Prevalence and factors associated with sarcopenia among older adults in a post-acute hospital in Singapore

Abstract

Background

Sarcopenia is common in older adults worldwide, but its prevalence varies widely owing to differences in diagnostic criteria, population sampled, and care setting. We aimed to determine the prevalence and factors associated with sarcopenia in patients aged 65 and above admitted to a post-acute hospital in Singapore.

Methods

This was a cross-sectional study of 400 patients recruited from a community hospital in Singapore. Data including socio-demographics, physical activity, nutritional status, cognition, clinical and functional status, as well as anthropometric measurements were collected. Sarcopenia was defined using the Asian Working Group for Sarcopenia 2019 criteria [AWGS2019].

Results

Of the 383 patients with complete datasets, overall prevalence of sarcopenia was 54% while prevalence of severe sarcopenia was 38.9%. Participants with increased age, male gender and a low physical activity level were more likely to be sarcopenic, while those with higher hip circumference and higher BMI of ≥27.5m/kg2 were less likely to be sarcopenic. Other than the above-mentioned variables, cognitive impairment was also associated with severe sarcopenia.

Conclusions

More than 1 in 2 older adults admitted to a post-acute hospital in Singapore are sarcopenic. There is an urgent need to address this important clinical syndrome burden and to identify patients at risk of sarcopenia in post-acute settings in Singapore for early intervention.

Introduction

The proportion of older adults worldwide is rapidly growing, with projected doubling by 2050 [1]. Close to seventeen percent of Singapore’s residents were aged 65 and above in 2022, and this is rising 4–5.6% year on year [2]. Increased life expectancy globally is not equitable with healthy life expectancy [3]. Echoing this, the United Nations General Assembly has declared 2021–2030 the Decade of Healthy Ageing [4]. Increased age is associated with frailty and sarcopenia [5–8]. These geriatric syndromes herald increased healthcare utilization and costs from associated morbidity and mortality [9–12]. Among inpatients undergoing rehabilitation, sarcopenia had been reported to be associated with worse recovery of function and lower rate of home discharge in hospitalized adults undergoing rehabilitation [13].

Sarcopenia is common in older adults worldwide, but it’s prevalence varies widely owing to differences in diagnostic criteria, population sampled, and care setting [14]. Sarcopenia prevalence reported in overseas studies was 26.9–58% in inpatient post-acute and rehabilitation wards and 50.9–60.2% in daycare facilities [11,15–18]. Sarcopenia has been reported to be associated with age, gender, marital status, comorbidities, smoking, physical activity, BMI, waist circumference, hip circumference, nutrition, and cognition [13,15–24].

Several studies in Singapore had reported sarcopenia prevalence among varying population groups in the community.

For example, one study reported sarcopenia prevalence of 76% among community ambulant adults aged 65 and above who were at medium or high risk of malnutrition [22]. The prevalence of sarcopenia in primary care and specialist outpatient clinics ranged from 27.4% in patients aged 60–89 years old with Type 2 Diabetes Mellitus using AWGS criteria, to 44% in patients aged 65 and above based on SARC-F questionnaire only [19,25]. Type 2 diabetes mellitus association with sarcopenia is also mirrored in an interesting outpatient study overseas where post-menopausal women with type 2 diabetes mellitus are 5 times more likely to have osteo-sarcopenia than those without type 2 diabetes mellitus [26].

However, till date there is none investigating sarcopenia prevalence using established recommended criteria in the inpatient post-acute setting. We aim to determine the prevalence of sarcopenia, severe sarcopenia and their associations in an inpatient post-acute hospital in Singapore.

Materials and methods

Setting

This cross-sectional study was conducted from May to November 2022 at Outram Community Hospital [OCH].

A community hospital in Singapore is a purpose built hospital to provide medical, nursing and rehabilitation care for patients who require a short period of continuation of care after their stay in the acute hospitals i.e. post-acute care before being discharged into the community [27].

Admissions into OCH come from the Singapore General Hospital [SGH], the largest co-located acute tertiary hospital in Singapore servicing the southeastern region of Singapore.

Study participants and recruitment

All patients aged 65 years old and above admitted to OCH under subacute or rehabilitation service from 17 May to 16 November 2022 were consecutively screened based on eligibility criteria. Participants who were unable to understand English or Mandarin, refused to participate, or unable to give informed consent or follow instructions due to conditions such as neuropsychiatric or neurocognitive disease were excluded. Additional excluded participants included those who were terminally ill; medically unstable; had conditions which precluded sarcopenia assessment such as anyone with a cardiac pacemaker, an implantable defibrillator, amputation of limb[s], intravenous hydration, fluid overload and had temporary restrictions in weightbearing status of upper or lower limbs.

Eligible participants were then recruited after written informed consent was obtained. All participants recruited were assigned to a unique serial number in order to delink and deidentify them. All data was collected during the inpatient stay in OCH, with no further follow-up or contact after discharge.

This study was approved by SingHealth Centralized Institutional Review board [CIRB Ref 2021/2817].

Data collection

All interviewers received standardized training on interview techniques with trial interviews held with trainer to ensure fidelity. Face-to-face interviews with the patients were conducted in Mandarin and English.

Data collected included 1] demographics [age, sex, race, marital status, living setup, type of dwelling and highest educational qualification]; 2] physical activity and sedentary time; 3] nutritional status; 4] cognition; and 5] other clinical parameters [weight, height, smoking history, Modified Barthel Index [MBI] scores on admission, Charlson Comorbidity Index [CCI]; history of COVID-19 infection; waist circumference [WC]; hip circumference [HC] and Rehabilitation Diagnostic Groups [RDG] classification, an administrative framework to group patients admitted for rehabilitation so as to facilitate transition and collaboration across the stakeholders in the care spectrum.

Physical activity level and sedentary time were assessed using the Global Physical Activity Questionnaire [GPAQ], which was developed by WHO with standardized approach and question guide available both in English and Mandarin translations, and used in multiple Singapore studies including the National Health Survey 2010 and 2019 [21,28–33]. As per the GPAQ, participants’ sedentary time in minutes per day and their physical activity level [low and moderate or high] were collected [34].

The 3-Minute Nutritional Screening [3-MinNS] tool, developed in Singapore and validated for medical and surgical inpatients in a Singapore hospital, was used to screen nutritional status [35,36]. Participants were grouped into low [0–2], moderate [3–4], or severe [5–9] malnutrition risk [35].

Cognitive assessment was done using the Chinese Mini Mental State Examination [CMMSE], which was also validated locally in English and Mandarin [37]. Those with CMMSE scores of 23 and below were considered as having impaired cognition [38].

The participants’ inpatient medical records were accessed to retrieve information regarding the following: MBI on admission; CCI; history of COVID-19 infection; RDGs; weight [in kilogrammes] and height [in metres] on admission.

Body mass index [BMI] was calculated as body weight divided by square of height [kg/m²]. Cut-offs were based on WHO BMI risk categories for cardiovascular disease and diabetes in Asian populations [39].

Anthropometry

Waist circumference [WC] was measured using a stretch-resistant tape at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest. Waist circumference cut-offs were determined as per The International Diabetes Federation [IDF] consensus worldwide definition for metabolic syndrome [40].

Hip circumference [HC] was measured around the widest portion of the buttocks [41]. Waist-hip ratio [WHR] was calculated by dividing WC over HC. Cut-offs for WHR were based on World Health Organization [WHO], redefining obesity—the Asia-Pacific perspective [42].

Sarcopenia diagnostic criteria

There have been several proposed diagnostic algorithms for sarcopenia. In 2010, the European Working Group on Sarcopenia in Older People [EWGSOP] proposed the first practical clinical definition and diagnostic criteria for sarcopenia based on assessment of muscle mass, muscle strength and physical performance [43]. This was revised in 2018 to EWSOP2, which used low muscle strength as the primary parameter of sarcopenia [10]. To address differences in cut-off values of measurements in Asian populations from Europeans, an Asian consensus was derived by the Asian Working Group for Sarcopenia [AWGS] in 2014 and revised in 2019 [44,45].

In our study, sarcopenia was assessed and diagnosed using criteria as per AWGS2019 [45]. Sarcopenia was diagnosed in the presence of low muscle mass, with either low muscle strength or low physical performance [PP]. Those with low muscle mass, low muscle strength, and low PP, were further subclassified as “severe sarcopenia” for sub-group analyses [45].

Muscle mass

Muscle mass.

Appendicular skeletal muscle mass [ASM] was determined using a multifrequency bioelectrical impedance analysis [BIA] InBody S10 Body Composition Monitor. ASMI was calculated by dividing ASM by height squared [kg/m²]. BIA measurements were conducted under standardised protocols i.e. before therapy sessions, in supine position with limbs abducted and ensuring no contact with metal frame of bed [46]. Low muscle mass was defined as appendicular skeletal muscle mass index [ASMI] <7kg/m2 in males and <5.7kg/m2 in females.

Muscle strength.

Muscle strength was assessed via handgrip strength [HGS] which was measured using a dynamometer [BASELINE 12–0240 standard hydraulic hand dynamometer] in seated posture, with shoulder adducted and elbow flexed to 90 degrees and forearm in neutral as recommended by American Society of Hand Therapists [47]. The maximum reading from at least two trials using either hand in a maximum-effort isometric contraction was used for analysis [45]. Low muscle strength was defined as handgrip strength [HGS] <28kg in males and <18kg in females.

Physical performance.

Low PP was defined as gait speed <1.0m/s over 6-metre walk [6mGS], or 5-time chair stand test [5CST] requiring ≥12s, or Short Physical Performance Battery [SPPB] score of ≤9.

6mGS was measured as time taken to walk 6m at a normal pace from a moving start, without deceleration, with average of two trials taken as the recorded speed [m/s] [45]. Walking aids were permitted if necessary and documented if it was used. 5CST was measured as the time taken to rise and sit five times as quickly as possible with no contact against the back of the chair and maximal extension of knees [48]. SPPB is a 3-part performance-based test of balance, gait speed, and 5CST, with a score of 0 to 12.

Statistical analyses

Categorical variables were presented as proportions and continuous variables summarized as means+/- SD or medians with interquartile ranges [IQR [25th percentile, 75th percentile]] as appropriate. Pearson chi-square test was used to compare categorical variables and logistic regression to compare continuous variables as appropriate for univariate analyses.

Multivariate logistics regression was done with all variables included as co-variates to arrive at adjusted odds ratio with 95% confidence interval [CI] associated with sarcopenia outcomes. Similarly multinomial regression was done on all variables to calculate adjusted odds ratio associated with severe sarcopenia outcomes.

All analyses were two-tailed and p value <0.05 was considered statistically significant. Statistical analysis was performed using Stata Version 17 [StataCorp, College Station, TX, USA].

Sample size estimation

Prevalence of sarcopenia in overseas studies on patients with a similar profile to our study population ranged between 26.9–60.2% [11,13,15,18]. In view of this large range in prevalence, it is recommended to take a conservative estimate of 50% for the expected prevalence, that will lead to the largest estimate for sample size [49].

Using the Finite Population Correction for prevalence for cross-sectional studies [as more than 5% of the population is being sampled, and the population has a known population size], sample size, n = [50].

z is the statistic corresponding to a confidence level of 95% and α of 0.05, which is 1.96; d is the level of precision which is selected to be 5% [as p is assumed to be between 10%-90%]; N is the finite population size, which is taken to be 1440 based on average of 240 admissions per month x 6 months of recruitment; p is the expected prevalence which is taken to be 50%, n is hence 304.

Results

A total of 400 patients were recruited, of which 17 [4%] had incomplete data due to discharge or transfer to acute hospital for acute medical issues. Hence, 383 [96%] patients with complete data were analysed in this study. Fig 1 shows the process of identifying sarcopenia in our study population using AWGS2019 diagnostic algorithm.

[Figure omitted. See PDF.]

Prevalence of sarcopenia and severe sarcopenia

Prevalence of sarcopenia using AWGS 2019 criteria in our study population was 54.0% [n = 207] with 66.4% of males being sarcopenic and 45.9% of females being sarcopenic. Prevalence of severe sarcopenia in our cohort was 38.9% [n = 149].

Characteristics of study population and unadjusted odds ratio

Table 1 illustrates the characteristics of our participants with corresponding unadjusted odds ratios of sarcopenia. The median age of participants was 75 years old [IQR: 70–81], with more females [60.3%] than male participants, and majority [93%] were Chinese. Slightly more than half [56.1%] were currently married. Majority [91.9%] live in public governmental housing [Housing Development Board [HDB] apartments]. About two thirds [60.4%] of our cohort have less than secondary school education and only 20% of participants were smokers or ex-smokers. 2/3 of our participants had CCI more than “0”. 62% of participants were not known to be infected by COVID-19 prior to recruitment into study.

[Figure omitted. See PDF.]

Slightly more than half [56.4%] were at moderate or high risk of malnutrition. Physical activity level was evenly distributed with half [50.9%] of the participants assessed to have low physical activity level. Approximately half of our participants [49.9%] had CMMSE 23 or less. Slightly over half [57.4%] of participants had BMI of 23kg/m² or more, while approximately 2/3 had an elevated waist circumference and waist-hip ratio.

Half [54.1%] of the participants had an MBI score of 0–60 on admission, indicating severe to total dependency in activities of daily living. Participants were mostly admitted for rehabilitation [RDG] due to musculoskeletal conditions [48.8%], followed by deconditioning [18.5%], others [14.4%], hip fracture [11.5%], and stroke [5.1%].

Factors associated with sarcopenia

Table 1 showed the univariable comparison between individuals with sarcopenia and without. Those with sarcopenia were older, more likely to be male, had higher CCI, had moderate or severe risk of malnutrition [compared to no risk] and low physical activity. They were also less likely to be currently married.

Table 2 showed the multivariable analysis. Older age [OR 1.06 [1.01–1.12]], male sex [OR 2.80 [1.12–7.02]] and low physical activity [OR 2.13 [1.17–3.89]] were associated with sarcopenia. Higher BMI more than or equal to 27.5 [OR 0.16 [0.05–0.52]] and greater hip circumference [OR 0.86 [0.81–0.92]] are inversely associated with sarcopenia [Table 2].

[Figure omitted. See PDF.]

Factors associated with severe sarcopenia

Among the variables investigated, severe sarcopenia in our setting is associated with age [OR 1.10 [1.03–1.16]], males [OR 4.35 [1.5–12.64]], low physical activity [OR 2.78 [1.34–5.79]], and cognitive impairment with MMSE 23 or less [OR 2.26 [1.01–5.02]]. Higher BMI more than or equal to 27.5 [OR 0.20 [0.05–0.75]] and greater hip circumference [OR 0.84 [0.78–0.91]] are inversely associated with sarcopenia [Table 3].

[Figure omitted. See PDF.]

Discussion

We found a high prevalence of sarcopenia with more than 1 in 2 older adults admitted to an inpatient post-acute rehabilitation setting in Singapore having sarcopenia. In addition, what is worrisome is that 1 in 3 older adults inpatients have severe sarcopenia. Our sarcopenia prevalence is in keeping with worldwide literature where such post-acute units have the highest prevalence of sarcopenia followed by nursing home residents, then hospitalized older adults and lowest in community dwelling older adults [51]. This confirms the burden of disease in units performing rehabilitation and suggests that patients should be intervened earlier upstream even before they are being referred or admitted to such settings. There is also a certain urgency to identify this group of patients for further study and conduct interventions with the aim to reverse the sarcopenia.

Like many studies, we found that ageing is associated with sarcopenia [21–23,52]. This is in keeping with the observation where older adults lose muscle mass with increasing age with intramuscular and intermuscular fat infiltration [53–56]. Many underlying hypotheses for this had been proposed. Examples include role of chronic inflammation via biochemical pathways mediated by IL-1β, IL-6, TNF-α et cetera; hormonal changes with age such as reduced GH, IGF-1, testosterone and oestrogens; mitochondrial dysfunction; accessibility to nutrition and dietary patterns; lifestyle factors such as reduction in physical activity, obesity rates and smoking and last but not least, concurrent metabolic chronic diseases being more prevalent in older adults leading to the onset of sarcopenia [57–59]. However, interesting developments looking at birth weights and later onset sarcopenia seem to hint at other factors that go beyond ageing as contributory factors for sarcopenia, setting the stage for a comprehensive review at reversing sarcopenia at all levels [60,61].

Most global literature found a significant association between males and sarcopenia like our study although there are some studies showed an association between females and sarcopenia instead [18,19,21,22]. Nonetheless, this highlights likely unique gender differences in developments of sarcopenia [21,62]. It has been postulated that myostatin causing catabolism in males and reduced IGF-1 leading to anabolic decline in females have a role in the sex differences in sarcopenia development [63,64]. Other clinical parameters such as men losing muscle strength and testosterone faster than women also support the association of men being at higher odds of sarcopenia than women [65–69]. Future trials are needed to formulate sex specific interventions tailored to reverse sarcopenia so as to conclusively determine the causal effects of these associations.

Consistent with literature, low physical activity has an inverse association with muscle mass and higher odds of sarcopenia [70–72]. What remains debatable is designing an evidence-based exercise regime to reverse sarcopenia. The differential role of aerobic, resistance training and a combination of the types of physical activity may play different roles in preventing or reversing sarcopenia [73]. Incorporating the right mix for the best effect is currently a knowledge gap worth studying in the future.

Higher hip circumference being a surrogate measure of gluteal musculature is understandably inversely associated with sarcopenia [19,74–76]. In addition, our study finds that a higher BMI is also protective of sarcopenia similar to other studies [18,77–79].

There may be a few explanations for the above. Firstly, a higher BMI may translate to increased load-bearing requirements during daily activities increasing muscle mass [80]. Secondly, a higher BMI may be due to better nutritional intake with a study showing associations between BMI and total protein intake [81]. Thirdly, BMI is likely an inadequate surrogate measure of adiposity in older adults [82,83]. In fact, sarcopenic obesity [SO] prevalence defined with BMI underestimates those defined by body fat percentages in the same cohort in a study [78]. Interestingly, a higher BMI range, confers a paradoxical survival benefit in older adults [84,85]. A study comparing all-cause mortality within the same cohort between groups with BMI v.s. body fat percentage measurements showed no increased mortality risk in apparently obese category of BMI while those defined obese with body fat percentage measurements did [86].

Perhaps it is time to reconsider the utility of previously accepted BMI range pre-defined in Asians older adults. Despite the limitations of BMI as a surrogate measure of body fat composition, it is undeniably an easily obtainable measure in a busy setting with limited resources. However, there is a research gap in defining what is a normal BMI range correlating to sarcopenic outcomes in long term prospective prognosis studies.

It may also be timely to standardise the definition of sarcopenic obesity using body fat percentages norms in future Delphi consensus studies among content experts. Understandably, more studies are needed to correctly classify obesity using body fat percentage adjusted for sex, age and race. Aligning sarcopenic obesity diagnostic criteria will stimulate further research in this area and allow for confident synthesis of data outcomes across different studies. This will pave the way for interventional trials.

In our study, cognitively impaired participants have a higher odd of severe sarcopenia although not to sarcopenia while global literature found an association between cognitive impairment and sarcopenia [87]. We believe the loss of association of cognitive impairment with sarcopenia could be due to type 2 error.

Our study did not find an association between categories of nutritional risks and sarcopenia on multivariate analysis although there was a statistically significant association on univariate analysis. Again, this could be due to type 2 error. An alternative explanation is because of the inherent differential property of a screening tool and a full diagnostic assessment of nutritional state. For example, a study showed GLIM assessment of nutrition predicted sarcopenia among malnourished participants while a screening tool failed to do so [88]. Nonetheless various studies showed the positive effects various nutritional interventions have on sarcopenia [89,90]. Of great interest, precision nutritional prescription is a hypothesis to be tested in the future for optimization of nutritional interventions based on a combination of factors such as phenotype, dietary habits and behaviours of individuals [91].

To our knowledge, this is the first inpatient study in Singapore to report sarcopenia prevalence using AWGS diagnostic criteria. Our study affirms some of the common associations associated with sarcopenia in line with global literature. This foundational evidence will provide the possibility of developing technology or artificial intelligence driven case finding for sarcopenia in an inpatient setting via a risk calculator. This is especially important as clinicians face challenges with equipment and manpower investiture with current diagnostic criteria of sarcopenia. Our study will also provide the basis for future intervention targeted at risk factors in our population to modify sarcopenic outcomes. Based on our findings, we can start on the modifiable risk factors found in within our cohort. Overcoming barriers of physical inactivity with muti-pronged strategies such as easy access to motivational interviewing, personalized physical activity coaching and setting physical targets tailored to the individuals may promote adoption of a less sedentary lifestyle in older adults [92]. Such multi-component and complex strategies should be co-designed and structured to participant groups sociocultural context [93]. For sarcopenic participants with cognitive impairments, current studies exploring the biochemical process involved in muscle-brain cross talk are underway. Future research may crystallize treatments targeting these processes especially in those at highest risk of cognitive impairments [94].

Some other strengths of our study include a reasonably large number of recruitments with low dropouts and missing data, as well as the consecutive sampling methodology for all patients admitted to OCH to achieve a more representative sample of the population.

Limitations

This study has several limitations. Our single site study participants may not be reflective of inpatients in other post-acute settings in other countries. Our findings cannot be generalized to those with participants characteristics that were excluded as per study protocol. Thirdly, we do not have data on the length of stay each participant spent in the acute hospital prior to their admission into our community hospital although we will generally screen all patients to ensure acute conditions have resolved prior to admissions. The uncertainty in the length of stay in acute hospital prior to admission may be a potential bias in our study. Additionally, causal relationship of the associated factors cannot be established as it is a cross-sectional study.

Our study estimated appendicular lean mass using the InBody S10 BIA which was not validated locally. However, the use of a multifrequency BIA is an accepted method of measurement by AWGS2019 with gender-specific cut-offs. The team utilised the InBody S10 for its ability to perform measurements in a supine position, enabling assessment among participants with functional limitations. Additionally, utilisation of a BIA machine will allow for comparative data in future interventional studies spanning across inpatient to the community, where access to DXA may be difficult.

The Jamar hydraulic hand dynamometer is the recommended brand for assessing handgrip strength, however this study utilised the Baseline hydraulic hand dynamometer instead for logistical reasons as multiple sets were readily available and routinely used by the rehabilitation department therapists, who were the assessors of handgrip for this study. The Baseline dynamometer has been validated against the Jamar dynamometer to measure equivalently and hence can be used interchangeably [95].

Conclusions

Our study found a prevalence of 54% of sarcopenia among inpatient older adults. We also found that participants with increased age, male gender, and a lower physical activity level were more likely to be sarcopenic, while those who had a higher hip circumference and higher BMI of 27.5 and above were less likely to be sarcopenic. In addition, cognitively impaired participants were more likely to be severely sarcopenic. There is an urgent need to address the burgeoning burden with sarcopenia and identify patients at higher risk of sarcopenia in post-acute settings in Singapore for early intervention.

Citation: Tan You Mei C, Seah Si Ying S, Yanshan DL, Koh SV, Karthikeyan G, Xia Jiawen O, et al. (2024) Prevalence and factors associated with sarcopenia among older adults in a post-acute hospital in Singapore. PLoS ONE 19(1): e0291702. https://doi.org/10.1371/journal.pone.0291702

About the Authors:

Charmaine Tan You Mei

Roles: Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Validation, Writing – original draft, Writing – review & editing

Affiliations: Post-Acute and Continuing Care Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore, SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore

Sharna Seah Si Ying

Roles: Formal analysis, Funding acquisition, Methodology, Resources, Software, Writing – review & editing

Affiliation: Research and Translational Innovation Office, SingHealth Community Hospitals, Singapore, Singapore

Doris Lim Yanshan

Roles: Data curation, Project administration, Resources

Affiliation: Rehabilitation Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore

Siew Van Koh

Roles: Data curation, Project administration, Resources, Supervision

Affiliation: Rehabilitation Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore

Ganeshan Karthikeyan

Roles: Data curation, Project administration, Resources, Supervision

Affiliation: Rehabilitation Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore

Olivia Xia Jiawen

Roles: Formal analysis, Methodology, Resources, Software, Validation, Writing – review & editing

Affiliation: Research and Translational Innovation Office, SingHealth Community Hospitals, Singapore, Singapore

Xuan Lin Low

Roles: Data curation, Project administration, Resources, Validation

Affiliation: Department of Health and Social Science, Singapore Institute of Technology, Singapore, Singapore

Hui Yi Quek

Roles: Data curation, Project administration, Resources, Validation

Affiliation: Department of Biological Sciences, National University of Singapore, Singapore, Singapore

Andrea Ong Shuyi

Roles: Data curation, Project administration, Resources, Supervision, Validation

Affiliation: Post-Acute and Continuing Care Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore

Lian Leng Low

Roles: Conceptualization, Methodology, Validation, Visualization, Writing – review & editing

Affiliations: Post-Acute and Continuing Care Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore, SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore

Junjie Aw

Roles: Conceptualization, Formal analysis, Methodology, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing

E-mail: [email protected]

Affiliations: Post-Acute and Continuing Care Department, Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore, SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore

ORICD: https://orcid.org/0000-0002-9169-1702

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Ageing and Health: World Health Organization; 2021 [14th Oct 2021]. Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.

2. Elderly, Youth and Gender Profile: Department of Statistics, Singapore; 2021 [Available from: https://www.singstat.gov.sg/find-data/search-by-theme/population/elderly-youth-and-gender-profile/latest-data.

3. Cao X, Hou Y, Zhang X, Xu C, Jia P, Sun X, et al. A comparative, correlate analysis and projection of global and regional life expectancy, healthy life expectancy, and their GAP: 1995–2025. J Glob Health. 2020;10[2]:020407. pmid:33110572

4. Decade of Healthy Ageing: Plan of Action. World Health Organization; 2020. p. 1–26.

5. Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, et al. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol [1985]. 2003;95[5]:1851–60. pmid:14555665

6. Mitchell WK, Williams J, Atherton P, Larvin M, Lund J, Narici M. Sarcopenia, dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review. Front Physiol. 2012;3:260. pmid:22934016

7. Buch A, Carmeli E, Boker LK, Marcus Y, Shefer G, Kis O, et al. Muscle function and fat content in relation to sarcopenia, obesity and frailty of old age—An overview. Exp Gerontol. 2016;76:25–32. pmid:26785313

8. Aw J, Lee ES, Chiang G, Tan BY. A study on prevalence and associations of non-robustness in older adults aged 65 years and above attending a general practitioner clinic in Ang Mo Kio. Singapore Med J. 2021;62[6]:311–4. pmid:34409474

9. Beaudart C, Zaaria M, Pasleau F, Reginster JY, Bruyère O. Health Outcomes of Sarcopenia: A Systematic Review and Meta-Analysis. PLoS One. 2017;12[1]:e0169548. pmid:28095426

10. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48[1]:16–31. pmid:30312372

11. Xu J, Reijnierse EM, Pacifico J, Wan CS, Maier AB. Sarcopenia is associated with 3-month and 1-year mortality in geriatric rehabilitation inpatients: RESORT. Age Ageing. 2021;50[6]:2147–56. pmid:34260683

12. Janssen I, Shepard DS, Katzmarzyk PT, Roubenoff R. The healthcare costs of sarcopenia in the United States. J Am Geriatr Soc. 2004;52[1]:80–5. pmid:14687319

13. Yoshimura Y, Wakabayashi H, Bise T, Nagano F, Shimazu S, Shiraishi A, et al. Sarcopenia is associated with worse recovery of physical function and dysphagia and a lower rate of home discharge in Japanese hospitalized adults undergoing convalescent rehabilitation. Nutrition. 2019;61:111–8. pmid:30710883

14. Pagotto V, Silveira EA. Methods, diagnostic criteria, cutoff points, and prevalence of sarcopenia among older people. ScientificWorldJournal. 2014;2014:231312. pmid:25580454

15. Yoshimura Y, Wakabayashi H, Bise T, Tanoue M. Prevalence of sarcopenia and its association with activities of daily living and dysphagia in convalescent rehabilitation ward inpatients. Clin Nutr. 2018;37[6 Pt A]:2022–8. pmid:28987469

16. Sánchez-Rodríguez D, Marco E, Miralles R, Guillén-Solà A, Vázquez-Ibar O, Escalada F, et al. Does gait speed contribute to sarcopenia case-finding in a postacute rehabilitation setting? Arch Gerontol Geriatr. 2015;61[2]:176–81. pmid:26051706

17. Sawaya Y, Ishizaka M, Kubo A, Shiba T, Hirose T, Onoda K, et al. The Asian working group for sarcopenia’s new criteria updated in 2019 causing a change in sarcopenia prevalence in Japanese older adults requiring long-term care/support. J Phys Ther Sci. 2020;32[11]:742–7. pmid:33281290

18. Chang CF, Yeh YL, Chang HY, Tsai SH, Wang JY. Prevalence and Risk Factors of Sarcopenia among Older Adults Aged ≥65 Years Admitted to Daycare Centers of Taiwan: Using AWGS 2019 Guidelines. Int J Environ Res Public Health. 2021;18[16].

19. Fung FY, Koh YLE, Malhotra R, Ostbye T, Lee PY, Shariff Ghazali S, et al. Prevalence of and factors associated with sarcopenia among multi-ethnic ambulatory older Asians with type 2 diabetes mellitus in a primary care setting. BMC Geriatr. 2019;19[1]:122. pmid:31035928

20. Lu Y, Karagounis LG, Ng TP, Carre C, Narang V, Wong G, et al. Systemic and Metabolic Signature of Sarcopenia in Community-Dwelling Older Adults. J Gerontol A Biol Sci Med Sci. 2020;75[2]:309–17. pmid:30624690

21. Pang BWJ, Wee SL, Lau LK, Jabbar KA, Seah WT, Ng DHM, et al. Prevalence and Associated Factors of Sarcopenia in Singaporean Adults-The Yishun Study. J Am Med Dir Assoc. 2021;22[4]:885.e1-.e10. pmid:32693999

22. Chew STH, Tey SL, Yalawar M, Liu Z, Baggs G, How CH, et al. Prevalence and associated factors of sarcopenia in community-dwelling older adults at risk of malnutrition. BMC Geriatr. 2022;22[1]:997. pmid:36564733

23. Keng BMH, Gao F, Teo LLY, Lim WS, Tan RS, Ruan W, et al. Associations between Skeletal Muscle and Myocardium in Aging: A Syndrome of "Cardio-Sarcopenia"? J Am Geriatr Soc. 2019;67[12]:2568–73. pmid:31418823

24. Gao Q, Hu K, Yan C, Zhao B, Mei F, Chen F, et al. Associated Factors of Sarcopenia in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis. Nutrients. 2021;13[12]. pmid:34959843

25. Tan LF, Lim ZY, Choe R, Seetharaman S, Merchant R. Screening for Frailty and Sarcopenia Among Older Persons in Medical Outpatient Clinics and its Associations With Healthcare Burden. J Am Med Dir Assoc. 2017;18[7]:583–7. pmid:28242192

26. Moretti A, Palomba A, Gimigliano F, Paoletta M, Liguori S, Zanfardino F, et al. Osteosarcopenia and type 2 diabetes mellitus in post-menopausal women: a case-control study. Orthop Rev [Pavia]. 2022;14[6]:38570. pmid:36267222

27. Ministry of Health S. Community Hospital Care—Handbook for Patients. 2017.

28. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire [GPAQ]: nine country reliability and validity study. J Phys Act Health. 2009;6[6]:790–804. pmid:20101923

29. Chu AH, Ng SH, Koh D, Müller-Riemenschneider F. Reliability and Validity of the Self- and Interviewer-Administered Versions of the Global Physical Activity Questionnaire [GPAQ]. PLoS One. 2015;10[9]:e0136944. pmid:26327457

30. Win AM, Yen LW, Tan KH, Lim RB, Chia KS, Mueller-Riemenschneider F. Patterns of physical activity and sedentary behavior in a representative sample of a multi-ethnic South-East Asian population: a cross-sectional study. BMC Public Health. 2015;15:318. pmid:25884916

31. Chu AHY, Ng SHX, Koh D, Müller-Riemenschneider F. Domain-Specific Adult Sedentary Behaviour Questionnaire [ASBQ] and the GPAQ Single-Item Question: A Reliability and Validity Study in an Asian Population. Int J Environ Res Public Health. 2018;15[4].

32. National Health Survey 2010. Epidemiology & Disease Control Division Ministry of Health, Singapore.

33. National Population Health Survey 2019. Household Interview: Epidemiology & Disease Control Division and Policy, Research & Surveillance Group Ministry of Health and Health Promotion Board, Singapore.

34. Global Physical Activity Questionnaire [GPAQ] Analysis Guide. World Health Organization.

35. Lim SL, Tong CY, Ang E, Lee EJ, Loke WC, Chen Y, et al. Development and validation of 3-Minute Nutrition Screening [3-MinNS] tool for acute hospital patients in Singapore. Asia Pac J Clin Nutr. 2009;18[3]:395–403. pmid:19786388

36. Lim SL, Ang E, Foo YL, Ng LY, Tong CY, Ferguson M, et al. Validity and reliability of nutrition screening administered by nurses. Nutr Clin Pract. 2013;28[6]:730–6. pmid:24107392

37. Sahadevan S, Lim PP, Tan NJ, Chan SP. Diagnostic performance of two mental status tests in the older chinese: influence of education and age on cut-off values. Int J Geriatr Psychiatry. 2000;15[3]:234–41. pmid:10713581

38. Ng TP, Niti M, Chiam PC, Kua EH. Ethnic and educational differences in cognitive test performance on mini-mental state examination in Asians. Am J Geriatr Psychiatry. 2007;15[2]:130–9. pmid:17272733

39. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363[9403]:157–63.

40. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23[5]:469–80. pmid:16681555

41. Organization WH. Waist circumference and waist-hip ratio: report of a WHO expert consultation. Geneva8-11 December 2008.

42. World Health Organization, Regional Office for the Western Pacific. The Asia-Pacific perspective: redefining obesity and its treatment. Sydney: Health Communications Australia 2000.

43. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39[4]:412–23. pmid:20392703

44. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15[2]:95–101. pmid:24461239

45. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21[3]:300-7.e2.

46. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gómez J, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. 2004;23[6]:1430–53. pmid:15556267

47. Fess EE, Moran CA. American Society of Hand Therapists Clinical Assessment Recommendations. United States of AmericaAugust 1981. 17 p.

48. Prosthetists AAoOa. Five Time Sit to Stand Test [FTSST]. 2017. p. https://www.youtube.com/watch?v=_jPl-IuRJ5A.

49. Arya R, Antonisamy B, Kumar S. Sample size estimation in prevalence studies. Indian J Pediatr. 2012;79[11]:1482–8. pmid:22562262

50. Daniel WW. Biostatistics A Foundation for Analysis in the Health Sciences 6th Edition ed. New York, Chichester, Brisbane, Toronto, Singapore: John Wiley & Sons, Inc.; 1995.

51. Lardiés-Sánchez B, Sanz-Paris A, Boj-Carceller D, Cruz-Jentoft AJ. Systematic review: Prevalence of sarcopenia in ageing people using bioelectrical impedance analysis to assess muscle mass. European Geriatric Medicine. 2016;7[3]:256–61.

52. Yuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism. 2023;144:155533. pmid:36907247

53. Wilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev. 2018;47:123–32. pmid:30048806

54. Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393[10191]:2636–46. pmid:31171417

55. Marcus RL, Addison O, Kidde JP, Dibble LE, Lastayo PC. Skeletal muscle fat infiltration: impact of age, inactivity, and exercise. J Nutr Health Aging. 2010;14[5]:362–6. pmid:20424803

56. Verdijk LB, Snijders T, Drost M, Delhaas T, Kadi F, van Loon LJ. Satellite cells in human skeletal muscle; from birth to old age. Age [Dordr]. 2014;36[2]:545–7. pmid:24122288

57. Dalle S, Rossmeislova L, Koppo K. The Role of Inflammation in Age-Related Sarcopenia. Front Physiol. 2017;8:1045. pmid:29311975

58. Biolo G, Cederholm T, Muscaritoli M. Muscle contractile and metabolic dysfunction is a common feature of sarcopenia of aging and chronic diseases: From sarcopenic obesity to cachexia. Clinical Nutrition. 2014;33[5]:737–48. pmid:24785098

59. Ziaaldini MM, Marzetti E, Picca A, Murlasits Z. Biochemical Pathways of Sarcopenia and Their Modulation by Physical Exercise: A Narrative Review. Front Med [Lausanne]. 2017;4:167. pmid:29046874

60. Sayer AA, Syddall HE, Gilbody HJ, Dennison EM, Cooper C. Does sarcopenia originate in early life? Findings from the Hertfordshire cohort study. J Gerontol A Biol Sci Med Sci. 2004;59[9]:M930–4. pmid:15472158

61. Patel HP, Syddall HE, Martin HJ, Stewart CE, Cooper C, Sayer AA. Hertfordshire sarcopenia study: design and methods. BMC Geriatr. 2010;10:43. pmid:20587018

62. Ren X, Zhang X, He Q, Du L, Chen K, Chen S, et al. Prevalence of sarcopenia in Chinese community-dwelling elderly: a systematic review. BMC Public Health. 2022;22[1]:1702. pmid:36076194

63. Tay L, Ding YY, Leung BP, Ismail NH, Yeo A, Yew S, et al. Sex-specific differences in risk factors for sarcopenia amongst community-dwelling older adults. Age [Dordr]. 2015;37[6]:121. pmid:26607157

64. Sattler FR, Castaneda-Sceppa C, Binder EF, Schroeder ET, Wang Y, Bhasin S, et al. Testosterone and growth hormone improve body composition and muscle performance in older men. J Clin Endocrinol Metab. 2009;94[6]:1991–2001. pmid:19293261

65. Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV, et al. The Loss of Skeletal Muscle Strength, Mass, and Quality in Older Adults: The Health, Aging and Body Composition Study. The Journals of Gerontology: Series A. 2006;61[10]:1059–64. pmid:17077199

66. Shimokata H, Ando F, Yuki A, Otsuka R. Age-related changes in skeletal muscle mass among community-dwelling Japanese: a 12-year longitudinal study. Geriatr Gerontol Int. 2014;14 Suppl 1:85–92. pmid:24450565

67. Gallagher D, Visser M, De Meersman RE, Sepúlveda D, Baumgartner RN, Pierson RN, et al. Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol [1985]. 1997;83[1]:229–39. pmid:9216968

68. Mouser JG, Loprinzi PD, Loenneke JP. The association between physiologic testosterone levels, lean mass, and fat mass in a nationally representative sample of men in the United States. Steroids. 2016;115:62–6. pmid:27543675

69. LeBlanc ES, Wang PY, Lee CG, Barrett-Connor E, Cauley JA, Hoffman AR, et al. Higher Testosterone Levels Are Associated with Less Loss of Lean Body Mass in Older Men. The Journal of Clinical Endocrinology & Metabolism. 2011;96[12]:3855–63. pmid:21976718

70. Steffl M, Bohannon RW, Sontakova L, Tufano JJ, Shiells K, Holmerova I. Relationship between sarcopenia and physical activity in older people: a systematic review and meta-analysis. Clin Interv Aging. 2017;12:835–45. pmid:28553092

71. Hughes VA, Roubenoff R, Wood M, Frontera WR, Evans WJ, Fiatarone Singh MA. Anthropometric assessment of 10-y changes in body composition in the elderly. Am J Clin Nutr. 2004;80[2]:475–82. pmid:15277173

72. Shephard RJ, Park H, Park S, Aoyagi Y. Objectively measured physical activity and progressive loss of lean tissue in older Japanese adults: longitudinal data from the Nakanojo study. J Am Geriatr Soc. 2013;61[11]:1887–93. pmid:24219190

73. Yoo SZ, No MH, Heo JW, Park DH, Kang JH, Kim SH, et al. Role of exercise in age-related sarcopenia. J Exerc Rehabil. 2018;14[4]:551–8. pmid:30276173

74. Janghorbani M, Momeni F, Dehghani M. Hip circumference, height and risk of type 2 diabetes: systematic review and meta-analysis. Obes Rev. 2012;13[12]:1172–81. pmid:22943765

75. Jayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar S. Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies. Bmj. 2020;370:m3324. pmid:32967840

76. Snijder MB, Dekker JM, Visser M, Bouter LM, Stehouwer CD, Kostense PJ, et al. Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr. 2003;77[5]:1192–7. pmid:12716671

77. Curtis M, Swan L, Fox R, Warters A, O’Sullivan M. Associations between Body Mass Index and Probable Sarcopenia in Community-Dwelling Older Adults. Nutrients. 2023;15[6]. pmid:36986233

78. Liu C, Cheng KY, Tong X, Cheung WH, Chow SK, Law SW, et al. The role of obesity in sarcopenia and the optimal body composition to prevent against sarcopenia and obesity. Front Endocrinol [Lausanne]. 2023;14:1077255.

79. Linge J, Heymsfield SB, Dahlqvist Leinhard O. On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank. J Gerontol A Biol Sci Med Sci. 2020;75[7]:1309–16. pmid:31642894

80. Abramowitz MK, Hall CB, Amodu A, Sharma D, Androga L, Hawkins M. Muscle mass, BMI, and mortality among adults in the United States: A population-based cohort study. PLoS One. 2018;13[4]:e0194697. pmid:29641540

81. Moon K, Krems C, Heuer T, Hoffmann I. Association between body mass index and macronutrients differs along the body mass index range of German adults: results from the German National Nutrition Survey II. J Nutr Sci. 2021;10:e8. pmid:33889391

82. Xu Z, Liu Y, Yan C, Yang R, Xu L, Guo Z, et al. Measurement of visceral fat and abdominal obesity by single-frequency bioelectrical impedance and CT: a cross-sectional study. BMJ Open. 2021;11[10]:e048221. pmid:34635516

83. Batsis JA, Mackenzie TA, Bartels SJ, Sahakyan KR, Somers VK, Lopez-Jimenez F. Diagnostic accuracy of body mass index to identify obesity in older adults: NHANES 1999–2004. Int J Obes [Lond]. 2016;40[5]:761–7. pmid:26620887

84. Winter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99[4]:875–90. pmid:24452240

85. Donini LM, Pinto A, Giusti AM, Lenzi A, Poggiogalle E. Obesity or BMI Paradox? Beneath the Tip of the Iceberg. Front Nutr. 2020;7:53. pmid:32457915

86. Padwal R, Leslie WD, Lix LM, Majumdar SR. Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study. Ann Intern Med. 2016;164[8]:532–41. pmid:26954388

87. Ohta T, Sasai H, Osuka Y, Kojima N, Abe T, Yamashita M, et al. Age- and sex-specific associations between sarcopenia severity and poor cognitive function among community-dwelling older adults in Japan: The IRIDE Cohort Study. Frontiers in Public Health. 2023;11. pmid:37081953

88. Lengelé L, Bruyère O, Beaudart C, Reginster J-Y, Locquet M. Malnutrition, assessed by the Global Leadership Initiative on Malnutrition [GLIM] criteria but not by the mini nutritional assessment [MNA], predicts the incidence of sarcopenia over a 5-year period in the SarcoPhAge cohort. Aging Clinical and Experimental Research. 2021;33[6]:1507–17.

89. Scaturro D, Vitagliani F, Terrana P, Tomasello S, Camarda L, Letizia Mauro G. Does the association of therapeutic exercise and supplementation with sucrosomial magnesium improve posture and balance and prevent the risk of new falls? Aging Clin Exp Res. 2022;34[3]:545–53. pmid:34510395

90. Massari MC, Bimonte VM, Falcioni L, Moretti A, Baldari C, Iolascon G, et al. Nutritional and physical activity issues in frailty syndrome during the COVID-19 pandemic. Ther Adv Musculoskelet Dis. 2023;15:1759720x231152648. pmid:36820002

91. Murphy CH, McCarthy SN, Roche HM. Nutrition strategies to counteract sarcopenia: a focus on protein, LC n-3 PUFA and precision nutrition. Proc Nutr Soc. 2023;82[3]:419–31. pmid:37458175

92. Taylor J, Walsh S, Kwok W, Pinheiro MB, de Oliveira JS, Hassett L, et al. A scoping review of physical activity interventions for older adults. Int J Behav Nutr Phys Act. 2021;18[1]:82. pmid:34193157

93. Constantin N, Edward H, Ng H, Radisic A, Yule A, D’Asti A, et al. The use of co-design in developing physical activity interventions for older adults: a scoping review. BMC Geriatr. 2022;22[1]:647. pmid:35941570

94. Arosio B, Calvani R, Ferri E, Coelho-Junior HJ, Carandina A, Campanelli F, et al. Sarcopenia and Cognitive Decline in Older Adults: Targeting the Muscle-Brain Axis. Nutrients. 2023;15[8]. pmid:37111070

95. Mathiowetz V, Vizenor L, Melander D. Comparison of Baseline Instruments to the Jamar Dynamometer and the B&L Engineering Pinch Gauge. The Occupational Therapy Journal of Research. 2000;20[3]:147–62.