If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Address correspondence to Shiou-Liang Wee, PhD, Geriatric Education and Research Institute (GERI), 2 Yishun Central 2, Tower E Level 4 GERI Admin, 768024 Singapore.
Frailty Identification, Prevention and Management, Geriatric Education and Research Institute (GERI), SingaporeFaculty of Health and Social Sciences, Singapore Institute of Technology, Singapore
Frailty Identification, Prevention and Management, Geriatric Education and Research Institute (GERI), SingaporeGeriatric Medicine, Khoo Teck Puat Hospital, Singapore
Frailty Identification, Prevention and Management, Geriatric Education and Research Institute (GERI), SingaporeDepartment of Psychological Medicine, National University of Singapore, Singapore
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.
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.
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).
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).
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.
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).
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,
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.
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).
Amidst 387 type 2 diabetic older adults in primary care, 58% showed low muscle mass (bioimpedance estimated) and 28% had sarcopenia according to AWGS2014.
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).
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,
Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis.
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).
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.
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
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.
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.
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.”
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.”
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
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,
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
Characteristics
Univariate
Multivariable
AWGS2019
EWGSOP2
AWGS2019
EWGSOP2
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
Age, y
1.08 (1.06‒1.10)∗∗∗
1.10 (1.08‒1.13)∗∗∗
1.07 (1.04‒1.09)∗∗∗
1.08 (1.04‒1.12)∗∗∗
Sex
Female
1
1
‒
1
Male
1.08 (0.73‒1.60)
1.67 (1.03‒2.70)∗
‒
2.20 (1.15‒4.19)∗
Ethnicity
Non-Chinese
1
1
‒
‒
Chinese
2.14 (1.19‒3.85)∗
1.88 (0.91‒3.91)
‒
‒
Education level
Tertiary and above
1
1
‒
‒
Secondary and below
3.24 (2.02‒5.18)∗∗∗
3.13 (1.71‒5.74)∗∗∗
‒
‒
Housing type
4-Room and above
1
1
‒
‒
3-Room and below
2.51 (1.68‒3.74)∗∗∗
1.82 (1.12‒2.96)∗
‒
‒
Living arrangement
Not alone
1
1
‒
‒
Alone
2.43 (1.28‒4.62)∗∗
1.76 (0.83‒3.76)
‒
‒
Marital status
Not married
1
1
1
1
Married
0.63 (0.41‒0.95)∗
0.64 (0.39‒1.05)
0.41 (0.23‒0.73)∗∗
0.48 (0.25‒0.94)∗
Diabetes
No
1
1
‒
‒
Yes
2.88 (1.76‒4.71)∗∗∗
3.13 (1.79‒5.44)∗∗∗
‒
‒
Hypertension
No
1
1
‒
‒
Yes
3.15 (2.11‒4.70)∗∗∗
3.23 (1.98‒5.29)∗∗∗
‒
‒
High cholesterol
No
1
1
‒
‒
Yes
2.20 (1.48‒3.27)∗∗∗
1.98 (1.23‒3.21)∗∗
‒
‒
No. of medical conditions
1.56 (1.36‒1.80)∗∗∗
1.45 (1.24‒1.70)∗∗∗
‒
‒
Smoker/ex-smoker
No
1
1
‒
‒
Yes
1.10 (0.69‒1.75)
1.19 (0.68‒2.09)
‒
‒
Alcoholic/ex-alcoholic
No
1
1
1
‒
Yes
1.81 (0.96‒3.40)
1.24 (0.56‒2.77)
4.04 (1.59‒10.22)∗∗
‒
Self-rated health
1.05 (0.82‒1.35)
1.32 (0.98‒1.79)
‒
‒
MNA score
0.76 (0.67‒0.87)∗∗∗
0.80 (0.68‒0.92)∗∗
‒
‒
Physical Activity
GPAQ, MET h/wk
0.99 (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/wk
1.00 (0.99‒1.00)
1.00 (0.99‒1.00)
‒
‒
BMI, kg/m2
0.82 (0.77‒0.87)∗∗∗
0.83 (0.77‒0.90)∗∗∗
0.66 (0.58‒0.77)∗∗∗
0.78 (0.71‒0.86)∗∗∗
Waist circumference, cm
0.98 (0.97‒1.00)
0.99 (0.97‒1.01)
1.05 (1.00‒1.11)∗
‒
Hip circumference, cm
0.92 (0.89‒0.95)∗∗∗
0.92 (0.88‒0.95)∗∗∗
‒
‒
RBANS total score
0.98 (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/cm2
0.06 (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.
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.
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.
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.
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
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.
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.”
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.
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.
Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS).
Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
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,
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.
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.
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.
Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
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.
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.
Indeed, a recent study reported the association between lower, but not upper, extremity muscle mass and cognitive impairment in persons with 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,
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.
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.
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.
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.
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.
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)
21‒30
31‒40
41‒50
51‒60
61‒65
66‒70
71‒75
76‒80
≥81
Overall
Sample Size (n)
Male
28
26
20
22
29
24
29
26
23
227
Female
32
35
39
37
31
35
29
34
37
309
Age (y)
Male
25.1 (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)
Female
25.1 (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)
Male
1.73 (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)
Female
1.60 (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)
Male
80.4 (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)
Female
57.7 (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)
Male
27.1 (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)
Female
22.5 (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)
Male
91.3 (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)
Female
77.1 (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)
Male
102.6 (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)
Female
96.0 (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)
Male
42.3 (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)
Female
25.7 (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)
Male
7.86 (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)
Female
5.36 (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)
Male
1.14 (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)
Female
1.14 (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
Male
2 (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)
Female
0 (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
Male
7 (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)
Female
20 (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
Male
3 (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)
Female
5 (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.
Prevalence of and factors associated with sarcopenia among multi-ethnic ambulatory older Asians with type 2 diabetes mellitus in a primary care setting.
Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis.
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.
Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS).
Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey.
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.
30436-9&id=gr1.jpg){kind=link}