic-

ipants. Therefore, presenting the study to the Medical Ethical Committee for official testing

was not necessary.

Population

Inclusion took place at the Division of Radiology in the Gelre Hospital in Apeldoorn, The

Netherlands. Included were all patients 70 years of age or above visiting the outpatient clinic

between January 11th

, 2016 and April 26th

, 2016 to undergo a CT scan of the abdomen on

which the m.m. psoas major at the level of the third lumbar vertebral body is shown (CT ab-

domen, CT colonography, CT urinary tract, CTA abdomen and CTA follow up stent graft).

Patients with heavy hand pain limiting their activities of daily living could not participate. In

addition, patients were excluded when they had an operation recently, paresis or amputation

of one of the hands.

Procedure

On arrival at the clinic, patients were asked to participate after reading an information letter.

Permission was obtained by written informed consent. Before or after the CT scan partici-

pants were taken to a separate room to undergo the different tests and measurements. Addi-

tional patient characteristics were taken from the Electronic Patient Files of the hospital.

Variables

The variables used for analysis can be divided into baseline and muscle related variables

(Figure 1). Baseline variables were age at time of visit, gender, body mass index (BMI), la-

boratory values of hemoglobin, creatinine, glomerular filtration rate and albumin, home situa-

tion, level of independency, frailty score, comorbidities, indication of CT scan, depression,

level of cognitive function, falls, smoking, polypharmacy and the surprise question. Muscle

related variables were handgrip strength and muscle mass.

Figure 1. Overview of variables.

BMI= Body Mass Index.


9

BMI

Height and weight were measured with the subjects wearing light clothes without shoes. Body

weight was measured to the nearest kilogram and height was measured to the nearest centime-

ter. Body Mass Index (BMI) was calculated as weight (kg) divided by height² (m). Partici-

pants with a BMI equal to or above 25 kg/m² were considered overweight, using the cut-off

values defined by the World Health organization.(19)

Laboratory values

Levels of hemoglobin, creatinine, glomerular filtration rate and serum albumin were retrieved

from the existing medical records if results were obtained within three months of the CT-scan.

Anemia was defined as a hemoglobin level below 13.2 g/dL in men and below 12.2 in wom-

en. (20)

Home situation

Type of house (nursing home or independent house) and need of daily home care were scored.

The variable was split in two categories: participants living in a nursing home or receiving

daily home care were categorized as ‘dependent’, while patients living independently without

home care were classified as ‘independent’. Patients were asked if they lived alone or together

(regardless whether the co-resident was a spouse, child or another relation).

Independency: KATZ-ADL6

The KATZ-ADL6 is an easily applied and assessed scale with six items (bathing, dressing,

toilet use, sphincter control, feeding, and bed-chair transfer), to quantify one’s independence

in activities of daily living (ADL).(21)(22) Score range is 0-6, reflecting the number of

ADL’s in which individuals are dependent. In this study, dependency was defined as a

KATZ-ADL6 score of one or more.

Frailty: Clinical Frailty Scale 1-9

Among the various screening tests to recognize frail persons, the Clinical Frailty Scale (CFS)

designed by Rockwood is the one based on clinical judgment.(23)(24) To ground this clinical

assessment the following questions were answered for each participant:

-Frequency and intensity of physical exercise or activity.

-Extent of independency (for example to be able to perform high order ADL tasks)

-Researcher’s estimation of patient’s illness and life expectancy.

Every score has a name (1= ‘Very Fit’, 2= Well, 3= Managing Well, 4= Vulnerable, 5= Mild-

ly Frail, 6= Moderately Frail, 7= Severely Frail, 8= Very Severely Frail, 9= Terminally ill).

Scores of five or higher are described in the title as ‘frail’. In this study, frailty was defined as

a CFS of five or more.

Charlson Comorbidity Index

The Charlson Comorbidity Index (CCI) is the scoring system for comorbidities most widely

used by researchers and clinicians. It provides a weighted score of the patients’ comorbidities

to predict short- and long-term outcomes.(25)(26) This simple-to-calculate index takes into

account both the number and severity of 19 pre-defined comorbid conditions. It was obtained

using the patients’ electronic file and by asking the patient about their comorbidities during

the conversation with the researcher.

Indication of the CT scan

The indication of the CT scan of the participants was taken from the electronic patient files.

This enabled to identify participants with cancer as an indication for the CT scan.


10

Depression: GDS-2 and GDS-15

Depression was scored by the Geriatric Depression Scale. If one or more questions of the

GDS-2 (2 questions) were answered positively, the GDS-15 (15 questions) was assessed. A

score 6 or more items on the GDS-15, was taken as suggestive for depression (sensitivity

80.5%, specificity 75.0%).(27)

Cognitive function

An impaired cognition can cause a discrepancy between the muscle mass and muscle strength,

in case the participant could not understand the instructions of using the dynamometer. The

cognitive level was assessed by the Mini-Mental State Examination (MMSE) and the clock-

drawing test. The MMSE, seen in literature as the gold standard of cognitive level assessment,

was classified in two groups: impaired (<24 points) and normal (24-30 points).(28) The clock-

drawing test, complementary to the MMSE, is a good screening test for dementia and cogni-

tive dysfunction. Interpretation of the drawings was done using the scoring criteria of Shul-

man et al (5 points for a faultless clock; 4 points for minor mistakes; 3 points for fault time

indication; 2 points for space oriented mistakes; 1 points for severe disorganization and 0

points for inability to draw a clock)(29) and classified in two groups: impaired (<4 points) and

normal (4-5 points). Patients included in the ‘impaired’ group of one or both tests were con-

sidered to have an impaired cognitive function.

Falls

Falls have been defined as ‘an unintentional change of body position, resulting in ending up

on the ground or another lower level’(30), occurring in the six months preceding the visit at

the outpatient clinic.

Smoking

Participants were classified as current smoker or non-smoker, regardless pack-years in the

past.

Polypharmacy

Polypharmacy was defined by the use of ‘five-or-more-different medications’(31).

Surprise Question

The surprise question (“would I, the researcher, be surprised if this patient will be deceased in

the next year?”) is proven to be very simple and effective to identify mortality risk in different

study populations(32)(33).

Muscle strength

Assessment of hand grip strength (GS) using a dynamometer is a simple, inexpensive and

very suitable tool for clinical settings. The GS of the dominant hand was assessed with a

JAMAR hydraulic dynamometer using a validated protocol(34)(35). The participant had to

squeeze the dynamometer with maximum strength in sitting position with an adducted and

neutrally rotated shoulder, 90° flexed elbow, and a neutral position of the wrist. To ensure that

the patient cooperated accurately, patients were shown the operating procedures of the dyna-

mometer prior to measurements. The highest result of the three grip strength trials was used.

Results were expressed in kilograms. Cut-off values for sarcopenia were 30 kilograms for

men and 20 kilograms for women, according to the European Working Group on Sarcopenia

in Older People (EWGSO)(4).


11

Muscle Mass

For the purpose of this study abdominal CT scans were used to measure the Total Psoas Index

(TPI) and to perform a Hounsfield Unit Average Calculation (HUAC). Abdominal CT scans

on which the psoas area at the level of the third lumbar vertebral body (L3) was shown were

used (CT abdomen, CT colonography, CT urinary tract, CTA abdomen and CTA follow up

stentgraft). All CT scans were performed in a supine position. Measurement was executed on

the first image on which the two pedicles were visible.

At this level, the psoas muscle surface area correlates with whole –body muscle

muscle(12)(13) and gives information about the surface area of the erector spinae muscle,

quadratus lumborum muscle, psoas muscle, transversus abdominis muscle, interior- and exte-

rior oblique muscle and the rectus abdominis muscles(11)(36). To calculate the TPI the fol-

lowing formula was used: (left psoas area + right psoas area) / (length*length).

The Hounsfield Unit (HU) is a measure of radiation attenuation that can be obtained from a

CT scan. The HUAC of the psoas muscles is a measure of muscle density and fatty infiltra-

tion. Only the CT scans with a comparable amount of contrast fluid and timing of administra-

tion of the contrast fluid were used (CT abdomen with 100ml or 125ml contrast fluid). Both

the right and left psoas were evaluated and the average was used for the final HUAC calcula-

tion. The formulas used are Right Hounsfield Unit Calculation (RHUC) = (Right HU*Right

Psoas area)/(Total psoas area), Left Hounsfield Unit Calculation (LHUC) = (Left HU*Left

Psoas area)/(Total psoas area), and HUAC = (RHUC+LHUC)/2.(14)(13)(37) Low muscle

density was defined as HUAC scores in the lowest gender specific quartile (<25th

percentile).

Both the HU and the TPA were measured with the computer software Sectra PACS, 2014.

One researcher (PK), who did not have specific knowledge of radiology, performed meas-

urements. Personal training by a professional radiologist was obtained to ensure reliable

measurements of the psoas area and density.

Figure 2. CT image at the level of the third lumbar vertebral body (L3).

CT = Computed Tomography.

The lines on the left psoas muscle represent the measurement of

the psoas area in mm². The circle on the right psoas muscle repre-

sents the measurement of the muscle density in Hounsfield Units.


12

Missing values

During the period of inclusion, the electronic medical system was used to register all patients

attending the radiology department matching the inclusion criteria. The age and gender of the

missing patients and the reason of non-cooperation were recorded.

Statistical analysis

Statistical analysis for categorical and dichotomous outcomes was performed by the χ2 test.

Mann Whitney-U tests or t-tests were used to analyse differences between the two groups

(low and high muscle mass) of continuous variables. Normally distributed continuous data

were presented with mean and standard deviation (SD), and continuous data not normally

distributed were presented with median and range, to indicate the variables’ distribution. The

Pearson’s correlation test studied the correlation between two variables (for example TPA and

GS). For all analyses, a P-value of < 0.05 was considered significant. The association between

GS and TPA was analysed by univariate and multivariate analyses. Variables were included

by means of the forward procedure. SPSS (version 20,0; IBM-SPSS Statistics 20, UK) was

used to perform all statistical analyses mentioned above.


13

Results

Participants

A total of 315 patients of 70 years of age or above underwent a CT scan during the study pe-

riod. Within this group, 99 patients were not invited to participate because of logistic reasons.

The main logistic reason was the fact that there was only one researcher and there were two

CT scans running simultaneously. During the time of testing, other patients in the waiting

room could not be asked to participate and were missed in the inclusion. The rest of the pa-

tients in this group were missed because of researcher’s activities other than including patients

at the time of patients visiting the outpatient clinic.

Figure 3. Flow diagram of inclusion of participants

CT = Computed Tomography.

Among the patients asked for participation and permission, 17 did not meet the criteria to be

included in the study. Another 22 patients did not participate because of patient related fac-

tors. These factors included not willing to participate, having other appointments shortly after

the CT scan or relying on transport leaving in time. Two patients participated in the study, but

were excluded because their CT scan did not contain the images of the L3 level. Eventually

there were 175 patients who met the criteria for analysis, were able to participate and gave

permission to be included in the study (Figure 3).

Table 1. Comparison of included and excluded patients.

Included (n=175) Excluded (n=140) P value

Age Median (range) 76 (70-92) 76 (70-95) 0.70

Gender N, (%)

Male

108 (62)

74 (53)

0.14


14

To make sure the included participants represent the total population of patients 70 years or

above visiting the outpatient clinic for a CT scan, the included and excluded patients were

compared by age and gender. No significant differences were found (Table 1). The median

age in years of the included patients was 76 (range 70-92) versus a median age of 76 (range

70-92) of the excluded patients (P=0.70). Male participants represented 62% of the included

patients versus 53% of the excluded patients (P=0.14).

Descriptive data

Table 2. Baseline Characteristics

GS low (n=50*) GS high (n=125*) P value

Age in years Median (range) 78.5 (70-92) 75 (70-89) <0.001

Gender (male) N (%) 23 (46) 85 (68) 0.007

BMI (kg/m²) Mean (SD) 25.1 (4.6) 26.4 (4.1) 0.071

Overweight (BMI ≥ 25) N (%) 21 (42) 72 (58) 0.055

Lab value

- Hb (g/dl)

- Anaemia (g/dl)

- Creatinine (umol/l)

- GFR (l/min)

- Albumin (g/l)

Mean (SD)

N (%)

Mean (SD)

Median (range)

Mean (SD)

13.55 (1.12) n=30

9 (30) n=30

79.4 (17.5) n=41

72 (38-90) n=41

33.7 (3.15) n=14

13.67 (1.77) n=90

34 (37.8) n=90

85.0 (23.36) n=110

72 (23-90) n=110

34.4 (4.89) n=39

0.66

0.29

0.16

0.44

0.62

Living situation

- Dependent

- Alone

N (%)

N (%)

3 (6)

18 (36)

3 (2.4)

25 (20)

0.23

0.023

ADL-dependent

(Katz-ADL ≥1)

N (%)

15 (30)

24 (19)

0.09

Frail

(CFS ≥ 5)

N (%)

7 (14)

5 (4)

0.018

Comorbidities

(CCI)

Median (range)

2 (0-9)

2 (0-9)

0.82

CT indication of cancer N (%) 32 (64) 87 (70) 0.24

Depression

(GDS > 6)

N (%)

3 (6)

3 (2)

0.23

Impaired cognition

- MMSE <24

- CDS <4

N (%)

N (%)

4 (9) n=47

7 (16) n=45

10 (8) n=124

9 (7) n=122

0.57

0.10

Falls N (%) 5 (10) 17 (14) 0.36

Smoking N (%) 9 (18) 16 (13) 0.25

Polypharmacy N (%) 18 (36) 35 (28) 0.20

Surprise Question N (%) 49 (98) 124 (9) 0.49

GS = hand grip strength in kilograms, BMI = Body Mass Index (weight/(height²)), Hb = He-

moglobin level, GFR = Glomerular Filtration Rate, ADL = Activity of Daily Living, CFS =

Clinical Frailty Scale, CCI = Charlson Comorbidity Index, GDS = Geriatric Depression Scale,

MMSE = MiniMental State Exam, CDS = Clock Drawing Score. N = number, SD = Standard

Deviation.

* In case of missing values, the deviating amount of participants per group is denoted.


15

Participants were divided among two groups based on the hand grip strength (GS). In the

group with a GS lower than the cut-off value (30 kilograms for males and 20 kilograms for

females) 50 participants were included. The residual 125 participants (71.4%) were included

in the group with a high GS.

The median age of all participants was 76 years and more than half of the population (62%)

was male (Table 1). Age differed significantly between the groups with low and high GS,

with a median age of 78.5 years (range 70-92) versus 75 years (range 70-89), respectively

(P<0.001)(Table 2). From the male participants, 46% had low hand grip strength compared to

68% having high hand grip strength (P0.007)(Table 2).

In addition, participants with a low GS were more often frail (P=0.018). BMI, independency,

impaired cognition and other possible confounders of sarcopenia did not significantly differ

between patients with low GS and patients with high GS.

Outcome data

The mean GS of all male participants was 36.4kg (SD 7.6) (Figure 4a). The mean GS of the

female participants was 21.1kg (SD 4.7). The mean TPA for male participants was 519

mm/m² (132.9SD) versus 391 mm/m² (111.2SD) for female participants (Figure 4b).

Figure 4a. Boxplot; GS by Gender Figure 4b. Boxplot; TPA by Gender

GS = hand grip strength. TPA = Total Psoas Area (in mm/m²)


16

Main results

The Pearson correlation of TPA and GS in men was 0.263 (P=0.06) with a scatterplot measur-

ing an R2

= 0.069. In women the Pearson correlation of TPA and GS was 0.11 (P=0.37) with a

scatterplot measuring an R2

= 0.013. Scatterplots are visualized in figure 5.

GS = hand grip strength. TPA = Total Psoas Area (in mm/m²)

Variables with a P <0.05 in the baseline characteristics were identified as potential

confounders (Table 2). Confounder-adjusted estimates are presented in Table 3.

The variable estimate (β) of TPA and GS was 0.47 (P<0.001). Age, gender and frail state

were defined as potential confounders and assessed in a linear regression. All variables were

significant confounders. After adjustment for the significant confounders the correlation

between TPA and GS remained significant (P=0.004). The proportion of variance explained

by the model (R²) changed from 22% to 43% after adjustment for age, gender and frailty.

β* (95%-CI) P value β* (95%-CI) P value

TPA

Age

Male Gender

Frail

0.47 (0.024 – 0.043)

-0.20 (-0.68 – -0.10)

0.75 (13-28 – 17.35)

-0.15 (-11.88 – -0.22)

<0.001

0.01

<0.001

0.04

TPA

Age

Male Gender

Frail

0.17 (0.004 – 0.019)

-0.22 (-0.61 – -0.25)

0.68 (11.90 – 16.02)

-0.14 (-8.8 – -1.74)

0.001

<0.001

<0.001

0.004

GS = hand grip strength in kilograms. TPA = Total Psoas Area in mm/m²;

β = standardized coefficient; CI = Confidence Interval.

* β denotes the variable estimate

** R² denotes the adjusted proportion of the variance explained by the model

Figure 5a. Scatterplot of the correlation between Figure 5b. Scatterplot of the correlation between

GS and TPA in males (Pearson 0.27, P=0.06). GS and TPA in females (Pearson 0.1, P=0.37).

R² = 0.069 R² = 0.013

Table 3a. Univariate linear regression analysis Table 3b. Multivariate linear regression analysis

for GS. R²** = 0.22 for GS. R²** = 0.43


17

Other analyses

A secondary objective was the association between Hounsfield Unit Average Calculation

(HUAC) and GS. The mean HUAC value differed significantly between the different types of

CT scans (Figure 6). In analyses including the HUAC, only participants that underwent a CT

scan of the abdomen with 100 milliliters or 125 milliliters of contrast fluid were included

since the mean HUAC of these groups was comparable. This group consisted of 109 partici-

pants in total.

Figure 6. HUAC by type of CT scan.

HUAC = Hounsfield Unit Average Calculation; CT = Computed Tomography

Participants were divided among two groups based on the HUAC quartiles. In the group with

a low HUAC, 27 participants (25%) were included. The residual 82 participants (75%) were

included in the group with a high HUAC. More than half of the participants were male (60

male participants versus 49 female participants).

Table 4. Sub analysis of the mean GS per group of HUAC with 109 out of 175 participants.

HUAC low (n=27) HUAC high (n=82) P-value

GS in kg

- Male (N=60)

- Female (N=49)

Mean (SD)

35.33 (10.6)

21.08 (7.9)

38.50 (6.9)

20.62 (4.2)

0.19

0.80

HUAC = Hounsfield Unit Average Calculation; GS = hand grip strength;

The mean GS of male participants and female participants did not significantly differ between

the two groups (P=0.19 and P=0.80 for male and female participants, respectively)(Table 4).


18

Figure 7a. Scatterplot; correlation between Figure 7b. Scatterplot; correlation

HUAC and GS in men. P=0.46 between HUAC and GS in women. P=0.39

HUAC = Hounsfield Unit Average Calculation. GS = hand grip strength.

There was no significant correlation between the HUAC and the GS of the participants (sepa-

rated by gender the P=0.46 for men, P=0.39 for women)(Figure 7).


19

Discussion

Key results

There is a significant correlation between muscle mass (defined by total psoas area, TPA) and

muscle strength (defined by hand grip strength, GS) in the elderly. Female gender, older age

and frailty are associated with low grip strength. The results illustrate the complexity in the

relationship between muscle mass and muscle strength, from which muscle function cannot be

derived easily. Finally, this study observed the influence of intravenous contrast on the as-

sessment of muscle density (defined by Hounsfield Unit Average Calculation, HUAC).

Strengths

All patients aged 70 years or older visiting the outpatient clinic for CT imaging of the abdo-

men were included. Only those with disability of the hands were excluded. The maximal in-

clusion could be considered as a strong feature of the study. Baseline characteristics and rea-

sons for missing patients were recorded. The fact that included and excluded patients were

comparable with respect to age and sex suggests that the study population is representative for

the total outpatient population. To our knowledge this study is the first in examining the asso-

ciation between muscle mass and muscle strength in a cohort of only older patients.

Limitations

The method of inclusion states that people had to decide whether they wanted to participate or

not. This resulted in some potential participants refusing to participate, among which frail

people in particular. This could give concerns about selection bias. However, only 22 of the

315 potential participants refused to participate because of patient related factors (Figure 1).

Among this group were not only patients too weak to participate, but also patients not able to

participate (e.g. dependent on transport leaving in time). Therefore, the magnitude of this po-

tential bias is considered to be negligible.

Interpretation of results

In 2010, the European Working Group on Sarcopenia in Older People (EWGSO) defines

sarcopenia as a syndrome characterized by three criteria: low muscle mass (criterion 1) in

combination with either low muscle strength (criterion 2) or low physical performance (crite-

rion 3)(4). In line with this comprehensive article, our study assessed all three criteria.

Population

In general, 25% of the elderly population suffers from sarcopenia(38). In the population of

this study, 50 of 175 (40%) participants had low hand grip strength. This result strengthens

the definition of sarcopenia of the EWGSO that the syndrome of sarcopenia is not solely ex-

plained by low muscle strength (one criterion): the other criteria are relevant as well. At the

other hand, this discrepancy might be explained by the composition of the population. Our

population represents the elderly population visiting the outpatient clinic (Table 1). This does

not guarantee that the study population represents the total elderly population (including pa-

tients not visiting the hospital). Finally, contrary to most other studies related to this topic, the

hospitals population used for this study was almost completely Caucasian.

This study illustrates that age, gender and frailty are confounders in the relationship between

muscle strength (GS) and muscle mass (TPA) (Table 3).

Ageing


20

The effects of the ageing process on muscle tissue and muscle quality is a topic of rising in-

terest in various fields.(39–41) In a large cohort of 1030 participants, Lauretani et al(42) illus-

trated that becoming older results in a significant decline of muscle strength. However, the

amount of muscle mass did not decrease in the same rate. In addition, Cesari et al(43) de-

scribes that ageing causes a decline in muscle quality rather than muscle mass, due to infiltra-

tion of fat and collagen in the muscle tissue. These results could explain the significant influ-

ence of gender on the correlation between GS and TPA.

Gender

Gender-specific cut-off values for GS have been identified by Cruz and have also been used

in this study(4). The study illustrates no significant association between muscle mass and

muscle strength in women.

There is an overall difference of absolute muscle mass in men and women.(44) Additionally,

women have on average 40% less upper body strength compared to 33% of lower body

strength.(45) The discrepancy between upper body hand grip strength and the lower body

muscle area of the psoas could explain our findings. In the development for cut-off points,

gender-specific division is indispensable. This could explain the fact that initially, no signifi-

cant correlation between GS and TPA in women could be found. However, multivariate anal-

ysis shows that TPA is still significant after correction for confounders, meaning that most

variance is explained by TPA in the relationship with GS.

Frailty

The relationship between frailty and sarcopenia has been elaborately described in literature.

The clinical phenotype of frail people shows substantial overlap with that of sarcopenia(7).

In addition, Cesari et al. states that sarcopenia and frailty are even two sides of the same

coin.(43) Another study proved the association of grip strength with more markers of frailty

than chronological age.(15) Low muscle strength specifically is not only one of the criteria for

sarcopenia defined by EWGSO, but also one of the criteria for frailty defined by Fried et

al.(16) The significance of frailty in the association of muscle mass and muscle strength sup-

ports existing evidence.

Muscle mass measurement

Simple psoas cross sectional area measurement has been performed in different studies as a

quick and easy method to assess sarcopenia (11)(46)(47)(48). The methods of measurement of

these studies are comparable with this study. In contrary to this study, some studies made use

of sophisticated yet costly imaging software. Nevertheless, the results obtained in these stud-

ies are comparable to this study.

As written earlier in this essay, ageing can lead to increased fat infiltration and collagen for-

mation. TPA measurement lacks the ability to distinguish muscle tissue and other tissue types

within the area measured on CT images. Fat infiltration in muscle tissue might influence mus-

cle mass measurement by TPA, implying the need to assess muscle density.

The Hounsfield Unit Average Calculation (HUAC) is a measure of radiation attenuation, re-

flecting the average muscle density corrected for psoas surface area(14). The Hounsfield units

express the muscle density and amount of fatty infiltration. A low HUAC indicates high

amounts of fatty infiltration in the muscle, and thus poorer muscle quality.


21

This study was initially designed to correlate both muscle mass (TPA) and muscle density

(HUAC) to grip strength. However, no previous study has described the effect of contrast

fluid on the HUAC. Since muscle tissue is highly vascularized, and fluid has different proper-

ties of radiation attenuation than muscle tissue, influence of contrast fluid on HUAC is sus-

pected. Significant differences in HUAC results were observed when dividing patients in

groups according to the amount of contrast fluid received (Table 6).

The extents of influence of contrast fluid on the HUAC or possible solutions to correct for

these differences are not described in literature. Even the president of Tomovision©, Quebec,

Canada, a software company highly specialized in CT imaging measurements, could not pro-

vide an answer to this question. To circumvent this problem, only the participants receiving a

comparable amount of contrast fluid have been included in the analyses using HUAC

(N=109).

A correlation between HUAC and GS could not be proven in this study. Baseline characteris-

tics of this sample population might differ from the study population. In addition, potential

other confounding factors (e.g. ejection fraction delaying contrast perfusion) are not assessed

in this study. This, in combination with the lack of knowledge about the influence of contrast

fluid on HUAC, makes it impossible to draw valid conclusion from the HUAC analyses in

this study.

Suggestions for future research

Research in the field of grip strength should consider the gender differences in muscle mass.

Measurements of muscle density (HUAC) might be a more comprehensive approach in re-

search of muscle function in elderly. However, sufficient knowledge on the HUAC measure-

ments is missing. A study primarily designed to assess the extent of influence of contrast fluid

on HUAC is needed. We advise future researchers using the HUAC measurements to take our

observations concerning this topic into account and describe the details of the CT scan proper-

ties transparently and precisely.

Implementation in clinical practice

We aim to assess physical condition in order to predict and prevent a perilous state such as

frailty. Hand grip strength is an affordable and patient-friendly instrument to assess a patient’s

physical condition. Facing times in which both time and money are scarce in healthcare, the

urge to find a quick and cheap gold standard to assess the physical condition of the old patient

is rising.

Grip strength measurement can be applied in all settings, regardless the availability of CT

images of a patient, Imaging software or even a computer. This makes the hand grip strength

measurement a highly suitable tool in outpatient clinics or out of hospital facilities, for exam-

ple in the preoperative setting where information of physical frailty might play a leading role

in the follow up of frail patients. Nevertheless, it is important to realize the complexity of this

physical condition, especially in elderly patients.

The study highlights and supports the ultimate goal of providing a simple tool to measure

muscle mass.

Conclusion

In conclusion, the results of this study support the hypothesis that there is an association be-

tween muscle mass (measured as the total psoas area on the level of the third lumbar vertebra)

and muscle strength (measured as hand grip strength using a dynamometer) in older patients.


22

Although muscle mass and muscle strength are associated, they each symbolize different as-

pects of a patient’s physical condition. Muscle density (measured with the Hounsfield Unit

Average Calculation of the psoas muscle on the level of the third lumbar vertebra) is not re-

lated with muscle strength in this study. However, remarkable influence of contrast fluid on

these measurements was observed.


23

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---------------------------------------------------------------------------------------------------------------------------

Appendices

Appendix I – Case Report Form (CRF)


26

Confidential

CASE REPORT FORM

*Onderzoeksnummer:

*Datum van invullen:

Exclusie criteria

Parese arm/hand Ja Nee

Eenzijdige amputatie arm/hand Ja Nee

Recente operatie arm/hand Ja Nee

Ernstige pijn arm/hand Ja Nee

Wilsonbekwaam Ja Nee

Ernstige cognitieve disfunctie Ja Nee

Geschikt voor deelname Ja Nee

Informed consent verkregen Ja Nee

2 0 1 6

d d m m j j j j

POPEYE OR PINOCCHIO? THE RELATIONSHIP

BETWEEN MUSCLE MASS AND GRIP STRENGTH

IN AN ELDERLY POPULATION BY MEASURING

MASS AND TESTING GRIP STRENGTH


27

*Geboortedatum:

* Geslacht: Man Vrouw

* Specialisme: _____________________

* Afspraak Polikliniek Kliniek

Gepland Acuut

* Type scan: ____________________

* Indicatie scan: _________________

Lab (< 3mnd)

* Hb: |__|__|.|__| mmol/L

* Creatinine: |__|__|__| μmol/L

* eGFR __________

* Serum Albumin: __________

Woonsituatie: Zelfstandig Met hulp VPH

Samen Alleen

Roken Ja Nee

Polyfarmacie (>5) Ja Nee

BMI

Lengte |___|___|___| cm

Gewicht

|___|___|___| kg

1 9

d d m m j j j j

BASELINE

POPEYE OR PINOCCHIO – CRF

ONDERZOEKSNUMMER |___|___|___|___|


28

Score Aandoening (zie ook toelichting onderstaand tabel 1)

1 Myocardinfarct met enzymverandering

Decompensatio cordis: dyspnoe reagerend op medicatie

Perifeer vaatlijden

- intermittent claudicatie

- bypass voor arteriele insufficiency

- gangrene

- acute arteriele insufficiency

- onbehandelde th/abd aneurysma (>6cm)

Cerebrovasculaire aandoening (CVA of TIA)

Dementie (chron. cognitief deficit)

Longziekten

- dyspnoe bij inspanning/in attacks (astma)

- dyspnoe in rust

Connective tissue disease

- SLE, RA, vasculitis

Ulcus pepticum waarvoor behandeling (incl bloeding)

Matige leverfunctiestoornis (cirrhosis)

Diabetes zonder eindorgaanschade, met behandeling

2 Paralyse (hemi- paraplegie)

Matig/ernstige nierfunctiestoornis

- creatinine >3mg%

Diabetes met eindorgaanschade

- retino-, neuro-, nefropathie

Solide tumor zonder metastase (diagnose <5 jaar)

Leukemie

Lymfomen: (N)HL, Waldenstrom, Myeloom

3 Matige/ernstige leverfunctiestoornis

- cirrhosis met portale hypertensie (met/zonder bloeding)

6 Gemetastaseerde maligniteit

- solide tumoren op afstand

AIDS (niet alleen HIV positief)

Leeftijd

1

2

3

4

5

40-59

60-69

70-79

80-89

90-99

Totaalscore

*CHARLSON COMORBIDITY INDEX




29

Deze lijst bevat vragen waarop u met ‘JA’ of ‘NEE’ kunt antwoorden. Het is de bedoeling dat u de

vragen leest en bedenkt welk antwoord u hierop zult geven. Om het door u gekozen antwoord zet u

een cirkeltje.

1. Heeft u hulp nodig bij het baden of douchen? JA NEE

2. Heeft u hulp nodig bij het aankleden? JA NEE

3. Heeft u hulp nodig bij het naar het toilet gaan? JA NEE

4. Maakt u gebruik van incontinentie materiaal? JA NEE

5. Heeft u hulp nodig bij een transfer van bed naar stoel? JA NEE

6. Heeft u hulp nodig bij het eten? JA NEE

7. Bent u het afgelopen half jaar gevallen? JA NEE

Maakt u dagelijks gebruik van deze hulpmiddelen:

- Rollator JA NEE

- Stok in dominante hand (bijvoorbeeld rechts als u rechts bent) JA NEE

- Rolstoel JA NEE

KATZ-ADL6 (KATZ 1963)




30

Deze lijst bevat vragen waarop u met ‘JA’ of ‘NEE’ kunt antwoorden. Het is de bedoeling dat u de

vragen leest en bedenkt welk antwoord u hierop zult geven. U geeft het antwoord dat het beste weer-

geeft hoe u zich de afgelopen week, met vandaag erbij, heeft gevoeld. Om het door u gekozen ant-

woord zet u een cirkeltje.

GDS-2

1. Hebt u zich de afgelopen maand somber, depressief of wanhopig gevoeld? JA NEE

2. Had u de afgelopen maand minder interesse of plezier in dingen? JA NEE

Indien één vraag met ‘JA’ wordt beantwoord, wordt doorgegaan met de GDS 15.

GDS-15

1. Bent u innerlijk tevreden met uw leven? JA NEE

2. Bent u met veel activiteiten en interesses opgehouden? JA NEE

3. Hebt u het gevoel dat uw leven leeg is? JA NEE

4. Verveelt u zich vaak? JA NEE

5. Hebt u meestal een goed humeur? JA NEE

6. Bent u bang dat u iets naars zal overkomen? JA NEE

7. Voelt u zich meestal wel gelukkig? JA NEE

8. Voelt u zich vaak hopeloos? JA NEE

9. Blijft u liever thuis dan uit te gaan en nieuwe dingen te doen? JA NEE

10. Het u het gevoel dat u meer moeite heeft met het geheugen dan anderen? JA NEE

11. Vindt u het fijn om te leven? JA NEE

12. Voelt u zich nogal waardeloos op het ogenblik? JA NEE

13. Voelt u zich energiek? JA NEE

14. Hebt u het gevoel dat uw situatie hopeloos is? JA NEE

15. Denkt u dat de meeste mensen het beter hebben dan u? JA NEE

GDS 2 EN GDS 15




31

Vragen:

1. Mate van activiteit: Regelmatig Soms Gehinderd door symptomen

2. Mate van afhankelijkheid: Geen in iADL in ADL (wassen, aankleden)

3. *Levensverwachting: >6mnd, stabiel > 6mnd <6 mnd

(in te vullen door onderzoeker)

4. * Surprise Question: (in te vullen door onderzoeker) Zou ik verbaasd zijn als deze patiënt binnen 12 maanden zou overlijden?

Ja Nee

CLINICAL FRAILTY SCALE




32

Ik ga u nu enkele vragen stellen en geef u enkele problemen om op te lossen. Wilt u alstublieft uw best doen om

zo goed mogelijke antwoorden te geven.

noteer antwoord score:

1. a. Welk jaar is het?

b. Welk seizoen is het?

c. Welke maand van het jaar is het?

d. Wat is de datum vandaag?

e. Welke dag van de week is het? (0-5) ________

2. a. In welke provincie zijn we nu?

b. In welke plaats zijn we nu?

c. In welk ziekenhuis (instelling) zijn we nu?

d. Wat is de naam van deze afdeling?

e. Op welke verdieping zijn we nu? (0-5) ________

3. Ik noem nu drie voorwerpen. Wilt u die herhalen nadat ik ze alle drie gezegd heb?

Onthoud ze want ik vraag u over enkele minuten ze opnieuw te noemen.

(Noem "appel, sleutel, tafel", neem 1 seconde per woord)

(1 punt voor elk goed antwoord, herhaal maximaal 5 keer tot de patiënt de drie woorden weet)

(0-3) ________

4. a Wilt u van 100 zeven aftrekken en van wat overblijft weer zeven aftrekken en zo

doorgaan tot ik stop zeg? (Scoor een punt per goed antwoord, d.w.z. als het verschil 7

bedraagt. Niet verder doorgaan dan 65). Noteer exact alle antwoorden van de patiënt:

100-7 = ____, ____, ____, ____, ____ (Herhaal eventueel 3 maal als de persoon stopt,

herhaal dezelfde instructie, geef maximaal 1 minuut de tijd)

of

4.b Wilt u het woord “‘WORST” achterstevoren spellen? (Scoor een punt per goede letter in juiste volgorde)

Noteer hier het antwoord. ……………………….. (0-5) ________

5. Noemt u nogmaals de drie voorwerpen van zojuist.

(Eén punt voor elk goed antwoord). (0-3) ________

6. Wat is dit? En wat is dat?

(Wijs een pen en een horloge aan. Eén punt voor elk goed antwoord). (0-2) ________

7. Wilt u de volgende zin herhalen: " Nu eens dit en dan weer dat ".

(Eén punt als de complete zin goed is) (0-1) ________

8. Wilt u deze woorden lezen en dan doen wat er staat’?

(Toon papier met daarop in grote letters: "Sluit uw ogen") (0-1) ________

9. (Lees eerst de instructie voor, geef dan het papier)

Wilt u dit papiertje pakken met uw rechterhand, het dubbelvouwen

en het op uw schoot leggen? (Eén punt voor iedere goede handeling). (0-3) ________

10. Wilt u voor mij een volledige zin opschrijven op dit stuk papier?

(Spellings- of grammaticale fouten zijn niet belangrijk. Schrijfwijze is juist als de zin een

onderwerp en een gezegde heeft, en betekenis heeft) (0-1) ________

11. Wilt u deze figuur natekenen?

(Figuur achterop dit papier. Eén punt als figuur geheel correct is nagetekend.

Er moet een vierhoek te zien zijn tussen de twee vijfhoeken) (0-1) ________

TOTALE TEST SCORE: (0-30) ________

MMSE MET KLOKTEKENEN




33

Sluit uw ogen


34

Kloktekenen

Teken een cirkel, teken hierin de cijfers van een klok, en zet de wijzers van de klok op 10 over 11.




35

Geef uitleg:

- Zittende of halfzittende houding

- Geadduceerde en neutraal geroteerde schouder, verticale arm en een neutrale positie van de

pols

- Met maximale kracht drie maal in de dynamometer knijpen.

Doe dit eenmalig voor.

Bepaal de dominante hand (= voorkeurshand)

Bij twijfel is de dominante hand de “sterkste” hand. Bijvoorbeeld de hand waarmee een bood-

schappentas op de tafel wordt getild.

Welke hand is de dominante hand? Rechts Links

Bepaal voor de dominante hand de handknijpkracht.

Handknijpkracht dominante hand

1e meting |___|___|___| kg

2e meting |___|___|___| kg

3e meting |___|___|___| kg

De handknijpkracht is niet gemeten wegens:

Patiënt weigert.

Patiënt is er fysiek niet toe in staat, omdat hij ziek is.

Patiënt is er fysiek niet toe in staat, omdat hij beperkingen heeft aan zijn armen

of handen.

Anders, nl: ……………………………………………………………………..

= Exclusie

HANDKNIJPKRACHT



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