https://orcid.org/0000-0002-9169-1702
Figures
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.
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
Editor: Antimo Moretti, University of Campania Luigi Vanvitelli: Universita degli Studi della Campania Luigi Vanvitelli, ITALY
Received: September 3, 2023; Accepted: January 9, 2024; Published: January 29, 2024
Copyright: © 2024 Tan You Mei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available from SingHealth Community Hospitals HQ, Research and Translational Innovation Office Senior Manager Mr. Kevin Chong. He can be contacted at kevin.chong@singhealthch.com.sg.
Funding: Charmaine Tan was the author who received the the 2021 SingHealth Regional Health System (RHS), Population-based, Unified, Learning System for Enhanced and Sustainable Health (PULSES) Grant ID: CGOCt21S06. Funders did not play any role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. Funds were used to recruit interns for coordinating administrative duties of the study on a day to day basis with oversight from the P.I. and Co-Is. Part of the fund was used to purchase the BIA InBody S10 and also a laptop that is encrypted to perform data analysis together with acquisition of Stata software licence.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: 3-MinNS, 3-minute Nutritional Screening; 5CST, 5 times chair stand test; 6mGS, 6 meters Gait speed; ASM, Appendicular skeletal muscle mass; ASMI, Appendicular skeletal muscle mass index; AWGS, Asian working group for sarcopenia; BIA, Bioelectrical impedance analysis; CCI, Charlson comorbidity index; CI, Confidence interval; CMMSE, Chinese Mini Mental state examination; EWGSOP, European Working Group on Sarcopenia in Older People; GPAQ, Global Physical Activity Questionnaire; HC, Hip circumference; IDF, International Diabetes Federation; IQR, Interquartile range; MBI, Modified Barthel Index; OCH, Outram Community Hospital; PP, Physical performance; RDG, Rehabilitation diagnostic groups; SPPB, Short Physical Performance Battery; WC, Waist circumference; WHR, Waist hip ratio
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.
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.
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].
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].
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.
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.