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OBESITY MEASURES AND DEFINITIONS OF SARCOPENIC OBESITY IN SINGAPOREAN ADULTS – THE YISHUN STUDY

 

B.W.J. Pang1,*, S.-L. Wee1,2,*, L.K. Lau1, K.A. Jabbar1, W.T. Seah1, D.H.M. Ng1, Q.L.L. Tan1, K.K. Chen1, M.U. Jagadish1,3, T.P. Ng1,4

1. Geriatric Education and Research Institute (GERI), Singapore; 2. Faculty of Health and Social Sciences, Singapore Institute of Technology, Singapore; 3. Geriatric Medicine, Khoo Teck Puat Hospital, Singapore; 4. Department of Psychological Medicine, National University of Singapore, Singapore.
Corresponding author: Shiou-Liang Wee, Geriatric Education and Research Institute (GERI), 2 Yishun Central 2, Tower E Level 4 GERI Admin, 768024, Singapore, Phone: +65 6807 8011, weeshiouliang@gmail.com; Benedict Wei Jun Pang, Geriatric Education and Research Institute (GERI), 2 Yishun Central 2, Tower E Level 4 GERI Admin, 768024, Singapore, Phone: +65 6807 8030, L3enanapang@gmail.com (B.W.J. Pang)

J Frailty Aging 2020;in press
Published online December 11, 2020, http://dx.doi.org/10.14283/jfa.2020.65

 


Abstract

Objectives: Due to the lack of a uniform obesity definition, there is marked variability in reported sarcopenic obesity (SO) prevalence and associated health outcomes. We compare the association of SO with physical function using current Asian Working Group for Sarcopenia (AWGS) guidelines and different obesity measures to propose the most optimal SO diagnostic formulation according to functional impairment, and describe SO prevalence among community-dwelling young and old adults. Design: Obesity was defined according to waist circumference (WC), percentage body fat (PBF), fat mass index (FMI), fat mass/fat-free mass ratio (FM/FFM), or body mass index (BMI). SO was defined as the presence of both obesity and AWGS sarcopenia. Muscle function was compared among phenotypes and obesity definitions using ANOVA. Differences across obesity measures were further ascertained using multiple linear regressions to determine their associations with the Short Physical Performance Battery (SPPB). Setting: Community-dwelling adults 21 years old and above were recruited from a large urban residential town in Singapore. Participants: 535 community-dwelling Singaporeans were recruited (21-90 years old, 57.9% women), filling quotas of 20-40 participants in each sex- and age-group. Measurements: We took measurements of height, weight, BMI, waist and hip circumferences, body fat, muscle mass, muscle strength, and functional assessments. Questionnaire-based physical and cognitive factors were also assessed. Results: Overall prevalence of SO was 7.6% (WC-based), 5.1% (PBF-based), 2.7% (FMI-based), 1.5% (FM/FFM-based), and 0.4% (BMI-based). SO was significantly associated with SPPB only in the FMI model (p<0.05), and total variance explained by the different regression models was highest for the FMI model. Conclusions: Our findings suggest FMI as the most preferred measure for obesity and support its use as a diagnostic criteria for SO.

Key words: Sarcopenic obesity, sarcopenia, obesity, prevalence, Singapore.

Abbreviation: ALMI: Appendicular Lean Mass Index; AWGS: Asian Working Group for Sarcopenia; FM/FFM: Fat Mass to Fat-Free Mass ratio; FMI: Fat Mass Index; GPAQ: Global Physical Activity Questionnaire; GS: Gait Speed; HGS: Handgrip Strength; KES: Knee Extensor Strength; LASA: Longitudinal Aging Study Amsterdam; MNA: Mini Nutritional Assessment; PBF: Percentage Body Fat; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status; SO: Sarcopenic Obesity; SPPB: Short Physical Performance Battery; TUG: Timed Up-and-Go; WC: Waist Circumference.


 

Introduction

The rising tide of obesity prevalence is currently a global public health problem of epidemic proportions in all ages. At the same time, population ageing will double the number of persons aged 65 years and over worldwide in the next three decades, reaching 1.5 billion in 2050 (1), many of whom will be physically frail and disabled from the progressive loss of muscle mass and function (sarcopenia) (2). Obesity, the excessive accumulation of body fat, is an important factor in the development of metabolic syndrome and cardiovascular disease (3), and is also an important contributing cause of muscle loss. The two conditions have overlapping causes and feedback mechanisms that are interconnected and mutually-aggravating (3). Coexistence of both, a condition known as sarcopenic obesity (SO), has been shown to act synergistically to exacerbate metabolic impairment, disability, cardiovascular disease and mortality more so than either condition alone (3,4).
Although the diagnosis of sarcopenia has been increasingly harmonized by consensus working groups such as the Asian Working Group for Sarcopenia (AWGS) (2), the lack of a uniform obesity definition in the context of SO has led to great variation in the methods and cut-offs applied to define obesity, resulting in marked variability in reported SO prevalence as well as conflicting data on observed adverse health outcomes (5). Obesity is officially considered a disease that requires clinical treatment, however, there are currently no universally-accepted definitions for it (3). Commonly used measures include the body mass index (BMI), waist circumference (WC), percentage body fat (PBF), fat mass index (FMI) and fat mass to fat-free mass (FM/FFM) ratio. BMI provides a good indication of disease risk, but does not distinguish between fat and fat-free mass, thus making its clinical value questionable (3,5,6). WC indicates central obesity and serves as a surrogate measure of visceral adiposity (5-7), while PBF gives an objective indication of total body fat and its distribution (5). FM and FFM are the most frequently used adiposity indexes for SO classification (7), with the FM/FFM ratio deemed clinically-suitable in the diagnosis of SO (6). However, each of these measures assesses a different construct of obesity and are not interchangeable (5). To better understand its underlying physiological processes, and to determine disease prevalence and design clinical interventions, it is necessary to progress towards a unified criteria for the diagnosis and classification of obesity.
Aside from the wide heterogeneity of obesity measures used in studies, inconsistent observations of associations between SO and disease risk (7) may also result from the criteria used for defining sarcopenia in most studies, which other than muscle mass did not always consider muscle strength and physical function (3,5). Muscle strength and function have been shown to decline more rapidly with age and contribute more significantly to physical decline and frailty than muscle mass.
The primary aim of the present study was to propose the most optimal SO diagnostic formulation by comparing the association of SO with physical function using different obesity measures (WC, PBF, FMI, FM/FFM and BMI). We hypothesize that the most optimal diagnostic formulation would be one that is most significantly associated with physical functional impairment. The secondary aim was to compare and describe estimates of SO prevalence among community-dwelling younger and older adults in the Singapore population using AWGS guidelines for sarcopenia diagnosis and the different obesity definitions.

 

Methods

Setting

Community-dwelling adults (≥21 years) were recruited from the town of Yishun, one of the largest north-residential towns in Singapore, residential population of 220,320 (50.6% females), with 12.2% older adults (≥65 years), similar to the overall Singapore residential population of 4,026,210 (51.1% females), with 14.4% older adults (≥65 years) (8).

Participants

Random sampling was employed to obtain a representative sample of approximately 300 male and 300 female participants, filling quotas of 20-40 participants in each sex- and age-group (10-year age-groups between 21-60; 5-year age-groups after 60). Detailed recruitment methods and exclusion criteria have been reported previously (9). Ethics approval was obtained from the National Healthcare Group DSRB (2017/00212). All respondents signed informed consent before participating in the study.

Questionnaires

Participants answered questionnaires pertaining to education level, housing type (a proxy for socio-economic status), living arrangement, marital status, smoking and drinking (more than four days a week), a health and medical questionnaire indicating medical conditions and comorbidities, a mini nutritional assessment (MNA) (10), a global physical activity questionnaire (GPAQ) (11) and the LASA physical activity questionnaire (12).

Anthropometry

Body weight to the nearest 0.1 kg and height to nearest millimeter were measured using a digital balance and stadiometer (Seca, GmbH & Co. KG, Hamburg, Germany). Waist and hip circumferences were measured using a non-elastic, flexible measuring tape around the navel and widest part of the hips respectively. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. ‘Obesity’ according to waist circumference (WC) was defined as ≥90 and ≥80 cm for men and women respectively (13). A BMI of ≥27.5 kg/m2 was used to define obesity as recommended by the World Health Organization for Asian populations (14).

Cognitive Assessment

Global cognition and cognitive domains including immediate and delayed memory, visuospatial, language and attention were assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (15).

Body Composition

Bone mineral density, total percentage body fat (PBF), fat mass (FM), fat-free mass (FFM) and appendicular lean mass (ALM) were measured using DXA (Discovery WI, Hologic, Inc., Marlborough, USA). Fat mass index (FMI) and appendicular lean mass index (ALMI) were calculated as FM (kg) and ALM (kg) divided by height (m) squared, where FM equals to total body fat mass and ALM equals to the sum of lean mass in the upper and lower limbs. FM/FFM ratio was calculated by taking FM (kg) divided by FFM (kg). ‘Obesity’ according to PBF, FMI and FM/FFM were defined using the upper two quintiles of PBF and FMI, and a ratio of >0.80 for FM/FFM (6).

Functional Performance

Muscle function, as a gauge of functional deterioration and impairment, was measured using objective and validated assessments including handgrip strength (HGS) (16), knee extensor strength (KES) (17), usual gait speed (GS) (18), the Short Physical Performance Battery (SPPB) (19) and the Timed Up-and-Go (TUG) (20). HGS was assessed using the Jamar Plus+ Digital Hand Dynamometer (Patterson Medical, Evergreen Boulevard, Cedarburg, USA), and the highest of four readings (two trials per arm) recorded. KES was assessed using a spring gauge strapped 10 cm above the ankle joint, and the highest of four readings (two trials per leg) recorded. GS was measured using the 6 m GAITRite Walkway (CIR Systems Inc., Sparta. New Jersey, USA) with a 2 m lead in and out phase, and the average speed (three trials) recorded. TUG measures the progress of balance, sit-to-stand and walking. The average timing (two trials) was recorded. A composite score was calculated for SPPB, which comprises three components: balance, gait speed and repeated chair stands.

Sarcopenia and Sarcopenic Obesity

Sarcopenia was assessed using the AWGS criteria (2). Poor physical function was defined as GS <1.0 m/s, low muscle mass as ALMI <7.0 and <5.4 kg/m2, and muscle strength by HGS <28 and <18 kg for men and women respectively. Presence of low muscle mass and poor muscle strength and/or physical performance constitutes ‘sarcopenia’ (2). Participants with both sarcopenia and obesity were classified as ‘sarcopenic obese (SO)’. Those who had neither were classified as ‘normal’.

Statistical Analysis

SPSS version 22 (Chicago, Illinois, USA) was used for analysis. Prevalence of SO were extrapolated to the general population weights by age groups. In statistical analyses, the sarcopenia component was defined according to low muscle mass and strength only, and one-way analysis of variance (ANOVA) with Bonferroni correction for post-hoc comparisons were performed to compare the four phenotypes – ‘Normal’, ‘Obese’, ‘Sarcopenic’ and ‘Sarcopenic Obese’ – against muscle functions for those 50 years and older. To further ascertain the impact of obesity definitions on physical function, univariate and multiple linear regressions were performed to determine their associations with SPPB. Statistical significance was set at p<0.05.

 

Results

A total of 542 participants (57.9% females) aged 21-90 years were recruited. Due to incomplete data from seven participants, data from 535 participants were analyzed (Figure 1.). Of these, 81.9% were Chinese, 8.6% Malays, 6.7% Indians, and 2.8% from other races. Mean age was 58.6 (18.8) years. Reference values and descriptive statistics are presented in Supplementary Tables S1. and S2.

Figure 1
Participant flowchart

 

Cut-off values for obesity using the sex-specific upper two quintiles of PBF and FMI were 31.0% and 7.63 kg/m2 for men, and 41.4% and 9.93 kg/m2 for women. Overall population-adjusted prevalence of sarcopenic obesity (SO) was 7.6% (WC-based: men 7.2%; women 7.9%), 5.1% (PBF-based: men 4.4%; women 5.7%), 2.7% (FMI-based: men 2.2%; women 3.2%), and 1.5% (FM/FFM-based; men 0%; women 2.9%). Population-adjusted prevalence of SO for older adults (≥60 years) was 21.6% (WC-based), 16.1% (PBF-based), 9.5% (FMI-based) and 3.7% (FM/FFM-based; Table 1.).

Table 1
Prevalence estimates in study sample and adjusted to the Singapore general population age groups weights

SO: Sarcopenic obese. Values are presented as percentages (%)

 

Participant Characteristics and Sarcopenic Obesity

Across all five obesity measures, the SO phenotypes had higher age compared to the overall sample (Table 2.). Individuals with the lowest education levels, smallest housing types, lived alone, were widowed, had diabetes, hypertension or high cholesterol, or had one or more medical conditions were more likely to have SO. Individuals who smoked were more likely to have SO using the PBF, FMI and BMI phenotypes, while individuals who drank were more likely to have SO using the WC, PBF, FMI and BMI phenotypes. The FM/FFM-based definition did not identify any males with SO (0%).

Table 2
Participant characteristics and sarcopenic obesity statuses

SO: Sarcopenic Obesity; WC: Waist Circumference; PBF: Percentage Body Fat; FMI: Fat Mass Index; FM: Fat Mass; FFM: Fat-Free Mass. BMI: Body Mass Index. Values are presented as mean (SD) or number (%)

 

Muscle Function (ANOVA)

The SO phenotype consistently performed poorer than the normal group in functional measures for the WC, PBF and FMI obesity definitions (p<0.05, Table 3.). The SO phenotype also persistently performed poorer than the obese group for the PBF definition (p<0.05), and in HGS, KES, GS and TUG for the WC and FMI definitions (p<0.05). Compared to the sarcopenic group, the SO phenotype performed poorer in HGS for the WC and FM/FFM definitions, and in TUG for the PBF definition (p<0.05).

Table 3
Comparison of muscle function among phenotypes and obesity definitions using ANOVA (≥50 years old)

P value (<0.05); 1 denotes a significant post-hoc Bonferroni test between Normal and Obese (P<0.05); 2 denotes a significant post-hoc Bonferroni test between Normal and Sarcopenic (P<0.05); 3 denotes a significant post-hoc Bonferroni test between Normal and SO (P<0.05); 4 denotes a significant post-hoc Bonferroni test between Obese and Sarcopenic (P<0.05); 5 denotes a significant post-hoc Bonferroni test between Obese and SO (P<0.05); 6 denotes a significant post-hoc Bonferroni test between Sarcopenic and SO (P<0.05)

 

Multiple Linear Regression for SPPB

We adjusted for age, gender, education level, housing type, diabetes, GPAQ activity level and RBANS global (Table 4.). Small sample sizes for SO as defined by FM/FFM (n=9) and BMI (n=2) precluded multiple linear regression analysis using these definitions. Using the WC-based, PBF-based and FMI-based definitions, the total variance explained by the regression models were 22.6% [F(8, 352)=14.147, p<0.001], 23.1% [F(8, 352)=14.534, p<0.001] and 23.6% [F(8, 352)=14.867, p<0.001] respectively. SO was significantly associated with SPPB only in the FMI model (p<0.05).

Table 4
Multiple linear regression analysis for Short Physical Performance Battery (≥50 years old)

β: Standardized Coefficient; B: Unstandardized Coefficient; SE: Standard Error; GPAQ: Global Physical Activity Questionnaire; MET: Metabolic Equivalent of Task; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status SO: Sarcopenia Obesity; * denotes a significant P value (<0.05)

 

Discussion

In this study, we present reference values for waist circumference (WC), percentage body fat (PBF), fat mass index (FMI), fat mass to fat-free mass (FM/FFM) ratio, and body mass index (BMI), as well as sex-specific cut-off values for PBF and FMI, to define alternative phenotypic representations of sarcopenic obesity (SO). We describe the corresponding SO prevalences using AWGS guidelines for sarcopenia, across the age groups of healthy adults. Our estimated prevalence of SO for adults aged ≥21 years and ≥60 years were 7.6% and 21.6% (WC-based), 5.1% and 16.1% (PBF-based), 2.7% and 9.5% (FMI-based), 1.5% and 3.7% (FM/FFM-based), and 0.4% and 1.6% (BMI-based) respectively.
Comparatively, a recent study on 200 cognitively-intact and functionally-independent community-dwelling adults in Singapore (≥50 years) reported a prevalence of 10.5% and 10.0% based on WC-based and PBF-based definitions of SO respectively (5), slightly lower than the 15.4% and 10.7% in the present study (≥50 years). Notably, the authors used the original AWGS criteria to define sarcopenia (21), with lower cut-offs for muscle strength and gait speed and thus lower detection rates compared to the updated AWGS criteria (2). Another study involving 591 healthy volunteers in Korea found a prevalence of 10.9% (40-59 years) and 18.0% (≥60 years) using the PBF-based definition (4), higher than the 2.1% (40-59 years) and 16.1% (≥60 years) in the present study. However, their criteria did not include the functional components of sarcopenia (2), and their population-derived cut-offs for low muscle mass were much higher at 8.81 and 7.36 kg/m2 compared to AWGS’ 7.0 and 5.4 kg/m2 used in the present study (2, 21).
Using one-way analysis of variance (ANOVA) with Bonferroni correction for post-hoc comparisons, the SO phenotype consistently performed poorer than the normal group in functional measures for the WC, PBF and FMI obesity definitions. The SO phenotype also persistently performed poorer than the obese group for the PBF definition, and in HGS, KES, GS and TUG for the WC and FMI definitions. Compared to the sarcopenic group, the SO phenotype performed poorer in HGS for the WC and FM/FFM definitions, and in TUG for the PBF definition.
Multiple linear regression results for SPPB revealed that only the FMI-based definition of SO was significantly associated with poorer SPPB scores, and the total variance explained by the different regression models was highest for the FMI definition, followed by PBF and WC. Physical function impairment in the absence of disability likely represents the shared core of sarcopenia and physical frailty. Such functional deterioration with deficits in gait speed, balance, and muscle strength, can be objectively assessed through the SPPB (22). Given that the SPPB is considered one of the most reliable and valid assessments for functional performance (5,19,22), our findings suggest FMI to be the most preferred obesity measure for defining SO.
Although PBF gives an objective indication of total body fat, it does not discern between visceral and subcutaneous fat (5). While WC provides an estimate of visceral adiposity which is associated with higher morbidity than its subcutaneous counterpart (23), it is not adjusted for height and is thus insensitive to body size (5-7). BMI gives a good indication of disease risk, but does not differentiate fat from fat-free mass (3, 5, 6). In addition, the BMI definition led to a noticeably much lower SO detection rate (0.4%) compared to the other definitions, similar to what was reported in a previous study.5 In corroboration with the literature, fat mass was previously reported to be the most frequently used adiposity index for the classification of SO, and its adjustment to height squared (FMI) has been the preferred method to account for differences in body size across age and between the sexes (6). In terms of physical performance, FMI is also considered an accurate indicator of total body adiposity that could improve the predictive value of SO in functional deterioration (6, 7). In addition, FMI was found to be a better screening tool in the prediction of metabolic syndrome in Chinese men and women (24) and more accurately assessed obesity in Mexican Americans (25) compared to BMI or PBF.
The FM/FFM definition did not identify any men with SO. This is similar to the findings of previous studies, where using the FM/FFM definition led to markedly disproportionate low numbers of men identified with SO (6, 7, 26). Women inherently have much higher relative fat mass than men (27), and conversely, men have higher relative fat-free mass (total body water, muscle and bone mass) than women at all ages (27,28). This is primarily due to the hormonal differences between men and women; men have higher testosterone levels which exhibits anabolic effects on muscle and bone (29), while higher estrogen levels in women promote subcutaneous fat deposition especially in the hips, thighs and chest (30). In addition, approximately 75% of skeletal muscle tissue is composed of water (31). Thus, with higher muscle mass, men inadvertently hold more total body water, further contributing to the discrepancy in fat mass and fat-free mass between men and women. To address the underlying gender-bias of the FM/FFM criteria and improve its accuracy in identifying gender-specific obesity and SO prevalence, different cut-off values for men and women (lower cut-off values for men) should be explored.
A recent study on 1235 adults with type 2 diabetes (T2D) in Singapore (≥45 years) reported a SO prevalence of 19.4% using the FM/FFM-based definition, higher than the 2.3% reported in this study, although the criteria for diagnosing SO in that study did not include the AWGS functional components for sarcopenia, which could possibly have inflated the proportions identified with SO (6). Furthermore, previous studies have shown a close link between sarcopenia and obesity through insulin resistance (3). Visceral fat accumulation (which promotes secretion of pro-inflammatory cytokines) is a contributing factor to the loss of skeletal muscle (which is the largest insulin-responsive tissue). Obesity and sarcopenia have a synergistic effect on promoting insulin resistance which could exacerbate T2D (4). In addition, patients with T2D exhibit insulin resistance, systemic inflammation and metabolic complications that could in turn perpetuate excess adiposity accumulation and loss of muscle mass (3), leading to a vicious cycle of worsening insulin resistance, T2D, sarcopenia and obesity (4).
The strengths of this study are its population-based nature, thoroughness of data collection and application of up-to-date and evidence-based consensus. It also has a few limitations. It presents cross-sectional data on obesity, muscular health and function of Singaporeans, which precludes inferences on causality. Agreement amongst the obesity definitions was also not investigated, though it has previously been established that different obesity definitions intrinsically measure different constructs and are therefore not interchangeable (5). While the AWGS criteria is for older adults, we also applied the same criteria to estimate prevalence of SO in younger adults, and so this might have been an underestimate, though we only included those 50 years and older in our statistical analyses. Finally, the participants were community-dwelling adults; thus, the findings may not be generalizable to hospitalized, institutionalized or disabled individuals.

 

Conclusions

This study presents new and much-needed data that help to better define and document sarcopenic obesity across age groups of healthy, community-dwelling Asian adults. To address the variability in sarcopenic obesity prevalence and conflicting data on its associations with adverse health consequences, a universally-accepted obesity definition is of utmost importance. Our findings suggest that FMI is the most preferred method for measuring obesity, and support its use as a diagnostic criteria for sarcopenic obesity.

 

Disclosure statement: The authors declare no conflict of interest. The research work conducted for this study comply with the current laws of the country in which they were performed.
Acknowledgements: This research was supported as part of a core funding from the Ministry of Health of Singapore to GERI. The authors gratefully acknowledge the strong support of Prof. Pang Weng Sun in making this Yishun Study possible, and the support of Dr. Lilian Chye, Sylvia Ngu Siew Ching, Aizuriah Mohamed Ali, Mary Ng Pei Ern, Chua Xing Ying and Shermaine Thein in this study. BWJP, SLW, MUJ, TPN contributed to the research design. BWJP, LKL, KAJ, WTS, DHMN, QLLT, KKC conducted the research. BWJP, LKL, KAJ, WTS, DHMN, QLLT, KKC analyzed data and performed statistical analysis. BWJP, SLW, TPN wrote the paper. BWJP, SLW, TPN had primary responsibility for final content. All authors have read and approved the final version.

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SUPPLEMENTARY MATERIAL2

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27. Cheng Q, Zhu X, Zhang X, Li H, Du Y, Hong W, Xue S, Zhu H. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: reference values, prevalence, and association with bone mass. Journal of Bone and Mineral Metabolism. 2013;32(1):78-88. https://doi.org/10.1007/s00774-013-0468-3.
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OBESITY DEFINITIONS IN SARCOPENIC OBESITY: DIFFERENCES IN PREVALENCE, AGREEMENT AND ASSOCIATION WITH MUSCLE FUNCTION

 

E.Q. Khor1, J.P. Lim2,3, L. Tay4, A. Yeo3, S. Yew3, Y.Y. Ding2,3, W.S. Lim2,3

 

1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; 2. Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore; 3. Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore; 4. Department of General Medicine (Geriatric Medicine), Sengkang Hospital, Singapore.
Corresponding author: Ezra Qi-En Khor, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Email address: khor0040@e.ntu.edu.sg, Telephone number: +65 63596474

J Frailty Aging 2019;in press
Published online August 7, 2019, http://dx.doi.org/10.14283/jfa.2019.28

 


Abstract

Background: Sarcopenic obesity (SO) is associated with poorer physical performance in the elderly and will increase in relevance with population ageing and the obesity epidemic. The lack of a consensus definition for SO has resulted in variability in its reported prevalence, poor inter-definitional agreement, and disagreement on its impact on physical performance, impeding further development in the field. While sarcopenia definitions have been compared, the impact of obesity definitions in SO has been less well-studied. Objectives: To compare 3 widely-adopted definitions of obesity in terms of SO prevalence, inter-definitional agreement, and association with muscle function. Design: Cross-sectional. Setting: GERILABS study, Singapore
Participants: 200 community-dwelling, functionally-independent older adults. Measurements: We utilized three commonly-used definitions of obesity: body mass index (BMI), waist circumference (WC) and DXA-derived fat mass percentage (FM%). Sarcopenia was defined using Asian Working Group for Sarcopenia criteria. For muscle function, we assessed handgrip strength, gait speed and Short Physical Performance Battery (SPPB). Subjects were classified into 4 body composition phenotypes (normal, obese, sarcopenic and SO), and outcomes were compared between groups. Results: The prevalence rate for SO was lowest for BMI (0.5%) compared to FM% (10.0%) and WC (10.5%). Inter-definitional agreement was lowest between BMI and WC (κ=0.364), and at best moderate between FM% and WC (κ=0.583). SO performed the worst amongst body composition phenotypes in handgrip strength, gait speed and SPPB (all p<0.01) only when defined using WC. In regression analyses, SO was associated with decreased SPPB scores (β=-0.261, p=0.001) only for the WC definition. Conclusion: There is large variation in the prevalence of SO across different obesity definitions, with low-to-moderate agreement between them. Our results corroborate recent evidence that WC, and thus central obesity, is best associated with poorer muscle function in SO. Thus, WC should be further explored in defining obesity for accurate and early characterization of SO among older adults in Asian populations.

Key words: Sarcopenic obesity, definition, agreement, waist circumference, muscle function.


 

 

Introduction

Body composition changes that occur with aging can lead to sarcopenic obesity (SO), an emerging worldwide phenomenon that has been described as the confluence of two public health crises, namely the obesity epidemic and population aging (1, 2). People aged 60 and above comprise 13% of the global population, and are projected to reach 2.1 billion in 2050 (3). Commensurate with this trend of population aging, the prevalence of sarcopenia is expected to rise in tandem, along with the attendant consequences of reduced muscle mass and strength or physical function such as falls, physical disability, reduced quality of life and mortality (4). Concurrently, obesity prevalence has skyrocketed worldwide, doubling in prevalence in middle-aged and older adults since 1980 (5).
Unsurprisingly, there is increasing attention on SO, a high-risk geriatric syndrome that is predominantly observed in an ageing population and has reported prevalence rates ranging from 0.9% to 30.1% (6, 7). The development of SO is attributed to hormonal, inflammatory and myocellular mechanisms from the cross-talk between adipose and muscle tissue which promote fat deposition and loss of lean mass and strength (8). The resultant synergistic complications from both sarcopenia and obesity lead to worse outcomes in SO than either condition alone, resulting in negative health impacts such as loss of independence, disability, reduced quality of life and increased mortality (9-11). Identification of SO is the cornerstone towards determining its prevalence, understanding its pathophysiology and clinical implications, and devising preventative and therapeutic strategies.
However, the lack of a consistent definition has been a major barrier that has resulted in marked variability in the reported prevalence of SO and conflicting data on its negative health consequences (11). For instance, the prevalence of SO in older adults was reported to differ by up to 26 times when various definitions are applied to the same study population (7). Harmonizing a definition is necessary for continued advancement in the field. While published consensus definitions for sarcopenia have emerged in recent years (4, 12, 13), there is a comparative lack of consensus regarding obesity definitions in the context of SO, leading to great variation in the methods and cut-offs being applied to define obesity (14). This is integral as different obesity definitions measure different constructs and are not interchangeable. Specifically, body mass index (BMI) provides a good indication of the disease risk of comorbidities but is unable to distinguish between lean and fat mass; waist circumference (WC) measures central obesity and is a surrogate measure of visceral adiposity; whilst fat mass percentage (FM%) measures total body fat but not its distribution (15).
This provided impetus for the current study to compare between 3 widely used definitions of obesity (BMI, WC and FM%) in SO. Specifically, we aim to determine the relative impact of the three obesity definitions in terms of SO prevalence, inter-definitional agreement, and association with muscle function.

 

Methods

Study population and groups

We studied 200 cognitively-intact and functionally-independent community dwelling subjects aged 50 years and above who participated in the “Longitudinal Assessment of Biomarkers for characterization of early Sarcopenia and predicting frailty and functional decline in community-dwelling Asian older adults Study” (GERI-LABS) (16).  Inclusion criteria included being: 1) 50–99-years-old at study enrolment; 2) community-dwelling; and 3) independent in terms of basic activities of daily living (BADLs) and instrumental activities of daily living (IADLs). Exclusion criteria included: 1) a past medical history of dementia; 2) cognitive impairment as defined by Chinese Mini-Mental State Examination (CMMSE) ≤21 (17); 3) inability to walk 4.5m independently; and 4) residents of sheltered or nursing homes. Additional study details have been described previously (16). Ethics approval was obtained from the Domain Specific Review Board of the National Healthcare Group, and written informed consent was obtained from each participant.
Based upon criteria for sarcopenia and obesity, we classified subjects into four body composition phenotypes: 1) non-obese and non-sarcopenic (“normal”); 2) non-sarcopenic and obese (“obese”); 3) non-obese and sarcopenic (“sarcopenic”); and 4) obese and sarcopenic (“SO”). Sarcopenia was defined using the Asian Working Group for Sarcopenia (AWGS) criteria as follows: 1) low muscle mass (<7.0kg/m2 in men and <5.4kg/m2 in women); and 2) low handgrip strength (<26kg in men and <18kg in women) and/or slow usual gait speed (<0.8m/s) (4). Obesity was defined using three widely used measures: BMI, WC and FM%. For BMI, we employed the cut-off of ≥27.5kg/m2 to define obesity as recommended for Asian populations by the World Health Organization (18, 19). A validation study in Singapore reported that the mortality risk increases only modestly from BMI=21.5-27.5, but was clearly higher when BMI≥27.5 (20). WC cut-offs of >90cm and >80cm were used for males and females respectively, as per the Asia Pacific Consensus by the International Diabetes Foundation Consensus Worldwide Definition of the Metabolic Syndrome (21, 22). The FM% cut-off used was ≥30% and ≥40% in males and females respectively, as per the definitions used in recent studies (23, 24).

Data collection

We collected information on demographic characteristics, comorbidities, and geriatric syndromes. Evaluating geriatric syndromes included screening for muscle symptoms using the SARC-F questionnaire (25), frailty using the FRAIL questionnaire (26), cognitive function via the locally-validated CMMSE (17), depressive symptoms using the 15-item Geriatric Depression Scale (GDS-15) (27) and nutritional status via the Mini Nutritional Assessment (MNA) (28).
Anthropometric data collected include height and weight to derive BMI, and WC which was obtained 2.5cm above the umbilicus; this anatomical landmark has been shown to be best associated with abdominal fat mass measured by dual-energy X-ray absorptiometry (DXA) (29). We also measured FM% and appendicular skeletal mass (ASM) from whole-body DXA (Discovery™ APEX 13.3; Hologic, Bedford, MA, USA). ASM was defined as the sum of fat-free lean body mass in the four limbs and standardized using height2 to derive the relative appendicular skeletal muscle mass index (RASM).
We assessed muscle function in three ways. For muscle strength, we measured hand grip strength using a hydraulic hand dynamometer (North Coast Medical, Inc, Gilroy, CA, USA), with two trials of grip strength for each hand, and the average of four trials taken. For physical performance, usual gait speed was derived through the best result of two 3m walk tests. The Short Physical Performance Battery (SPPB), a 3-component test comprising balance, gait speed and repeated chair stands, was administered as a gauge of overall physical performance (30). Functional ability was assessed in terms of BADLs, IADLs and the 15-item Frenchay Activity Index (FAI), which has previously been used to assess physical activity among older adults in the Singaporean population (31).

Statistical analysis

Statistical Package for the Social Sciences (SPSS) version 22 was used for statistical analysis. The level of significance was set at 5%. For each definition of obesity, we ascertained the prevalence of each body composition phenotype and compared demographics, comorbidities, geriatric syndromes, body composition, muscle function measures, and functional ability between them. We also compared agreement between obesity definitions on SO diagnosis.
Continuous variables were analyzed using one-way analysis of variance (ANOVA) with Bonferroni correction for post-hoc comparison and the Kruskal-Wallis test for parametric and non-parametric variables respectively. Categorical variables were analyzed using the χ2 test. Agreement between the definitions was measured using Cohen’s kappa.
To further ascertain the impact of obesity definitions on physical performance, we performed multiple linear regression to determine the association between body composition phenotypes and SPPB. We adjusted for important covariates with theoretical relevance (age, gender and education level) or statistical significance across all obesity definitions on univariate analysis. The normal and obese groups were combined to form the reference group for analysis, in order to determine the deleterious impact of sarcopenia alone as well as the additional effect of concurrent obesity as in SO.

 

Results

Baseline characteristics

Participants were predominantly female (68.5%) and Chinese (92.0%) with a mean age of 67.9±7.9 years and median education level of 10 years. The main comorbidities were hyperlipidemia (66%), hypertension (48%), and diabetes mellitus (21.5%). The sample comprised relatively healthy older adults, as evidenced by the high scores for BADLs, IADLs and SPPB; low SARC-F and FRAIL scores; and high CMMSE scores.

Prevalence

The prevalence of phenotypes varied greatly between definitions of obesity (Table 1). BMI identified only a single individual as SO (n=1, 0.5%), which is much lower compared to WC (n=21, 10.5%) and FM% (n=20, 10.0%). Notably, the single male individual being identified as SO using the BMI definition had WC (108.0cm) and FM% (44.90%) values which were much higher than the corresponding cut-offs used to define obesity in this study. Conversely, using WC and FM% to define obesity resulted in the SO group having a much lower median BMI than 27.5kg/m2 (23.0kg/m2 and 23.6kg/m2 respectively).

Table 1 Prevalence of body phenotypes for different definitions of obesity

Table 1
Prevalence of body phenotypes for different definitions of obesity

 

Agreement

Using Cohen’s kappa, inter-definitional agreement between groups was at best moderate, being the best between WC and FM% (κ=0.583) (Table 2). Amongst the 3 definitions, BMI had the poorest agreement (κ=0.364 and 0.529 with WC and FM% respectively).

Table 2 Agreement between definitions of obesity

Table 2
Agreement between definitions of obesity

 

Comparison of clinical characteristics between obesity definitions

Across all three obesity definitions, the SO phenotypes had the highest age, followed by sarcopenia, normal and obese phenotypes (p<0.001) (Table 3). In particular, the WC definition resulted in the SO phenotype containing a disproportionately low number of males compared with BMI and FM% definitions (19% versus 100% and 35%). SO individuals tended to have the lowest level of education. For comorbidities, the SO phenotype had a higher prevalence of strokes and transient ischemic attacks (TIAs) for all definitions of obesity (p<0.05). The prevalence of previous or current alcohol use was highest for the SO phenotype using the BMI and WC definitions, but highest for sarcopenic phenotype using the FM% definition (p<0.05). However, there was no difference in geriatric syndromes between body composition phenotypes (Supplementary Table 1).

Comparison of anthropometric data, muscle function and functional ability measures between obesity definitions

Consistent with the operational definition of body composition phenotypes, significant differences were found in terms of anthropometric data such as BMI, WC, FM% and RASM across all 3 definitions of obesity (p<0.05) (Table 3). In particular, SO groups as defined by WC and FM% had much lower median BMIs (23.0kg/m2 and 23.6kg/m2 respectively) than the BMI cut-off for obesity of ≥27.5kg/m2. In addition, the WC definition resulted a divergent trend of RASM being lower in the SO compared to sarcopenia group (5.16kg/m2 vs 5.23kg/m2, post-hoc p=1.000), in contrast to the FM% definition where the converse was observed (5.24kg/m2 vs 5.15kg/m2, post-hoc p=1.000)  (Table 3).
There was a statistically significant difference between phenotypes across all definitions of obesity in term of muscle strength and physical performance measures (p<0.05) (Table 3). The sarcopenic and SO phenotypes persistently performed poorer than the normal and obese groups, with the exception of the BMI definition where only a single individual was classified as SO. Notably, using the WC definition, SO consistently performed worse than the sarcopenic phenotype in all three measures of muscle function, in contrast to the inconsistent results between SO and sarcopenic phenotypes for the FM% definition. In terms of functional ability, the SO phenotype was found to have significantly lower IADL scores than normal, obese and sarcopenic phenotypes (post-hoc comparison with Bonferroni correction, p<0.05) only when defined using WC. There was no other significant difference between phenotypes in BADL for all definitions of obesity, whereas FAI scores were lowest for SO and sarcopenic phenotypes for all 3 definitions of obesity.

Table 3 Comparison of demographics, comorbidities, anthropometric data, muscle function and functional ability among body composition phenotypes for different definitions of obesity

Table 3
Comparison of demographics, comorbidities, anthropometric data, muscle function and functional ability among body composition phenotypes for different definitions of obesity

ADLs = Activities of Daily Living; BMI = Body Mass Index; FM% = Fat Mass Percentage; RASM = Relative Appendicular Skeletal Muscle Mass Index; WC = Waist Circumference; * Post-hoc analyses were not performed due to the small sample size of the SO group; a. Significant post-hoc Bonferroni test between normal and SO (P<0.05); b. Significant post-hoc Bonferroni test between obese and SO (P<0.05); c. Significant post-hoc Bonferroni test between sarcopenic and SO (P<0.05); d. Significant post-hoc Bonferroni test between normal and sarcopenic (P<0.05); e. Significant post-hoc Bonferroni test between obese and sarcopenic (P<0.05); f. Significant post-hoc Bonferroni test between normal and obese (P<0.05).

 

Multiple linear regression for SPPB

We performed multiple linear regression to assess the impact of sarcopenia and SO phenotypes on SPPB adjusted for significant covariates (Table 4). We adjusted for age, gender, education, stroke/transient ischemic attack, previous/current alcohol use and RASM. Small SO sample size (n=1) precluded multivariate analysis using the BMI definition. Using WC and FM% to define obesity, the total variance explained by the regression models was 15.1% [F(7, 192)=5.415, p<0.001] and 15.5% [F(7, 192)=4.388, p<0.001] respectively. Neither model violated assumptions of normality, linearity, multicollinearity and homoscedasticity.
For the WC model, SO, but not sarcopenia, was significantly associated with a decreased SPPB score (β=-0.261, p=0.001). In contrast, the FM% model showed that sarcopenia, but not SO, was associated with decreased SPPB scores (β=-0.184, p=0.017). Other significant covariates included the association of increasing age and current or previous alcohol use with decreased SPPB (β=-0.170, p=0.030 and β=-0.144, p=0.048 respectively) in the FM% model, and the protective effect of more years of formal education in both WC and FM% models (β=0.156, p=0.034 and β=0.178, p=0.017 respectively).

Table 4 Multiple linear regression analysis for Short Physical Performance Battery

Table 4
Multiple linear regression analysis for Short Physical Performance Battery

FM% = Fat Mass Percentage; RASM = Relative Appendicular Skeletal Muscle Mass Index; TIA: Transient Ischemic Attack; WC = Waist Circumference

 

Discussion

This study is, to our knowledge, the first to elucidate the impact of different obesity definitions on the prevalence, inter-definitional agreement, and muscle function of SO. These vary greatly between obesity definitions, supporting our initial position that different obesity definitions measure different constructs and are not interchangeable, and reiterating the importance of finding a consensus definition of SO. Of the three definitions studied, WC had the highest case detection rate for SO, and consistently identified individuals with the worst muscle function and IADL outcomes. It is noteworthy that we chose a method of WC measurement which is best associated with abdominal adiposity, as different anatomical locations for measuring WC can yield different results (29). Considering how WC is currently not widely adopted in SO studies despite its ease of measurement (14), our results support the case to further explore the consistent use of WC to define obesity in SO for research and clinical purposes.
While BMI has been used extensively in defining SO, our results indicate it was significantly less sensitive in identifying SO compared to the other two definitions despite using Asian-appropriate cut-offs and has the poorest agreement with the other obesity definitions. This suggests that BMI is unsuitable as a measure of obesity for the definition of SO in older adults in our local population. WHO cut-offs for BMI were designed to detect adverse cardiovascular and metabolic outcomes (19), and appear unsuitable for identifying poor muscle function outcomes in SO. BMI fails to account for the higher body fat composition and loss of lean body mass with age (11), and does not assess body fat distribution. This is pertinent as it is central obesity rather than peripheral fat deposition which is associated with higher morbidity (18).
Our results suggest that WC and FM% intrinsically measure different constructs and are not interchangeable. Despite having similar SO case detection rates (10.5% and 10.0% respectively), the agreement between them was only moderate (κ=0.583), as has been highlighted in the existing literature (32). Only the WC definition consistently resulted in the SO phenotype having the worst scores amongst the body composition phenotypes for muscle function performance measures and IADLs. In particular, for the composite physical performance measure of SPPB, SO performed significantly worse in multivariate analysis only for the WC definition. This is pertinent as lower SPPB scores are associated with a higher risk of hospitalization, institutionalization, morbidity and mortality (30).
The observed differences in association with muscle function measures and IADLs can be attributed to WC being a more specific measure of abdominal obesity as opposed to FM%, which fails to distinguish body fat distribution. This corroborates the putative role of central obesity, rather than generalized obesity per se, in the pathogenesis of adverse physical performance and functional outcomes arising from SO. In support of this, the English Longitudinal Study of Aging (ELSA) recently reported that abdominal obesity is associated with a decline in muscle strength (33). We posit that WC, as a specific measure of central obesity, provides an indication of the risk of ectopic fat deposition within muscle tissue and thus increased intramuscular adipose tissue (IMAT) (8). IMAT has been linked with adipose tissue inflammation due to the secretion of cytokines such as monocyte chemoattractant protein-1, and the resultant proinflammatory milieu and accelerated muscle catabolism can then lead to decreased muscle mass and impaired strength (34). Thus, using WC to define obesity among older adults may result in more accurate characterization of SO and potentially earlier detection of the downstream consequences of reduced physical performance.
This study was made possible through a comprehensive evaluation which permitted accurate characterization of body composition phenotypes, and thorough assessment of clinically relevant outcomes comprising various measures of muscle function and functional ability. Limitations include the cross-sectional study design, such that reverse causality cannot be excluded; study results thus represent point-prevalence and associations rather than definitive conclusions about causality. The results may not be generalizable to other ethnic groups or other Asian populations due to the predominance of Chinese individuals. They also may not apply to less robust older adults. Further studies in non-Chinese populations and in more heterogeneous populations of older adults, as well as longitudinal studies to determine the trajectory of SO relative to other body composition phenotypes, are necessary to validate our findings. Lastly, the small sample size may result in inadvertent type II error and precluded subgroup analysis for important covariates such as gender.

 

Conclusion

To our knowledge, this is the first study that compares the impact of obesity definitions on SO, reiterating the importance of finding a consensus definition of obesity for SO. Our results demonstrate that obesity definitions significantly affect the prevalence of SO, agreement between definitions is at best moderate, and muscle function and functional ability differ depending on the definition being employed.
Our findings suggest that WC should be further explored as a means of defining obesity in SO. It is associated with poorer physical and functional performance outcomes, has an established pathophysiological association with impaired muscle strength and mass, and is easily measured clinically, thus enhancing its utility and relevance for defining sarcopenic obesity. In contrast, BMI appears unsuitable for use in the context of SO. It has a low case detection rate, poor agreement with other obesity definitions, and is intrinsically unable to distinguish between the components of body composition.

 

Disclosure: The authors report no conflicts of interest in this work.
Acknowledgments: This study was supported by Lee Foundation grant 2013. We extend our appreciation to the Senior Activity Centers and the study participants who have graciously consented to participate in the study.

 

SUPPLEMENTAL MATERIAL

 

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IMPACT OF BODY COMPOSITION ON PHYSICAL PERFORMANCE TASKS IN OLDER OBESE WOMEN UNDERGOING A MODERATE WEIGHT LOSS PROGRAM

 

 

G.D. MILLER1, S.L. ROBINSON2

 

1. Department of Health and Exercise Science, Wake Forest University, Winston-Salem NC 27109; 2. Spelman College, Atlanta GA 30314.

Corresponding author: Gary D. Miller, PhD, RD, Box 7868 Reynolda Station, Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC 27109-7868, email: millergd@wfu.edu; Phone: +1 (336) 758-1901; Fax: +1 (336) 758-4680

J Frailty Aging 2013;2(1):27-32

Published online February 13 2016, http://dx.doi.org/10.14283/jfa.2013.5


Abstract

Background: Although obesity is a recognized risk factor for impaired physical function in older adults, there is still debate on whether older obese adults should undergo intentional weight loss due to concern of loss in lean body mass, including appendicular lean soft tissue mass.  This may put them at risk for worsening muscle strength and mobility. Objectives: Therefore, the purpose of this study was to examine the effect of a weight loss intervention on body composition and physical function in obese older women.  Design: Women were randomized into either a weight stable (WS) (n=20) or an intensive weight loss (WL) (n=26) group.  Setting: The study setting was at a university research facility. Participants: Women (age, 67.8±1.3 yrs; BMI, 34.9 (0.7) kg/m2; mean±standard error of the mean) were recruited.  Intervention: The WL intervention was for 6 months and included moderate dietary energy restriction and aerobic and strength exercise training.  Measurements: Variables were obtained at baseline and 6-months and included body weight, dual energy x-ray absorptiometry (DXA), 6-minute walk distance, stair climb time, and concentric knee extension muscular strength. Results: Estimated marginal means (SEM) for weight loss at 6-months was -8.5 (0.9)% for WL and +0.7 (1.0)% for WS. There was a significant loss of body fat mass, lean body mass, appendicular lean soft tissue mass, relative muscle mass, and skeletal muscle index for WL vs. WS at 6-months.  However, improvements for WL vs. WS were seen in 6-minute walk distance and stair climb time, and trends for improved relative strength and leg muscle quality. Change in body fat mass was positively related to improved physical function and muscle strength and quality. Conclusion: These results further support the use of a sound intentional weight loss program incorporating moderate dietary energy restriction and exercise training in older obese women to improve physical function.  Although lean soft tissue mass was lost, over the 6-month program there was no deleterious effect on muscle strength or muscle quality.

Key words: Older adults, physical activity, mobility disability, weight loss, sarcopenic obesity.


 

Introduction

Obesity is recognized as a prominent risk factor for mobility disability, especially in older adults (1).  This is particularly troublesome with the projected rises in the number of older adults and prevalence of obesity in older adults (2, 3).  In the United States alone, there are over 20 million older adults.  Currently, across all racial and ethnic groups, more than one in three adults over the age of 60 years are considered obese (body mass index, BMI, ≥ 30.0 kg/m2) and nearly three in four adults ≥ 60 years are considered overweight or obese (BMI ≥ 25.0 kg/m2) (3).  Additionally, the prevalence of obesity is more staggering in women, with 42.3% of adults 60 years or older being obese (3).  Furthermore, it has been shown that compared to men, older obese women have lower physical function, and higher risk for future disability (4).

Weight loss in obese older adults remains controversial, but in clinical trials it has been shown to improve measures of physical function (5, 6).  The reluctance of clinicians to promote weight loss in overweight and obese older adults stems from the observation that intentional weight loss reduces not only body fat, but also includes loss of lean body mass, including appendicular lean soft tissue mass, and independently, low lean body mass is negatively associated with physical function and performance in older adults (7).  Thus, weight loss treatments in older adults aim to minimize lean body mass loss.  However, there is some evidence showing that the relative reduction in the amount of lean soft tissue mass is greater in older than younger adults (8).  Change in fat mass is a stronger predictor of change in physical function in older obese adults than lean body mass, which suggests that in obesity, there is impaired muscle quality (9).  With subsequent weight loss, muscle quality and physical function is enhanced even with loss of lean soft tissue mass; reduction in fatty infiltration of lean tissue may underlie these improvements (10).

Exercise training and dietary energy restriction leading to weight loss, independently and together, have been shown to enhance physical function in older obese adults with mobility disability (5).  Resistance training helps ameliorate the loss of lean soft tissue mass during dietary energy restriction (11).  A systematic review comparing dietary energy restriction and exercise training showed that including exercise training with energy restriction reduced the loss of fat free mass from 24% to 11% (12).
The purpose of the current analysis was to test the hypotheses that the improvement in physical function in older obese women undergoing a weight loss intervention of dietary energy restriction and exercise training will be related to alterations in whole body and regional body composition.  There have been few randomized trials of weight loss in older obese adults, and several of the earlier works have been limited based on the low level of weight loss, lack of physical function performance measures- including muscular strength and muscle quality, and not having a valid and sensitive measure for body fat and lean soft tissue mass.

 

Methods

Study Population and Design

Data for this investigation and analysis were obtained from the Physical Activity, Inflammation, and Body Composition Trial (PACT).  Details of this trial are described elsewhere (13).  Briefly, sedentary obese older adults (≥60 years) with self-reported knee osteoarthritis and impaired lower body physical function were recruited.  For these analyses, data from 46 women were utilized.  Participants were randomly assigned to one of two groups: intensive weight loss (WL) (n=26) and weight stable (WS) control (n=20).

Interventions

Weight Loss Intervention

There was a 10% weight loss goal during the WL intervention which utilized partial meal replacements, nutrition education, and lifestyle behavior modifications.  Participants in WL also engaged in a structured, facility-based exercise aerobic and strength training program 3 days/week for 60 minutes/session.  

Weight Stable Intervention

Participants randomized to this arm of the study were the attention control group and met bimonthly in a group format with presentations on general health, including osteoarthritis and exercise.

Measurements

All outcome measures were collected on participants at baseline and following the 6-month intervention.

Physical Function and Knee Strength

These tasks included the 6-minute walk distance, stair climb time and concentric knee extension muscular strength.  Briefly, for the first test, participants were instructed to walk as far as possible in a 6-minute time period on an established course.  They were not allowed to carry a watch and were not provided with feedback during the trial.  Performance was measured in the total distance covered.  The timed stair climb involved ascending and descending a flight of 5 stairs as quickly as possible. During the ascent, participants were instructed to grasp the handrail with their left hand, and without hesitation turn around on the platform at the top and descend using the same hand to hold the rail.  Performance was measured as the time required to complete the task.

Concentric knee extension muscular strength was assessed using a Kin-Com 125E isokinetic dynamometer (Chattanooga Group, Hixson, TN) at a velocity of 30os-1.  Prior to testing, a warm-up period was provided to habituate the participants to the testing equipment.  The participant was secured with the torso and tested leg strapped to the testing chair, hands across the chest, the axis of the dynamometer aligned with the knee, and the resistance pad attached to the lower leg proximal to the ankle joint.  The tested leg was the one most affected by arthritis according to the participant.

Body Weight, Height, and Waist Circumference

These measures were obtained using standard techniques.  Weight and height were determined with shoes and jackets or outer garments removed.  Instruments were calibrated on a weekly basis.  Waist circumference was obtained by placing a measuring tape in a horizontal plane around the abdomen at the level of the iliac crest.

Body Composition

Dual energy x-ray absorptiometry (DXA, Hologic Delphi QDR) was used to assess whole body and regional body composition.  Lean tissue mass, fat mass, and bone mineral content for whole body and specific regions of interest were obtained from the DXA system software.  Lean soft tissue mass for the total body and each region (left and right arms and legs) was determined by subtracting bone mineral content from lean tissue mass associated with the specific sites.  Compartments of arms and legs were calculated by the system software using specific anatomic landmarks.  Right and left arms and legs were summed to obtain total arm and leg lean soft tissue mass, respectively.  Total body skeletal muscle mass was calculated based on measurement of appendicular lean soft tissue mass measured with DXA using the following equation (14):  Total Body Skeletal Muscle Mass (kg) = (1.13 x ALST) – (0.02 x age) + (0.61 x sex) + 0.97; where sex = 0 for female and ALST = appendicular lean soft tissue mass.   Relative Muscle Mass (RMM) was the total body skeletal mass divided by height (m)2 (15).  Skeletal muscle index (SMI) was calculated as total body skeletal muscle mass/total body mass x 100 (16).  Muscle quality was calculated from concentric knee extension muscular strength/appendicular lean soft tissue mass.

Data Analyses

Independent samples t-test were used to compare baseline measures between groups.  Baseline values are presented as means±standard error of the mean.  Univariate analysis of covariance was used to compare differences between groups in follow-up measures and change between baseline and 6-months for body weight, body composition, physical function, and muscle strength and quality.  The baseline value of the variable and age were used as covariates in the model.  These results are shown as estimated marginal means±standard error of the mean.  Pearson Product correlations were performed for determining relationships between measures of physical function, muscle strength and quality, and body composition at baseline as well as absolute change between baseline and 6-months.  Statistical significance was deemed significant at p < 0.05.  Analyses were performed on SPSS® version 19.0 (Chicago, IL).

Results

A total of 55 women (n=27 WS and n=28 WL) were randomized and 46 (n=20 WS and n=26 WL) completed both baseline and 6-month follow-up assessments and were used in the current analysis.  There were no differences between those that completed and the 9 dropouts in baseline values for demographics, physical function, muscle strength, body composition, and indices of skeletal muscle measures.  Furthermore, for the completers, there were no statistical differences (p>0.05) between WS and WL at baseline (Table 1).

Table 1 Body composition, physical function, muscle strength and muscle quality for weight stable and weight loss groups at baseline, 6-months, and the change from the intervention. Values are means±SEM for baseline and estimated marginal means±SEM for 6-mos, and change

* Statistically significant difference (p<0.05) between comparison  

At 6-months, those in the WL group, as compared to WS, had significantly lower body weight, waist circumference, body fat, lean body mass, appendicular lean soft tissue mass, and relative muscle mass, and a higher skeletal muscle index (Table 1).  Women in the WL group lost over 7 kg of total weight (-8.5±0.9% from baseline for WL vs. +0.7±1.0% for WS) with approximately 6 kg of this being from body fat and an additional 1.6 kg from lean body mass.  Loss of appendicular lean soft tissue mass was approximately 1 kg in WL vs. a loss of 0.1 kg in the WS group.  Relative muscle mass showed a greater reduction between baseline and 6-months for WL vs. WS (-0.4 kg/m2 vs. 0.0 kg/m2, respectively).   However, the change in skeletal muscle index was greater for WL vs. WS (1.3% vs. -0.1%, respectively).

Six-minute walking distance and stair climb time were improved in the WL vs. WS group (Table 1).  However, there were no statistical significant differences between groups in measures of absolute (knee extension) and relative (knee extension/kg lean body mass) concentric knee extension musclar strength and muscle quality (concentric knee extension muscular strength/kg leg lean soft tissue mass).  Albeit, there was a strong trend for relative concentric knee extension muscular strength and muscle quality to be higher for WL vs. WS (p<0.10).

 

Table 2 Correlations at baseline between body composition, physical function, muscle strength and quality

Sample size = 42 for correlations between body composition measures and physical function assessments.  Sample size = 46 for correlations between the various body composition assessments.

 

Pearson Product correlations were performed between the body composition and physical function and muscle strength variables at baseline (Table 2) and in their changes across the treatments (Table 3).  At baseline, higher body fat was associated with lower performance for 6-minute walk distance (r=-0.356) and stair climb time (r=0.330).  Additionally, appendicular lean soft tissue mass and relative muscle mass showed strong trends (p<0.10) for statistically significant negative correlations with 6-minute walk distance, such that those with higher appendicular lean soft tissue mass and relative muscle mass had poorer walking distances.  Consistent with these findings, negative correlations were also apparent (statistically significant and strong trends towards significance, p<0.10) between indices of lean soft tissue and relative concentric knee extension muscular strength and muscle quality.  Individuals with lower lean soft tissue had higher relative concentric knee extension muscular strength and muscle quality.  Concentric knee extension muscular strength and muscle quality were negatively correlated with stair climb time such that individuals with a faster stair climb time had greater strength and muscle quality.

Table 3 Correlations for change across time between body composition, physical function, muscle strength, and muscle quality

Sample size = 42 for correlations between body composition measures and physical function assessments.  Sample size = 46 for correlations between the various body composition assessments.

Correlations for changes in variables from baseline to 6-months showed that greater loss of body fat correlated with greater increase in 6-minute distance walked (r=-0.455), faster stair climb time (r=0.453), increased concentric knee extension muscular strength (r=-0.262; p=0.090), and improved muscle quality (r=-0.318).  A more positive change in skeletal muscle index was correlated with improvement in 6-minute walk distance.  Interestingly, there was a significant correlation between the largest decreases in indices of muscle mass (total lean body mass, appendicular lean soft tissue mass, and relative muscle mass) from the interventions with faster stair climb time and increased muscle quality (Table 3).  Furthermore, there were significant negative correlations between change in indices of muscle mass from the interventions and concentric knee extension muscular strength and muscle quality such that those that showed the greatest gains in strength and muscle quality were those that lost the most lean body mass, appendicular lean soft tissue mass, and relative muscle mass.

Discussion

The current results are derived from a more extensive analysis of a subpopulation in older obese women of previously published results with regards to physical function and strength (6, 17).  The novelty of this analysis includes examining the effect of the interventions on more comprehensive indices of total and regional lean soft tissue and body fat, as well as determining the relationships of these variables with physical function.  Distinctive findings show that appendicular lean soft tissue mass and relative muscle mass were significantly lower and skeletal muscle index was increased in the weight loss vs. weight stable group; however this loss of lean soft tissue mass components was not detrimental to physical performance or muscular strength.

Intuitively, the loss in appendicular lean soft tissue mass would be expected to lead to a worsening in physical function, specifically strength, and subsequently muscle quality.  However, as shown, neither strength nor muscle quality was compromised through this weight loss intervention; noteworthy, relative concentric knee extension muscular strength and muscle quality showed a trend towards improvement with the weight loss intervention.  The 6-minute walk distance and stair climb time showed marked improvements of ~55 meters (13%) and 2 seconds (19%) for the stair climb time.  In older adults with mobility disability, Perera et al showed that best estimates for a “substantial change” in 6-minute walk distance is 50 meters (18), which approximates the change we observed.

Correlational data supports previous research on harmful effects of body fat and physical function (19), such that, at baseline, those with the highest levels of body fat had the shortest distance on the 6-minute walk and slowest stair climb time.  Change in body fat was also related to change in physical function as women who lost the most body fat walked farther, had faster stair climb time, and greater increase in concentric knee extension muscular strength and muscle quality.  The mechanism for fat’s detrimental effects are not known, but it may be an action of fat infiltration in muscle tissue (9, 10, 20).  The current study did not assess fat infiltration in muscles which  requires the use of computed tomography which is expensive and exposes participants to higher levels of radiation than DXA.

A number of the correlational findings at baseline are noteworthy as they show that women with the highest appendicular lean soft tissue mass, relative muscle mass, and skeletal muscle index, walked less, were weaker, and had lower muscle quality.  Correlations were assessed and are presented by combining both intervention groups (n=46).  Further analyses were performed on correlations stratified by treatment group.  In general, the magnitude of the correlations were similar for WL alone (data not shown) and in combining groups; however, because of the smaller sample size, the p value was often larger and in some instances was not significant (p>0.05).  Surprisingly, individuals that improved the most in stair climb time and concentric knee extension muscular strength and muscle quality, lost the most lean soft tissue mass.  The results from this study may be confounded by the strong collinearity between fat mass, lean body mass, appendicular lean soft tissue mass, relative muscle mass and their changes from the interventions (correlation values ranging from 0.646-0.743).  Alternatively, this suggests that improvement in mobility disability and muscular strength is not exclusively from the quantity of skeletal muscle mass, but likely the function of the tissue is important; the latter has been shown to be influenced by a number of factors, including muscle fiber types, fat infiltration of the skeletal muscle, inflammation, mitochondria function, energy metabolism, and oxidative capacity (21, 22).

Confounding the relationships between changes in body composition and physical function in these analyses is the inclusion of physical activity into WL.  Independently, physical activity is associated with improved physical function in older adults, including during weight loss (9, 23).  In a longitudinal cohort study, both the initiation and the continuation of physical activity in older adults was associated with maintaining functional status and delaying functional loss compared to sedentary individuals (24).  The modality of exercise training used in the current study, both aerobic and resistance training, has been shown to provide the optimal exercise strategy for lowering limitations in physical function in older obese adults (25).  Thus, the results and improvements observed must be considered in light of the incorporation of physical activity in the weight loss intervention.  An additional cofounder is that although there were no differences in the baseline measures between the completers and those that dropped out, it is not known if this may have affected the outcomes in the study.  The dropouts may have been less compliant to the intervention and dropped out based on this response; by dropping out and not having their data, this may have biased the results towards more favorable outcomes from the intervention.

The current analysis demonstrates the impact of a 6-month dietary restriction and exercise training weight loss program on measures of physical function, muscle strength and quality, and body composition in older obese women.  Although total lean body and appendicular lean soft tissue mass decreased following WL, improvements were evident in physical performance tasks.  The loss in lean soft tissue mass did not worsen function measures.  Poorest function was seen in those with the highest level of body fat.  Furthermore, enhanced function was related to loss in fat mass.  The surprising findings that measures of increased skeletal muscle were related to poor function and muscle strength and quality is proposed to be the result of the high collinearity between fat mass and lean body mass.  The WL intervention, incorporating both exercise training and dietary restriction provides a viable program for significant reduction in fat mass with minimal changes in lean soft tissue mass.

Acknowledgements: This project was funded by SlimFast® Nutrition Institute, the Wake Forest University Claude D. Pepper Older American Independence Center (NIH grant #P30 AG21332) and the Wake Forest University General Clinical Research Center (NIH grant M01-RR07122).

Disclosure Statement: Author has no conflict of interest to disclose. Work was carried out at Wake Forest University, Winston-Salem, NC 27109

 

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CHANGES OF BODY COMPOSITION, MUSCULAR STRENGTH AND PHYSICAL PERFORMANCE DUE TO RESISTANCE TRAINING IN OLDER PERSONS WITH SARCOPENIC OBESITY

 K. STOEVER1, A. HEBER2, S. EICHBERG1, W. ZIJLSTRA1, K. BRIXIUS2

1. Institute of Movement and Sport Gerontology, German Sport University Cologne; 2. Department of Molecular and Cellular Sport Medicine, Institute of Cardiovascular Research and Sport Medicine, German Sport University Cologne.

Corresponding author: Prof. Dr. Klara Brixius, German Sport University Cologne, Department of Molecular and Cellular Sport Medicine, Am Sportpark Muengersdorf 6, 50933 Cologne, Germany, Email: brixius@dshs-koeln.de, Phone: +49 221 4982 5220

J Frailty Aging 2015;4(4):216-222
Published online June 11, 2015, http://dx.doi.org/10.14283/jfa.2015.67


Abstract

 Background: At present, it is unclear whether older, obese persons with or without sarcopenia respond differently to training. Furthermore, there are no differentiated recommendations for resistance training for this special target group. Objectives:  The objectives are to investigate the changes in the physical parameters of older, obese men caused by training and to reappraise the modalities of resistance training for older persons. Design: Pre-test-post-test design. Participants: The participants were 33 physically inactive and obese older men (≥ 65 years, BMI ≥ 30 kg/m2), with-out severe diseases. Subjects were divided into two groups: NSAR (no or presarcopenia, n= 15) or SAR (sarcopenia, n= 18). Intervention: The intervention consisted of progressive resistance training, twice a week for 16 weeks with finally 80-85% of maximum strength and three sets with 8-12 repetitions. The training contained six exercises for the major muscle groups. Measurements: Sarcopenia was assessed using the Short Physical Performance Battery (SPPB), hand-grip strength, skeletal muscle mass index (SMI), and gait speed over a 6-meter walkway. Furthermore, the maximum dynamic strength (1 RM) was assessed.  Results: At baseline, the NSAR group had significantly better values in SMI, SPPB score, hand-grip strength, and 1 RM. After training, the results in both groups displayed an increase in 1 RM at the lower limbs (NSAR 18%, SAR 38%) and the upper limbs (NSAR 12%, SAR 14%). Also, the SPPB score (NSAR 11%, SAR 15%) and the 6-m-gait speed (NSAR 5%, SAR 10%) increased. The SAR group was able to increase their right hand-grip strength by 12%, whereas the NSAR group maintained their initial high strength values. SMI did not change in both groups. Conclusions: Both groups show improvements after resistance training with slightly more benefits for  men with sarcopenia. Results of this study can be used to define specific training regimens for N(SAR) subjects.

Key words: Sarcopenic obesity, muscle strength, physical performance, resistance training.


 

Background

Age-related loss of skeletal muscle mass and strength (i.e. sarcopenia) can have an impact on functioning. However, the coexistence of obesity and sarcopenia (i.e. sarcopenic obesity) can even have more profound consequences for the affected individuals, as the negative consequences of both aspects add up. These negative consequences include a decreased physical performance, an increased number of falls, a reduced quality of life, a decreased cardiovascular fitness as well as changes in metabolism, e.g. an increased insulin resistance (1-3). In the United States as well as in Europe, the number of obese older people is rapidly increasing because of an increase in the older population and the percentage of older people who are obese (4-5). The prevalence of sarcopenia varies between 5-13% in persons aged 60 to 70 years and between 11-50% in people over 80 years of age, for example, which depends on the respective population (6).

To counteract the negative aspects of sarcopenic obesity, endurance training used to be performed by obese, older persons, often combined with a diet intervention (7-9). Only in recent years, components of strength training have been added (7). In general, interventions have been carried out as a combined training according to the recommendations of the American College of Sport Medicine (ACSM, 10-12). The results of these studies show that a sole diet leads to a significant reduction in weight, but at the same time causes a reduction in muscle mass. With exercise based training, the muscle mass may be increased, but on the other hand, there is no reduction of the fat mass. Only a combined intervention of exercise and diet can reduce the fat mass while maintaining or increasing muscle mass (8-9). In addition, the exercise part of these interventions has positive effects on physical performance (depending on the training design, there is an improvement of strength or endurance parameters) and functional skills that have a crucial importance for the accomplishment of everyday activities, e.g. increasing gait speed (10-12).

Previous study designs had their focus mainly on the different effects of different interventions, e.g. exercise vs. diet or exercise vs. control group. This means that subjects had to fulfill the same conditions, so that they were randomized and then divided into different groups (10-13).

The key messages of these studies refer to the respective effects of the different interventions in terms of physical performance capacity and body composition in comparable groups of subjects. The training recommendations of the ACSM used therein refer to older, healthy people or people with type 2 diabetes mellitus. In this area, specific recommendations for the design of differentiated training in older, obese people are still lacking. However, due to the increasing prevalence of serious adverse effects (in particular the declining physical activity), it is even more important for the individuals concerned to fill this gap.

The statement that the negative consequences of the simultaneous presence of sarcopenia and obesity add up has not yet been considered with regard to the effects of resistance training. This brings up the question to what extent maximum strength, body composition and functional parameters change differently in obese adults with sarcopenia in comparison with obese adults without sarcopenia due to resistance training. The answer to this question can provide insights into the influence of lower baseline values of muscle mass and function in terms on the response to the training programme.

For the investigation of this aspect, the baseline values (i.e. before training) of the maximum strength of the upper and lower extremities, body composition (especially regarding muscle mass) as well as functional and every-daylife testing are crucial. Based on the baseline values, possible differences and similarities of adults with and without sarcopenia should be shown. The hypothesis is that the group with sarcopenia has a higher increase due to the training because of their lower baseline performance, particularly in the maximum strength and the physical performance measurements

Methods

Participants 

Volunteers were recruited by advertisement. In this first part of the current study, only men could participate. They were included for the study if they were 65 years and older, had a BMI of 30 kg/m2 or more and had not been physically active in the past 12 months. The exclusion criteria were any serious inflammatory, neurological or cardiovascular diseases. Finally, 36 men who met the above criteria participated at the beginning.

The participants were divided according to muscle function and proportion of muscle mass into a high-performance and a low-performance group. The proceeding and the precise conditions for each group will be explained in the following section.

This study was approved by the Ethics Committee of the German Sports University Cologne. Each participant provided written informed consent.

Measurements 

Using a questionnaire, the current and previous diseases and also the physical activity within the last 12 months were recorded. This was followed by the determination of sarcopenia based on the test battery of Cruz-Jentoft and colleagues (14). They determined three components in order to evaluate the presence of sarcopenia and the stage of sarcopenia:

• Muscle mass

• Muscle strength

• Physical performance

The existence of low muscle mass and low maximum strength or reduced physical performance is called sarcopenia. If all three components are reduced, Cruz Jentoft et al. (14) call it severe sarcopenia. Presarcopenia is characterised by only a low muscle mass. Based on the data collected by Cruz-Jentoft et al. (table 1), the exact cut-off points for each criterion are shown.

Table 1 Overview of the methods used and the cut-off points for the sarcopenia test battery (by Cruz-Jentoft et al. (14))

BMI: body mass index; SPPB: Short Physical Performance Battery

Muscle mass

The measurement of muscle mass was performed in the supine position using bioelectrical impedance analysis (EgoFit BIA series 4, monofrequency, Germany). Before the measurement, the participants had to adhere to different specifications according to Heyward (15). This method has the advantage that the measurement can be carried out regardless of location, and the results of the analysis of the body composition are available immediately. The important parameter is the skeletal muscle mass, which was determined by the equation of Janssen et al. (16). This value was converted to percent, so that the socalled skeletal muscle mass index (SMI) resulted (17).

Muscle strength 

The measurement of the isometric handgrip strength was performed using JAMAR hand dynamometer (Sammons Preston Rolyan, Bolingbrook, USA) and was carried out alternately with the right and left hand. There were at least two attempts on each side. If there was a difference of ≥ 10 % between the two attempts, a third attempt was performed. For the statistical analysis the best attempt overall was taken.

Physical performance 

To determine this parameter, the Short Physical Performance Battery (SPPB; 18-19) was performed according to the guidelines given by Cruz-Jentoft et al. (14). It consists of the following three parts: balance (consecutively side-by-side, semi-tandem, and tandem stands, each for 10 seconds), walking a 4-meter distance at normal gait speed and rise from a chair and return to the seated position five times. A maximum of four points will be awarded in each category. This results in a total score. As an additional item, walking a 6-meter distance was also tested in this category because of the existence of separate cut-off points.

Maximum muscle strength 

If there were no objections by physician, a maximum-strength measurement was performed on the leg- and chest-press machine (ERGO-FIT 4000, Pirmasens, Germany). Here, the subjects were first familiarized with the test procedure and the measuring apparatus. The determination of dynamic maximum strength (one-repetition-maximum, 1 RM) was performed following the protocol of Baechle, Earle and Wathen (20). After three warm-up attempts, up to five maximum attempts were carried out to determine the 1 RM. The highest weight moved was used for the evaluation. This weight also determines the starting weight for the subsequent strength training.

All test procedures performed at baseline measurements were repeated at the end of training intervention after 16 weeks.

Group division 

According to the scheme of Cruz-Jentoft and colleagues, the participants were divided into non- and presarcopenic (NSAR) or sarcopenic (SAR, stage 1 and 2) subjects. It should be noted that there are two possibilities or two assessments in the field of physical performance to be considered as sarcopenic. Firstly, the total score of the Short Physical Performance Battery, and secondly the gait speed over a 6-meter walking distance.

If the participants met the inclusion criteria and if they achieved an adequate result in the sarcopenia test battery, a sports-medical examination was carried out, in particular to exclude cardiac contraindications.

Intervention

The participants took part in a progressive resistance training, which was carried out on machines (Cybex EAGLE, SANIMED Nordicline). The training lasted 16 weeks with two sessions of 60 minutes per week. During the first three weeks, the participants trained at 60% of 1RM and carried out two sets of 12-15 repetitions each muscle group. During weeks 4 to 16, the participants increased the intensity gradually to 80-85% of 1 RM, and carried out three sets of 8-12 repetitions. First, there was a warming up for ten minutes on a bicycle ergometer. Then the training followed, which contained seven exercises for the major muscle groups (knee extensors, biceps and chest muscles, hip adductors and abductors, abdominal muscles, back muscles). For cooling down, the participants did about five minutes of bicycle-ergometer work.

Statistical analysis

The statistical analysis was performed using the IBM SPSS statistics software (version 22; IBM, Ehningen, Germany). Normal distribution was analyzed using the Kolmogorow-Smirnow-Test. The significance level was set at α = 5% at analysis of variance. Baseline characteristics between groups were compared using the t-test for unpaired samples for continuous variables. A two-factorial analysis of variance with repeated measures on two main factors (time and group) was conducted. The homogeneity of group variances was ensured for all variables.

Using this method, pre- and post-training differences between the groups were to be determined. The t-test for paired samples was performed to determine whether there were statistically significant within-group changes.

The SPPB has a score at the ordinal scale level. Therefore, for all calculations nonparametric tests (Wilcoxon signed-rank test and Mann-Whitney U test) were used.

Results

33 test subjects completed the intervention. Three men dropped out due to health problems.

Overall, the training participation of the 33 men was 86 % on average.

The basic conditions of the participants were similar, i.e. there were no significant differences of the anthropometric data (table 2).

Table 2 Description of the participants at baseline

Means ± standard deviation are shown. BMI: body mass index; NSAR: group with no or presarcopenia; SAR: group with sarcopenia

When testing dynamic maximum strength, the preset protocol could not be completed in all subjects, for example due to pain in the shoulder or knee joints. Therefore, for these subjects there is no maximum strength value available.

Table 3 indicates baseline performance as well as performance after the intervention. The two-factorial analysis of variance with repeated measures showed no statistical significant interaction between group and time. Thus, for the following analysis the results of the different t-tests are shown.

Table 3 Comparison of the means between and within the groups before and after training

Means ± standard deviation are shown. NSAR: group with no or presarcopenia; SAR: group with sarcopenia; SMI: skeletal muscle mass index; 1 RM: one-repetition-maximum; SPPB: Short Physical Performance Battery; *p-value within group NSAR (t-test for paired sample); †p-value within group SAR (t-test for paired sample); §p-value between groups post-training (t-test for unpaired samples) #Wilcoxon signed-rank test for differences within groups and Mann-Whitney U test for differences between groups were used

 

Baseline performance 

Before the start of the intervention, the two groups differed significantly in several tests, which confirmed the planned group difference. The NSAR had significantly better values in the skeletal muscle mass index, the gait speed over six meters, the hand-grip strength, and the dynamic maximum strength at the chest press machine compared to the SAR.

There were no group differences of the dynamic maximum strength test for the lower extremities.

Both groups differ significantly in their total SPPB score. Here, one subtest of the SPPB, the repeated chair stands, is considered separately. The two groups did not differ, however, in the chair stands when time is considered.  

Performance after training 

In both groups no changes were observed in the SMI after training.

The SAR could increase their hand-grip strength (by 12%). The NSAR kept their handgrip strength constant at a high level. In both groups there was a statistically significant improvement of the dynamic maximum strength performance on the leg-press machine (NSAR by 18%, SAR by 38%) and of the dynamic maximum strength on the chest-press machine (NSAR by 12%, SAR by 14%).

The gait speed over a 6-meter course of both groups improved statistically significantly (NSAR by 5% and SAR by 10%).

Both groups increased their value in the total SPPB score significantly. The improvements of SAR were slightly larger, so that the difference between the two groups after training was no longer significant.

Discussion

Both groups of obese individuals, regardless of whether they are sarcopenic or not, benefit from a progressive resistance training. The improvements could be recognized in two domains: maximum strength of the upper and lower extremities as well as aspects of physical performance. This means that the training did not only caused positive changes to the muscular level; there is also a possible transfer to certain skills of everyday life, e.g. gait speed and getting up from a chair. No statistically significant changes could be noticed after training relating to muscle mass in both groups.

The division into groups of obese men with and without sarcopenia was done in accordance with the recommendations of Cruz-Jentoft and colleagues (14). The system developed by this working group initially applies to all people aged 65 and over. However, they point out that comorbidities and individual circumstances must be taken into account. Before starting our study, it was unclear whether this system can be applied to obese older people to differ sarcopenic from non-sarcopenic people. According to the predetermined scheme, there were differences in baseline characteristics, such as SMI, hand-grip strength and gait speed, between the groups. This is a confirmation that the predetermined scheme is also applicable to older, obese individuals and can thus be used to measure group differences. However, it should be noted that 95% of the participants were not identified by using gait speed but by using the other categories, muscle mass and muscle strength. We decided to adopt the definition by Cruz-Jentoft et al. because of its addition of muscle function and because of the possibility to use the recommended tests and cut-off points in a clinical setting. The initial concepts of a simple decrease in muscle mass have been modified (30).

Detailed information about the design of resistance training with obese older men with or without sarcopenia is still lacking in the literature. This applies in particular with regard to the question to what extent strength training leads to different effects in the case of a different status in muscle mass and function. The intervention in the present study followed up on the recommendations for healthy older people as well as on the recommendations for diabetics. It was a big challenge to implement these guidelines for this particular target group. Given the very high participation rate and the good increases in maximum-strength values in both groups, the intervention should be seen as a success. With a loss of only three participants out of 36 (8%) the drop-out rate was very low. The high compliance is also reflected in the participation rate of the remaining 33 people. For the four-month strength training it was 86% on average, which is a good value and comparable to other studies (10-11).

All maximal strength measurements could not be conducted with the entire group. As far as the measurement of dynamic maximum strength is concerned, the final values are missing for some subjects. Due to health problems, it was not possible to perform the entire protocol with all subjects. Before the fifth attempt the measurement had to be terminated, for example because of shoulder or knee pain, so that only a submaximal value was available. As in the other subjects, too, this value was used as a clue to the initial weight during resistance training. These submaximal values were not considered for the evaluation of maximum strength because here the limitation is not due to maximum muscle performance.

Muscle mass

No statistically significant changes could be noticed after training. This result applies to both groups. This suggests that there was no hypertrophy of the muscles. In other studies (9-13), an increase in muscle mass was observed. However, these studies had a longer intervention period (6 or 12 months). Hence, the question arises to what extent an increase in muscle mass in overweight, partially sarcopenic older people can be expected in such a short intervention period. Perhaps the changes in muscle strength take place to a greater extent on the neurophysiological level, e.g. in the form of an improved inter- and intramuscular coordination. But this can only be speculated about since no explicit measurements were conducted.

It should be pointed out that previous studies which reported intervention effects on SMI used dual energy X-ray absorptiometry (DXA) or Magnetic Resonance Imaging (MRI) for the measurements of body composition, so that the results cannot really be compared to our results based on BIA. The BIA has established itself as an inexpensive and mobile alternative to DXA or MRI. However, Janssen et al. (16) and Boneva-Asiova et al. (21) among others found in their studies that for measurements with the BIA, fat mass is slightly overestimated or the muscle mass is underestimated and therefore the reliability of measurements decreases if there is a BMI of > 35 kg/m2 (21). However, this was found especially in obese women. It is important to consider whether another BIA device can be used alternatively or other complementary measurements such as girth measurements can be added as additional parameters for hypertrophy. 

Muscle strength

The relatively large increase in the 1 RM in our subjects can be observed in the older adults in other studies too (10, 12). One explanation could be the low base line strength in our subjects. It can be seen that the 1 RM on the leg-press machine increased to a higher degree in the sarcopenic group. This could be explained with the lower starting level of this group at the beginning and a very good compliance. In the post-test, this group achieved the level of NSAR. This result suggests that particularly the leg muscles benefited from this training. In this test, no statistically significant group differences could be seen, which could be due to the large standard deviation in each group.

In contrast, the rates of increase of maximum strength on the chest-press machine after training turned out to be considerably lower. This trend was also observed with this exercise during training. However, it should be noted that after the training SAR could increase to the initial level of NSAR. This result differs from the other studies (22-23). The causes of this difference are still unclear, which might possibly be due to the different machine producers (measurement vs. training). Thus, the participants had to complete two different paths of movement at measurement and training.  

Physical performance

The domain of the functional skills is an important field concerning the transfer to everyday-life activities. There was a significant increase in gait speed in both groups. This improvement could be seen over both walking distances (4m and 6m). This could be related to the remarkable increase in leg strength or to a presumably better neuromuscular control. Generally, however, it should be noted that both groups (at least on average) exhibited a relatively high gait speed, even before the start of training. They were significantly faster than the gait speed of the “Grim Reaper” which is defined in the literature at 0.82 m/s (24).

The total SPPB score changed only slightly when the values before and after training are compared. This applies to both groups to an equal extent. This could be due to the fact that even in the pre-test there were ceiling effects concerning the balance test and the gait speed over four meters. For the next studies with this target group, it may be possible to use the modified SPPB, which was designed for older people with a better performance. However, this would have the disadvantage that it would no longer be possible to use the cut-off points of Cruz-Jentoft and colleagues.

One interesting feature is the isolated consideration of the repeated chair stands. Here, no group differences before and after training could be shown. However, both groups showed statistically significant improvements in this test after training. These results suggest that this phenomenon is also related to the improvement in maximum strength of the lower extremities. This is a particularly important result since getting up from a chair is a fundamental factor for an independent life at older age (25).

The clinical relevance of our results is supported by Perera and colleagues (26) who analyzed data in common physical performance measures in older adults. Based on their results they estimate that substantial changes are near 0.1 m/s for gait speed, and 1.0 point for SPPB. In both of our groups (SAR and NSAR) the pre-post changes are similar or larger to these values.

Intervention

The ACSM recommends a progressive and gradual increase of the weights (27-28). This applies first to healthy older people. It is the extent to which these recommendations can be adapted to this particular target group due to the initial physical situation and the health problems that in the target group can occur more often than in healthy older people. However, current guidelines do not specifically address obesity (7). A main message of Hills et al. is to adapt the method of increasing the weights to this target group, so that the compliance can be improved. Westcott and Baechle (29) recommended for healthy older people to increase the weights by 5% or less (depending on the muscle group), if they were able to lift the current weight with the highest number of repetitions in two consecutive sessions. This was a little too progressive for this sample. Instead, it proved to be worthwhile to increase the weight after approximately every fourth unit. However, individual limitations had to be taken into account, such as joint pain which led to a slower increase. In addition, the slow introduction of strength training is important. The first three weeks at the beginning of strength training were crucial not only for getting accustomed to the exercises and resistance training machines, but also for the participants’ body awareness. Thus, it was also more likely that the partici-pants were able to give an adequate assessment of their effort level and current performance limit.

The representativeness of the sample for larger populations is difficult to determine. Hence, more studies in similar target populations are needed.

In conclusion, implementing intervention guidelines was possible, adherence of the participants was high and positive effects after training were achieved. The results lead to the conclusion that – regardless of the proportion of muscle or fat mass and regardless of muscle function – older obese men benefit from intensive resistance training very much. This is particularly true of the maximum strength, the gait speed and getting up from a chair. These are important aspects with regard to the longest possible independent lifestyle and successful fall prevention.

Moreover, the additional administration of nutrition supplements, such as protein, could lead to further insights to what extent this could support an increase in muscle mass. In future studies, women should also be examined to verify a transfer of the results to the other sex.

The group of obese, sarcopenic older people will increase dramatically in the next years. Therefore, informed and evaluated preventive and therapeutic interventions are needed to prevent or at least slow down the serious consequences of a possible metabolic syndrome.

Funding: This study was funded by the German Sport University Cologne.  The sponsors had no role in the design and conduct of the study, in the collection, analysis, interpretation of data, and in the preparation of the manuscript.

Acknowledgement: We thank all men who agreed to participate in the study and all assistants who supported the measurements and the intervention.

Conflict of interest: Authors declare no conflict of interest on this paper.

Ethical standards: This study was approved by the Ethics Committee of the German Sports University Cologne. 

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