Risk prediction models for sarcopenia among community-dwelling older adults in China: a systematic review and meta-analysis.

BACKGROUND

An increasing number of sarcopenia risk prediction models for older adults in Chinese communities have been developed, but the quality and applicability of these models in clinical practice and future research remain unclear. We conducted a systematic review to evaluate their performance.

OBJECTIVES

To systematically review published studies on risk prediction models for sarcopenia among community-dwelling older adults in China.

METHODS

We searched the China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, Cochrane Library and Embase databases up to February 17, 2025, and extracted relevant information from the selected prediction models, including study design, data sources, outcome definitions, sample size, predictors, model development and performance. The risk of bias and applicability were assessed via the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.

RESULTS

Initially, we retrieved 2092 studies. After the screening process, 8 development models and 7 validation models were included from 9 studies. The prevalence of sarcopenia ranged from 8.3% to 30.6%, and the most commonly used predictors were BMI and age. All included studies had a high risk of bias, mainly due to inappropriate data sources and insufficient reporting in the analysis area. In the meta-analysis, we observed that the prevalence of sarcopenia was 20% (95% confidence interval: 0.14–0.26), the area under the curve (AUC) value of the development models was 0.89 (95% confidence interval: 0.84–0.95), and the AUC value of the validation models was 0.87 (95% confidence interval: 0.80–0.95).

CONCLUSION

The overall accuracy of sarcopenia risk prediction models for older adults in Chinese communities is relatively good, but according to the PROBAST checklist, all studies have a high risk of bias. Future research should focus on developing new models with larger sample sizes, rigorous study designs, and multicenter external validation. TRIAL REGISTRATION: The review protocol was registered in PROSPERO (registration ID: CRD420250653096).