AUTHOR=Song Yuanying , Liu Jianping , Wang Sufang , Shi Haicun , Han Lijian 

TITLE=Association between frailty trajectories and stroke incidence among Chinese older adults: evidence from CHARLS 2011–2015

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1592565

DOI=10.3389/fneur.2025.1592565

ISSN=1664-2295

ABSTRACT=BackgroundWhile frailty is recognized as a significant predictor of adverse health outcomes in older adults, the relationship between different frailty assessment approaches and stroke risk remains understudied in the Chinese elderly population.MethodsData were derived from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2015, including 8,049 participants. Frailty was assessed using two approaches: cluster analysis (K-means method) based on longitudinal frailty index trajectories and baseline frailty index. Cox proportional hazards models were employed to examine the association between frailty and stroke incidence, adjusting for sociodemographic characteristics and cardiovascular risk factors including age, marital status, education, smoking, drinking, BMI, glycated hemoglobin, systolic blood pressure, HDL cholesterol, C-reactive protein, and physical dysfunction.ResultsDuring the 5-year follow-up period, 768 stroke events were recorded. In the fully adjusted models, both frailty assessment approaches showed significant associations with stroke risk. The cluster-based approach demonstrated a higher risk of stroke (HR = 3.85, 95% CI: 3.26–4.56, p < 0.001) compared to the baseline frailty index (HR = 1.14, 95% CI: 1.12–1.16, p < 0.001). Traditional cardiovascular risk factors remained significant predictors in both models. Subgroup analyses consistently demonstrated significant associations across nearly all demographic and health-related subgroups.ConclusionBoth longitudinal frailty trajectories and baseline frailty index are independently associated with stroke risk in Chinese older adults, with the cluster-based approach showing stronger predictive value. These findings suggest the importance of considering frailty patterns in stroke risk assessment and highlight the potential value of longitudinal frailty monitoring in stroke prevention strategies for aging populations.