What is the relationship between the change in body composition and change in physical fitness in obese elderly adults with type 2 diabetes?

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DOES CHANGE IN BODY COMPOSITION

AFFECT PHYSICAL FITNESS IN OBESE

ELDERLY ADULTS WITH TYPE 2 DIABETES?

Authors: Lydia Bakker and Susie Hu

Institute: Amsterdam University of Applied Sciences

Department: Nutrition and Dietetics

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What is the relationship between the change

in body composition and change in physical

fitness in obese elderly adults with type 2

diabetes?

Authors:

Lydia Bakker Susie Hu

Thesis number:

2017200

University:

Amsterdam University of Applied Sciences (HvA)

Bachelor:

Nutrition and Dietetics

In authority of:

Dr. Ir. Peter J.M. Weijs Lector Weight Management

Department of Nutrition & Dietetics University of Applied Sciences

Dr Meurerlaan 8, 1067 SM Amsterdam Supervisor: R.G. Memelink Mentor: A.M. Verreijen Assessor:

A. Van der Steen

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Acknowledgements

This thesis is written as part of our final project, to complete our Bachelor’s degree in Nutrition and Dietetics at the Amsterdam University of Applied Sciences. In this thesis, we described the

relationship between the change in body composition and the change in physical fitness in obese elderly with type 2 diabetes.

The data we used for this study originated from the PROBE-study. The PROBE-study was

commissioned by Nutricia Research in collaboration with the Research Group Weight Management (University of Applied Sciences, Amsterdam), TNO, Tromp Medical and Vialente-Diëtheek. The PROBE-study examines whether obese older adults with type 2 diabetes benefit from extra protein to maintain muscle mass, muscle strength, physical function and metabolic stability. This study is combined with an innovative nutrition and exercise program. Before and after the weight loss program, the relationship between body composition and physical fitness is examined.

In this acknowledgement, we would like to extend our sincerest thanks and gratitude to the people who contributed to and helped assisting us in writing our thesis.

First, we want to thank R.G. Memelink, our supervisor for his feedback and guiding. And for the time he has spent on supervision.

We would also like to thanks A.M. Verreijen (our mentor) for her cheering support, enthusiasm, and useful feedback during the last 20 weeks.

Finally, we want to thank Mr. P.J.M. Weijs, lecturer Weight Management, for the opportunity provided to us to carry out our research and write our thesis.

Amsterdam, June 2017 Lydia Bakker and Susie Hu

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Table of content

Acknowledgements 5

Abstract 8

1. Introduction 9

2. Methods 11

2.1 Study design and recruitment 11

2.2 Inclusion criteria 11

2.3 Measurements 12

2.3.1 Body composition 12

2.3.2 Physical fitness 12

2.3.2.1 Six physical fitness tests 13

2.4 Statistics 14

3. Results 15

3.1 Subjects and characteristics 15

3.2 The relation between change in body composition and change in physical fitness 17

3.2.1 Scatterplots and correlations 17

3.2.2 Comparison of 4 groups 23

3.2.3 Regression analysis and adjusted models 25

Discussion 27

Recommendations 30

Conclusion 30

References 31

Appendix 33

Appendix I. Exclusion criteria PROBE-study 33

Appendix II. Calculate % of repetition max. 34

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Abstract

Background:

According to the CBS the number of elderly people in the Netherlands is growing rapidly. In people over 55 and older, the prevalence of people with type 2 diabetes is high. Not only old age, but also obesity increases the risk for type 2 diabetes. Losing weight is the first advice in the treatment of obesity and type 2 diabetes. However, weight loss is often accompanied by undesirable loss of muscle mass and loss of physical function. There are many studies that have investigated the

relationship between protein, exercise and muscle mass in the elderly. But there are few studies that have examined the relationship between changes in body composition and changes physical fitness. Aim:

The aim of this study is to examine the relationship between changes in body composition and changes in physical fitness in obese older adults with diabetes type 2. The outcome of this study together with the literature provides evidence for realizing new guidelines for the treatment of obese elderly with type 2 diabetes.

Methods: In this analysis, overweight and obese participants aged 55 years or older with type 2 diabetes are included. All participants followed a weight loss program including a three-weekly strength training session and a hypo-caloric diet (with protein or control supplement). At baseline and after 13 weeks the body composition (fat mass and appendicular muscle mass, Dual-energy X- ray absorptiometry) and the physical fitness (leg press, steep ramp test, 400-meter walking test, gait speed test, chair stand test and knee extension power test) are measured. The relationship between body composition and physical fitness was assessed using a regression analysis, with the dependent variables being the change in physical fitness, and the independent variables being the change in appendicular lean mass and fat mass. The analysis were adjusted for potential confounders. The subjects were also split in 4 groups to compare differences between each physical test and the change in appendicular lean mass and fat mass.

Results: The 13-week intervention program is completed by 101 subjects. Mean BMI at baseline is 33.2 ± 4.3 kg/m2_{. There are no significant differences between the 4 groups in fat mass loss (yes/no)}

and appendicular lean mass gain (yes/no) and the change in the physical fitness tests. Regression analysis showed a significant relation between the change in fat mass and leg press (P=0.008) and the 400-meter walking test (P=0.007). Losing 1 kg of fat mass led to an improved leg press and 400- meter walk. After adjusting for confounders there were no significant relations. The relation between change in appendicular lean mass and chair stand test had a negative significant effect (P=0.048), and it also lost its significance after adjusting for confounders.

Conclusion: Results showed that there is not a strong relationship between changes in body composition and changes in physical fitness in obese older adults with type 2 diabetes. Fat mass change has a positive effect on the leg press and the 400-meter walk. On the other physical tests, no effect was seen. Change in appendicular lean mass showed to have a negative effect on the chair stand test. Our hypothesis that improvements in the body composition will lead to a significantly improved physical fitness, can only be partially confirmed.

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1. Introduction

Over the next few years the number of elderly people will increase rapidly according to the Dutch population forecast of CBS (1). The number of people over 65 will increase from 2.9 million in 2014 to about 4.8 million in 2040. The demand for care will rise due to the increased number of elderly. 70% of the people over 65 have a chronic illness (1). 63% of the elderly (75+) often have more than one chronic disease, and about one third even has three or more chronic conditions. (1) Especially type 2 diabetes is becoming more common among elderly over 55. In 2013, 3.8% (633.000 people) of the Dutch population had type 2 diabetes (2). In older patients (age 70+) account for almost 50% of the type 2 diabetes population (3).

Not only old age, but also obesity increases the risk for type 2 diabetes. Research shows that the higher prevalence of type 2 diabetes is partly caused by the ageing population. However, a large part of the increase can be attributed to changes in lifestyle such as the growth in prevalence of

overweight people (2). Losing weight is the first advice in the treatment of obesity and type 2

diabetes (4). However, losing weight is often accompanied by undesirable loss of muscle mass, which may lead to reduction in physical functioning (5).

The University of Applied Sciences initiated a trial with the aim to investigate the effect of a protein enriched supplement on the preservation of muscle mass and to control blood sugar levels during a 12-week period of weight loss (PROBE-study). This study investigates whether obese older adults with type 2 diabetes benefit from an enriched oral nutritional supplement (containing high whey protein, leucine and vitamin D) to preserve muscle mass and muscle strength, and to maintain physical functioning and metabolic stability during a period of weight loss. At baseline and after 13 weeks’ body composition and physical functioning is assessed.

Performing regular resistance exercises and consuming adequate sources of quality protein, are two important ways that potentially help to slow down the loss of muscle mass and function in the elderly. First the effects of exercise on physical fitness and body composition will be explained. Research carried out by Villareal et al., showed that in obese elderly (65+) a combination of weight loss with a hypo-caloric diet and physical activity leads to a greater improvement in physical

functioning than exercise or following a hypo-caloric diet only. The score on the Physical Performance Test and the Functional Status Questionnaire (in which higher scores indicate better physical status) increased more in the diet-exercise group than in the diet group or the exercise group. Furthermore, the peak oxygen consumption, strength, balance and gait improved more in the diet-exercise group compared to the other groups. Additional in the diet-exercise group, there was less loss of lean body mass and bone mineral density than in the diet group (6). Another study among older overweight people by Avila et al. showed that the effect of weight training during weight loss has no positive effect on physical functioning. Both groups (the group with the DASH diet combined with resistance training and the DASH diet group) experienced decreases in 400-meter walk times. Yet this study showed that the group following the DASH diet combined with weight training had a greater reduction in body fat and greater changes in lean mass and strength than the DASH diet group (7). Weinheimer et al. showed in their systemic review that exercise preserves fat free mass in overweight and obese middle-aged older adults (with a BMI >25 kg/m2_{) who followed an}

energy restricted diet (8).

Next the outcome of researches on the effect of protein on body composition will be brought forward. A recent systematic review and meta-analysis by Kim et al. revealed that older adults retained more lean mass and lost more fat mass during weight loss when consuming ≥25% protein (or 1.0 g/kg/d) of the total energy intake compared to the group who consumed <25% protein of the energy intake. (9).

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Lastly the effects of exercise and protein combined on body composition and physical fitness will be explained. A number of randomized controlled trials have shown that daily protein supplementation (above the protein requirement) in combination with a resistance training program, significantly improves the fat-free mass, strength and physical performance among (vulnerable) elderly (10,11). However, a literature search by Thomas et al. revealed that protein or essential amino acids (EAA) supplementation does not significantly augment the effects of progressive resistance exercise training in older adults (12). Verreijen et al. investigated whether a high protein diet and/or

resistance exercise preserves fat-free mass during weight loss in overweight and obese older adults. The outcome showed only the group with the combined intervention (of high protein diet and resistance exercise) significantly increased in fat-free mass (13). Another study by Verreijen et al. showed that appendicular muscle mass was preserved in obese adults who took an enriched supplement (containing a high whey protein, leucine and vitamin D) combined with a hypo-caloric diet and resistance exercise program (14). A study by Tieland et al. showed daily dietary protein supplementation and prolong resistance training improved strength and physical performance in frail elderly people (15).

Many researches have been conducted regarding protein intake and muscle mass in the elderly. However, only a few studies investigated the effect of changes in body composition during weight loss on physical functioning. Using data from the PROBE-study we investigated this relationship, by answering the following research question: What is the relationship between the change in body composition and change in physical fitness in obese older adults with type 2 diabetes?

Based on studies as mentioned above (6,7), our hypothesis is that there is a positive relationship between the change in body composition and change in physical functioning in obese elderly people with diabetes type 2. In other words, improvements in the body composition (preservation or increase in appendicular lean mass and a decrease in fat mass) will lead to a significantly improved physical fitness. We expect that more muscle mass ensures better physical functioning. And fat mass reduction leads to carrying less weight, so the outcome of the physical fitness tests will be more positive.

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2. Methods

To answer the research question, we used data from the PROBE-study. First, we described an overview of the study design and recruitment of participants, the inclusion criteria of the

participants, how the change in body composition and change in physical fitness is measured and finally how the statistical analysis will be done.

2.1 Study design and recruitment

The PROBE-Study started in 2014 with the aim to examine the effects of an innovative high-protein diet and exercise, to maintain muscle mass, losing fat and metabolic health among 120 obese older adults with type 2 diabetes. This study was a 13-week weight loss trial in which the effect of an oral nutritional supplement (high whey protein (21 grams), leucine, and vitamin D) and resistance training on preservation of lean mass was investigated and compared to a control product in the research group.

The newly developed product was an oral supplement comprising a mixture of proteins (high whey protein and leucine), vitamin D and calcium. This was compared to a control product that looked the same as the test product. Both products contained the same amount of calories, but not the specific active ingredients. Both products came in two flavors: vanilla and strawberry.

All participants received dietary counseling (hypocaloric diets) and a resistance exercise program (3 times per week) with the goal to lose weight. Within this weight loss program participants were randomized into two groups. Group one received the newly developed product and the other group received the control product. This study was a double-blind study, so the research team did not know which of the two products have been given to the participants.

The study period consisted of 13 weeks and started after the inspection visit and after visiting the sports physician which decided if the participant is suitable. During the 13 weeks, the test and control products were used. Participants were asked to take the product once a day before

breakfast. On training days, participants used a second portion of the product. The participants were asked to visit the HvA three times for a study visit, fill in various questionnaires at home and visit the VU University Medical Center twice for a blood test.

The subjects were recruited through nutritionists, general practitioners, diabetes units of hospitals or other relevant health care providers, available databases, flyer and poster campaigns in the

community and ads on websites, social media and regional newspapers.

2.2 Inclusion criteria

The inclusion criteria for the participants were: 1. Age 55-85 years old

2. Ambulant type 2 diabetes patients (verified by used medication for diabetes). In the event, no medication is used HbA1c should be ≥43 mmol/mol or ≥6.1%

3. - BMI > 30.0 kg/m2 _or

- BMI > 27.0 kg/m2_{, in combination with a waist circumference >88 cm for women and >102}

cm for men

4. For patients who use SU-derivatives:

-Agreement of patient that his/her diabetes medication may be adapted.

-Agreement to possible adjustment(s) of SU-derivate dose at the start and during the study 5. Written informed consent

6. Willingness and ability to comply with the protocol

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The exclusion criteria are listed in Appendix I.

2.3 Measurements

The data we used for this thesis were the body composition and physical fitness, both at baseline and after 13 weeks of intervention. The body composition (fat mass and appendicular lean mass) and the physical fitness (six components) were measured. These tests were performed to measure physical functioning, physical strength and endurance. The measurements of the body composition and physical fitness tests will be explained below.

2.3.1 Body composition

The body composition shows the relative amounts of fat and fat-free mass in the body. The second component of the body composition, lean muscle mass, refers to the fat-free mass, connective tissue, muscle and organ tissue (16). In this research fat mass and appendicular lean mass were used.

DXA-scan

The fat-free mass, body fat mass and appendicular lean mass is measured by the Dual Energy X-ray Absorptiometry scan (DXA-scan, named Hologic Discovery A). During a DXA scan, X-rays are passed through the body. Some radiation is absorbed by the bone and soft tissue and some travels through the body. Special detectors in the DXA scanner measure how much radiation passes through the bones, and this information is sent to a computer. The participant should wear swimwear or

underwear for this assessment and must hold very still. The test is usually completed within 10 to 30 minutes, depending on the equipment and the parts of the body being examined. The DXA scan is a reliable method to estimate fat mass and muscle mass compared to the BIA (bio-electrical Impedance Analysis) to quantify appendicular lean mass as a proxy for muscle mass (17). For this thesis, the appendicular lean mass and fat mass will be used for the analysis. Appendicular lean mass (in kg) is defined by the sum of the lean, bone free mass of the left arm, right arm, trunk, left leg, right leg and head. For the fat mass, we used the total body fat.

2.3.2 Physical fitness

Physical fitness is a set of attributes that are health or skill-related. These attributes can be measured by specific tests. Health-related components of physical fitness are: cardiorespiratory endurance, muscular endurance, muscular strength and flexibility (18). The six measurements of physical fitness which are measured in the PROBE-study are the leg press 10-RM (kg), steep ramp test (l/min; ml/kg/min), 400-meter walking test (m/s), gait speed test (m/s), chair stand test (s) and the knee extension power (Watts).

Muscle strength is a key element for measuring physical fitness. Muscle strength is strongly

associated with the performance of daily activities (19). The leg press 10-RM (kg) test is a good test to measure the power in the lower part of the body (20). Data from the study of Patton et al., suggest that the steep ramp test gives a reliable and valid estimate of the VO2 max (21). The gait

speed test is a fast and reliable measurement of physical functioning, for health-related outcomes (22). The chair stand test is a commonly used measure of functional strength, particularly among older adults. The reliability can be interpreted as good to high in most patient groups and institutions (23). The knee extension power test is a reliable test to measure the power (in Watts) in a double-leg extension (24).

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2.3.2.1 Six physical fitness tests

Leg press 10-RM test

The leg press 10-RM (kg) test measures the maximum leg strength of the participant on a leg press weight training machine. Prior to the test, the participant has performed a warming-up exercise on the leg press weight machine. First the participant performed the exercise unloaded, then the participant performed the exercise loaded (kg) to a maximum of 50% of the estimated maximum power (calculated based on a %RM calculate table*) of the participant. Next, the participant will perform the 10-RM (kg) power exercise to the estimated maximum force at which he is able to exert a maximum of 10 repetitions on the machine. The leg press 10-RM (kg) test is performed with both legs at the same time.

Prior to the test, the participant has performed a warming-up exercise on the leg press weight machine. First the participant performed the exercise unloaded, then the participant performed the exercise loaded to a maximum (kg) of 50% of the estimated maximum power (based on a %RM calculate table*).

*Example of the table is listed in appendix II.

Steep Ramp test

The Steep Ramp Test is the most commonly used method to determine the endurance. During this test, the maximum oxygen uptake (

VO

2max) is measured. The

VO

2 max is determined using a steep incline test on a bicycle ergometer. The Steep Ramp Test has been validated against the everyday ramp test and was found to accurately measure the exercise capacity in type 2 diabetes patients (25). The test starts with a four-minute rest period followed by a warming-up. After that the bike resistance increases in small increments until the maximum effort is reached, when the participant is exhausted or when the test is discontinued. The oxygen uptake and carbon dioxide expiration are measured with a gas analyzer (Quark device). The highest average 10-second oxygen uptake is documented as

VO

2max.

400-meter walking test

During the 400-meter walking test, the participant walks a trail of 20 meters, ten times back and forth. The course must be completed as quickly as possible so the participant can maintain the 400- meter walk test at the same pace. During the test, the time is recorded by a stopwatch in seconds. The speed is calculated in meters per second (m/s). This test may not be performed if the participant has a heart rate <40 bpm (beats per minute) or >110 bpm, or when the diastolic blood pressure >109 mmHg or systolic blood pressure >199 mmHg is.

Gait speed test

The gait speed test is a 4-meter walk test in which the subject walks in a normal walking pace from the beginning to the endpoint. The researcher measured with a stopwatch how many seconds the participant has done to run four meters. This is measured in meters per second (m/s). The subject performed this test twice, the fastest of the two measurements in m/s is used for the analysis.

Chair stand test

With the chair stand test, the participant takes a seat on the chair. The first time the participant will be asked if he/she can get up once and if it feels safe to repeat to stand up five times as quickly as possible. If the participant confirms, the test can begin. The participant must get up five times from the chair and sit back with arms crossed over the chest as quickly as possible. The total time in seconds is recorded and used for the analysis.

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Knee extension power test

The knee extension power test is performed on a knee extension weight training machine with a linear encoder (360 Humac closed loop trainer, CSMi) connected to the weight stack of the machine. The participant is in a comfortable position on the knee extension machine and the subject is requested to stretch the knees as quickly as possible from a bent position. The time and distance over which the weight stack is lifted, is measured by the linear encoder and it calculates the velocity. The participant performs this exercise at least 5 times while the weight is increased every repetition. The weight is increased by 2.5 kg for woman and for men with 5 kg. The average power (Watts) is measured for each repetition. The test is finished when the average power successfully is measured. In this research, the maximum power of 5 repetitions is used. Prior to the test, the participant has finished a 5-minute warming-up on a stationary bike, followed by 10 repetitions on the leg extension with a sub-maximal weight and 5 repetitions with a higher than sub-maximal weight.

2.4 Statistics

First scatterplots were generated to visualize the crude relation between the x (change in appendicular lean muscle mass or change in fat mass) and y (change in the six different tests mentioned above for physical fitness). With change, we mean the change after the 12-week intervention program.

Second, regression analysis was performed for the relationship between x (= change in appendicular lean muscle mass and change in fat mass) and y (= change in the five different testes mentioned above for physical fitness) adjusted for the following potential confounders. Age, change in fat mass, change in muscle mass, self-reported physical activity and smoking are considered as factors that can bias associations between body composition and physical function (26).

The beta’s for the main determinant are presented with standard error and P-value. If the P-value is < 0.05, then this association is significant. Regression analysis was not performed on the steep ramp test. This variable seems to be divided into categories and therefor is not normally distributed. To view the distribution of the steep ramp Wmax, we compared the change of the steep ramp Wmax in 4 different groups.

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3. Results

3.1 Subjects and characteristics

Figure 1 presents an overview of the subjects in the PROBE-study from the screening to the 13-week measurement. Initially, 220 obese older subjects diagnosed with type 2 diabetes were recruited for the PROBE-study. 94 of them were reported as screened failures, due to the fact the subjects didn’t meet the inclusion criteria. 126 subjects met the eligibility criteria and were invited for the baseline measurements. Participants were subsequently randomized and assigned to one of two

interventions: the test group receiving the high-whey, leucine and vitamin D supplement and the control group receiving a placebo supplement.

From the 126 participants, 22 had an early termination and therefore no 13-week measurement because participating in the study was too time consuming (n=11), or because of personal circumstances (n=9) or due to serious adverse events (n=4). Only subjects from whom body composition measurements and physical fitness tests were performed at baseline and at the 13- week measurement were included in the analysis. Subjects who had partial results of the body composition and/or physical fitness tests (at baseline and/or 13-week measurement) were excluded in the analysis. The 13 week intervention program was eventually completed by 101 subjects.

Figure 1: Flowchart of the PROBE-study subjects

The study participants (N= 101) had an average age (± SD) of 66.6 ± 6.1 years with a mean BMI of 33.2 (SD 4.6) kg/m2_{, of which 64.4% was obese at baseline. 91.3% of the participants were}

nonsmokers. The mean physical activity level (PAL) at baseline was (± SD) 1.4 ± 0.14 PAL.

Figure 1 shows body weight and body composition change over the 12-week weight loss trial. Overall body weight loss was -2.8 ± 3.2 kg. The mean change in fat mass was (± SD) -2.6 kg ± 2.5 kg and the mean change in appendicular lean mass after 13 weeks of intervention was (± SD) +0.15 ± 1.04 kg. 3 participants had missing values of the body composition (lean and fat mass) due to body parts that were partially outside the DXA scan area. These 3 participants did perform the physical test, hence they were excluded from the analysis.

101 subjects were included in the data analysis

22 subjects dropped out

due to personal circumstances,

serious events or other reasons. And 3

subjects had incomplete data.

126 subjects were randomized

94 subjects did not meet

inclusion criteria

220 subjects were recruited

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Figure 2: Mean body composition change between baseline and 13-week measurement

To show if weight loss and fat mass loss are significant, a paired samples T-test was performed. This test showed the subjects did significantly lose bodyweight and fat mass (P=0.000 and P=0.000).

Paired Samples Test

Mean Std. Deviation t df Sig. (2-tailed)

Pair 1 BPWEIGHT3 - BPWEIGHT1 -2,807 3,159 -8,929 100 ,000 Pair 2 TOTALFATMASS3 - TOTALFATMASS1 -2,585 2,475 -10,500 100 ,000 2 1 0,15 0 -1 -2 -3 _-2,81 _-2,59 -4 -5 -6 -7

Body weight change Fat mass change Appendicular lean mass change

M ean lo ss/ ga in in kg

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3.2 The relation between change in body composition and change in physical fitness

First scatterplots were generated to visualize the relation between the primary determinants (change in fat mass and change in appendicular mass) and the outcome variables (change in the five different tests for physical fitness, the steep ramp test excluded).

3.2.1 Scatterplots and correlations

Chart 2. Relationship between change total fat mass and change in 400-meter walk test

Fat mass change (kg)

Chart 1. Relationship between change in total fat mass and change in leg press

Fat mass change (kg)

Beta= -.315

SE= 1.854

P= 0.008

R

2

_{= 0.009}

N=70

Beta= -.274

SE= 0.004

P= 0.007

R

2

_{= 0.075}

N=95

400 -me

ter

w

al

k

te

st

ch

an

ge

(m

/s)

Leg

p

res

s ch

an

ge

(kg

)

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Chart 3. Relationship between change in total fat mass and change in gait speed test

Fat mass change (kg)

Beta= -.035

SE= 0.009

P= 0.729

R

2

_{= 0.001}

N=99

Beta= 0.104

SE= 0.080

P= 0.306

R

2

= 0.011

N=98

Ch

ai

r s

ta

nd

te

st

cha

nge

(s

ec

)

Ga

it

sp

ee

d

te

st

ch

an

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(m

/s)

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The relation between change in total fat mass and change in leg press are significant (P=0.008) as well as the relation between change in total fat mass and change in the 400-meter walking test (P=0.007). With fat loss, the power in the legs increased, hence the subjects could walk faster. Chart 1 showed 1 kg loss of fat mass led to an increased strength of the leg press with 5,1 kg. Vice a versa 1 kg gain in fat mass led to a decreased strength of 5,1 kg in the leg. Chart 2 showed losing 1 kg of fat mass led to a faster 400-meter walk of 0.012 meters per second. Vice a versa gaining 1 kg fat mass displayed the walking test was slower with 0.012 meters per seconds. The remaining relationships are less clear.

The next five scatterplots showed the forms of relationships between the change in appendicular lean mass (ALM) and the change in five physical fitness tests (steep ramp test excluded).

Chart 5. Relationship between change in total fat mass and change in knee extension power

Fat mass change (kg)

Beta= 0.037

SE= 2.387

P= 0.760

R

2

_{= 0.001}

N=70

Kn

ee

ex

ten

sion

po

w

er

ch

an

ge

(W

at

t)

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Chart 6. Relationship between appendicular lean mass and leg press is insignificant

Appendicular lean mass change (kg)

Beta= -.061

SE= 4.807

P= 0.616

R

2

_{= 0.004}

N=70

Beta= -.160

SE= 0.010

P= 0.121

R

2

= 0.026

N=95

400 -me

ter

w

al

k

te

st

ch

an

ge

(m

/s)

Le

g

pre

ss

c

ha

nge

(kg

)

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Chart 9. Relationship between change in appendicular lean mass and change in chair stand test

Appendicular lean mass change (kg)

Chart 8. Relationship between change in appendicular lean mass and change in gait speed test

Appendicular lean mass change (kg)

Beta= -.103

SE= 0.020

P= 0.309

R

2

_{= 0.011}

N=99

Beta= 0.200

SE= 0.189

P= 0.048

R

2

_{= 0.040}

N=98

Ch

ai

r s

ta

nd

te

st

cha

nge

(s

ec

)

Ga

it

sp

ee

d

te

st

cha

nge

(m

/s)

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The relation between change in ALM and change in chair stand test is the only test that has a significant relation (p=0.048). However, this relation seems to have a negative effect on the chair stand test. With ALM gain the chair stand test time is increasing in seconds, where we expect it would have the opposite effect. Chart 9 showed gaining 1 kg of ALM increased the chair stand test time with 0.378 seconds. Vice a versa 1 kg loss of ALM led to a decreased chair stand test time of 0.378 seconds. The remaining relationships are insignificant and less clear.

Chart 10. Relationship between change in appendicular lean mass and change in knee extension power

Appendicular lean mass change (kg)

Beta= -.043

SE= 5.242

P= 0.724

R

2

_{= 0.002}

N=70

Kn

ee

ex

te

ns

ion

po

w

er

te

st

ch

an

ge

(W

at

t)

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3.2.2 Comparison of 4 groups

Independent-Samples Kruskal-Wallis Test

As mentioned before, the steep ramp Wmax test is not normally distributed therefor a regression analysis cannot be performed. The median of this variable is (± SD) 25 ± 29.2. With the Independent- Samples Kruskal-Wallis Test is shown that there is no change in distribution of the steep ramp Wmax between the 4 groups (P=0.309).

ANOVA test

We first viewed the crude relations of the data. Because fat mass change as well as appendicular lean mass change can affect physical functioning, the participants were divided in 4 groups in this post- hoc analysis. The cut-off value for fat mass loss is -2,4 kg, the cut-off value for fat mass gain is - 2,39999. The cut-off value for appendicular lean mass loss is 0,0 kg and the cut-off value for appendicular lean mass gain is 0,00001 …

The study participants were divided in 4 groups: gain in appendicular lean mass (yes/no) combined with loss in fat mass (yes/no).

Group 1= FM loss, LM gain Group 2= FM loss, LM loss Group 3= FM gain, LM gain Group 4= FM gain, LM loss

These 4 groups were compared in the change in physical fitness using the One-Way ANOVA. In the 5 charts below the 5 physical fitness tests were displayed per test to see the change in performance between the 4 groups. The outcome of the ANOVA test showed there was no difference between these 4 groups in all values.

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Figure 3a Figure 3b

Figure 3c Figure 3d

Group 1= FM loss, LM gain

Group 2= FM loss, LM loss

Group 3= FM gain, LM gain

Group 4= FM gain, LM loss

625 _35,7

5 8 30,6

Knee extension power test (Watt)

P=0,806

100 90 80 70 60 50 40 30 20 10 0 41,0 647 2 364

Groep 1 Groep 2 Groep 3 Groep 4

0,1 0,05 0 -0,05 -0,1 -0,15 -0,2 -0,25 -0,3

Gait speed test (m/s)

P=0,415

0,0574

-0,0007 _-0,02

-0,0406

400 meter walk test (m/s)

P=0,242

0,25 0,2 0,15 0,0963 0,1 _0,0671 0,0818 0,05 0,0376 0

Chair stand test (sec)

P=0,935

0 -0,5 -1 -1,3047 -1,141 -1,1545 -1,4765 -1,5 -2 -2,5 -3

Leg press (kg)

P=0,702

120 100 80 60 53,6471 54,95 41,230769 44,15 40 20 0

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3.2.3 Regression analysis and adjusted models

After viewing the crude relations between change in body composition and change in physical fitness, we performed a regression analysis to adjust for potential confounders. Not only potential confounders may influence the change in fat mass and change in appendicular lean mass, they also may influence each other.

Table 1 shows the crude and adjusted models. The crude model (model 1) shows the physical fitness tests were compared with the dependent and independent variable. The second model shows models for change in fat mass were adjusted for change in appendicular lean mass and vice a versa. Also in this model, the analysis was adjusted for potential confounders physical activity level (PAL), smoking, age and sex. Regression analysis was performed with an unadjusted linear regression model.

Table 1: Crude and adjusted models for the relation between change in body composition and change in physical fitness

Physical tests N Change in fat mass Change in Appendicular Lean Mass β (SE) P β (SE) P Leg Press Model 11 ₇₀ _{-5.071 (1.854)} _0.008 _{-2.420 (4.807)} _0.616 Model 22 _{-3.460 (2.066)} _0.099 _{-0.371 (4.886)} _0.940 400-m walking test Model 11 ₉₅ _{-0.012 (0.004)} _0.007 _{-0.016 (0.010)} _0.121 Model 22 _{2.109 (0.923)} _0.086 _{-0.016 (0.011)} _0.146

Gait speed test

Model 11 ₉₉ _{-0.003 (0.009)} _0.729 _{-0.021 (0.020)} _0.309

Model 22 _{-0.012 (0.010)} _0.221 _{0.027 (0.022)} _0.222

Chair stand test

Model 11 ₉₈ _{0.083 (0.080)} _0.306 _{0.378 (0.189)} _0.048

Model 22 _{-0.024 (0.081)} _0.772 _{0.345 (0.186)} _0.068

Knee extension power test

Model 11 ₇₀ _{0.731 (2.387)} _0.760 _{-1.857 (5.242)} _0.724

Model 22 _{-0.409 (2.726)} _0.881 _{-2.503 (5.518)} _0.652

1 _{Relationship between fat mass and physical fitness; Relationship between appendicular lean mass} and physical fitness.

2 _{Relationship between change in fat mass and physical fitness, adjusted for change in appendicular} lean mass, Physical Activity Level (PAL), smoking, age and sex; Relationship between change in appendicular lean mass and physical fitness, adjusted for change in fat mass, Physical Activity Level (PAL), smoking, age and sex.

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When looking at the crude models with the main determinant fat mass change, it’s showing that fat mass loss has both a positive significant effect on the leg press and 400-meter walking test. 1 kg fat mass loss leads to 5,1 kg more power on the leg press (P=0.008) and amps up the speed with 0.012 m/s on the 400-meter walking test (P=0.007). After adjusting these two tests for confounders, both tests do not seem to have a significant relation with change in fat mass.

When looking at the crude models with the main determinant appendicular lean mass change, it’s showing that appendicular lean mass loss only has a negative significant effect on the chair stand test. 1 kg appendicular lean mass gain leads to a slower chair stand test time of 0.378 seconds (P=0.048). This test also doesn’t have a significant relation with change in appendicular lean mass after adjusting for all confounders.

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Discussion

This is one of the first studies that examine the relationship between the change in body composition and the change in physical fitness in obese elderly adults with type 2 diabetes, during a 13-week weight loss trail combined with resistance training.

The number of elderly people in the Netherlands is growing rapidly. In people over 55 and older, the prevalence of people with type 2 diabetes is high (1). Losing weight is the first general advice in the treatment of obesity and type 2 diabetes, but there is no specific advice for elderly adults with obesity and type 2 diabetes (4). However, weight loss is often accompanied by undesirable loss of muscle mass. Additionally, loss of muscle mass is associated with an increased risk of disability and loss of physical function. There are many studies that have investigated the relationship between protein, exercise and muscle mass in the elderly. But there are few studies that have examined the relationship between changes in body composition and changes in physical fitness (5). It is important to provide a recommendation on how to treat this group of elderly adults.

The main aim of this thesis is to examine the relationship between the change in body composition and change in physical fitness in obese elderly adults with type 2 diabetes.

Our hypothesis is that there is a positive relationship between the change in body composition and change in physical functioning in obese elderly people with diabetes type 2. Improvements in the body composition (preservation or increase in appendicular lean mass and a decrease in fat mass) will lead to a significantly improved physical fitness. We are expecting that more muscle mass ensures better physical functioning. And fat mass reduction leads to carrying less weight, so the outcome of the physical fitness tests will be more positive.

The results show that the fat mass is the parameter that is most strongly associated with the effect on physical fitness. The change in fat mass does have significant association with the change in leg press (kg) and the 400-meter walking test (m/s) in elderly with diabetes type 2. The participants walk faster and can push more kilo grams on the leg press when they lose fat mass.

The change in appendicular lean mass does have significant association with the chair stand test (s). But not in the way we expected. The change in appendicular lean mass have a negative effect on the chair stand test. When the participant appendicular lean mass gains, the time (sec) on the chair stand test increases. After adjusting the physical fitness tests for confounders, there are no significant relations any longer.

Villareal et al. shows that in obese elderly (65+) a combination of weight loss with a hypo-caloric diet and physical activity leads to a greater improvement in physical functioning (the peak oxygen consumption, strength, balance and gait speed improved) than in the diet group or the exercise group (6). Another study among older overweight people by Avila et al. shows that the effect of weight training during weight loss has positive effect on physical functioning. Both groups (the group with the DASH diet combined with resistance training and the DASH diet group) experienced

decreases in 400-meter walk times. Yet this study shows that the group following the DASH diet combined with weight training had a greater reduction in body fat and greater changes in lean mass and strength than the DASH diet group (8). A study by Hunter et al. shows that significant muscle mass improvement starts after 9 weeks of resistance training. The effects of resistance training are particularly high the first three months on physical fitness where the change in lean mass is less relevant (29). In addition, preservation of the appendicular lean mass could have a positive effect on physical function, but also on the glucose level. This is important for patients with type 2 diabetes. Based on our results, our hypothesis can only be partially confirmed.

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A study by Shin et al., shows that more lean mass was related to better physical performance on items assessing body strength, while higher body fat was related to the poorer physical performance in each of the assessed measures (26). The results of our study show that there is no significant association between the change in appendicular lean mass and the physical fitness parameters except for the chair stand test. This relation is even more significant when the chair stand test is adjusted for smoking and PAL as confounders. But in negative way. The results show that the subjects of this study have gained an average of 150 gram appendicular lean mass. Research by Weinheimer et al. shows exercise preserves fat-free mass in overweight and obese middle-aged older adults (with a BMI >25 kg/m2_{) who followed an energy restricted diet (8).}

Several randomized controlled trials have shown that daily protein supplementation (above the protein requirement) in combination with a resistance training program, significantly improves the fat-free mass, strength and physical performance among (vulnerable) elderly (10,11). However, a literature search by Thomas et al. revealed that protein/EAA (essential amino acids) supplementation does not significantly augment the effects of progressive resistance exercise training in older adults (12). The protein supplementation in this study could have a disturbing effect. Because this study is double-blind, we can’t adjust for protein supplementation. Further investigation may be beneficial for a better understanding of how protein the change in body composition can improve the physical fitness of elderly adults with type 2 diabetes and obesity.

The primary determinant in this study are change in fat mass and change in appendicular lean mass. The confounders in the regression analysis are sex, age, smoking and physical activity level (PAL). The relation between change in fat mass/appendicular lean mass and change in physical fitness were not only confounded for age, sex, smoking and PAL, but also vice a versa. Generally, men have more appendicular lean mass than women (27). Which can indicate that there is a difference in physical fitness between men and woman. In this study sex was a confounder for the relationship between the change in fat mass and the leg press, steep ramp VO2 and knee extension power test. And for the

relationship between change in appendicular lean mass and leg press. We use age as a confounder because it can have influence on the physical performance of the subjects. A study by Sandvik et al., shows that smoking causes a decrease in physical fitness and lung function among middle aged people, that could not be explained by differences in age and physical activity (28).

In a post-hoc analysis in which the following four groups were compared, group 1 loss fat mass and gain lean mass, group 2 loss fat mass and loss lean mass, group 3 gain fat mass and gain lean mass and group 4 gain fat mass and loss lean mass. The largest differences are between group 2 and group 3, these differences are most likely due to the fact that group 3 loss body weight and group 2 gains body weight. However, there were no significant relationships between these 4 groups while performing the 6 physical tests. The study of Villareal et al., confirmed also that a combination of weight loss and exercise in obese elderly (65+) improves the physical fitness and only exercise with no weight loss improves less. Also, between the exercise and weight loss group and only exercise group was no significant relationship found (6).

The strengths of our study are the use of a valid instrument to determine body composition. The use of the Dual Energy X-ray Absorptiometry scan (DXA-scan, type Hologic Discovery A) gives a reliable outcome of body composition (17). The measurements for physical fitness were all conducted in the

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than only muscle mass. Further research is needed to investigate the influence of the change in quality of muscle mass on physical fitness.

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Recommendations

Using the data of the present study we can conclude that there is a relation between change in body composition and 3 of the 6 physical fitness tests. However, these relations only apply to change in fat mass in association with change in leg press and change in the 400-meter walking test. And the relation between change in appendicular lean mass and the change in the chair stand test. These findings suggest that loss of fat mass leads to an improved leg press and 400-meter walk, and that gain in appendicular lean mass leads to a slower chair stand test time. In any event, this intervention has ensured that the participants have not lost appendicular lean mass and on average they have improved their physical functioning and fitness. In addition, the study results show that the effect of fat loss seems stronger than maintaining appendicular lean mass. However, we recommend that further research is needed to determine whether change in body composition is of influence on (an improved) physical fitness. Further research should be done to examine the relation between change in body composition and the three physical fitness tests: the steep ramp test, gait speed test and knee extension power test.

Conclusion

In conclusion, there is not a strong relationship between the change in body composition and change in physical fitness. These findings indicate that a three-weekly strength training session and a hypo- caloric diet (with protein or control supplement) resulted in a significant relation between body composition and 3 of the 6 physical fitness tests. However, after adjusting these 3 physical fitness tests for confounders, there was no significant relation any longer.

Our hypothesis that improvements in the body composition will lead to a significantly improved physical fitness, only applies to the leg press and 400-meter walking test. In other words, our

hypothesis can only be partially confirmed. Further research is needed to determine whether change in fat mass and change in appendicular lean mass is of influence on physical fitness.

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References

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3. Ubink-Veltmaat LJ, Bilo HJ, Groenier KH, Houweling ST, Rischen RO, Meyboom-de Jong B. Prevalence, incidence and mortality of type 2 diabetes mellitus revisited: a prospective population-based study in The Netherlands (ZODIAC-1).Eur J Epidemiol. 2003;18(8):793-800. 4. Dieetbehandelingsrichtlijnen. Richtlijn 5: Diabetes Mellitus. Beschikbaar via:

http://www.dieetbehandelingsrichtlijnen.nl.rps.hva.nl:2048/richtlijnen/05HK_diabetes_melli tus_1.html

5. Janssen I, Heymsfield SB & Ross R. Low relative skeletal muscle mass (Sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002; 50: 889−896.

6. Villareal DT, Chode S, Parimi N, Sinacore DR, Hilton T, Armamento-Villareal R, Napoli N, Qualls C, Shah K. Weight loss, exercise, or both and physical function in obese older adults. N Engl J Med. 2011 Mar 31;364(13):1218-29.

7. Avila JJ, Gutierres JA, Sheehy ME, Lofgren IE, Delmonico MJ. Effect of moderate intensity resistance training during weight loss on body composition and physical performance in overweight older adults. Eur J Appl Physiol. 2010 Jun;109(3):517-25.

8. Weinheimer EM, Sands LP, Campbell WW. A systematic review of the separate and combined effects of energy restriction and exercise on fat-free mass in middle-aged and older adults: implications for sarcopenic obesity. Nutr Rev. 2010 Jul;68(7):375-88.

9. Kim JE, O'Connor LE, Sands LP, Slebodnik MB, Campbell WW. Effects of dietary protein intake on body composition changes after weight loss in older adults: a systematic review and meta-analysis. Nutr Rev. 2016 Mar;74(3):210-24.

10. Tieland M, Dirks ML, van der Zwaluw N, Verdijk LB, van de Rest O, de Groot LC, van Loon LJ. Protein supplementation increases muscle mass gain during prolonged resistance-type exercise training in frail elderly people: a randomized, double-blind, placebo-controlled trial. J Am Med Dir Assoc. 2012 Oct;13(8):713-9.

11. Cermak NM, Res PT, de Groot LC, Saris WH, van Loon LJ. Protein supplementation augments the adaptive response of skeletal muscle to resistance-type exercisetraining: a meta-analysis. Am J Clin Nutr. 2012 Dec;96(6):1454-64.

12. Thomas DK, Quinn MA, Saunders DH, Greig CA. Protein Supplementation Does Not Significantly Augment the Effects of Resistance Exercise Training in Older Adults: A Systematic Review. J Am Med Dir Assoc. 2016 Oct 1;17(10):959.e1-9.

13. Verreijen AM, Engberink MF, Memelink RG, van der Plas SE, Visser M, Weijs PJ. Effect of a high protein diet and/or resistance exercise on the preservation of fat-free mass during weight loss in overweight and obese older adults: a randomized controlled trial. Nutr J. 2017 Feb 6;16(1):10.

14. Verreijen AM, Verlaan S, Engberink MF, Swinkels S, de Vogel-van den Bosch J, Weijs PJ. A high whey protein-, leucine-, and vitamin D-enriched supplement preserves muscle mass during intentional weight loss in obese older adults: a double-blind randomized controlled trial. Am J Clin Nutr. 2015 Feb;101(2):279-86.

15. Tieland M, Dirks ML, van der Zwaluw N, Verdijk LB, van de Rest O, de Groot LC, van Loon LJ. Protein supplementation increases muscle mass gain during prolonged resistance-type exercise training in frail elderly people: a randomized, double-blind, placebo-controlled trial. J Am Med Dir Assoc. 2012 Oct;13(8):713-9.

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16. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985; 100:126– 131.

17. Laskey MA. Dual-energy X-ray absorptiometry and body composition. Nutrition. 1996 Jan;12(1):45-51

18. Acevedo EO, Starks MA. Handleiding fitness oefeningen. Tweede editie. 2011.

19. Dawn A. Skelton, Carolyn A. Greig, Janet M. Davies, Archie Young. Strength, Power and Related Functional Ability of Healthy People Aged 65-89 Years. Age and Aging. 1994; 23:371- 77.

20. Jared W. Coburn, Moh H. Malek. NSCA’s Essentials of Personal Training. Human Kinetics. Fitness testing protocols and norms. Muscular strenght. p. 225-6.

21. Patton JF, Vogel JA, Mello RP. Evaluation of a maximal predictive cycle ergometer test of aerobic power. Eur J Appl Physiol Occup Physiol. 1982;49(1):131-40.

22. Peel NM, Kuys SS, Klein K. Gait speed as a measure in geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci. 2013 Jan;68(1):39-46.

23. Bohannon RW. Test-retest reliability of the five-repetition sit-to-stand test: a systematic review of the literature involving adults. J Strength Cond Res. 2011Nov;25(11):3205-7. 24. European Journal of Applied Physiology and Occupational Physiology. A new method for

measuring power output in a single leg extension: feasibility, reliability and validity. September 1990, Volume 60, Issue 5. p. 385–390

25. Sternfeld B, Ngo L, Satariano WA, Tager IB. Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol. 2002 Jul 15;156(2):110-21.

26. Shin H, Liu PY, Panton LB, Ilich JZ. Physical performance in relation to body composition and bone mineral density in healthy, overweight, and obese postmenopausal women. J Geriatr Phys Ther. 2014 Jan-Mar;37(1):7-16.

27. Janssen I, Heymsfield SB, Wang ZM, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr. J Appl Physiol (1985). 2000 Jul;89(1):81-8. Erratum in: J Appl Physiol (1985). 2014 May 15;116(10):1342.

28. Leiv Sandvik, senior statistician, Gunnar Erikssen, Erik Thaulow. Long term effects of smoking on physical fitness and lung function: a longitudinal study of 1393 middle aged Norwegian men for seven years. TheBMJ. 1995 Sept 16;311:715.

29. Hunter GRM, John P, Bamman, Marcas M. Effects of resistance traning on older adults. Sports Med. 2004;34(5):329–348.

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Appendix

Appendix I. Exclusion criteria PROBE-study

The exclusion criteria:

1. Specific medical history: instable angina pectoris, cardiac infarcts and/or cardiac surgery within 3 months prior the baseline, any malignant disease during the last five years (except for adequately treated prostate cancer without evidence of metastases), localized bladder cancer, cervical carcinoma in situ, breast cancer in situ and non-melanoma skin cancer, and other relevant medical history that could affect the study outcome as judged by the investigator.

2. Any gastro-intestinal disease that interferes with the bowel function and nutritional intake (e.g. constipation or diarrhea secondary to neuropathy, diarrhea due to chronic

inflammatory bowel disease, gastroparesis, (partial) gastrectomy or any other procedure for stomach volume reduction, including gastric banding).

3. Wearing an electronic implant and/or pacemaker.

4. Renal disease (estimated glomerular infiltration rate (eGFR) <60 ml/min).

5. Hepatic disease (liver enzymes ALAT, ASAT, GGT or ALP greater than 3 times Upper Limit of Normal)

6. Use within 2 weeks prior to baseline and/or expected use during the study: - Corticosteroids for systemic use

- Antibiotics for systemic use 7. Use of insulin

8. Change in dose within three months prior to baseline of: - Antidepressants

- Neuroleptics

- Lipid lowering medication

9. Specific dietary and/or lifestyle factors present within three months prior to baseline: - Involuntary weight loss of at least 5%

- Use of protein containing or amino acid containing nutritional supplements 10. Known allergy to cow’s milk and milk products or the ingredients of the study product 11. Known galactosaemia

12. Known lactose intolerance

13. More than 22 µg of daily vitamin D intake from non-food sources (such as supplements and prescribed medication).

14. More than 500 mg of daily calcium intake from non-food sources (such as supplements and prescribed medication).

15. Current alcohol or drug abuse in opinion of the investigator

16. Investigator’s uncertainty about the willingness or ability of the subject to comply with the protocol requirements

17.

Participation in any other study involving investigational or marketed products concomitantly or within four week prior to baseline

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Appendix III. Syntax

*Lengte berekenen in meters.

COMPUTE HeightMeter=Height / 100 . EXECUTE.

*BMI berekenen.

COMPUTE BMI=BPWEIGHT1 / (HeightMeter * HeightMeter). EXECUTE.

*Baseline characteristics age. FREQUENCIES VARIABLES=AGE

/STATISTICS=STDDEV RANGE MINIMUM MAXIMUM MEAN MEDIAN /ORDER=ANALYSIS.

*Baseline characterics BMI. GET

FILE='\\homedir.ad.hva.nl\hus\Desktop\datafiles PROBE 1 2 3\Datasetscriptie3.sav'. DATASET NAME DataSet1 WINDOW=FRONT.

COMPUTE Heightmeter=Height / 100. EXECUTE.

COMPUTE BMI=BPWEIGHT1 / (Heightmeter * Heightmeter). EXECUTE.

FREQUENCIES VARIABLES=BMI

*Baseline characterics weight.

FREQUENCIES VARIABLES=BPWEIGHT1

*Baseline characterics smoking. FREQUENCIES VARIABLES=SMOKING

/STATISTICS=RANGE MINIMUM MAXIMUM /ORDER=ANALYSIS.

*Baseline mean PALV1 & PALV3.

COMPUTE PalmeanV1=(PAL11 + PAL21 + PAL31) / 3. EXECUTE.

COMPUTE PalmeanV3=(PAL13 + PAL23 + PAL33) / 3. EXECUTE.

COMPUTE PALdiff=PalmeanV3 - PalmeanV1. EXECUTE.

SAVE OUTFILE='\\homedir.ad.hva.nl\hus\Desktop\datafiles PROBE 1 2 3\Datasetscriptie4.sav' /COMPRESSED.

FREQUENCIES VARIABLES=PalmeanV1 PalmeanV3

*13-week measurement mean BMI & weight.

COMPUTE BMI3=BPWEIGHT3 / (Heightmeter * Heightmeter). EXECUTE.

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*Characterics change in FM & ALMdifference.

FREQUENCIES VARIABLES=TFMkgverschil ALMkgverschil

*Paired samples T-test weightloss and fat loss. DATASET NAME DataSet1 WINDOW=FRONT.

T-TEST PAIRS=BPWEIGHT3 TOTALFATMASS3 WITH BPWEIGHT1 TOTALFATMASS1 (PAIRED) /CRITERIA=CI(.9500)

/MISSING=ANALYSIS.

*TFMvan gram naar kilo berekenen. COMPUTE TFMKG=TFMVerschil / 1000. EXECUTE.

*ALM van gram naar kilo berekenen. COMPUTE ALMKG=ALMVerschil / 1000. EXECUTE.

*Mediaan berekenen ALMverschil tussen v1 en v3, en TFMverschil tussen v1 en v3. FREQUENCIES VARIABLES=TFMKG ALMKG

/STATISTICS=RANGE MINIMUM MAXIMUM MEAN MEDIAN /BARCHART FREQ

/ORDER=ANALYSIS.

*Groepsindeling ALM, afvallen en aankomen.

RECODE ALMKG (Lowest thru 0=1) (0.00001 thru Highest=2) INTO ALMgroups. EXECUTE.

*Groepsindeling TFM, afvallen en aankomen.

RECODE TFMKG (Lowest thru -2.4=1) (-2.3999 thru Highest=2) INTO TMFgroups. EXECUTE.

*Groepsindeling afvallen en aankomen TFM en ALM. IF (TMFgroups = 1 & ALMgroups = 1) Groups=1. EXECUTE.

IF (TMFgroups = 1 & ALMgroups = 2) Groups=2. EXECUTE.

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*Chart Builder FM and LegPress relationship. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=TFMKG LegPressVerschil MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=INLINE. BEGIN GPL

SOURCE: s=userSource(id("graphdataset"))

DATA: TFMkgverschil=col(source(s), name("TFMkgverschil")) DATA: LegPressVerschil=col(source(s), name("LegPressVerschil")) GUIDE: axis(dim(1), label("TFMKG"))

GUIDE: axis(dim(2), label("LegPressVerschil")) ELEMENT: point(position(TFMKG*LegPressVerschil)) END GPL.

*Variance FM and LegPress. REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN

/DEPENDENT LegPressVerschil /METHOD=ENTER TFMKG. *Gecorrigeerd LegPress met LM. REGRESSION

/MISSING LISTWISE

/DEPENDENT LegPressVerschil /METHOD=ENTER TFMKG ALMKG.

* Chart Builder FM and 400-m walk test m/s. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=TFMKG

@400mWalkTestVerschilms[name="_400mWalkTestVerschilms"] MISSING=LISTWISE REPORTMISSING=NO

SOURCE: s=userSource(id("graphdataset")) DATA: TFMKG=col(source(s), name("TFMKG"))

DATA: mWalkTestaVerschilmbs=col(source(s), name("_400mWalkTestVerschilms")) GUIDE: axis(dim(1), label("TFMKG"))

GUIDE: axis(dim(2), label("@400mWalkTestVerschilms")) ELEMENT: point(position(TFMKG*mWalkTestaVerschilmbs)) END GPL.

*Variance FM and 400-m walk test m/s. REGRESSION

/MISSING LISTWISE

/DEPENDENT @400mWalkTestVerschilms /METHOD=ENTER TFMKG.

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*Gecorrigeerd 400-m walk test m/s met LM. REGRESSION

/MISSING LISTWISE

/DEPENDENT @400mWalkTestVerschilms /METHOD=ENTER TFMKG ALMKG.

* Chart Builder FM and Gait Speed test m/s. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=TFMKG GaitspeedVerschilMS MISSING=LISTWISE REPORTMISSING=NO

DATA: GaitspeedVerschilMS=col(source(s), name("GaitspeedVerschilMS")) GUIDE: axis(dim(1), label("TFMKG"))

GUIDE: axis(dim(2), label("GaitspeedVerschilMS")) ELEMENT: point(position(TFMKG*GaitspeedVerschilMS)) END GPL.

*Variance FM and Gait Speed test m/s. REGRESSION

/MISSING LISTWISE

/DEPENDENT GaitspeedVerschilMS /METHOD=ENTER TFMKG.

*Gecorrigeerd Gait Speed test m/s met LM. REGRESSION

/MISSING LISTWISE

/DEPENDENT GaitSpeedVerschil /METHOD=ENTER TFMKG ALMKG.

* Chart Builder FM and Chair Stand test sec. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=TFMKG Time5StandsVerschil MISSING=LISTWISE REPORTMISSING=NO

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*Variances FM and Chair stand test sec. REGRESSION

/MISSING LISTWISE

/DEPENDENT Time5StandsVerschil /METHOD=ENTER TFMKG.

*Gecorrigeerd Chair Stand test sec met LM. REGRESSION

/MISSING LISTWISE

/DEPENDENT Time5StandsVerschil /METHOD=ENTER TFMKG ALMKG.

* Chart Builder FM and Knee Extension Power. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=TFMKG KEPVerschil MISSING=LISTWISE REPORTMISSING=NO

DATA: KEPVerschil=col(source(s), name("KEPVerschil")) GUIDE: axis(dim(1), label("TFMKG"))

GUIDE: axis(dim(2), label("KEPVerschil")) ELEMENT: point(position(TFMKG*KEPVerschil)) END GPL.

*Variances FM and Knee Extension Power. REGRESSION

/MISSING LISTWISE

/DEPENDENT KEPVerschil /METHOD=ENTER TFMKG.

*Gecorrigeerd Knee Extension Power met LM. REGRESSION

/MISSING LISTWISE

/DEPENDENT KEPVerschil

/METHOD=ENTER TFMKG ALMKG. * Chart Builder ALM & Leg press. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=ALMKG LegPressVerschil MISSING=LISTWISE REPORTMISSING=NO

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BEGIN GPL

SOURCE: s=userSource(id("graphdataset")) DATA: ALMKG=col(source(s), name("ALMKG"))

DATA: LegPressVerschil=col(source(s), name("LegPressVerschil")) GUIDE: axis(dim(1), label("ALMKG"))

GUIDE: axis(dim(2), label("LegPressVerschil")) ELEMENT: point(position(ALMKG*LegPressVerschil)) END GPL.

*Variances ALM & Leg press. REGRESSION

/MISSING LISTWISE

/DEPENDENT LegPressVerschil /METHOD=ENTER ALMKG. *Gecorrigeerd Leg press met FM. REGRESSION

/MISSING LISTWISE

/DEPENDENT LegPressVerschil /METHOD=ENTER ALMKG TFMKG.

* Chart Builder ALM & 400 meter walk test m/s. GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=ALMKG

@400mWalkTestVerschilms[name="_400mWalkTestVerschilms"] MISSING=LISTWISE REPORTMISSING=NO

SOURCE: s=userSource(id("graphdataset")) DATA: ALMKG=col(source(s), name("ALMKG"))

DATA: mWalkTestaVerschilmbs=col(source(s), name("_400mWalkTestVerschilms")) GUIDE: axis(dim(1), label("ALMKG"))

GUIDE: axis(dim(2), label("@400mWalkTestVerschilms")) ELEMENT: point(position(ALMKG*mWalkTestaVerschilmbs)) END GPL.

*Variances ALM & & 400 meter walk test m/s. REGRESSION

/MISSING LISTWISE