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Prevalence and Associated Factors of Sarcopenia in Singaporean Adults—The Yishun Study

Open AccessPublished:July 19, 2020DOI:https://doi.org/10.1016/j.jamda.2020.05.029

      Abstract

      Objectives

      To describe the normative values of sarcopenia among community-dwelling adults (≥21 years of age); compare the prevalence of sarcopenia using Asian Working Group for Sarcopenia criteria, 2014 (AWGS2014), Asian Working Group for Sarcopenia criteria, 2019 (AWGS2019), and European Working Group on Sarcopenia in Older People criteria, 2018 (EWGSOP2) guidelines; and identify factors associated with sarcopenia.

      Design

      Participants were recruited through random sampling. Sarcopenia assessments were performed using a dual-energy x-ray absorptiometry scan (muscle mass), handgrip test (muscle strength), and usual walking test (physical performance). Questionnaires were administered to evaluate lifestyle and cognition.

      Setting and Participants

      In total, 542 community-dwelling Singaporeans were recruited (21‒90 years old, 57.9% women).

      Methods

      We assessed anthropometry, body composition, and questionnaire-based physical and cognitive factors, and estimated sarcopenia prevalence according to the AWGS2014, AWGS2019, and EWGSOP2 recommendations, and examined associations using logistic regression.

      Results

      According to AWGS2019, the Singapore population-adjusted sarcopenia prevalence was 13.6% (men 13.0%; women 14.2%) overall, and 32.2% (men 33.7%, women 30.9%) in those aged 60 years and above. The cut-offs derived from young adult reference group for low appendicular lean mass index were 5.28 kg/m2 for men and 3.69 kg/m2 for women (lower than AWGS recommended cut-off); for gait speed it was 0.82 m/s, (AWGS2019 recommended cut-off 1.0 m/s, AWGS2014 cut-off was 0.8 m/s); and for handgrip strength it was 27.9 kg/m2 for men and 16.7 kg/m2 for women (close to AWGS2019 recommendation). Age, sex, marital status, alcoholism, physical activity, body mass index, waist circumference, and global cognition were associated with sarcopenia (P < .05).

      Conclusions and Implications

      This is the first study to provide reference values of muscle mass, strength, and gait speed across the adult lifespan of Singaporeans. Using AWGS2019 criteria, sarcopenia is prominent in older age (32.2% in ≥60 years old), but it is already nontrivial (6.9%) among young and middle-age persons. Multidomain lifestyle modifications addressing muscle strength, cognition, and nutrition over the adult lifespan are important to delay the development of sarcopenia.

      Keywords

      Sarcopenia is an age-associated muscle disease characterized by the progressive loss of muscle mass, strength, and function.
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      • Bahat G.
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      Sarcopenia: Revised European consensus on definition and diagnosis.
      Given its associations with disability, falls, need for long-term care, and mortality,
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      Sarcopenia: Revised European consensus on definition and diagnosis.
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      rising sarcopenia prevalence because of longer life expectancy constitutes a public health concern. Since the recent upsurge in sarcopenia research (2000), studies have reported widely differing sarcopenia prevalence. To standardize its diagnosis and harmonize working definitions across studies, the European Working Group on Sarcopenia in Older People (EWGSOP) in 2010 published the first guidelines on sarcopenia classifications and diagnostic cut-offs.
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      Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People.
      In 2018, it revised its classification to recognize that low muscle strength constitutes “probable sarcopenia,” low muscle mass confirms the diagnosis, and physical function determines the severity (European Working Group on Sarcopenia in Older People criteria, 2018, EWGSOP2).
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      Sarcopenia: Revised European consensus on definition and diagnosis.
      To address ethnic differences in body size and lifestyles, the Asian Working Group for Sarcopenia (AWGS) proposed its own diagnostic criteria for Asians in 2014 (Asian Working Group for Sarcopenia criteria, 2014, AWGS2014).
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      A 2016 review found widely differing prevalence in Asian populations, and concluded that further revisions to cut-offs are required, while calling for more data from Asia.
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      Recent advances in sarcopenia research in Asia: 2016 Update from the Asian Working Group for Sarcopenia.
      In 2019, the criteria was revised to recognize poor muscle strength and/or physical performance as “possible sarcopenia,” and low muscle mass and poor muscle strength or physical performance as “sarcopenia,” whereas the presence of all 3 constitutes “severe sarcopenia” (Asian Working Group for Sarcopenia criteria, 2019, AWGS2019).
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      Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment.
      The cut-offs for slow gait speed (GS) were raised from 0.8 to 1.0 m/s and low handgrip strength (HGS) for men from 26 to 28 kg. These changes are expected to inflate sarcopenia prevalence,
      • Chen L.
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      Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment.
      but to what extent is unknown.
      In Singapore, a few small-sample studies have reported sarcopenia prevalence using different measurement instruments and sarcopenia domains on various population groups. Using the SARC-F, a questionnaire that assesses the 5 components of Strength, Assistance with walking, Rising from a chair, Climbing stairs, and Falls, a prevalence of 44.3% was reported for 115 outpatients (≥65 years of age) attending specialist clinics.
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      • Choe R.
      • et al.
      Screening for frailty and sarcopenia among older persons in medical outpatient clinics and its associations with healthcare burden.
      Among 186 community-dwelling older adults, 53.8% had low muscle mass [dual-energy x-ray absorptiometry (DXA)- appendicular lean mass (ALM)/ht2] using EWGSOP cut-offs (Conference Abstract).
      • Koh A.
      • Wong J.
      • Yew W.
      • et al.
      Sarcopenia and vascular function among community elderly.
      Amidst 387 type 2 diabetic older adults in primary care, 58% showed low muscle mass (bioimpedance estimated) and 28% had sarcopenia according to AWGS2014.
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      • Koh Y.
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      Prevalence of and factors associated with sarcopenia among multi-ethnic ambulatory older Asians with type 2 diabetes mellitus in a primary care setting.
      A fourth study, using AWGS2014 guidelines, reported a low muscle mass (bioimpedance estimated) prevalence of 20.6% among 400 community-dwelling adults (≥65 years of age).
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      • How C.
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      Factors associated with muscle mass in community-dwelling older people in Singapore: Findings from the SHIELD study.
      These studies have several limitations. The SARC-F has low sensitivity in screening for sarcopenia.
      • Yang M.
      • Hu X.
      • Xie L.
      • et al.
      SARC-F for sarcopenia screening in community-dwelling older adults.
      Compared with the DXA, the bioimpedance estimate is also less reliable in measuring muscle mass because of its dependence on assessment conditions.
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      To date, there is no data on sarcopenia prevalence, muscle mass, and function based on gold standard measurements among Singaporeans in a representative community-dwelling sample that includes younger and older adults.
      Studies suggest earlier onset and deterioration of muscle mass, strength, and function attributed to physiological and neuromuscular changes,
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      sedentary lifestyles,
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      • Koster A.
      • Schols J.
      • et al.
      Physical activity and incidence of sarcopenia: The population-based AGES—Reykjavik Study.
      inadequate nutrition,
      • Cederholm T.
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      GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community.
      obesity,
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      Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis.
      neurocognitive decline,
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      • et al.
      Association of neurocognitive and physical function with gait speed in midlife.
      and more recently, the emerging role of gut microbiome in muscle health.
      • De Spiegeleer A.
      • Elewaut D.
      • Van Den Noortgate N.
      • et al.
      Quorum sensing molecules as a novel microbial factor impacting muscle cells.
      Studying the age-associated changes in muscle mass and function, development of sarcopenia across the lifespan, and its associated factors in the multi-ethnic population of Singapore contributes important data toward a better understanding and definition of sarcopenia among Asians.
      The aims of the present study are (1) to describe the normative values of muscle mass, strength and function among community-dwelling adults in Singapore; (2) estimate sarcopenia prevalence using AWGS2014, AWGS2019, and EWGSOP2 guidelines; and (3) identify factors associated with AWGS2019 and EWGSOP2 sarcopenia.

      Methods

      Setting

      Community-dwelling adults (≥21 years of age) were recruited from the large north-eastern residential town of Yishun in Singapore, residential population of 220,320 (50.6% female), with 12.2% older adults (≥65 years of age).
      Singapore Department of Statistics (DOS) [Internet]. Base. 2020.
      This is similar to the overall Singapore residential population of 4,026,210 (51.1% female), with 14.4% older adults (≥65 years of age).
      Singapore Department of Statistics (DOS) [Internet]. Base. 2020.

      Participants

      Random sampling was employed to obtain a representative sample of approximately 300 male and 300 female participants, filling quotas of 20 to 40 participants in each sex- and age-group (10-year age-groups between 21 and 60 years old; 5-year age-groups after 60 years old). Conventionally, the sample size of 30 or greater per age-group is sufficient for normative measures.
      • Hogg R.
      • Tanis E.
      • Zimmerman D.
      Probability and statistical inference.
      Between October 2017 and February 2019, using 2-stage random sampling, 50% of all housing blocks were selected, and 20% of the units were approached for participant recruitment. Between March and November 2019, 50% of all housing blocks were randomly selected and all units approached. Up to 3 eligible participants were recruited from each unit. Nonresponse units were recontacted a second time at a different time of day on a later date. Older adults (>75 years of age) were additionally recruited through community sources and from a list of registered participants in 4 senior activity centers. Exclusion criteria were individuals with disabilities, injuries, fractures or surgeries affecting function, neuromuscular, neurologic, and cognitive impairments, or more than 5 poorly controlled comorbidities. Pregnant women or those planning for pregnancy were also excluded. Overall response rate was 39.0%. 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, living arrangement, marital status, smoking, and alcoholism; a health and medical questionnaire indicating medical conditions and comorbidities, a mini-nutritional assessment
      • Kaiser M.
      • Bauer J.
      • Ramsch C.
      • et al.
      Validation of the Mini Nutritional Assessment short-form (MNA-SF): A practical tool for identification of nutritional status.
      ; a global physical activity questionnaire (GPAQ)
      • Armstrong T.
      • Bull F.
      Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ).
      ; and the Longitudinal Aging Study Amsterdam physical activity questionnaire.
      • Stel V.
      • Smit J.
      • Pluijm S.
      • et al.
      Comparison of the LASA Physical Activity Questionnaire with a 7-day diary and pedometer.

      Anthropometry

      Body weight to the nearest 0.1 kg and height to nearest millimeter were measured using a digital balance and stadiometer (Seca, GmbH and Co. KG, Hamburg, Germany). Waist and hip circumferences were measured using a nonelastic, 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.

      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.
      • Collinson S.
      • Fang S.
      • Lim M.
      • et al.
      Normative data for the repeatable battery for the assessment of neuropsychological status in elderly Chinese.

      Body Composition

      Bone mineral density and ALM were measured using DXA (Discovery WI, Hologic, Inc, Marlborough, MA). ALM index (ALMI) was calculated as ALM (kg) divided by height (m) squared, where ALM equals to the sum of lean mass in the upper and lower limbs.

      HGS

      HGS was assessed using the Jamar Plus+ Digital Hand Dynamometer (Patterson Medical, Cedarburg, WI). Seated with arms 90 degrees to the sides, 2 trials were taken per arm in an alternating fashion with 30 seconds of rest between trials. The highest reading was recorded.

      GS

      Usual GS was measured using the 6 m GAITRite Walkway (CIR Systems Inc, Sparta, NJ) with a 2 m lead in and out phase. Three trials were taken. The average GS was recorded.

      Sarcopenia

      Sarcopenia was assessed using the AWGS2014,
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      AWGS2019,
      • Chen L.
      • Lee W.
      • Peng L.
      • et al.
      Recent advances in sarcopenia research in Asia: 2016 Update from the Asian Working Group for Sarcopenia.
      and EWGSOP2
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      criteria. Poor physical function was defined as GS < 1.0 m/s (AWGS2014 ≤0.8 m/s), low muscle mass as ALMI <7.0 and <5.4 kg/m2, and muscle strength by HGS <28 kg (AWGS2014 <26 kg) and <18 kg for men and women, respectively. AWGS2014 categorizes low muscle mass and poor muscle strength and/or physical function as “sarcopenia.”
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      AWGS2019 recognizes poor muscle strength and/or physical function as “probable sarcopenia,” whereas low muscle mass and poor muscle strength or physical performance constitutes “sarcopenia confirmed.”
      • Chen L.
      • Lee W.
      • Peng L.
      • et al.
      Recent advances in sarcopenia research in Asia: 2016 Update from the Asian Working Group for Sarcopenia.
      EWGSOP2 recognizes low muscle strength as “probable sarcopenia,” with low muscle mass confirming the diagnosis.
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      Presence of all 3 constitutes “severe sarcopenia” in both AWGS2019 and EWGSOP2.
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      ,
      • Chen L.
      • Lee W.
      • Peng L.
      • et al.
      Recent advances in sarcopenia research in Asia: 2016 Update from the Asian Working Group for Sarcopenia.

      Statistical Analyses

      SPSS v 22 (SPSS, Inc, Chicago, IL) was used for analysis. Continuous variables were reported as mean [standard deviation (SD)] and categorical variables as number (%). Sample estimates of sarcopenia were extrapolated to the general population weights by age groups. Univariate and multivariable logistic regressions using backward stepwise selection (removal threshold: P = .05) were performed to examine factors associated with sarcopenia, without correction for multiple significance testing. No sarcopenia and sarcopenia probable were grouped as “no sarcopenia,” and “sarcopenia” was defined as sarcopenia confirmed and severe sarcopenia. Statistical significance was set at P < .05.

      Results

      A total of 542 participants (57.9% female) aged 21 to 90 years were recruited. Because of incomplete data from 6 participants, data from 536 participants were analyzed. Of these, 81.7% were Chinese, 8.6% Malays, 6.9% Indians, and 2.8% from other races. Mean age was 58.5 (18.8) years. The descriptive statistics are presented in Supplementary Table 1.
      The prevalence of sarcopenic phenotypes according to age-groups are presented in Table 1, and comparisons among the 3 different criteria (ie, AWGS2014, AWGS2019, and EWGSOP2) shown in Figure 1. Participant characteristics and sarcopenia statuses are presented in Table 2. Overall population-adjusted prevalence of low muscle mass was 40.6%. Using AWGS2014 guidelines, the prevalence of low muscle strength was 7.3% and slow GS 4.1%. With AWGS2019, the prevalence of low muscle strength increased to 9.0% and slow GS to 24.0%, while prevalence of “probable sarcopenia” was 14.0%, “sarcopenia confirmed” 9.5%, and “severe sarcopenia” 4.1%, compared with EWGSOP2s 1.8% (probable), 3.1% (confirmed), and 4.1% (severe).
      Table 1Prevalence of Sarcopenic Phenotypes According to Age Groups
      Age Group (y)21‒3031‒4041‒5051‒6061‒6566‒7071‒7576‒80≥81Overall
      AWGS2014 Sarcopenia
       Confirmed
      Male0 (0)0 (0)0 (0)1 (4.5)3 (10.3)4 (16.7)7 (24.1)8 (30.8)13 (56.5)36 (15.9)
      Female0 (0)1 (2.9)1 (2.6)2 (5.4)1 (3.2)3 (8.6)8 (27.6)8 (23.5)23 (62.2)47 (15.2)
      AWGS2019 Sarcopenia
       Probable
      Male3 (10.7)6 (23.1)4 (20.0)4 (18.2)1 (3.4)2 (8.3)7 (24.1)3 (11.5)2 (8.7)32 (14.1)
      Female2 (6.3)4 (11.4)4 (10.3)5 (13.5)2 (6.5)7 (20.0)5 (17.2)13 (38.2)8 (21.6)50 (16.2)
       Confirmed
      Male0 (0)0 (0)0 (0)2 (9.1)5 (17.2)5 (20.8)5 (17.2)7 (26.9)7 (30.4)31 (13.7)
      Female3 (9.4)4 (11.4)1 (2.6)2 (5.4)5 (16.1)6 (17.1)11 (37.9)8 (23.5)12 (32.4)47 (15.2)
       Severe
      Male1 (3.6)0 (0)0 (0)1 (4.5)2 (6.9)2 (8.3)6 (20.7)7 (26.9)10 (43.5)29 (12.8)
      Female0 (0)0 (0)0 (0)1 (2.7)1 (3.2)1 (2.9)2 (6.9)5 (14.7)15 (40.5)25 (8.1)
      EWGSOP2 Sarcopenia
       Probable
      Male1 (3.6)0 (0)0 (0)0 (0)0 (0)2 (8.3)3 (10.3)1 (3.8)1 (4.3)8 (3.5)
      Female0 (0)0 (0)0 (0)1 (2.7)1 (3.2)2 (5.7)0 (0)5 (14.7)3 (8.1)12 (3.9)
       Confirmed
      Male0 (0)0 (0)0 (0)1 (4.5)2 (6.9)2 (8.3)2 (6.9)3 (11.5)3 (13.0)13 (5.7)
      Female0 (0)1 (2.9)0 (0)1 (2.7)0 (0)2 (5.7)4 (13.8)1 (2.9)3 (8.1)12 (3.9)
       Severe
      Male1 (3.6)0 (0)0 (0)1 (4.5)2 (6.9)2 (8.3)6 (20.7)7 (26.9)10 (43.5)29 (12.8)
      Female0 (0)0 (0)0 (0)1 (2.7)1 (3.2)1 (2.9)2 (6.9)5 (14.7)15 (40.5)25 (8.1)
      Values are presented as number (%).
      Figure thumbnail gr1
      Fig. 1Comparisons among the diagnostic criteria; AWGS2014, AWGS2019, and EWGSOP2.
      Table 2Participant Characteristics and Sarcopenia Status According to AWGS2019
      CharacteristicsTotal n = 536No Sarcopenia n = 317Probable n = 82Confirmed n = 83Severe n = 54
      Age (y)58.5 (18.8)51.5 (17.2)62.4 (18.6)69.6 (14.5)76.7 (10.3)
       21‒40121 (22.6)98 (30.9)15 (18.3)7 (8.4)1 (1.9)
       41‒60118 (22.0)94 (29.7)17 (20.7)5 (6.0)2 (3.7)
       61‒80237 (44.2)119 (37.5)40 (48.8)52 (62.7)26 (48.1)
       ≥8160 (11.2)6 (1.9)10 (12.2)19 (22.9)25 (46.3)
      Sex
       Male227 (42.4)135 (42.6)32 (39.0)31 (37.3)29 (53.7)
       Female309 (57.6)182 (57.4)50 (61.0)52 (62.7)25 (46.3)
      Ethnicity
       Chinese438 (81.7)259 (81.7)57 (69.5)76 (91.6)46 (85.2)
       Malay46 (8.6)33 (10.4)11 (13.4)1 (1.2)1 (1.9)
       Indian37 (6.9)18 (5.7)9 (11.0)5 (6.0)5 (9.3)
       Others15 (2.8)7 (2.2)5 (6.1)1 (1.2)2 (3.7)
      Highest qualification
       ≤Primary173 (32.3)62 (19.6)39 (47.6)42 (50.6)30 (55.6)
       Secondary165 (30.8)110 (34.7)16 (19.5)26 (31.3)13 (24.1)
       Tertiary115 (21.5)84 (26.5)15 (18.3)11 (13.3)5 (9.3)
       ≥Degree83 (15.5)61 (19.2)12 (14.6)4 (4.8)6 (11.1)
      Years of education (y)
       ≤6169 (31.5)59 (18.6)38 (46.3)42 (50.6)30 (55.6)
       7‒12201 (37.5)134 (42.3)23 (28.0)27 (32.5)17 (31.5)
       ≥13166 (31.0)124 (39.1)21 (25.6)14 (16.9)7 (13.0)
      Housing type
       1‒2 rooms63 (11.8)21 (6.6)11 (13.4)15 (18.1)16 (29.6)
       3 rooms111 (20.7)64 (20.2)12 (14.6)26 (31.3)9 (16.7)
       4‒5 rooms316 (59.0)201 (63.4)53 (64.6)37 (44.6)25 (46.3)
      High-end Public/private46 (8.6)31 (9.8)6 (7.3)5 (6.0)4 (7.4)
      Living arrangement (n = 487)
       Alone42 (8.6)19 (6.8)4 (5.3)11 (13.9)8 (15.1)
       Not alone445 (91.4)261 (93.2)71 (94.7)68 (86.1)45 (84.9)
      Marital status (n = 509)
       Married348 (68.4)215 (71.9)53 (67.9)49 (62.0)31 (58.5)
       Single75 (14.7)61 (20.4)6 (7.7)6 (7.6)2 (3.8)
       Divorced/separated17 (3.3)8 (2.7)2 (2.6)5 (6.3)2 (3.8)
       Widowed69 (13.6)15 (5.0)17 (21.8)19 (24.1)18 (34.0)
      Medical conditions
       No known conditions236 (44.0)174 (54.9)28 (34.1)24 (28.9)10 (18.5)
       Diabetes80 (14.9)28 (8.8)16 (19.5)17 (20.5)19 (35.2)
       Hypertension196 (36.6)80 (25.2)38 (46.3)44 (53.0)34 (63.0)
       High cholesterol202 (37.7)85 (26.8)46 (56.1)41 (49.4)30 (55.6)
       Others33 (6.2)18 (5.7)4 (4.9)6 (7.2)5 (9.3)
       1‒3258 (48.1)132 (41.6)45 (54.9)48 (57.8)33 (61.1)
       ≥442 (7.8)11 (3.5)9 (11.0)11 (13.3)11 (20.4)
      Smoking and drinking
       Smokers/ex-smokers115 (21.5)66 (20.8)18 (22.0)16 (19.2)15 (27.8)
       Alcoholics/ex-alcoholics46 (8.6)25 (7.9)4 (4.9)10 (12.0)7 (13.0)
       Smoke and drink25 (4.7)12 (3.8)3 (3.7)4 (4.8)6 (11.1)
      Values are presented as mean (SD) or number (%).
      Overall sarcopenia prevalence according to AWGS2014 was 6.7% (male 6.9%; female 6.4%) compared with AWGS2019s 13.6% (male 13.0%; female 14.2%) and EWGSOP2s 7.1% (male 9.1%; female 5.3%; Supplementary Table 2).

      Study Norms

      The number of young adults sampled (21‒40 years of age), mean age 30.5 (6.1) years, was n = 121 (55.4% female). Population-specific cut-offs, derived by subtracting 2 SD from the young reference mean,
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      ,
      • Cruz-Jentoft A.
      • Baeyens J.
      • Bauer J.
      • et al.
      Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People.
      for GS is 0.82 m/s (close to AWGS2014); for HGS, 27.9 and 16.7 kg (close to AWGS2019); and for ALMI, 5.28 and 3.69 kg/m2 (lower than AGWS) for men and women, respectively. Using these cut-offs, the prevalence of low muscle strength is 7.2% (overall) and 18.9% (≥60 years of age), muscle mass 0.3% (overall) and 1.1% (≥60 of age), and physical performance 4.9% (overall) and 11.9% (≥60 of age).

      Factors associated with sarcopenia

      Table 3 shows the results of significant variables associated with sarcopenia in a regression model from backward stepwise selection. Across age, AWGS2019 sarcopenia prevalence was 6.9% (21‒59 years of age), 32.2% (≥60 years of age), 39.1% (≥65 years of age), and 53.4% (≥75 years of age).
      Table 3Factors Associated with Sarcopenia Using Logistic Regression
      CharacteristicsUnivariateMultivariable
      AWGS2019EWGSOP2AWGS2019EWGSOP2
      OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)
      Age, y1.08 (1.06‒1.10)∗∗∗1.10 (1.08‒1.13)∗∗∗1.07 (1.04‒1.09)∗∗∗1.08 (1.04‒1.12)∗∗∗
      Sex
       Female111
       Male1.08 (0.73‒1.60)1.67 (1.03‒2.70)∗2.20 (1.15‒4.19)∗
      Ethnicity
       Non-Chinese11
       Chinese2.14 (1.19‒3.85)∗1.88 (0.91‒3.91)
      Education level
       Tertiary and above11
       Secondary and below3.24 (2.02‒5.18)∗∗∗3.13 (1.71‒5.74)∗∗∗
      Housing type
       4-Room and above11
       3-Room and below2.51 (1.68‒3.74)∗∗∗1.82 (1.12‒2.96)∗
      Living arrangement
       Not alone11
       Alone2.43 (1.28‒4.62)∗∗1.76 (0.83‒3.76)
      Marital status
       Not married1111
       Married0.63 (0.41‒0.95)∗0.64 (0.39‒1.05)0.41 (0.23‒0.73)∗∗0.48 (0.25‒0.94)∗
      Diabetes
       No11
       Yes2.88 (1.76‒4.71)∗∗∗3.13 (1.79‒5.44)∗∗∗
      Hypertension
       No11
       Yes3.15 (2.11‒4.70)∗∗∗3.23 (1.98‒5.29)∗∗∗
      High cholesterol
       No11
       Yes2.20 (1.48‒3.27)∗∗∗1.98 (1.23‒3.21)∗∗
      No. of medical conditions1.56 (1.36‒1.80)∗∗∗1.45 (1.24‒1.70)∗∗∗
      Smoker/ex-smoker
       No11
       Yes1.10 (0.69‒1.75)1.19 (0.68‒2.09)
      Alcoholic/ex-alcoholic
       No111
       Yes1.81 (0.96‒3.40)1.24 (0.56‒2.77)4.04 (1.59‒10.22)∗∗
      Self-rated health1.05 (0.82‒1.35)1.32 (0.98‒1.79)
      MNA score0.76 (0.67‒0.87)∗∗∗0.80 (0.68‒0.92)∗∗
      Physical Activity
       GPAQ, MET h/wk0.99 (0.99‒1.00)∗∗∗0.99 (0.99‒1.00)∗∗0.99 (0.99‒1.00)∗∗0.99 (0.99‒1.00)∗
       LAPAQ, MET h/wk1.00 (0.99‒1.00)1.00 (0.99‒1.00)
      BMI, kg/m20.82 (0.77‒0.87)∗∗∗0.83 (0.77‒0.90)∗∗∗0.66 (0.58‒0.77)∗∗∗0.78 (0.71‒0.86)∗∗∗
      Waist circumference, cm0.98 (0.97‒1.00)0.99 (0.97‒1.01)1.05 (1.00‒1.11)∗
      Hip circumference, cm0.92 (0.89‒0.95)∗∗∗0.92 (0.88‒0.95)∗∗∗
      RBANS total score0.98 (0.97‒0.98)∗∗∗0.98 (0.97‒0.98)∗∗∗0.99 (0.98‒1.00)∗0.99 (0.98‒1.00)∗
      BMD (w/o head), g/cm20.06 (0.01‒0.33)∗∗0.34 (0.05‒2.35)
      BMD, bone mineral density; CI, confidence interval; LAPAQ, Longitudinal Aging Study Amsterdam Physical Activity Questionnaire; MET: Metabolic Equivalent of Task; MNA, Mini Nutritional Assessment; OR, odds ratio; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status.
      P < .05; ∗∗P < .01; ∗∗∗P < .001.
      With AWGS2019, age, ethnicity, education level, housing type, living arrangement, and marital status were associated with sarcopenia in univariate analysis (P < .05). Age and marital status remained significant after multivariable analyses (P < .01). With EWGSOP2, age, sex, and marital status were associated with sarcopenia after multivariable analyses (P < .05).

      Health and Medical Conditions

      Diabetes, hypertension, high cholesterol, and number of medical conditions were associated with sarcopenia in univariate analyses (P < .01). Alcoholism was associated with AWGS2019 sarcopenia after multivariable analyses (P < .01).

      Nutrition and Physical Activity

      Mini-nutritional assessment and GPAQ were associated with sarcopenia in univariate analyses (P < .05). GPAQ remained significant after multivariable analyses in both AWGS2019 (P < .01) and EWGSOP2 (P < .05).

      Anthropometry and Body Composition

      With univariate analysis, BMI and hip circumference were associated with sarcopenia (P < .001). Bone mineral density was associated with AWGS2019 sarcopenia (P < .01). After multivariable analyses, BMI remained significant (P < .001), while waist circumference was associated with AWGS2019 sarcopenia (P < .05).

      Cognitive Performance

      Global cognition was associated with sarcopenia in univariate analyses (P < .001) and remained significant after multivariable analyses (P < .05).

      Discussion

      Our study contributes to a growing Asian database for sarcopenia. It is the first population-based study to present reference values for muscle mass, strength, GS, and sarcopenia prevalence across the age groups of community-dwelling Singaporean adults. Sarcopenia prevalence vary widely across studies.
      • Chen L.
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      Recent advances in sarcopenia research in Asia: 2016 Update from the Asian Working Group for Sarcopenia.
      Our estimated AWGS2014 prevalence was 18.0% (≥60 years of age) and 24.1% (≥65 years of age), at the upper range (5.5%‒25.7%) of those surveyed in the recent AWGS update.
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      Other East Asian studies have reported similar prevalence of 27.8% (Korea)
      • Jung H.
      • Jang I.
      • Lee Y.
      • et al.
      Prevalence of frailty and aging-related health conditions in older Koreans in rural communities: A cross-sectional analysis of the aging study of Pyeongchang rural area.
      and 29.7% (China),
      • Wang Y.
      • Wang Y.
      • Zhan J.
      • et al.
      Sarco-osteoporosis: Prevalence and association with frailty in Chinese community-dwelling older adults.
      as well as much lower ones of 8.6% (Japan)
      • Yuki A.
      • Ando F.
      • Otsuka R.
      • et al.
      Epidemiology of sarcopenia in elderly Japanese.
      and 6.8% (Taiwan).
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      Despite similarities in ethnicities and body size, the wide-ranging prevalences reported across Asian studies are attributable to the different assessment methods. Muscle mass assessed through bioimpedance analysis
      • Jung H.
      • Jang I.
      • Lee Y.
      • et al.
      Prevalence of frailty and aging-related health conditions in older Koreans in rural communities: A cross-sectional analysis of the aging study of Pyeongchang rural area.
      ,
      • Wang Y.
      • Wang Y.
      • Zhan J.
      • et al.
      Sarco-osteoporosis: Prevalence and association with frailty in Chinese community-dwelling older adults.
      is less consistent and reliable than DXA because of its dependence on hydration status, humidity, and other assessment conditions.
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      There are yet insufficient studies to validate the use of bioimpedance analysis for specific Asian populations.
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      ,
      • Chen L.
      • Liu L.
      • Woo J.
      • et al.
      Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia.
      The Lunar DXA machine
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      may also give differing results from the Hologic machine used in this study.
      • Aasen G.
      • Fagertun H.
      • Halse J.
      Body composition analysis by dual x-ray absorptiometry: In vivo and in vitro comparison of three different fan-beam instruments.
      Interinstrumental DXA measurements have low reliability and significant intermanufacturer differences.
      • Aasen G.
      • Fagertun H.
      • Halse J.
      Body composition analysis by dual x-ray absorptiometry: In vivo and in vitro comparison of three different fan-beam instruments.
      Furthermore, HGS assessed with the Smedley dynamometer
      • Yuki A.
      • Ando F.
      • Otsuka R.
      • et al.
      Epidemiology of sarcopenia in elderly Japanese.
      ,
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      has low agreement with the Jamar dynamometer used in this study.
      • Sousa-Santos A.
      • Amaral T.
      Differences in handgrip strength protocols to identify sarcopenia and frailty—A systematic review.
      Recommended by the American Society of Hand Therapists, the Jamar is the most widely used and tested, has higher inter- and intra-individual reliability, and is considered the “gold standard.
      • Sousa-Santos A.
      • Amaral T.
      Differences in handgrip strength protocols to identify sarcopenia and frailty—A systematic review.
      Sociodemographic differences among study populations (ie, rural-dwellers,
      • Jung H.
      • Jang I.
      • Lee Y.
      • et al.
      Prevalence of frailty and aging-related health conditions in older Koreans in rural communities: A cross-sectional analysis of the aging study of Pyeongchang rural area.
      suburban-dwellers,
      • Yuki A.
      • Ando F.
      • Otsuka R.
      • et al.
      Epidemiology of sarcopenia in elderly Japanese.
      working farmers,
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      and city community-dwellers
      • Wang Y.
      • Wang Y.
      • Zhan J.
      • et al.
      Sarco-osteoporosis: Prevalence and association with frailty in Chinese community-dwelling older adults.
      ) could have further contributed to the heterogeneity of sarcopenia prevalence in Asian studies. In addition, only 44.8% of Yuki et al's
      • Yuki A.
      • Ando F.
      • Otsuka R.
      • et al.
      Epidemiology of sarcopenia in elderly Japanese.
      participants were aged ≥75 years, compared with 54.8% in this study. Huang et al
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      further acknowledged that their participants, mostly working farmers, had remarkable physical activity levels that probably protected them from sarcopenia.

      AWGS2019 Cut-Offs

      Our study sheds light on the ramifications of the AWGS guidelines that are promulgated and revised with the intent to reduce heterogeneity of prevalence and to standardize sarcopenia diagnosis. Sarcopenia prevalence in this study increased from 6.7% (AWGS2014) to 13.6% (AWGS2019) because of the revisions in diagnostic criteria. The proportions of our sample with low HGS increased from 7.3% to 9.0%, and slow GS from 4.1% to 24.0%, the latter being most responsible for inflating sarcopenia prevalence. At the very least, this calls for caution when interpreting data according to the AWGS2014 and the current AWGS2019 criteria. To better refine diagnostic criteria, more normative data of HGS, GS, and especially DXA-muscle mass based on young reference adult Asian populations are needed. Our population-derived cut-offs for HGS for men (27.9 kg) is identical to AWGS2019 (28 kg), and for women (16.7 kg) just a little lower than AWGS2019 (18 kg). For GS, our cut-off (0.82 m/s) is lower than the revised AWGS2019 (1.0 m/s), but close to the original AWGS2014 (0.8 m/s). For DXA-ALMI, our cut-offs for men and women (5.28 and 3.69 kg/m2) are considerably lower than AWGS2019 (7.0 and 5.4 kg/m2). The latter cut-off values are placed at about the mean of this reference population for women, and roughly 1 SD below the mean for men, not 2 SD below the mean, which has the effect of inflating low muscle mass prevalence.

      Factors Associated with Sarcopenia

      Multivariable logistic regression using backward stepwise selection procedures revealed that age, sex, marital status, alcoholism, physical activity, BMI, waist circumference, and global cognition were associated with sarcopenia. In sensitivity analyses, we also used forward selection which led to identical findings as backward selection for AWGS2019 analyses, but for EWGSOP2, it identified the same but 2 fewer risk factors. The full saturated models identified the same but 1 fewer risk factor for AWGS2019, and the same but 3 fewer risk factors for EWGSOP2. Forward selection has the drawback of suppressor effects, whereas leaving a large number of clearly insignificant factors in the model reduces the effects of potentially significant factors.
      Older men were more likely to develop sarcopenia
      • Yuki A.
      • Ando F.
      • Otsuka R.
      • et al.
      Epidemiology of sarcopenia in elderly Japanese.
      ,
      • Huang C.
      • Hwang A.
      • Liu L.
      • et al.
      Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese.
      ,
      • Cruz-Jentoft A.
      • Landi F.
      • Schneider S.
      • et al.
      Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS).
      ,
      • Ryu M.
      • Jo J.
      • Lee Y.
      • et al.
      Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
      as age-related hormonal changes affect men more than women.
      • Ryu M.
      • Jo J.
      • Lee Y.
      • et al.
      Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
      Although age-associated decrease in sex hormones is a major contributor to loss of lean mass and increase in fat mass for both sexes, fat promotes the conversion of androgens to estrogens, a process that exhibits anabolic effects only in women,
      • Flöter A.
      • Nathorst-böös J.
      • Carlström K.
      • et al.
      Effects of combined estrogen/testosterone therapy on bone and body composition in oophorectomized women.
      thereby attenuating the loss of lean mass and strength in women but not in men.
      Higher BMI lowered the risk of sarcopenia.
      • Tey S.
      • Chew S.
      • How C.
      • et al.
      Factors associated with muscle mass in community-dwelling older people in Singapore: Findings from the SHIELD study.
      ,
      • Cheng Q.
      • Zhu X.
      • Zhang X.
      • et al.
      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.
      Overweight and obese individuals are better-nourished with diets higher in calories, proteins, and nutrients, translating to better health outcomes than the underweight and malnourished.
      • Cederholm T.
      • Jensen G.
      • Correia M.
      • et al.
      GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community.
      ,
      • Cheng Q.
      • Zhu X.
      • Zhang X.
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      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.
      However, a larger waist circumference increased sarcopenia risk. Excess body fat exacerbates fat infiltration into muscle, decreasing muscle quality and physical performance.
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      ,
      • Cheng Q.
      • Zhu X.
      • Zhang X.
      • et al.
      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.
      Increased abdominal and visceral fat stimulates the production of proinflammatory cytokines that perpetuate chronic and muscle inflammation, further contributing to muscle loss.
      • Ryu M.
      • Jo J.
      • Lee Y.
      • et al.
      Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
      Moreover, obese individuals are often less physically active, resulting in a gradual decrease in muscle mass and strength.
      • Cheng Q.
      • Zhu X.
      • Zhang X.
      • et al.
      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.
      Given the contrasting evidence, it is important to assess adiposity in addition to BMI.
      Poorer global cognition increased sarcopenia risk. Gait and function require input from the executive functional, attentional, visuospatial and memory resources.
      • Amboni M.
      • Barone P.
      • Hausdorff J.
      Cognitive contributions to gait and falls: Evidence and implications.
      Declining cognitive functions and brain structures affect gait and balance,
      • Herter T.
      • Scott S.
      • Dukelow S.
      Systematic changes in position sense accompany normal aging across adulthood.
      and corroboratively, lower IQ, smaller brain volume, and cortical thinning were associated with slower GS, suggesting that gait was influenced by brain health and neurocognition.
      • Rasmussen L.
      • Caspi A.
      • Ambler A.
      • et al.
      Association of neurocognitive and physical function with gait speed in midlife.
      Indeed, a recent study reported the association between lower, but not upper, extremity muscle mass and cognitive impairment in persons with type 2 diabetes.
      • Low S.
      • Ng T.
      • Lim C.
      • et al.
      Association between lower extremity skeletal muscle mass and impaired cognitive function in type 2 diabetes.
      More studies can elucidate the relationship between specific cognitive domains and gait. Taken together, lower-extremity strength and cognition, both domains of intrinsic capacity,
      • Cesari M.
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      • Amuthavalli Thiyagarajan J.
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      Evidence for the domains supporting the construct of intrinsic capacity.
      are important factors in sarcopenia prevention.
      Low physical activity levels were associated with sarcopenia. Physical activity, although inconsistent in maintaining muscle mass and strength, lowers sarcopenia risk possibly, through its effects on preserving physical function.
      • Mijnarends D.
      • Koster A.
      • Schols J.
      • et al.
      Physical activity and incidence of sarcopenia: The population-based AGES—Reykjavik Study.
      Interestingly, alcoholism was associated with AWGS2019 but not EWGSOP2 sarcopenia. This could be attributed to the different diagnostic criteria; poor physical function is confirmative of AWGS2019 sarcopenia, but merely indicative of severity in EWGSOP2. Excessive alcohol intake propagates systemic inflammation, leading to mobility limitations and decreased physical performance.
      • Cawthon P.
      • Fink H.
      • Barrett-Connor E.
      • et al.
      Alcohol use, physical performance, and functional limitations in older men.
      More specific measures of alcohol consumption could clarify our understanding of its effects on sarcopenia.
      Notably, married adults had lower sarcopenia risk. Marital status is critical, especially in mid- to later-life, in regards to its protective effects on health and mortality through mutual care provision and reception.
      • Robards J.
      • Evandrou M.
      • Falkingham J.
      • Vlachantoni A.
      Marital status, health and mortality.

      Sarcopenia in Younger Adults

      Among younger and middle-age adults (21‒59 years), 32.4% have low muscle mass and 14.1% have probable sarcopenia, whereas 6.9% have sarcopenia, suggesting that sarcopenia was not exclusive to the older adults. Interventions to improve and maintain intrinsic capacity are needed well before old age so as to delay functional disability. Consistent with previous reports, our data showed that muscle mass and strength peak in early adulthood (31‒40 years) before declining thereafter.
      • Volpi E.
      • Nazemi R.
      • Fujita S.
      Muscle tissue changes with aging.
      Indeed, sarcopenia can develop from a multitude of factors secondary to aging,
      • Cruz-Jentoft A.
      • Bahat G.
      • Bauer J.
      • et al.
      Sarcopenia: Revised European consensus on definition and diagnosis.
      of which, physical inactivity, poor nutrition, and obesity have been discussed previously.
      • Mijnarends D.
      • Koster A.
      • Schols J.
      • et al.
      Physical activity and incidence of sarcopenia: The population-based AGES—Reykjavik Study.
      • Cederholm T.
      • Jensen G.
      • Correia M.
      • et al.
      GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community.
      • Kalinkovich A.
      • Livshits G.
      Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis.
      Identifying and implementing multidomain lifestyle modifications over the adult lifespan and across life-stage transitions may be important to effectively prevent or delay the development of sarcopenia. Such multidomain lifestyle interventions have been shown to reverse sarcopenia in community-dwelling older adults.
      • Lu Y.
      • Niti M.
      • Yap K.
      • et al.
      Assessment of sarcopenia among community-dwelling at-risk frail adults aged 65 years and older who received multidomain lifestyle interventions.
      This study has several limitations. It presents cross-sectional data on the muscular health and function of Singaporeans and is subject to cohort effects. This may actually mean the younger generation of Singaporeans are at increased risk of sarcopenia. Age-related changes may not fully reflect the temporal changes across the lifetime, as well as the longitudinal trajectories of muscle mass and function, and the causal relationships between sarcopenia and the associated parameters. The participants were also relatively healthy, community-dwelling adults; therefore, the findings may not be generalizable to the institutionalized or disabled individuals.

      Conclusions and Implications

      This study presents new and much-needed reference data for appendicular lean mass index, HGS, GS, and sarcopenia prevalence across age groups of community-dwelling adults in Singapore. Age, sex, marital status, alcoholism, physical activity, BMI, waist circumference, and global cognition are associated with sarcopenia. Moreover, some younger adults are already at risk of sarcopenia. These findings add to Asian data on sarcopenia definition and suggest the important role of multidomain lifestyle interventions to strengthen or maintain intrinsic capacity in younger and middle-age adults to reduce sarcopenia so as to delay functional disability in old age.

      Acknowledgments

      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.

      Appendix

      Supplementary Table 1Descriptive Statistics by Sex and Age Groups
      Age Group (y)21‒3031‒4041‒5051‒6061‒6566‒7071‒7576‒80≥81Overall
      Sample Size (n)
       Male282620222924292623227
       Female323539373135293437309
      Age (y)
       Male25.1 (2.8)35.9 (2.9)45.8 (2.5)57.0 (2.5)63.1 (1.4)68.3 (1.4)72.9 (1.7)77.9 (1.3)83.7 (2.3)58.8 (19.1)
       Female25.1 (2.8)35.9 (2.9)45.6 (3.0)55.1 (3.0)63.1 (1.4)67.8 (1.5)72.5 (1.6)77.9 (1.5)83.1 (2.1)58.4 (18.6)
      Height (m)
       Male1.73 (0.07)1.70 (0.05)1.68 (0.06)1.69 (0.07)1.66 (0.06)1.65 (0.05)1.65 (0.06)1.62 (0.07)1.62 (0.07)1.67 (0.07)
       Female1.60 (0.05)1.59 (0.05)1.57 (0.07)1.57 (0.06)1.55 (0.05)1.54 (0.05)1.53 (0.05)1.52 (0.05)1.48 (0.04)1.55 (0.06)
      Weight (kg)
       Male80.4 (22.4)81.2 (20.0)76.8 (13.4)73.5 (10.9)66.2 (8.0)65.9 (10.9)65.4 (8.5)63.0 (10.3)61.6 (11.4)70.3 (15.4)
       Female57.7 (11.7)61.6 (12.3)63.4 (11.7)63.1 (14.1)58.8 (8.7)59.3 (7.6)53.8 (8.4)57.5 (8.2)52.8 (8.6)58.8 (10.9)
      BMI (kg/m2)
       Male27.1 (8.2)28.0 (6.7)27.2 (3.8)25.7 (3.2)24.0 (2.9)24.0 (3.4)24.2 (3.2)23.7 (3.0)23.5 (4.1)25.2 (4.9)
       Female22.5 (4.5)24.5 (4.7)25.7 (4.3)25.6 (5.5)24.4 (3.6)25.0 (3.0)22.9 (3.7)25.0 (3.5)24.2 (4.0)24.5 (4.2)
      WC (cm)
       Male91.3 (18.6)94.2 (17.4)94.3 (8.3)91.7 (8.8)89.7 (8.0)89.8 (9.1)91.9 (9.4)90.1 (9.4)90.6 (9.9)91.4 (11.7)
       Female77.1 (11.8)83.1 (11.4)84.9 (11.0)86.9 (12.4)87.4 (9.3)89.6 (6.9)86.0 (10.4)89.7 (8.9)89.4 (8.9)86.1 (10.8)
      HC (cm)
       Male102.6 (13.9)103.0 (12.6)99.1 (7.6)98.9 (6.6)95.3 (4.8)96.1 (6.7)96.8 (5.9)95.5 (6.7)96.1 (7.1)98.1 (8.9)
       Female96.0 (8.5)99.0 (10.0)100.7 (9.2)101.7 (11.1)98.9 (7.8)100.1 (6.8)95.9 (6.7)99.2 (6.7)97.9 (8.3)98.9 (8.6)
      HGS (kg)
       Male42.3 (8.1)44.6 (7.4)42.1 (6.5)40.0 (6.7)35.5 (5.9)32.9 (5.9)29.0 (7.0)28.3 (4.8)24.4 (7.4)35.3 (9.4)
       Female25.7 (4.7)26.2 (4.6)27.7 (5.3)23.7 (4.1)23.1 (3.7)22.8 (4.5)21.1 (4.2)19.6 (4.1)17.9 (3.4)23.1 (5.3)
      ALMI (kg/m2)
       Male7.86 (1.44)8.16 (1.29)7.72 (1.07)7.66 (1.11)6.73 (0.70)6.63 (0.74)6.48 (0.71)6.37 (0.71)6.19 (0.97)7.07 (1.21)
       Female5.36 (0.90)5.76 (0.95)6.01 (1.03)5.96 (1.31)5.47 (0.70)5.58 (0.65)5.20 (0.74)5.42 (0.73)5.16 (0.69)5.56 (0.93)
      GS (m/s)
       Male1.14 (0.15)1.12 (0.19)1.14 (0.16)1.14 (0.17)1.12 (0.19)1.11 (0.17)0.99 (0.15)0.95 (0.21)0.83 (0.21)1.06 (0.20)
       Female1.14 (0.18)1.14 (0.13)1.18 (0.20)1.14 (0.16)1.09 (0.14)1.05 (0.18)1.02 (0.14)0.90 (0.17)0.83 (0.16)1.05 (0.20)
      Low HGS
       Male2 (7.1)0 (0)0 (0)2 (9.1)4 (13.8)6 (25.0)11 (37.9)11 (42.3)14 (60.9)50 (22.0)
       Female0 (0)1 (2.9)0 (0)3 (8.1)2 (6.5)5 (14.3)6 (20.7)11 (32.4)21 (56.8)49 (15.9)
      Low ALMI
       Male7 (25.0)4 (15.4)5 (25.0)8 (36.4)20 (65.5)16 (69.0)21 (72.4)23 (88.5)19 (82.6)123 (54.2)
       Female20 (62.5)12 (34.3)8 (20.5)15 (40.5)17 (54.8)15 (42.9)22 (75.9)16 (47.1)28 (75.7)153 (49.5)
      Slow GS
       Male3 (10.7)6 (23.1)4 (20.0)6 (27.3)6 (20.7)6 (25.0)15 (51.7)14 (53.8)16 (69.6)76 (33.5)
       Female5 (15.6)7 (20.0)5 (12.8)7 (18.9)8 (25.8)11 (31.4)14 (48.3)24 (70.6)32 (86.5)113 (36.6)
      HC, hip circumference; WC, waist circumference; WHR, waist‒hip ratio.
      Values are presented as mean (SD) or number (%).
      Supplementary Table 2Prevalence Estimates in Study Sample and Adjusted to the Singapore General Population Age Groups Weights
      Sample EstimatesPopulation-Adjusted Estimates
      Overall21‒59 y≥60 y≥65 y≥75 yOverall21‒59 y≥60 y≥65 y≥75 y
      AWGS2014
       Low HGS16.02.626.431.041.27.33.218.625.139.9
       Low ALMI51.533.065.766.971.340.632.463.465.169.3
       Slow GS10.41.717.220.631.64.11.611.015.631.2
       Confirmed15.52.125.730.241.26.72.618.024.138.0
      AWGS2019
       Low HGS18.53.430.035.545.69.04.222.330.644.5
       Low ALMI51.533.065.766.971.340.632.463.465.169.3
       Slow GS35.318.048.554.069.924.018.738.846.169.4
       Probable15.313.316.819.422.814.014.113.717.624.6
       Confirmed15.55.223.425.827.99.55.520.724.526.1
       Severe10.11.316.819.428.74.11.411.614.627.3
      EWGSOP2
       Low HGS18.53.430.035.545.69.04.222.330.644.5
       Low ALMI51.533.065.766.971.340.632.463.465.169.3
       Slow GS35.318.048.554.069.924.018.738.846.169.4
       Probable3.70.95.97.38.81.80.84.66.910.0
       Confirmed4.71.37.38.98.13.11.96.19.17.2
       Severe10.11.316.819.428.74.11.411.614.627.3
      Study Norm
       Low HGS15.12.125.129.437.57.23.018.925.636.0
       Low ALMI0.70.01.31.62.20.30.01.11.72.3
       Slow GS11.62.618.522.233.84.92.411.916.933.6
       Probable20.94.733.339.550.710.45.324.634.049.1
       Confirmed0.60.01.01.21.50.20.00.91.41.4
       Severe0.20.00.30.40.70.10.00.20.30.9
      Values are presented as percentages (%).

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