Obesity Research & Clinical Practice (2015) 9, 256—265
ORIGINAL ARTICLE
The impacts of sarcopenia and obesity on physical performance in the elderly Ching-I Chang a, Kuo-Chin Huang a,b, Ding-Cheng Chan c,d, Chih-Hsing Wu e,f, Cheng-Chieh Lin g, Chao A. Hsiung h, Chih-Cheng Hsu a,b, Ching-Yu Chen a,b,∗ a
Division of Geriatrics and Gerontology, Institute of Population Health Sciences, National Health Research Institutes, Taiwan b Department of Family Medicine, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan c Department of Geriatrics and Gerontology, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan d Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan e Department of Family Medicine, National Cheng Kung University Hospital, Tainan, Taiwan f Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan g Department of Family Medicine, China Medical University & Hospital, Taichung, Taiwan h Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taiwan Received 30 May 2014 ; received in revised form 15 July 2014; accepted 1 August 2014
KEYWORDS Bioelectric impedance analysis; Body composition; Elderly; Sarcopenic obesity; Physical performance
Summary Objective/Background: The current definition of sarcopenic obesity in the elderly does not seem to take the ageing difference of body composition into sufficient consideration. The study accordingly attempted to better define sarcopenia/obesity based on various references, and the impacts of sarcopenia/obesity on elderly physical performance were also examined. Design and methods: 2629 elderly subjects (age 65) and 998 young adults were recruited for Sarcopenia and Translational Ageing Research in Taiwan (START). For each eligible subject, body composition was measured by bio-impedance analysis
∗ Corresponding author at: Department of Family Medicine, College of Medicine, National Taiwan University, 100 R440, 4F, No. 17 Xu-Zhou Road, Taipei, Taiwan. Tel.: +886 2 3393 2198; fax: +886 2 2356 3260. E-mail addresses: [email protected], [email protected] (C.-Y. Chen).
http://dx.doi.org/10.1016/j.orcp.2014.08.003 1871-403X/© 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Effect of sarcopenic obesity on physical function
257
and physical performance, including upper and lower extremity function, was examined. The thresholds of sarcopenic obesity were defined as a value at two standard deviations from the gender-specific means of the young population or at the adopted value of our elderly population. Results: Compared to the young adults, the elderly subjects reported a lower appendicular skeletal muscle index (ASMI, kg/m2 ) and a significantly higher fat percentage (%). From three different criteria, thresholds of obesity or sarcopenia were 31.41%, 30.16%, 30.64% (fat percentage) or 6.76 kg/m2 , 7.36 kg/m2 , 7.09 kg/m2 (ASMI) for men and 39.17%, 41.43%, 43.25% or 5.28 kg/m2 , 5.74 kg/m2 , 5.70 kg/m2 for women. The elderly subjects were classified into four groups. With covariates adjusted, the ‘‘sarcopenia only,’’ ‘‘obesity only,’’ and ‘‘sarcopenic obesity’’ elderly subjects were worse than their normal counterparts in physical performance (all p < 0.05 except for the handgrip strength compared in groups 1 and 3). Conclusions: Sarcopenic obesity seems to exert a synergistic impact on elderly physical performance. Body composition should be an essential part in geriatric assessment and elderly care. © 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction During the ageing process, a series of modifications, such as increased fat mass and decreased skeletal muscle mass, would occur owing to changes in the endocrine and metabolic systems [1]. Several studies have shown that low muscle mass tends to aggravate the decline in physical function and might lead to adverse health outcomes like fall, disability, poor quality of life, and even mortality [2—4]. However, the impacts of obesity on physical performance have remained controversial. Some reports consider obesity, especially central obesity with visceral fat and subcutaneous abdominal fat, a high risk of cardio-metabolic disease, morbidity and mortality [1,5]. However, a new concept of obesity paradox, e.g. a U-shaped association between body mass index (BMI) and all-cause mortality among adult Chinese people, has been introduced with the lowest risk of death observed in subjects with a BMI at 24.0—25.9 [6,7]. Due to its reversible nature with proper interventions, sarcopenic obesity has been an intriguing topic of the obesity research in the elderly. However, no universal cutoff point has so far been established for sarcopenia or obesity. According to the generally accepted definitions of sarcopenia by the European Working Group on Sarcopenia in Older People (EWGSOP) and the International Working Group on Sarcopenia, low skeletal muscle mass is an essential criterion based on the norm of young healthy adults or the pooled elderly population [8—11]. Obesity, on the other hand, has been defined based on fat proportion in total body weight [10—14], fat mass per square height [15], body mass
index (BMI) [4], or waist circumference [16,17]. The thresholds for either high fat mass proportion or BMI vary by ethnicity and reference group [10,11]. Previously, cutoff points for low skeletal muscle mass based on the norm of appendicular skeletal muscle index (ASMI) in Taiwanese young healthy adults or the sex-specific lowest 20% of a pooled elderly population have been reported [18,19]. Utilizing the advantages of the measurement tool of bioimpedance analysis, the study aims at proposing alternative cutoff points of obesity and sarcopenic obesity for Taiwanese elderly population and examining the impacts of body composition on physical function.
Methods and procedures Subject recruitment In order to develop domestic operational tools for sarcopenia evaluation, a Sarcopenia and Translational Ageing Research in Taiwan (START) team collaborating with National Taiwan University, China Medical University and Hospital, Chung Shan Medical University, and National Cheng Kung University Hospital in Taiwan has been established to recruit eligible subjects from the following four individual cohort studies conducted during the period from 2007 to 2012: Healthy Aging Longitudinal Study in Taiwan (HALST), Taichung Community Health Study for the Elderly (TCHS-E), Tianliao Old People Study (TOP), and Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients [18—21]. The subjects were deemed representative
258 as they were recruited respectively from the northern (HALST, Comprehensive Geriatric Assessment and Frailty Study), central (TCHS-E), and southern (HALST, TOP) parts of Taiwan. For the reference group, 998 young healthy participants aged 20—40 years old (498 men and 500 women subjects, mean age 23.1 ± 2.8) were recruited for body composition analysis during the regular physical examination in National Taiwan University Hospital. Subjects were excluded if found with (1) morbid obesity (BMI over 35); (2) long term use of body composition modifying medications (e.g. steroid) and medications for endocrine or autoimmune diseases; (3) energy-consuming diseases (e.g. cancer or organ failure); and (4) pregnancy [18]. All of the young and elderly participants were approved by respective institutional review boards and requested to sign an informed consent prior to enrollment.
Body composition analysis All of the recruited participants underwent body composition analysis using the same Tanita BC418 (Tanita Corporation, Tokyo, Japan) bio-impedance analysis (BIA) machine in compliance with the manufacturer’s instructions. This BIA model with a constant high frequency current (50 kHz, 500 A) and an eight-contact electrode was designed to measure the body composition of the trunk, each extremity, and the whole body. Therefore, a valid appendicular skeletal muscle mass (ASM) was first estimated by the sum of muscle mass in each extremity except for the ‘‘trunk part’’ and then divided by squared height to reach the appendicular skeletal muscle index (ASMI, kg/m2 ) [22,23]. The valid fat percentage was calculated based on the total fat mass divided by the total body weight (%) [24].
Physical function and covariates assessment Several common physical functions were examined in each individual cohort, including gait speed, hand grip strength, and timed up and go test (TUG). The standard procedures were described previously [18—21]. Gait speed and TUG were timed by a trained interviewer using a handheld stopwatch. In general, participants were asked to walk a distance ranging from 3 to 5 m, depending on the study site, at their usual pace to calculate their gait speed. TUG was performed while the subjects rose from a standard armchair, walked 3 m, turned and walked back to the chair, and sat down at their usual pace [25]. Hand grip strength
C.-I. Chang et al. was the average value from the dominant hand or that of the hand with superior performance tested by a handheld dynamometer [26,27]. In addition, demographic information including age, gender, and education status were collected. Education status was classified as elementary school and high school. Selected chronic diseases including diabetes, hypertension, heart diseases, chronic obstructive pulmonary disease (COPD), chronic kidney disease, stroke, cancer, arthritis, and osteoporosis were recorded by self-reported doctor’s diagnosis. Any fall event during the past one year was recorded at the same time.
Statistical analysis Descriptive analysis with mean ± standard deviation (SD) or number (proportion) was used to present demographic information, basic characteristics, and physical functions. One-way analysis of variance (ANOVA) test and Chi-square test were used to compare the differences in the distribution of continuous and categorical variables across the four groups (normal, sarcopenic only, obesity only, and sarcopenic obesity). Generalized linear model was adopted to examine the association between body composition and each physical performance (hand grip strength, gait speed, and timed up and go test). Covariates included age, gender, education status, the number of chronic disease, and each study group.
Results A total of 2629 subjects aged 65 and over (51.0% female, mean age 74.6 ± 6.3) were pooled from several studies as previously reported [18—21]. 53.8% of the recruited subjects received primary education or lower, and 20.9% of them experienced fall during the past one year. The mean value of BMI was 24.6 ± 3.6 kg/m2 , and the number of chronic disease was 2 (minimum, maximum 0, 7). In order to examine ageing-related difference in body composition, the young (aged 20—40) reference group was used to compare with the pooled elderly (aged 65 and over) population. The ASMI was significantly lower in the pooled elderly population, while the fat percentage increased in both genders (Table 1) (all p-value
ORIGINAL ARTICLE
The impacts of sarcopenia and obesity on physical performance in the elderly Ching-I Chang a, Kuo-Chin Huang a,b, Ding-Cheng Chan c,d, Chih-Hsing Wu e,f, Cheng-Chieh Lin g, Chao A. Hsiung h, Chih-Cheng Hsu a,b, Ching-Yu Chen a,b,∗ a
Division of Geriatrics and Gerontology, Institute of Population Health Sciences, National Health Research Institutes, Taiwan b Department of Family Medicine, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan c Department of Geriatrics and Gerontology, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan d Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan e Department of Family Medicine, National Cheng Kung University Hospital, Tainan, Taiwan f Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan g Department of Family Medicine, China Medical University & Hospital, Taichung, Taiwan h Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taiwan Received 30 May 2014 ; received in revised form 15 July 2014; accepted 1 August 2014
KEYWORDS Bioelectric impedance analysis; Body composition; Elderly; Sarcopenic obesity; Physical performance
Summary Objective/Background: The current definition of sarcopenic obesity in the elderly does not seem to take the ageing difference of body composition into sufficient consideration. The study accordingly attempted to better define sarcopenia/obesity based on various references, and the impacts of sarcopenia/obesity on elderly physical performance were also examined. Design and methods: 2629 elderly subjects (age 65) and 998 young adults were recruited for Sarcopenia and Translational Ageing Research in Taiwan (START). For each eligible subject, body composition was measured by bio-impedance analysis
∗ Corresponding author at: Department of Family Medicine, College of Medicine, National Taiwan University, 100 R440, 4F, No. 17 Xu-Zhou Road, Taipei, Taiwan. Tel.: +886 2 3393 2198; fax: +886 2 2356 3260. E-mail addresses: [email protected], [email protected] (C.-Y. Chen).
http://dx.doi.org/10.1016/j.orcp.2014.08.003 1871-403X/© 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Effect of sarcopenic obesity on physical function
257
and physical performance, including upper and lower extremity function, was examined. The thresholds of sarcopenic obesity were defined as a value at two standard deviations from the gender-specific means of the young population or at the adopted value of our elderly population. Results: Compared to the young adults, the elderly subjects reported a lower appendicular skeletal muscle index (ASMI, kg/m2 ) and a significantly higher fat percentage (%). From three different criteria, thresholds of obesity or sarcopenia were 31.41%, 30.16%, 30.64% (fat percentage) or 6.76 kg/m2 , 7.36 kg/m2 , 7.09 kg/m2 (ASMI) for men and 39.17%, 41.43%, 43.25% or 5.28 kg/m2 , 5.74 kg/m2 , 5.70 kg/m2 for women. The elderly subjects were classified into four groups. With covariates adjusted, the ‘‘sarcopenia only,’’ ‘‘obesity only,’’ and ‘‘sarcopenic obesity’’ elderly subjects were worse than their normal counterparts in physical performance (all p < 0.05 except for the handgrip strength compared in groups 1 and 3). Conclusions: Sarcopenic obesity seems to exert a synergistic impact on elderly physical performance. Body composition should be an essential part in geriatric assessment and elderly care. © 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction During the ageing process, a series of modifications, such as increased fat mass and decreased skeletal muscle mass, would occur owing to changes in the endocrine and metabolic systems [1]. Several studies have shown that low muscle mass tends to aggravate the decline in physical function and might lead to adverse health outcomes like fall, disability, poor quality of life, and even mortality [2—4]. However, the impacts of obesity on physical performance have remained controversial. Some reports consider obesity, especially central obesity with visceral fat and subcutaneous abdominal fat, a high risk of cardio-metabolic disease, morbidity and mortality [1,5]. However, a new concept of obesity paradox, e.g. a U-shaped association between body mass index (BMI) and all-cause mortality among adult Chinese people, has been introduced with the lowest risk of death observed in subjects with a BMI at 24.0—25.9 [6,7]. Due to its reversible nature with proper interventions, sarcopenic obesity has been an intriguing topic of the obesity research in the elderly. However, no universal cutoff point has so far been established for sarcopenia or obesity. According to the generally accepted definitions of sarcopenia by the European Working Group on Sarcopenia in Older People (EWGSOP) and the International Working Group on Sarcopenia, low skeletal muscle mass is an essential criterion based on the norm of young healthy adults or the pooled elderly population [8—11]. Obesity, on the other hand, has been defined based on fat proportion in total body weight [10—14], fat mass per square height [15], body mass
index (BMI) [4], or waist circumference [16,17]. The thresholds for either high fat mass proportion or BMI vary by ethnicity and reference group [10,11]. Previously, cutoff points for low skeletal muscle mass based on the norm of appendicular skeletal muscle index (ASMI) in Taiwanese young healthy adults or the sex-specific lowest 20% of a pooled elderly population have been reported [18,19]. Utilizing the advantages of the measurement tool of bioimpedance analysis, the study aims at proposing alternative cutoff points of obesity and sarcopenic obesity for Taiwanese elderly population and examining the impacts of body composition on physical function.
Methods and procedures Subject recruitment In order to develop domestic operational tools for sarcopenia evaluation, a Sarcopenia and Translational Ageing Research in Taiwan (START) team collaborating with National Taiwan University, China Medical University and Hospital, Chung Shan Medical University, and National Cheng Kung University Hospital in Taiwan has been established to recruit eligible subjects from the following four individual cohort studies conducted during the period from 2007 to 2012: Healthy Aging Longitudinal Study in Taiwan (HALST), Taichung Community Health Study for the Elderly (TCHS-E), Tianliao Old People Study (TOP), and Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients [18—21]. The subjects were deemed representative
258 as they were recruited respectively from the northern (HALST, Comprehensive Geriatric Assessment and Frailty Study), central (TCHS-E), and southern (HALST, TOP) parts of Taiwan. For the reference group, 998 young healthy participants aged 20—40 years old (498 men and 500 women subjects, mean age 23.1 ± 2.8) were recruited for body composition analysis during the regular physical examination in National Taiwan University Hospital. Subjects were excluded if found with (1) morbid obesity (BMI over 35); (2) long term use of body composition modifying medications (e.g. steroid) and medications for endocrine or autoimmune diseases; (3) energy-consuming diseases (e.g. cancer or organ failure); and (4) pregnancy [18]. All of the young and elderly participants were approved by respective institutional review boards and requested to sign an informed consent prior to enrollment.
Body composition analysis All of the recruited participants underwent body composition analysis using the same Tanita BC418 (Tanita Corporation, Tokyo, Japan) bio-impedance analysis (BIA) machine in compliance with the manufacturer’s instructions. This BIA model with a constant high frequency current (50 kHz, 500 A) and an eight-contact electrode was designed to measure the body composition of the trunk, each extremity, and the whole body. Therefore, a valid appendicular skeletal muscle mass (ASM) was first estimated by the sum of muscle mass in each extremity except for the ‘‘trunk part’’ and then divided by squared height to reach the appendicular skeletal muscle index (ASMI, kg/m2 ) [22,23]. The valid fat percentage was calculated based on the total fat mass divided by the total body weight (%) [24].
Physical function and covariates assessment Several common physical functions were examined in each individual cohort, including gait speed, hand grip strength, and timed up and go test (TUG). The standard procedures were described previously [18—21]. Gait speed and TUG were timed by a trained interviewer using a handheld stopwatch. In general, participants were asked to walk a distance ranging from 3 to 5 m, depending on the study site, at their usual pace to calculate their gait speed. TUG was performed while the subjects rose from a standard armchair, walked 3 m, turned and walked back to the chair, and sat down at their usual pace [25]. Hand grip strength
C.-I. Chang et al. was the average value from the dominant hand or that of the hand with superior performance tested by a handheld dynamometer [26,27]. In addition, demographic information including age, gender, and education status were collected. Education status was classified as elementary school and high school. Selected chronic diseases including diabetes, hypertension, heart diseases, chronic obstructive pulmonary disease (COPD), chronic kidney disease, stroke, cancer, arthritis, and osteoporosis were recorded by self-reported doctor’s diagnosis. Any fall event during the past one year was recorded at the same time.
Statistical analysis Descriptive analysis with mean ± standard deviation (SD) or number (proportion) was used to present demographic information, basic characteristics, and physical functions. One-way analysis of variance (ANOVA) test and Chi-square test were used to compare the differences in the distribution of continuous and categorical variables across the four groups (normal, sarcopenic only, obesity only, and sarcopenic obesity). Generalized linear model was adopted to examine the association between body composition and each physical performance (hand grip strength, gait speed, and timed up and go test). Covariates included age, gender, education status, the number of chronic disease, and each study group.
Results A total of 2629 subjects aged 65 and over (51.0% female, mean age 74.6 ± 6.3) were pooled from several studies as previously reported [18—21]. 53.8% of the recruited subjects received primary education or lower, and 20.9% of them experienced fall during the past one year. The mean value of BMI was 24.6 ± 3.6 kg/m2 , and the number of chronic disease was 2 (minimum, maximum 0, 7). In order to examine ageing-related difference in body composition, the young (aged 20—40) reference group was used to compare with the pooled elderly (aged 65 and over) population. The ASMI was significantly lower in the pooled elderly population, while the fat percentage increased in both genders (Table 1) (all p-value
