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. 2025 Dec 4;72(2):110–125. doi: 10.1159/000549808

Prevalence and Factors Associated with Sarcopenia in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis

Leixia Wang a, Jianqian Chao a,✉, Na Zhang b, Xinyue Li c, Jianxia Li d, Shengxuan Jin a, Gangrui Tan a, Tong Chen e, Yiyao Wu a
PMCID: PMC12782612  PMID: 41343409

Abstract

Introduction

Sarcopenia is a growing concern as a geriatric syndrome associated with various adverse health outcomes. Determining its prevalence and identifying risk factors are essential for effective prevention. This systematic review and meta-analysis aimed to estimate the prevalence of sarcopenia and identify the factors associated with sarcopenia in community-dwelling older adults.

Methods

Guided by the PICo framework, we systematically searched six databases for relevant literature. Two reviewers independently assessed the quality of included studies. We performed a meta-analysis to estimate the prevalence of sarcopenia in overall older adults and subgroups. For risk factor analysis, pooled odds ratios (ORs) with 95% CIs were calculated, employing either random or fixed-effects models as appropriate.

Results

A total of 52 eligible studies involving 70,202 older adults were included, among whom 7,488 were male and 9,054 were female. Forty studies were analyzed for both prevalence and related factors, while the remaining 12 were used for prevalence estimation only. The pooled analysis revealed a wide variation in the prevalence of sarcopenia among community-dwelling older adults, ranging from 5.2% to 50.0%, with an overall estimated prevalence of 18.8% (95% CI: 15.6%–22.4%) and substantial heterogeneity (I2 = 99.3%). Subgroup analyses showed that the highest rates were identified in studies using the EWGSOP 2018 definition (25.8%), Europe populations (23.4%), and using the anthropometric equations for muscle mass measurement (23.1%). Moreover, the factors significantly associated with sarcopenia in community-dwelling older adults were older age (OR = 3.3, 95% CI: 2.8–3.8), BMI (OR = 0.7, 95% CI: 0.6–0.9), malnutrition (OR = 3.4, 95% CI: 2.2–5.1), low physical activity (OR = 2.3, 95% CI: 1.8–2.8), current smoking (OR = 1.7, 95% CI: 1.3–2.2), and comorbidities such as osteoporosis (OR = 1.8, 95% CI: 1.3–2.4), osteoarthritis (OR = 1.4, 95% CI: 1.3–1.6), depression (OR = 3.0, 95% CI: 1.9–4.7), diabetes (OR = 2.8, 95% CI: 1.4–5.4), and cognitive impairment (OR = 2.5, 95% CI: 1.9–3.2).

Conclusion

Our findings demonstrate a high prevalence of sarcopenia among community-dwelling older adults, with estimates significantly influenced by geographic region, diagnostic criteria, and muscle mass measurement methods. The findings highlight heterogeneity due to non-standardized diagnostic methods and identify key risk factors including advanced age, low BMI, malnutrition, low physical activity, and comorbidities such as osteoporosis. These results underscore the need for unified diagnostic standards and early community-based interventions targeting modifiable risks.

Keywords: Community, Aged people, Sarcopenia, Prevalence, Associated factors

Introduction

With the rapid aging of global populations, geriatric syndromes – including chronic diseases, malnutrition, and cognitive decline – have emerged as critical public health concerns [1, 2]. Among these, sarcopenia, a progressive skeletal muscle disorder characterized by loss of muscle strength, mass, and function, remains underdiagnosed despite its profound impact on physical mobility, cognitive health, and quality of life in older adults [3, 4].

Globally, the overall prevalence of sarcopenia in people aged 65 years and older is estimated to be 6–22%, and the number of people with sarcopenia is expected to be as high as 500 million by 2050 [1, 4]. The insidious onset of sarcopenia has many adverse effects on the health status of older adults, causing not only physical dysfunction, cognitive dysfunction, and depression, which seriously impair the quality of life of older adults, but also significantly increases the risk of adverse outcomes such as falls, fractures, physical disability, and death [5]. Considering the detrimental influence of sarcopenia, it is essential to recognize the factors correlated with sarcopenia in the older population. In a socioeconomic context, identifying intervenable correlated factors enables early screening of sarcopenia in a defined population, thus reducing healthcare expenditures [5, 6].

Geriatric researchers have increasingly recognized the critical importance of sarcopenia in both population-based studies and clinical practice. Several international organizations including the European Working Group on Sarcopenia in Older People (EWGSOP), the Asian Working Group on Sarcopenia (AWGS), the International Working Group on Sarcopenia (IWGS), and the Foundation for the National Institutes of Health (FNIH) Sarcopenia Program have developed distinct diagnostic guidelines and definitions for sarcopenia [7–9]. However, these criteria vary in their recommended assessment methods and diagnostic cutoff values, leading to the absence of a universally accepted consensus. Consequently, reported prevalence rates of sarcopenia differ considerably across studies [10, 11]. A retrospective study of 58 cohorts from 26 countries showed that the prevalence of sarcopenia ranged from 9.9% to 40.4%, with more than a four-fold difference in prevalence using different guidelines for diagnosing sarcopenia [12]. Consequently, there is a necessity for a systematic review of the prevalence of sarcopenia, as recommended by the European and Asian working groups. In addition, the prevalence of sarcopenia varies widely among studies conducted in populations with different settings such as the community, nursing homes, and hospitals, as verified in recent systematic reviews and meta-analyses [13]. The prevalence of sarcopenia in older adults from nursing home and hospital is significantly higher than in older community-dwelling residents due to their poorer health status and higher risk of suffering from sarcopenia [13, 14].

The pathogenesis of sarcopenia has not yet been fully elucidated. Numerous studies have demonstrated that sarcopenia is not only associated with aging but also with factors such as nutritional status, underlying diseases, and physical inactivity [1, 5, 14]. In addition, the factors associated with sarcopenia in previous studies have varied, and sometimes the results have been controversial. Evidence on the association between low BMI and sarcopenia is mixed, with some studies supporting a connection [15] and others finding none [16]. Early identification of risk factors associated with sarcopenia can contribute to the development of preventive interventions, which may include the development of individualized activity plans and the provision of dietary guidance. These measures ensure that older adults at risk for sarcopenia receive appropriate lifestyle interventions and social support, ultimately improving their quality of life.

This meta-analysis specifically focused on community-dwelling older adults for three primary reasons. First, this population constitutes the vast majority of the aging demographic and represents the primary target for initiatives aimed at “healthy aging” and early intervention. Accurately assessing the burden of sarcopenia in this large group is a prerequisite for developing effective prevention strategies and optimizing resource allocation. Additionally, compared to hospitalized or institutionalized individuals, community dwellers are in a more stable health state, which allows for a more accurate reflection of the natural progression of sarcopenia during aging while minimizing confounding effects from acute illnesses or immobilization [13, 14]. Finally, identifying risk factors within community settings provides direct evidence for establishing screening pathways and implementing cost-effective early interventions in primary care, which is crucial for delaying functional decline and reducing the subsequent burden of disability and healthcare costs. In this regard, we performed a systematic review and meta-analysis, aiming to estimate the prevalence of sarcopenia and identify the factors associated with sarcopenia in community settings and also to assess potential sources of heterogeneity. The review protocol has been registered in PROSPERO (CRD420251171264). It is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.

Methods

Literature Search

In this study, we employed a comprehensive search strategy across six databases both in China and internationally. The databases searched included PubMed, Embase, Web of Science, Cochrane Library, and Chinese databases such as China National Knowledge Infrastructure (CNKI) and Wanfang, which were searched from the inception of the databases until April 30, 2025. Online supplementary Table S1 (for all online suppl. material, see https://doi.org/10.1159/000549808) presents the detailed search strategies for English databases. Additionally, references and relevant reviews of the included studies were also examined. Any inconsistencies were resolved by a third reviewer.

We constructed the search terms for this review through the PICo framework proposed by the Joanna Briggs Institute (JBI) [17]. P (Population): older adults aged 60 and over (“seniors”, “geriatric people”, “elderly”, “older people”, “aged people”, “older patients”); I (phenomenon of Interest): the prevalence and factors associated with sarcopenia (including terms such as “sarcopenia”, “sarcopenic”, “low skeletal muscle”, “low muscle mass”, “low muscle strength”, “amyotrophy”, “muscle loss”, “risk factors”, “associated factors”, “precipitating factors”, “influence factors”, “contributing factors”, “prevalence”, “incidence”, “epidemiology”, “frequency”); Co (Context): community (“community dwelling”).

Inclusion and Exclusion Criteria

Studies were included if they met the following criteria: (1) study design included cohort, case-control, and cross-sectional studies; (2) study participants reported as community-dwelling older adults (60 years and older); (3) the study was not limited to any particular diagnostic criteria for sarcopenia, but the chosen criteria must be supported by reliable evidence. SARC-F is the most widely used screening tool for sarcopenia and plays a critical role in its early identification and management. Studies employing the SARC-F questionnaire to screen for sarcopenia were also included; (4) information on the prevalence or factors associated with sarcopenia was clearly reported in the abstract or full text of the paper; (5) reported an unadjusted or adjusted odds ratio (OR) and the corresponding 95% confidence interval (CI), or had sufficient data to calculate OR and 95% CI. The exclusion criteria were as follows: (1) case report, reviews, or letters; (2) did not specify diagnostic criteria for sarcopenia; (3) inability to extract data or obtain full text; (4) non-English or Chinese language publications; (5) duplicate publications from the same research team with overlapping publication dates were identified, and only the most comprehensive or relevant study was retained.

Data Extraction and Quality Assessment

First, two reviewers (Leixia Wang and Jianxia Li) independently conducted a comprehensive search according to the search strategy, imported the retrieved literature into EndNote X9, and compared the search results to ensure that no studies were missed. After removing duplicate literature, we screened the articles for suitability based on titles and abstracts and excluded those that were clearly ineligible. Second, two reviewers (Leixia Wang and Xinyue Li) conducted a detailed full-text review using inclusion and exclusion criteria for further screening. Finally, two researchers independently extracted relevant data from the included studies and created a systematic review record table summarizing the characteristics of the included studies. This table documented the following information: (1) the first author’s name, publication year; (2) study region; (3) study design; (4) diagnostic criteria for sarcopenia; (5) muscle mass assessment methods; (6) participant details (e.g., age, gender distribution); (7) prevalence of sarcopenia; (8) factors associated with sarcopenia.

Two reviewers independently performed the quality assessment of included studies. We employed the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, which consists of eight items, and these items were evaluated as “yes,” “no,” “unclear,” and “not applicable” for each cross-sectional study. More items assessed as “yes” indicates higher quality research. For case-control and cohort studies, we adopted the Newcastle-Ottawa scale (NOS) score, which consists of three categories and eight items with a maximum score of 9. Studies with an NOS score of 5 or less indicates a higher risk of bias, those with a score of 5–7 were at moderate risk, and studies with scores of 7 or more have a low risk. Any disagreements were resolved by the arbitrator (Jianqian Chao).

Data Analysis

The meta-analysis was performed with the metaprop command in Stata to calculate the overall pooled prevalence of sarcopenia with 95% CIs among community-dwelling older adults. When one study reported more than one prevalence result according to different diagnostic criteria for sarcopenia, only one of the studies was retained to estimate the overall prevalence of sarcopenia. Heterogeneity between studies was assessed using the I2 statistic (ranging from 0% to 100%) and Cochran’s Q test. If I2 ≤50% and p > 0.1, indicating low heterogeneity, a fixed-effects model was employed. Otherwise, a random-effects model was used, and subgroup and sensitivity analyses were conducted to identify the sources of heterogeneity. Sensitivity analyses were conducted by omitting one study at a time and examining the effect of each study on the pooled estimates.

The association of each factor with sarcopenia was assessed by unadjusted or adjusted OR and 95% CI. For factors that could not be statistically analyzed, results are presented through descriptive narrative. Publication bias was assessed by using the Egger test and Funnel plots. If potential publication bias was identified, a trim-and-fill approach was used to assess the impact of potentially unpublished studies. We employed Stata 18 statistical software (StataCorp LP) for all analyses, and a two-sided p < 0.05 was considered statistically significant.

Results

Literature Search

Figure 1 illustrates the comprehensive process of search process and results. We initially identified 1,754 records from six English and Chinese databases, which included 566 duplicates. After removing duplicates, 1,188 titles and abstracts were screened. Of these, 343 records were selected for a full-text review for the eligibility assessment. Based on the inclusion and exclusion criteria, 291 records were further removed. Finally, after a thorough review, 52 articles were considered suitable for meta-analysis.

Fig. 1.

Fig. 1.

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Flowchart of literature screening.

Characteristics of Included Studies

Table 1 summarizes the basic characteristics of the 52 included studies that met the inclusion criteria. They were published over an 11-year period from 2013 to 2024. All the included studies were conducted in community settings across 23 countries. The majority of them were from Asia (35 studies) [18–53], while one study spanned Austria, Germany, Israel, Italy, The Netherlands, Poland, and Spain [54]. Of the included studies, the vast majority employed cross-sectional study designs (42 studies) [15, 16, 18–26, 28–35, 38–42, 44–46, 48–51, 53–64], followed by cohort studies (9 studies) [16, 27, 36, 37, 47, 52, 65–67] and 1 case-control study design [43]. The sample sizes of the studies ranged from 132 to 6,172 participants, and the mean age ranged from 63.0 to 86.4 years. Regarding the criteria for diagnosing sarcopenia in older adults, 19 studies utilized the 2019 AWGS criteria [21–25, 27, 30–34, 36, 38, 41–43, 45, 46, 48, 51, 53], 14 articles applied the 2018 EWGSOP criteria [19, 29, 32, 50, 52, 55, 56, 59–63, 65, 67], only one study evaluated participants using the 2011 IWGS criteria [43], and 3 articles used the SARC-F (a 5-factor questionnaire assessing strength, gait speed, chair rise, stair climbing, and falls) criteria [37, 40, 67]. Bioelectrical impedance analysis (BIA) was the instrument most often used to estimate muscle mass (25 studies) [15, 20–23, 25–27, 30, 31, 33–36, 41–45, 50, 53, 54, 58, 63, 64], followed by DXA (11 studies) [18, 28, 29, 32, 38, 47, 51, 52, 59, 65, 66] and validated anthropometric equations (3 studies) [16, 46, 55]. Depending on the diagnostic method used, the overall prevalence of sarcopenia ranged from 0.9% to 50.0% (0.4–55.7% in female and 1.5–58.2% in male).

Table 1.

Characteristics of the included studies (n = 52)

Author, year Region Study design Diagnostic/screening criteria Muscle mass assessment methods Age, years Sample size (male/female) Sarcopenia size (male/female) Prevalence (%)
Lin et al. [18] (2013) Taiwan Cross-sectional EWGSOP, 2010 DXA NA 761 (407/354) 99 (52/47) 13.0
Simsek et al. [19] (2019) Turkey Cross-sectional EWGSOP, 2018 CC 72.4±5.9 909 (327/582) 47 (25/22) 5.2
Khongsri et al. [20] (2016) Thailand Cross-sectional EWGSOP, 2010 BIA 69.7±6.9 243 (62/181) 74 (21/53) 30.5
Vanitcharoenku et al. [21] (2024) Thailand Cross-sectional AWGS, 2019 BIA 69.0±6.1 2,456 (894/1,562) 445 (237/208) 18.1
Nakamura et al. [22] (2021) Japan Cross-sectional AWGS, 2014 BIA 74.2±6.5 1,371 (601/770) 101 (43/58) 7.4
Kurose et al. [23] (2020) Japan Cross-sectional AWGS, 2019 BIA 74.6±6.7 552 (173/379) 123 (30/93) 22.3
Pereira et al. [65] (2022) Brazil Cohort EWGSOP, 2018 DXA;CC 70.0±6.3 132 (52/80) 23 (9/14) 17.4
Yao et al. [24] (2022) China Cross-sectional AWGS, 2019 NA 76.6±7.1 1,082 (466/616) 520 (246/274) 48.1
Xu et al. [25] (2020) China Cross-sectional AWGS, 2019 BIA 86.4±3.5 582 (246/336) 155 (52/103) 26.6
Lyu et al. [26] (2022) Japan Cross-sectional AWGS, 2019 BIA 74.6±5.5 1,257 (658/599) 94 (51/43) 7.5
Moreno-Gonzalez et al. [54] (2024) Across 7 European countriesa Cross-sectional EWGSOP, 2018 BIA 79.0±6.0 1,420 (616/804) 150 (51/91) 10.6
Kitamura et al. [27] (2020) Japan Cohort AWGS, 2019 BIA 72.0±5.9 1,851 (917/934) 261 (105/156) 14.1
Moreira et al. [55] (2018) Brazil Cross-sectional EWGSOP, 2010 Anthropometric equation 76.6±6.9 745 (221/524) 80 (18/62) 10.7
Kuo et al. [28] (2019) Taiwan Cross-sectional AWGS, 2014 DXA NA 731 (386/345) 50 (36/14) 6.8
Gandham et al. [66] (2021) Australia Cohort EWGSOP, 2010 DXA 63.0±7.5 1,099 (537/562) 10 (8/2) 0.9
Swan et al. [56] (2022) United Kingdom Cross-sectional EWGSOP, 2018 NA 70.7±7.7 6,052 (2757/3,295) 2037 (837/1,200) 33.7
Altaf et al. [29] (2024) Pakistan Cross-sectional EWGSOP, 2018 DXA 68.1±6.5 142 (65/77) 67 (39/28) 47.2
Samper-Ternent et al. [57] (2016) Colombia Cross-sectional EWGSOP, 2010 NA 70.7±7.7 1,442 (562/880) 166 (55/111) 11.5
Asavamongkolkul et al. [30] (2024) Thailand Cross-sectional AWGS, 2019 BIA 69.2±6.5 2,991 (1,103/1,888) 445 (237/208) 18.1
Wang et al. [31] (2022) China Cross-sectional AWGS, 2019 BIA 67.4 729 (240/489) 75 (22/53) 10.3
Gholizade et al. [32] (2022) Iran Cross-sectional EWGSOP, 2018 DXA 69.3±6.3 2,368 (1,145/1,223) 846 (459/387) 35.7
Sri-on et al. [33] (2022) Thailand Cross-sectional AWGS, 2019 BIA 70.0 (66.0, 75.0)b 892 (278/614) 198 (67/131) 22.2
Connolly et al. [58] (2021) Ireland Cross-sectional EWGSOP, 2018 BIA 81.7±7.1 134 (52/82) 35 (7/28) 26.1
Franco et al. [59] (2024) Brazil Cross-sectional EWGSOP, 2018 DXA 71.0 (68.0, 76.0)b 278 (109/169) 110 (36/74) 39.6
Tramontano et al. [15] (2017) Peru Cross-sectional IWGS, 2011 BIA NA 222 (102/120) 39 (1/38) 17.6
Swan et al. [60] (2021) Ireland Cross-sectional EWGSOP, 2018 NA 68.9±6.3 3,342 (1,567/1,775) 783 (377/406) 23.4
He et al. [34] (2022) China Cross-sectional AWGS, 2019 BIA NA 1,407 (581/826) 167 (84/83) 11.9
Dodds et al. [67] (2020) United Kingdom Cohort EWGSOP, 2018; SARC-F NA NA 1,686 (824/862) 328 (147/181) 19.5
Hai et al. [35] (2017) China Cross-sectional AWGS, 2014 BIA 68.6±6.4 834 (419/415) 88 (47/41) 10.6
Pérez-Sousa et al. [61] (2020) Colombia Cross-sectional EWGSOP, 2018 NA 70.4±7.8 5,237 (2172/3,065) 2434 (1,041/1,393) 46.5
Iidaka et al. [36] (2024) Japan Cohort AWGS, 2019 BIA 65.8±12.3 1,551 (521/1,030) 125 (46/79) 8.1
Wu et al. [37] (2016) Taiwan Cohort SARC-F NA 76.1±6.4 670 (340/330) 41 (12/29) 6.1
Pang et al. [38] (2021) Singapore Cross-sectional AWGS, 2019 DXA NA 536 (227/309) 73 (30/43) 13.6
Gao et al. [39] (2015) China Cross-sectional AWGS, 2014 CC 70.6±6.7 612 (254/358) 60 (7/53) 9.8
Harimurti et al. [40] (2023) Indonesia Cross-sectional SARC-F NA NA 386 (162/224) 68 (23/45) 17.6
de Souza et al. [62] (2021) Brazil Cross-sectional EWGSOP, 2018 NA 69.9±7.1 306 (130/176) 153 (55/98) 50.0
Tsekoura et al. [63] (2021) Greece Cross-sectional EWGSOP, 2018 BIA 71.5±7.6 402 (110/292) 53 (NA) 13.2
Huang et al. [41] (2023) China Cross-sectional AWGS, 2019 BIA 71.0 (68.0, 77.0)b 678 (269/409) 81 (32/49) 12.0
Chen et al. [42] (2021) China Cross-sectional AWGS, 2019 BIA NA 938 (462/476) 172 (92/80) 18.3
Zhang et al. [43] (2023) China Case-control AWGS, 2019 BIA NA 462 (192/270) 154 (64/90) 33.3
Han et al. [44] (2015) China Cross-sectional AWGS, 2014 BIA 67.3±6.0 1,069 (467/602) 99 (30/69) 9.3
Mo et al. [45] (2022) China Cross-sectional AWGS, 2019 BIA 70.3±7.5 1,050 (347/703) 263 (84/179) 25.0
Wu et al. [46] (2021) China Cross-sectional AWGS, 2019 Anthropometric equation 68.1±6.5 6,172 (3,070/3,102) 2367 (1,115/1,261) 18.4
Alexandre et al. [16] (2014) Brazil Cohort EWGSOP, 2010 Anthropometric equation 69.6±0.6 1,149 (437/712) 266 (103/163) 23.2
Seok et al. [47] (2023) Korea Cohort AWGS, 2014 DXA 71.7±4.6 3,911 (1,679/2232) 1,223 (737/486) 31.3
Chen et al. [48] (2022) China Cross-sectional AWGS, 2019 NA NA 401 (199/202) 106 (53/53) 26.4
Tseng et al. [49] (2020) Taiwan Cross-sectional AWGS, 2014 NA 72.0±6.9 1,025 (310/715) 179 (93/86) 17.5
Ozgu et al. [50] (2023) Turkey Cross-sectional EWGSOP, 2018 BIA NA 190 (58/132) 28 (11/17) 14.7
Hoang et al. [51] (2024) Vietnam Cross-sectional AWGS, 2019 DXA NA 1,899 (591/1,308) 266 (83/183) 14.0
Blanco-Reina et al. [64] (2022) Spain Cross-sectional EWGSOP, 2010 BIA 72.8±5.1 333 (138/195) 68 (47/21) 20.4
Shafiee et al. [68] (2021) Iran Cohort EWGSOP, 2018 DXA NA 2188 (1,078/1,110) 505 (247/258) 23.1
Wang et al. [53] (2023) China Cross-sectional AWGS, 2019 BIA 70.5±8.1 1,327 (650/677) 194 (84/110) 14.6
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AWGS, the Asia Working Group for Sarcopenia; EWGSOP, European Working Group Sarcopenia in Older People; IWGS, International Working Group on Sarcopenia; BIA, bioelectrical impedance absorption method; DXA, dual-energy X-ray absorption method; CC, calf circumference; SARC-F, the 5-component questionnaire that measures strength, assistance walking, rise from a chair, climb stairs, and falls; NA, not available.

aAustria, Germany, Israel, Italy, The Netherlands, Poland, and Spain.

bMedian (IQR).

Risk of Bias in Included Studies

We assessed the quality of the 42 cross-sectional studies using the JBI Critical Appraisal Checklist. All studies measured exposure in a valid and reliable way and used appropriate statistical analysis. Except for Lin [18] and Chen [48], all other included studies reported the criteria for inclusion in the sample clearly defined. Overall, 39 studies met seven or more checklist items, indicating high quality. For the 9 cohort studies and 1 case-control study, the Newcastle-Ottawa Scale was used to assess quality. Three studies scored 8 and the remaining 7 studies scored 9, indicating high quality. More information on each study according to their quality assessment is available in online supplementary Tables S2 and S3.

Meta-Analysis Results

Prevalence of Sarcopenia in Older Adults and Subgroups

The prevalence data and subgroup analyses for sarcopenia are presented in Figures 2 and 3. The meta-analysis showed I2 = 99.3%, p < 0.001, which indicates a high degree of heterogeneity between 52 studies included in the review. The sensitivity analysis was conducted by the one-by-one elimination method, and no literature was found to have a significant impact on the overall results, so the random-effect model was used for combined analysis. A total of 52 studies, which involved 70,202 participants, were included in the meta-analysis. The results revealed that the prevalence of sarcopenia in community-dwelling older adults varied widely, ranging from 5.2% to 50.0%, with a pooled estimate of 18.8% (95% CI: 15.6%–22.4%).

Fig. 2.

Fig. 2.

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Forest plot of the pooled prevalence of sarcopenia in all participants.

Fig. 3.

Fig. 3.

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Forest plot of the pooled prevalence of sarcopenia in different subgroups. *1 Australia and 7 South American countries.

Subgroup analyses (Fig. 3) were performed to explore potential sources of heterogeneity, stratified by sex, region, diagnostic criteria, and muscle mass measurement methods. The differences between all subgroups were statistically significant (all p < 0.05), indicating that these factors substantially contributed to the observed heterogeneity. The subgroup analysis by gender revealed that the pooled prevalence of sarcopenia was 18.7% (95% CI: 15.1%–22.6%; I2 = 98.6%) in older males and 18.8% (95% CI: 15.4%–22.5%; I2 = 98.8%) in older females, with a statistically significant subgroup difference (p = 0.039). Substantial variation was also observed across geographic regions. European populations demonstrated the highest prevalence at 23.4% (95% CI: 15.9%–31.8%; I2 = 99.4%), which was notably greater than the rates observed in Asia (17.9%; 95% CI: 14.4%–21.9%; I2 = 99.1%) and other regions (18.3%; 95% CI: 7.4%–32.7%; I2 = 99.1%). The diagnostic criteria employed significantly influenced prevalence estimates. Specifically, the highest estimate was observed with EWGSOP 2018 (25.8%; 95% CI: 19.3%–32.7%; I2 = 99.3%), followed by AWGS 2019 (19.1%; 95% CI: 14.3%–24.4%; I2 = 99.1%), EWGSOP 2010 (14.1%; 95% CI: 7.0%–23.0%; I2 = 98.7%), and AWGS 2014 (12.5%; 95% CI: 5.7%–21.3%; I2 = 99.2%). Regarding muscle mass measurement methods, the prevalence of sarcopenia in the Anthropometric equation group (23.1%, 95% CI: 9.2%–41.0%) and DXA group (19.9%; 95% CI: 12.2%–28.8%; I2 = 99.3%) appear to have a higher prevalence than BIA group (16.0%; 95% CI: 13.6%–18.5%; I2 = 96.4%) and others (6.9%, 95% CI: 5.6%–8.2%).

Factors Associated with Sarcopenia in Older Adults

After excluding factors reported in fewer than two studies or those that could not be meta-analyzed, 12 factors associated with sarcopenia were identified in 40 studies. These factors were grouped into four categories: (1) Sociodemographic factors: age, gender; (2) physiological factors: BMI, fall history, malnutrition; (3) lifestyle factors: low physical activity, current smoking; (4) comorbidities: osteoporosis, osteoarthritis, depression status, diabetes, cognitive impairment. The results of the meta-analysis of factors associated with sarcopenia are illustrated in online supplementary Figure S1.

Sociodemographic Factors

The results showed that gender were not significantly associated with sarcopenia (ORs = 1.1, 95% CI: 0.9–1.2, p = 0.855). Older age was associated with sarcopenia in the elderly (34 studies, n = 48,289) and the pooled ORs as 3.3 (95% CI: 2.8–3.8, p < 0.001).

Physiological Factors

The results of meta-analysis for 16 studies revealed that higher BMI was protective for sarcopenia, with a pooled OR of 0.7 (95% CI: 0.6–0.9, p = 0.001). There were only two studies including fall history (n = 1,147); the pooled ORs were not significant (2.9, 95% CI: 0.9–8.4, p = 0.055). Malnutrition was identified as a risk factor for sarcopenia among the older individuals (ORs = 3.4, 95% CI: 2.2–5.1, p < 0.001).

Lifestyle Factors

The meta-analysis results suggested that low physical activity was associated with a significantly higher risk of sarcopenia (ORs = 2.3, 95% CI: 1.8–2.8, p < 0.001). Four studies of current smoking revealed an OR of 1.7 (95% CI: 1.3–2.2, p < 0.001) associated with sarcopenia, with no significant heterogeneity (I2 = 0.0%, p = 0.518).

Comorbidities

The results showed that osteoporosis (ORs = 1.8, 95% CI: 1.3–2.4, p = 0.001), osteoarthritis (ORs = 1.4, 95% CI: 1.3–1.6, p < 0.001), depression status (ORs = 3.0, 95% CI: 1.9–4.7, p < 0.001), diabetes (ORs = 2.8, 95% CI: 1.4–5.4, p < 0.001), and cognitive impairment (ORs = 2.5, 95% CI: 1.9–3.2, p < 0.001) were associated with sarcopenia compared to those without the events.

Publication Bias

Funnel plots and Egger’s tests indicated no significant publication bias for the studied factors, with the exception of age (t = −0.99, p < 0.001), which showed evidence of asymmetry (online suppl. Fig. S2–S12). To address this, we applied the trim-and-fill method, imputing eight missing studies. The adjusted pooled ORs for age decreased substantially from 3.3 to 1.15 (95% CI: 1.1–1.3, p = 0.028; online suppl. Fig. S13), suggesting that initial estimates were inflated by selective publication of small studies with higher effects. High heterogeneity (I2 = 99.1%) may further explain the observed asymmetry.

Discussion

Prevalence of Sarcopenia and Disparities across Subgroups

This systematic review included 52 studies encompassing 70,202 community older adults, with the reported prevalence of sarcopenia ranging from 5.2% to 50.0%. The results of meta-analysis showed that the total sarcopenia prevalence was 18.8%, consistent with prior estimates (10%–27%) [11]. The prevalence estimates differed significantly in subgroup analyses based on sex, geographic region, the diagnostic criteria for sarcopenia, and the method of muscle mass measurement. The review indicates that the EWGSOP and AWGS are the most commonly used diagnostic criteria for sarcopenia. The highest pooled prevalence was identified in studies using the EWGSOP 2018 definition (25.8%), which is consistent with the literature suggesting that its incorporation of low muscle strength as a primary marker enhances sensitivity for case finding [3, 7]. In contrast, the lower estimates derived from the AWGS 2014 criteria (12.5%) may reflect the application of cutoff values specifically validated for Asian populations, which might be more conservative [8]. This disparity highlights a critical challenge in comparing sarcopenia burden across regions and over time as evolving definitions directly impact prevalence estimates. Geographical differences were also notable, with a higher prevalence in Europe (23.4%) compared to Asia (17.9%). This divergence may be attributable to a combination of factors, including differences in average population age, ethnic characteristics, physical activity levels, and nutritional patterns. Furthermore, the regional preference for certain diagnostic criteria (e.g., higher use of EWGSOP in Europe and AWGS in Asia) likely contributes to these observed disparities.

Regarding assessment methods, the prevalence estimate from studies using BIA (16.0%), the most frequently applied method, was lower than that from studies using DXA (19.9%), often considered a golden reference standard. The notably high prevalence in the small subset of studies using anthropometric equations (e.g., Lee formula) (23.1%), while based on three studies, points to the potential of such cost-effective methods for screening in resource-limited primary care settings, albeit with acknowledged limitations in precision for specific subgroups like the obese elderly. Although a statistically significant difference in prevalence was found between sexes (p = 0.039), the absolute difference between males (18.7%) and females (18.8%) is minimal, suggesting limited clinical significance. This may be due to the potential influence of gender-specific cutoffs for muscle mass.

Analysis of Factors Affecting Sarcopenia among Community-Dwelling Older Adults

Our systematic review identifies four key factors associated with sarcopenia: sociodemographic, physiological, lifestyle, and comorbid conditions. We derived eleven potential associated factors for sarcopenia and calculated their pooled ORs. Older age, lower BMI, malnutrition, low physical activity, current smoking, and comorbidities (osteoporosis, osteoarthritis, depression status, diabetes, cognitive impairment) were associated with sarcopenia in community-dwelling older adults.

Sociodemographic Factors

Age is a well-established risk factor, with sarcopenia originally defined as age-related muscle loss. Studies consistently demonstrate a significant decline in skeletal muscle mass after age 60 [4, 5]. This progression is further exacerbated by age-related changes such as poor nutrition, reduced physical activity, and declining serum testosterone and growth hormone levels, all of which contribute to muscle deterioration. In contrast, the role of sex differences in sarcopenia remains unclear. While existing evidence does not conclusively establish gender-specific patterns in disease pathogenesis, our analysis found no significant sex-based disparities in its development [33, 69]. This finding aligns with a previous meta-analysis reporting a pooled OR of 1.50 (95% CI: 0.96–2.34) for sarcopenia risk by gender [69].

Physiological Factors

Our meta-analysis confirms that low BMI is a significant risk factor for sarcopenia, likely due to protein-energy malnutrition and hormonal dysregulation. Insufficient caloric intake may impair muscle protein synthesis, while decreased adipose tissue could reduce adipokine secretion, further accelerating muscle loss. However, BMI has limitations in assessing sarcopenia, particularly in older adults, as it cannot distinguish between muscle mass and fat mass [5, 33, 68]. This limitation is particularly relevant for sarcopenic obesity – a clinically important subtype characterized by high fat mass coupled with low muscle mass [70]. Current prevalence estimates of sarcopenic obesity may be underestimated. Therefore, a comprehensive sarcopenia risk assessment should integrate BMI with additional body composition and functional parameters. This systematic review establishes malnutrition as a significant and modifiable risk factor for sarcopenia in community-dwelling older adults, consistent with current evidence [5, 68]. The underlying mechanisms involve both macronutrient and micronutrient deficiencies: protein-energy imbalance disrupts muscle homeostasis by inhibiting anabolic pathways (particularly mTOR signaling) while stimulating protein breakdown, and insufficient micronutrients (notably vitamin D) impair myocyte function. Furthermore, age-related physiological challenges including masticatory dysfunction, swallowing difficulties, and tooth loss create substantial barriers to adequate nutrition, forming a vicious cycle that perpetuates both malnutrition and muscle loss in aging populations [71]. In this systematic review, no significant association was found between fall history and sarcopenia, potentially due to the limited number of included studies (only two articles).

Lifestyle Factors

Our meta-analysis identifies both low physical activity and smoking as independent risk factors for sarcopenia, albeit through distinct pathways. Converging evidence suggests that physical inactivity predominantly compromises muscle homeostasis by disrupting the anabolic-catabolic balance-reducing protein synthesis while promoting proteolysis [68, 69, 72]. In contrast, smoking appears to mediate muscle loss primarily through oxidative stress-induced damage and microvascular dysfunction, which impair nutrient delivery to skeletal muscle [5]. While these distinct pathways were consistently observed across studies, the interpretation of our findings requires caution due to incomplete adjustment for potential confounders (e.g., nutritional status, comorbid conditions) in several included studies, which may affect the magnitude of the observed associations.

Comorbidities

Comorbidities are highly prevalent in aging populations. This system review found that older adults with osteoarthritis, osteoporosis, diabetes, depression status, or cognitive impairment have a significantly higher risk of developing sarcopenia compared to those without these conditions. For patients with osteoarthritis and osteoporosis, joint pain and stiffness can significantly limit mobility, contributing to muscle atrophy and functional decline [73]. For individuals with diabetes, muscle loss may be worsened by combined effects of oxidative stress, chronic inflammation, and insulin resistance [74]. Among older adults with depression, oxidative stress related to depression can impair mitochondrial function, reducing muscle energy production and promoting atrophy. Additionally, depressive symptoms like fatigue and anhedonia also reduce physical activity, leading to disuse-related muscle loss [75]. In cases of cognitive impairment, pathological changes in muscle fiber activity contribute to declining muscle function and strength [76]. These findings suggest that effective management of chronic conditions may be important for preventing sarcopenia in community-dwelling older adults. Furthermore, previous systematic reviews have shown that sarcopenia can significantly predict the development of these same chronic conditions, indicating a bidirectional relationship between sarcopenia and comorbidities [5, 74].

Strengths and Limitations

This study has numerous strengths. This systematic review provides updated insights into the global prevalence of sarcopenia among community-dwelling older adults, with nearly 80% of the included studies published within the past 5 years. Additionally, it systematically evaluated 12 risk factors associated with sarcopenia, categorized into four groups: sociodemographic factors, physiological factors, lifestyle factors, and comorbidities. By identifying these key risk factors within the community context, our findings pave the way for developing targeted screening protocols and implementing early, effective interventions to mitigate the burden of sarcopenia in primary care and community health settings. However, several limitations should be acknowledged. First, the primary limitation of this synthesis is the inherent methodological heterogeneity of the primary studies, which, while a source of bias, also serves as a key finding of this review. Consequently, our findings strongly advocate for the critical importance of standardizing diagnostic approaches in future research to enable valid cross-study and cross-regional comparisons. For clinical practice, particularly in community settings, these results underscore the necessity of selecting context-appropriate diagnostic tools and being mindful of the reference standards when interpreting local prevalence data to guide targeted screening and intervention strategies. Second, the review could not adequately evaluate several potentially important factors (e.g., alcohol consumption, fall history) due to limited available data in included studies. Third, the predominance of cross-sectional studies in our analysis precludes definitive causal inferences regarding the identified associations. Future research should prioritize large-scale prospective cohort studies to establish temporal relationships and strengthen evidence for causality.

Conclusion

This systematic review provides updated evidence on the global burden of sarcopenia among community-dwelling older adults. The pooled analysis revealed an overall prevalence of 18.8%, with notable variations influenced by geographic region and diagnostic criteria. Higher estimates were observed in European populations and when the EWGSOP 2018 criteria were applied. BIA emerged as the most frequently used method for assessing muscle mass. Substantial clinical heterogeneity was observed across studies, largely attributable to the lack of standardized diagnostic criteria, assessment tools, and cutoff thresholds. Additionally, several key risk factors were consistently associated with sarcopenia, including advanced age, low body mass index, malnutrition, physical inactivity, smoking, and specific comorbidities such as osteoporosis, osteoarthritis, depression, diabetes, and cognitive impairment. These findings underscore the imperative for standardizing diagnostic approaches in both research and clinical practice. They also highlight the importance of implementing early screening and targeted interventions – particularly focusing on modifiable risk factors – to mitigate the progression and burden of sarcopenia in aging populations.

Acknowledgments

The authors would like to thank the Southeast university library, who greatly assisted with the construction of the search strategy; we also acknowledge the support from the Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Program (2024-JCYJ-QP-17).

Statement of Ethics

An ethics statement is not applicable because this study is based exclusively on published literature.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

This work was supported by supported by the Major Project of the National Social Science Fund of China (No. 23 & ZD188).

Author Contributions

Research design: Jianqian Chao and Leixia Wang; data collection: Leixia Wang, Xinyue Li, and Jianxia Li; data analysis: Leixia Wang and Shengxuan Jin; and manuscript drafting and revision: Leixia Wang, Jianqian Chao, Na Zhang, Gangrui Tan, Tong Chen, and Yiyao Wu. All authors contributed to the article and approved the submitted version.

Funding Statement

This work was supported by supported by the Major Project of the National Social Science Fund of China (No. 23 & ZD188).

Data Availability Statement

All related data materials have been provided in this article and its online supplementary material files. Further inquiries can be directed to the corresponding author.

Supplementary Material.

Supplementary Material.

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Supplementary Materials

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Data Availability Statement

All related data materials have been provided in this article and its online supplementary material files. Further inquiries can be directed to the corresponding author.

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