Evidence map›Paper›PMID 41343409›Full record

SynthesisGerontology2026

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

Leixia Wang, Jianqian Chao, Na Zhang, Xinyue Li, Jianxia Li, Shengxuan Jin, Gangrui Tan, Tong Chen, Yiyao Wu

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Gerontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Leixia WangHealth Management Research Center, School of Public Health, Southeast University, Nanjing, China.
Jianqian ChaoHealth Management Research Center, School of Public Health, Southeast University, Nanjing, China, chaoseu@163.com.
Na ZhangDepartment of Nursing Research Institute, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xinyue LiInstitute of Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Jianxia LiSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Shengxuan JinHealth Management Research Center, School of Public Health, Southeast University, Nanjing, China.
Gangrui TanHealth Management Research Center, School of Public Health, Southeast University, Nanjing, China.
Tong ChenSchool of Clinical Medicine, Southwest Medical University, Luzhou, China.
Yiyao WuHealth Management Research Center, School of Public Health, Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<p>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.

methodsGuided 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.

resultsA 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).

conclusionOur 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. </p>.

Indexed as

Independent LivingSarcopeniaAgedAged, 80 and overFemaleHumansMalePrevalenceRisk FactorsAged peopleAssociated factorsCommunityPrevalenceSarcopenia

Identifiers

PMID41343409
PMCPMC12782612

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.