The relation between protein intake and appendicular skeletal muscle mass among older adults with obesity and type 2 diabetes: A cross-sectional study : A bachelor’s thesis

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The relation between protein intake and

appendicular skeletal muscle mass among

older adults with obesity and type 2 diabetes:

A cross-sectional study

A bachelor’s thesis

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The relation between protein intake and appendicular skeletal

muscle mass among older adults with obesity and type 2 diabetes:

A cross-sectional study

A bachelor’s thesis

Author information

Author: Melissa Verlaan

Student administration number: 500751589 Project number: 2020204

Student group: VOE 4.2 | 14 Email address: melissa.verlaan@hva.nl

Bachelor’s thesis

Advisors information

Counselor: R. G. Memelink, M.Sc Examiner: H. Ozturk, M.Sc

1st_{client supervisor: D. C. van Dronkelaar, M.Sc}

2nd_{client supervisor: J.J.J. Verstappen, M.Sc}

Client: Faculty of Sports and Nutrition, Amsterdam University of Applied Sciences

Technical information

Education: B.Sc. Nutrition and Dietetics

Educational institution: Amsterdam University of Applied Sciences Semester: 8

Version: 1

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Acknowledgements

This is the bachelor’s thesis ‘The relation between protein intake and appendicular skeletal muscle mass among older adults with obesity and type 2 diabetes: A cross-sectional study.’ This paper is written as part of my B.Sc. graduation for the Nutrition and Dietetics education at the Amsterdam University of Applied Sciences. The research for this paper was conducted under the direction and guidance of the faculty of Sports and Nutrition and based on data gathered during the PROBE study. I have previously worked with older patients, patients with morbid obesity and patients with type 2 diabetes during internships as part of my education. The main research question for this paper was formed based on both of these experiences and the input of my supervisors. After conducting complex explorative research, I now have an answer to my research question.

This bachelor’s thesis was made possible with the help and encouragement of a number of teachers, colleagues, fellow graduate students, family members and friends.

First and foremost, my gratitude goes to my counselor R. G. Memelink for his continuous guidance throughout my graduate semester and sharing his expertise on the research topic with patience and enthusiasm.

Secondly, I thank my examiner H. Ozturk for providing vital feedback on my bachelor’s thesis and accompanying papers during my graduation process.

My sincere appreciation goes to the research team of ProIntense. D. C. van Dronkelaar, J. J. J. Verstappen and Dr. C.A.B. Tieland, thank you for giving me the opportunity to work with the Faculty of Sports and Nutrition and providing the necessary training and feedback for this research.

I take this opportunity to express my gratitude to the teachers of the Amsterdam University of Applied Sciences who have provided their help and feedback during the writing of this paper. To my fellow students, thank you for your support and for sharing your experiences of this important time in our education.

I am also grateful to my family members and friends who have supported me throughout this process and kept my spirits lifted during the tumultuous time of the COVID-19 crisis. To my parents, Siem and Gerrie, and my brother, Robin, thank you for providing your encouragement and a comfortable place to work. To my dear partner, Nicholas, my sincere gratitude for your endless support and sympathy and for providing your feedback on my professional English skills.

Melissa Verlaan Zeewolde, May 8 2020

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Abstract

Introduction

A rapidly increasing patient group is older adults with obesity and type 2 diabetes. The risk of

accelerated muscle loss is prominent in this patient group. This population could benefit from weight loss, at the cost of increased risk of muscle loss and sarcopenia. Previous studies showed that

adequate protein intake and exercise were important factors in maintaining muscle mass. It was unknown whether protein intake at baseline could provide more insight into the risk of sarcopenia or actual protein needs among the study population. This study aimed to investigate the following research question: What is the relation between protein intake and appendicular skeletal muscle mass (ASMM) among older adults with obesity and type 2 diabetes in the PROBE study?

Methods

A cross-sectional study was conducted for 123 older adults (55-85 year) with obesity and type 2 diabetes. The analyzed data was recorded at baseline in the PROBE study. Protein intake was recorded with a diet diary. ASMM was measured using dual-energy X-ray absorptiometry. The data was analyzed through linear regression analysis with SPSS version 25. A p-value of <0.05 was considered statistically significant for any data tested.

Results

The mean ASMM was 26.7 kg (SD = 5.2) for this study population. The mean total protein intake per kg of body weight per day was 0.85 g (SD = 0.23) for the overall study population. Linear regression analysis in the unadjusted model showed no significant linear relation between protein intake and ASMM for the study population (R = 0.100, ß = 2.20, p = 0.28). There was also no significant linear relation found after correcting for identified confounders or after testing for men and women

separately. Excess energy (kcal/d), physical activity (accelerometry), visceral fat (DXA) and duration of type 2 diabetes (months) were identified confounding factors in these calculations.

Conclusion

There was no significant relation between protein intake and appendicular skeletal muscle mass found among older adults with obesity and type 2 diabetes in the PROBE study at baseline, which is contradictory to previous theories on this subject.

Keywords

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1. Introduction

The prevalence of older adults with diabetes mellitus is increasing rapidly (1,2). Diabetes mellitus is a metabolic disorder that is typically characterized by high levels of blood sugar caused by insulin resistance or inadequate production of insulin by the pancreas. In the case of type 2 diabetes, this is often the result of insufficient physical activity and excessive body weight (3). Diabetes mellitus is known to be associated with serious complications including peripheral vascular disease, chronic damage to the kidneys and retinopathy (4). Type 2 diabetes and its comorbidities can drastically impair physical abilities and quality of life in older patients (5,6). Studies show that apart from the use of medication, effective treatment for type 2 diabetes in older adults with obesity involves weight loss, dietary intervention and exercise (4). Weight loss improves insulin sensitivity, which in turn can decrease blood sugar levels (7,8).

However, older adults are also at increased risk to lose muscle mass during weight loss. This could potentially have negative effects on a patient’s ability to move and can affect the quality of life, similarly to the effects of type 2 diabetes itself (9). One study suggests there is strong evidence that type 2 diabetes accelerates muscle loss in general (10). Another study suggests type 2 diabetes is associated with an increased risk of sarcopenia (11). Early detection of sarcopenia in older adults with diabetes should be standard practice in administering intervention (12). Sarcopenia is a syndrome defined by muscle loss, loss in fat-free mass, loss in muscle strength and loss in muscle function primarily caused by aging (13). This process is possibly accelerated by intentional weight loss achieved through caloric restriction alone (14).

The PROBE study evaluates the effect of a combined lifestyle intervention including dietary

counseling, resistance training and whey protein supplementation for older adults with obesity and type 2 diabetes. The PROBE study is administered by the faculty of Sports and Nutrition at the

Amsterdam University of Applied sciences. The research for this bachelor’s thesis attempts to analyze part of the data gathered at baseline in the PROBE study.

Previous studies show that adequate protein intake in combination with exercise is an important factor in the prevention of muscle loss in older adults with sarcopenic obesity (14-16). One study suggests older adults require 1.0 to 1.3 g/kg body weight of dietary protein to optimize physical function and reduce age-related muscle loss in combination with progressive resistance exercise (17). According to current dietary guidelines in the Netherlands, healthy older adults (>65) have a protein need of 1.0 g/kg body weight, however, patients with insulin resistance, muscle disuse and

inflammation require 1.2 g/kg body weight at a minimum. This is due to reduced protein synthesis in these patients (18). This study population has a higher protein requirement for muscle mass and due to low-grade inflammation (19).

It is currently unknown whether assessed protein intake at baseline can be used to detect sarcopenia in the study population or whether it can provide insight in actual protein needs to minimize muscle loss. Thus, the objective of this paper is to analyze a possible relation between protein intake and appendicular skeletal muscle mass (ASMM) among older adults with obesity and type 2 diabetes at baseline. ASMM is the sum of the muscle masses of the four limbs of the body. Potential confounders such as demographic, dietary- and physical activity factors are also examined in this study.

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6 The thesis aims to answer the following question: What is the relation between protein intake and appendicular skeletal muscle mass among older adults with obesity and type 2 diabetes in the PROBE study? The following sub-questions are answered to build on the main research question:

1. What is the average protein intake of the participants?

2. What is the average ASMM of the participants measured with dual-energy x-ray absorptiometry (DXA)?

3. Is there a significant linear relationship between protein intake and ASMM among the participants?

4. Is there a difference in protein intake and ASMM relation among male and female participants?

5. How do other confounding factors affect the possible relation between protein intake and ASMM among participants?

It is expected there will be a positive linear relation between protein intake and ASMM among older adults with obesity and type 2 diabetes in the PROBE study at baseline. This hypothesis is formed based on previous studies that suggest dietary protein is an important factor in muscle mass in this target population (14-16). It is also based on pre-existing guidelines for increased protein intake among patients with insulin resistance (18).

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2. Methods

2.1 Study design

This cross-sectional study was conducted between March 2020 and May 2020. The cross-sectional design best tested the relations among data gathered at baseline during one session of

measurements on study participants. The data used for this study was gathered during the PROBE study (protein and a combined lifestyle intervention to preserve muscle mass in obese older adults with type 2 diabetes: a double blind randomized controlled trial).

2.2 Participants

The PROBE study included 123 older adults aged 55-85 with obesity and type 2 diabetes. Both male and female participants were included. Recorded data of 120 participants was available for analyzing protein intake and body composition. Participants were recruited by varying means including through healthcare providers, media and existing available databases.

Participants were eligible to participate in this study if they were of the ages 55 through 85, diagnosed with type 2 diabetes and diagnosed with obesity. Type 2 diabetes patients with active medication use (with the exception of insulin) or an HbA1c value of ≥43 mmol/mol were included. Obesity was diagnosed based on BMI (≥30.0 kg/m2_{) and in some cases waist circumference (if BMI}

≥27 kg/m2_{in combination with a waist circumference of >102 cm (men) or >88 cm (women)).}

Patients had to be willing to give their written informed consent and needed the ability to comply with the protocol of the PROBE study.

Participants were excluded from this study under certain circumstances. Patients with a medical history including cardiovascular complications, malignant diseases, gastrointestinal disease, gastrectomy, renal disease and hepatic disease were excluded. Patients who used insulin, corticosteroids and antibiotics were also excluded. Other metabolic illnesses were defined as exclusion criteria as well. For a detailed list of inclusion- and exclusion criteria, see Appendix 1. This bachelor’s thesis is part of the PROBE study, which was approved by METC Assen, Foundation BEBO (Stichting Beoordeling Ethiek Biomedisch Onderzoek).

2.3 Anthropometric measurements

Body weight (kg) was measured with a weighing scale (Life Measurement) rounded to the closest second decimal. Participants were weighed wearing minimal clothing such as underwear or bathing clothes. Body height (cm) was measured with a wall-mounted stadiometer (Seca) rounded to the closest first decimal. Body mass index (BMI) of participants was calculated in kg/m2_{. Waist}

circumference (cm) was measured with a measuring tape rounded to the closest first decimal. Obesity was diagnosed if BMI was ≥30.0 kg/m2_{or ≥27.0 kg/m}2_{in combination with a waist}

circumference of >102 cm (men) or >88 cm (women). 2.4 DXA measurements

Dual-energy x-ray absorptiometry (DXA) measurements were conducted using a whole body scan (Discovery A, Hologic). Participants were measured in minimal clothing such as underwear or bathing

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8 clothes. The following measurements were recorded: fat mass (kg), total lean body mass (kg), visceral fat mass (cm2_{) and appendicular skeletal muscle mass (ASMM) (kg).}

2.5 Physical activity level and resting energy expenditure

The physical activity level of participants was recorded with accelerometry. Participants wore an activity monitor (PAM AM 200, PAM) for three days. The activity monitor was worn during waking hours on the right front side of the body attached to the trousers, skirt, belt or underwear. Scores were read with the manufacturer’s software and converted to physical activity level scores with the following equation: PAL = (PAM/100) + 1. The average physical activity level score was calculated from the daily scores measured over three days.

Resting energy expenditure was determined with indirect calorimetry with the ventilated hood method (Vmax Encore n29, Viasys Healthcare). Participants were measured in a fasting state, laying down for 15 minutes prior to assessment. Resting energy expenditure was assessed over a period of 30 minutes.

2.6 Nutritional assessment

Nutritional intake was recorded by participants using a diet diary over three days, including a weekend day. All food and drink items needed to be recorded. Dietary intake questionnaires were checked for completion during the baseline visit. The information recorded was translated into energy- and macronutrient intake with the NEVO online database version 2013/4.0 (20).

The following nutritional data was used for this study: total energy intake (kcal/d), protein intake (g/kg BW/d), animal protein intake (g/d), plant-based protein intake (g/d) and alcohol consumption (g/d). Additional nutritional variables were computed based on this data and some nutritional data was used to identify confounders. The following protein need references were used in the data analysis: healthy individuals aged 55-65: 0.8 g/kg body weight, healthy older individuals aged >65: 1.0 g/kg body weight, individuals with type 2 diabetes: 1.2 g/kg body weight (18).

The physical activity level average score was used to calculate energy excess (kcal/d) in combination with resting energy expenditure (REE) with the following equation: excess energy = total energy intake - (REE x PAL).

2.7 Data analysis

Only data recorded at baseline and the first screening session was analyzed in this study. Data analysis was performed using Statistical Package for Social Science (SPSS) version 25 (SPSS, Inc.). Average ASMM and protein intake were described as mean (SD). The relation between protein intake and ASMM was tested for significance with simple linear regression with the hypothesis that there is a significant positive linear relationship between protein intake and ASMM among the participants. These results were presented with a scatter diagram for the overall study population as well as separately for men and women. A p-value of <0.05 was considered statistically significant for any data tested. One outlier in the regression analysis has been excluded due to an error detected in the coding.

The following potential confounders were explored during the regression analysis: sex, visceral fat (cm2_{), physical activity level, excess energy intake (kcal/d), animal protein (%/total protein/d),}

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plant-9 based protein (%/total protein/d), alcohol consumption (g/d), duration of type 2 diabetes (months), use of diabetes medication and type of diabetes medication. The relation tested with simple linear regression was corrected for each individual confounder. Confounding variables with the most effect on the coefficient were applied for correction and corrected linear regression was then repeated with other potential confounding variables. Confounders were considered viable with an effect on the coefficient of ≥10%. The following information for all analysis models was recorded: Correlation coefficient (R), coefficient of determination (R2_{), standard error of the estimate (SE), regression}

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3. Results

3.1 Participant characteristics

Data was analyzed for a total of 123 participating subjects in the PROBE study. Of the 123 participants, 80 of them were male (65%) and 43 were female (35%). The characteristics of the participants are presented in Table 1. The mean (SD = 5.2) appendicular skeletal muscle mass (ASMM) (kg) measured with dual-energy x-ray absorptiometry (DXA) was 26.7 kg for this study population.

Table 2 presents the protein intake of the participating subjects. The mean (SD = 0.23) total protein intake per kg of body weight per day (g/kg BW/d) was 0.85 for the overall study population. On average 66.0% of the participants’ protein intake consisted of animal protein (g/d) and 34.0% consisted of plant-based protein (g/d).

Table 1: Participants' characteristics grouped by sex. Mean SD is presented for age, body height, body weight, body mass index and appendicular skeletal muscle mass.

Variable

Sex

Women (n = 43) Men (n = 80) Overall (n = 123)

Mean SD Mean SD Mean SD

Age (years) 65.2 6.4 66.9 6.1 66.3 6.2

Body height (cm) 164.4 5.9 177.3 6.9 172.8 9.0

Body weight (kg) 92.8 15.7 102.5 13.9 99.1 15.3

Body mass index (kg/m2₎ _34.3 _5.5 _32.6 _3.7 _33.2 _4.5

Appendicular skeletal muscle mass (kg)

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Table 2: Participants' protein intake grouped by sex. Mean SD is presented for the different types of protein intake in grams per kg of body weight per day and for different types of protein intake in grams per day.

Variable

Sex

Women (n = 41) Men (n = 79) Overall (n = 120)

Mean SD Mean SD Mean SD

Total protein intake (g/kg BW/d) 0.79 0.20 0.88 0.24 0.85 0.23

Animal protein intake (g/kg BW/d) 0.52 0.18 0.58 0.20 0.56 0.20

Plant-based protein intake (g/kg BW/d)

0.28 0.07 0.30 0.10 0.29 0.09

Total protein intake (g/d) 72.60 16.29 89.78 26.92 83.91 25.12

Animal protein intake (g/d) 47.30 15.51 59.54 22.46 55.36 21.11

Plant-based protein (g/d) 25.34 6.12 30.31 10.57 28.61 9.56

3.2 Regression analysis, unadjusted

The possible relation between protein intake (g/kg BW/d) and ASMM (kg) was explored with linear regression analysis and made visual with the assistance of scatter diagrams for the unadjusted model. Figure 1 shows there is no significant linear relation between protein intake (g/kg BW/d) and ASMM (kg) for the overall study population (R = 0.100, ß = 2.20, p = 0.28). Figure 2 shows there is no significant linear relation between protein intake (g/kg BW/d) and ASMM (kg) in men (R = 0.001, ß = 0.03, p = 0.99). Figure 3 shows there is no significant linear relation between protein intake (g/kg BW/d) and ASMM (kg) in women (R = 0.235, ß = -2.99, p = 0.15).

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Figure 1: Relation between protein intake per kg of body weight (g/kg BW/d) and appendicular skeletal muscle mass (kg) at baseline for the overall study population.

Figure 2: Relation between protein intake per kg of body weight (g/kg BW/d) and appendicular skeletal muscle mass (kg) at baseline for men.

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Figure 3: Relation between protein intake per kg of body weight (g/kg BW/d) and appendicular skeletal muscle mass (kg) at baseline for women.

3.3 Adjustment for confounders

Several potential confounders were explored during the linear regression analysis. The relation between protein intake (g/kg BW/d) and ASMM (kg) was corrected for four identified confounders. Table 3 presents the outcomes of linear regression analysis for the unadjusted model and one adjusted model using the identified confounders. The unadjusted model (model 1) shows no significant linear relation between protein intake (g/kg BW/d) and ASMM (kg) for the overall study population (p = 0.28).

Model 2 is adjusted for excess energy intake (kcal/d), physical activity level (accelerometry), visceral fat (cm2_{) and duration of type 2 diabetes (months). These identified confounders had the most effect}

on the coefficients. This adjusted model (model 2) shows no significant relation between protein intake (g/kg BW/d) and ASMM (kg) for this study population (p = 0.50). For more detailed results of the confounder identification process, see Appendix 2.

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Table 3: Linear regression analysis for the relation between protein intake (g/kg BW/d)a_and

appendicular skeletal muscle mass (kg)b_{for the overall study population and for men and women.}

Sex Model Results R R2 _SE _ß _p Overall 1c _0.10 _0.01 _5.12 _2.20 _0.28 2d _0.43 _0.18 _4.92 _1.90 _0.50 Men 1 0.00 0.00 3.88 0.03 0.99 2 0.21 0.04 4.15 0.19 0.95 Women 1 0.24 0.06 2.51 -2.99 0.15 2 0.38 0.15 2.45 -2.29 0.44

a_{Primary independent variable: protein intake (g/kg BW/d).} b_{Dependent variable: appendicular skeletal muscle mass (kg).}

c_{Model 1: unadjusted model describing the relation between protein intake (g/kg BW/d) and}

appendicular skeletal muscle mass (kg).

d_{Model 2: adjusted for excess energy intake (kcal/d), physical activity level (accelerometry), visceral}

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4. Discussion

This study aims to investigate the relation between protein intake and appendicular skeletal muscle mass (ASMM) among older adults with obesity and type 2 diabetes in the PROBE study. To current knowledge it was unknown what protein intake and ASMM at baseline could indicate about protein needs and risk of sarcopenia and sarcopenic obesity within the study population. The methodology described is pertinent to answering the main research question. The results suggest there is no significant linear relation between protein intake and ASMM in this study population. The results present excess energy intake, physical activity level, visceral fat and duration of diabetes type 2 as identified confounders in the exploration of this relation. The results of this study also suggest that, on average, the participating subjects consume an inadequate amount of protein in relation to their body weight, age and medical status.

It was expected to find a positive relation between protein intake and ASMM within the study population based on previous studies. The results suggest there is no significant linear relation between protein intake and ASMM for the overall study population, nor do the results present such a relation for men or women when tested separately. This outcome is contradictory to expectations by the researcher before conducting data analysis for several reasons. First of all, the results do not fit the existing theory that increased protein intake could lead to a lower risk of muscle loss (12). One possible explanation is inadequate physical activity and strength exercises to accompany the protein intake at baseline. As previous studies show, protein intake can have a positive effect on muscle mass in combination with exercise (21,22). Furthermore, physical activity level seems to be an important confounder when testing this relation.

Secondly, this study is contradictory to expectations because current protein guidelines suggest a higher protein intake within this study population is required. This is due to reduced protein synthesis among patients with insulin resistance (18). Therefore, a linear relation is expected between protein intake and ASMM among these patients at baseline. It should be noted that the average study participant does not consume the daily recommended protein intake (18). Another study suggests that lower skeletal muscle mass is independently associated with insulin resistance in older adults (23). This could be a possible explanation for the lower muscle mass despite the protein intake for a study population consisting of patients with type 2 diabetes.

Without a significant relation found between protein intake and ASMM among older adults with obesity and type 2 diabetes, it is suggested protein intake at baseline does not indicate protein needs within the study population based on ASMM. Neither can it indicate a potential increased risk of muscle loss or sarcopenia.

After exploring confounders in regression analysis, the results show several identified confounders that might have an effect on the coefficient when testing the relation between protein intake and ASMM. One of these identified confounders is excess energy intake. It is expected that an intake of more kilocalories than a subject’s physical need could affect the results. This is due to the changes in body composition, such as visceral fat mass and ASMM, caused by overeating (24).

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16 Physical activity level, as discussed above, could potentially have a large influence on the coefficient when testing for the relation between protein intake and ASMM. This is to be expected due to the known effects of exercise on muscle mass in combination with increased protein intake (21,22). However, the method used in this study to record physical activity level, accelerometry, does not reflect the amount of strength or resistance training participants are involved in at baseline. Other studies show it is predominantly strength or resistance training that has a positive effect on muscle mass in patients with type 2 diabetes or older adults with obesity (25,26). The findings should be taken into consideration when recording physical activity in participants. It could potentially have an effect on study outcomes to include any occurring strength and resistance training at baseline, as opposed to solely recording physical activity in general.

A third identified confounder in this regression analysis is visceral fat. Visceral fat is body fat stored in the abdominal area of the body. This type of fat is located close to organs, in particular the stomach, liver and the intestinal tract. It is in line with the researcher’s expectation that visceral fat has an effect on the coefficient considering a longitudinal study that shows visceral obesity is associated with future loss of skeletal muscle mass in adults (27). The author of this longitudinal study suggests this is an important insight in sarcopenic obesity in an aging society.

Another confounder that affects the relation between protein intake and ASMM in this study is the duration of type 2 diabetes. The results suggest the duration of type 2 diabetes has a large effect on the coefficient, although no literary evidence has been found that supports a clear explanation for this. The duration of type 2 diabetes may be related to the severity of the illness. One previous study has shown that type 2 diabetes can accelerate muscle loss and the effects of this could possibly be more prominent over a longer period of time (10).

The mean (SD = 0.23) total protein intake per kg of body weight per day (g/kg BW/d) is 0.85 for the overall study population. This is not in accordance with the reference protein intake for healthy older adults (1.0 g/kg BW/d), nor is it in accordance with the reference protein intake for patients with insulin resistance (1.2 g/kg BW/d) (18). The average total protein intake for the overall study population is inadequate at baseline. It should be noted that the subjects, older adults with obesity and type 2 diabetes, are recruited for the PROBE study to have their protein intake and muscle mass improved with intervention. No intervention took place at baseline. A possible explanation for the inadequate protein intake could be the difficulty level of self-management within the study population without intervention (28). One study shows that proper support and education in diabetes self-management is a promising factor in a patient’s diet and health (29). Although no significant relation between protein intake and ASMM was found in this study, it should still be noted that protein is an important macronutrient for this study population. For instance, protein plays a role in the healing of wounds caused by decubitus or diabetic neuropathy (3). There is also an increased protein requirement for this patient group due to low-grade inflammation (18,19). The results of this study should be interpreted in light of the limitations during this explorative research. It should be noted that the group of participating subjects consists predominantly of men. However, the sample size of the study is adequate and results for women are sufficiently accurate. The generalizability of this study is limited by the fact that the data collection takes place solely in the Netherlands. It is beyond the scope of the study to claim findings apply to older adults with obesity and type 2 diabetes on a global level. Additionally, the reliability of the nutritional data could possibly

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17 be constrained by the method of self-reporting. Participants could potentially under- or overestimate their dietary intake, although this is predominantly the case with energy intake and not necessarily other dietary information (30).

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5. Conclusion and recommendations

5.1 Conclusion

This cross-sectional study indicates there is no significant relation between protein intake and appendicular skeletal muscle mass found among older adults with obesity and type 2 diabetes in the PROBE study at baseline. After examining the dietary characteristics analyzed for the overall study population, it can be concluded the participating subjects on average eat an inadequate amount of protein at baseline. These research results contribute to constructing recommendations for health practitioners and researchers who wish to further explore this subject matter.

5.2 Recommendations

Based on the conclusions described, it is suggested further research is performed to confirm the results that are contradictory to expectations and existing theories. This is particularly needed to determine an explanation for the absence of a relation between protein intake and ASMM among older adults with obesity and type 2 diabetes. One explanation is a lack of physical activity at baseline. However, strength and resistance training are not recorded for this study. It is suggested that more research with strength and resistance training recorded at baseline is performed among older adults with obesity and type 2 diabetes. This is alongside other methods to record physical activity such as accelerometry as is used in this study. Strength and resistance training could possibly be an important confounder for analyzing ASMM in older populations.

Another explanation for the results found is the effect of type 2 diabetes and insulin resistance on muscle mass and protein synthesis. Researchers should consider further exploring this effect and what this means for protein needs among the study population. Additionally, according to the researcher’s knowledge the effect of the duration of type 2 diabetes on body composition has yet to be explored. No literary evidence yet exists that supports an explanation for identifying this

confounder during the regression analysis in this study. It is suggested the duration of the illness and its effect on body composition should be explored. Furthermore, based on the identified confounders during regression analysis, visceral fat should be taken into account as a potential confounder when exploring ASMM in older adults with obesity and type 2 diabetes. Previous studies show visceral obesity is associated with future muscle loss in adults.

Based on the conclusions from analyzing participants’ characteristics and protein intake, health practitioners should be informed that older adults with type 2 diabetes on average don’t consume an adequate amount of dietary protein. Although the results of this study suggest that protein intake is not related to ASMM in this study population, the inadequate protein intake should still be noted as it could potentially have consequences unrelated to muscle mass. For instance, an increased amount of protein is required in the case of low-grade inflammation and for the healing of wounds. Existing guidelines should not be changed based on a single study with contradictory results. Health

practitioners should take the finding of an inadequate protein intake into consideration when assisting patients with self-management. It is advised patients receive adequate education about type 2 diabetes self-management or self-regulation. It is suggested that dietary protein should be included in such programs. Researchers should take this finding into consideration when developing intervention programs for older adults with obesity and type 2 diabetes. Based on the conclusion of this study, it is also suggested future studies should be done on how to increase protein intake

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19 among this patient group to reach reference intakes. Furthermore, similar future studies should consider analyzing the relation between protein intake and ASMM with an intervention group. Within this group, study participants should receive a controlled amount of protein that is at least on par with reference intakes to examine a possible linear relation when patients do follow current dietary guidelines as this is not the case in this study at baseline.

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Appendix 1. Subject criteria in the PROBE

study.

Inclusion criteria

1. Ages 55 through 85 years old;

2. Patients with type 2 diabetes mellitus verified by: a. Use of medication for type 2 diabetes or; b. HbA1c of ≥43 mmol/mol;

3. Patients with obesity defined by: a. BMI >30.0 kg/m2_or;

b. BMI >27.0 kg/m2 in combination with increased waist circumference of >102 cm for men and 88 cm for women;

4. Participants must agree that their general practitioner will be notified on their participation in this study;

5. For participants who use SU-derivates as their type 2 diabetes medication: a. Patients must agree that their medication might be changed;

b. Treating physician must agree that the dose for SU-derivates might be changed at the start and during the study by the treating physician or the study physician based on the study physician’s advice;

6. Participants must give their written informed consent;

7. Participants must be willing and have the ability to comply with the study protocol;

8. Participants must be able to comply with the study exercise protocol as evaluated by a sports physician.

Exclusion criteria

1. Specific medical history that could influence the study outcomes as assessed by the study physician:

a. Cardiovascular: instable Angina Pectoris, cardiac surgery and/or cardiac infarcts within 3 months before the start of the study;

b. Malignancy: any malignant diseases within 5 years before the start of the study with the following exceptions:

i. Treated prostate cancer with no known metastases present; ii. Localized bladder cancer;

iii. Non-melanoma skin cancer iv. Breast cancer in situ;

v. Cervical carcinoma in situ;

2. Gastrointestinal: any diseases of the gastro-intestinal tract that could influence bowel function and nutritional intake:

a. Constipation; b. Diarrhea; c. Gastroparesis;

d. Gastrectomy, partial gastrectomy or other surgeries that restrict nutritional intake (e.g. gastric banding, intragastric balloon);

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23 3. Renopathy: estimated glomerular filtration rate (GFR) of <50 mL/min based on the MDRD

formula;

4. Hepatopathy: When the following liver enzymes are more than 3 times the Upper Limit of Normal: ASAT, ALAT, ALP or GGT.

5. Patients with a cardiac pacemaker, implantable cardioverter-defibrillator or other electronic devices used to treat arrhythmias;

6. The following medication use:

a. Corticosteroids for systemic use within 2 weeks before the start of the study or expected use during the study;

b. Antibiotics for systemic use within 2 weeks before the start of the study or expected use during the study;

c. Insulin;

d. Dosage change within 3 months before the start of the study for: i. Antidepressants;

ii. Neuroleptics;

iii. Lipid lowering medication;

7. Patients with 5% or more involuntary weight loss within 3 months before the start of the study;

8. Patients who have used nutritional supplements containing protein or amino acids within 3 months before the start of the study;

9. Patients with a record of allergy to cow’s milk, milk products or any other ingredients in the products used for the study;

10. Patients with a record of galactosaemia; 11. Patients with a record of lactose-intolerance;

12. Patients who use more than 22 ug (880 IU) of daily vitamin D from non-food sources like dietary supplements and prescribed medication containing vitamin D;

13. Patients who use more than 500 mg of daily calcium from non-food sources like dietary supplements and prescribed medication containing calcium;

14. Patients with ongoing alcohol abuse assessed by the investigator;

15. Patients unable or unwilling to comply with the study protocol as judged by the investigator; 16. Patients who are participating in other studies involving marketed or investigational products

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Appendix 2. Identifying confounders.

Table 1: Identification of confounder 1 in regression analysis for protein intake (g/kd BW/d) and ASMM (kg).

Potential confounders ß (basea_{= 2.203)} _{Effect %}b

Visceral fat (cm2₎ _2.491 _13.07

Physical activity level 2.077 5.72

Excess energy (kcal/d)c _5.048 _129.14

Animal protein intake % 1.795 18.52

Plant based protein intake % 1.798 18.38

Alcohol (g/d) 1.537 30.23

Duration of type 2 diabetes (months) 1.995 9.44 Use of diabetes medication (yes/no) 1.760 20.11

Use of SU-derivates (yes) 1.673 24.06

Use of biguanides (yes) 2.579 17.07

Use of DPP4-inhibitor (yes) 2.228 1.14

a _{Protein intake (g/kg BW/d) and appendicular skeletal muscle mass (kg).} b _{Confounders were considered viable with an effect on the coefficient of ≥10%.} C_{Confounder with the most effect on the coefficient (ß): excess energy (kcal/d).}

Visceral fat (cm2₎ _4.606 _8.76

Physical activity levelc _3.874 _23.26

Alcohol (g/d) 4.705 6.80

a _{Protein intake (g/kg BW/d), appendicular skeletal muscle mass (kg) and excess energy (kcal/d).} b _{Confounders were considered viable with an effect on the coefficient of ≥10%.}

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Visceral fat (cm2₎c _2.694 _30.46

Alcohol (g/d) 3.797 1.99

a _{Protein intake (g/kg BW/d), appendicular skeletal muscle mass (kg), excess energy (kcal/d) and}

physical activity level.

b _{Confounders were considered viable with an effect on the coefficient of ≥10%.} C_{Confounder with the most effect on the coefficient (ß): visceral fat (cm}2_).

Alcohol (g/d) 2.697 0.11

Duration of type 2 diabetes (months)c _1.901 _29.44

Use of diabetes medication (yes/no) 2.405 10.73

a _{Protein intake (g/kg BW/d), appendicular skeletal muscle mass (kg), excess energy (kcal/d), physical}

activity level and visceral fat (cm2_).

b _{Confounders were considered viable with an effect on the coefficient of ≥10%.}

The relation between protein intake and appendicular skeletal muscle mass among older adults with obesity and type 2 diabetes: A cross-sectional study : A bachelor’s thesis

The relation between protein intake and

appendicular skeletal muscle mass among

older adults with obesity and type 2 diabetes:

A cross-sectional study

A bachelor’s thesis

The relation between protein intake and appendicular skeletal

muscle mass among older adults with obesity and type 2 diabetes:

A cross-sectional study

A bachelor’s thesis

Acknowledgements

Abstract

Table of contents

1. Introduction

2. Methods

3. Results

4. Discussion

5. Conclusion and recommendations

References

Appendix 1. Subject criteria in the PROBE

study.

Appendix 2. Identifying confounders.