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Chinese Journal of Geriatrics Research(Electronic Edition) ›› 2026, Vol. 13 ›› Issue (03): 23-30. doi: 10.3877/cma.j.issn.2095-8757.2026.03.003

• Clinical Research • Previous Articles    

Frailty trajectories and cardiovascular disease risk in middle-aged and older adults: the mediating role of physical activity

Xiangyu Wang1,2, Yanran Duan3,4, Shuai Jiang3,4, Hang Fu3,4, Yifan Shan2,4,†()   

  1. 1School of Public Health, Zhengzhou University, Zhengzhou 450051, China
    2Chaoyang Hospital, Capital Medical University, Beijing 100020, China
    3The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
    4Institute for Hospital Management of Henan Province, Zhengzhou 450052, China
  • Received:2026-01-21 Online:2026-08-28 Published:2026-10-27
  • Contact: Yifan Shan

Abstract:

Objective

To investigate the dynamic patterns of frailty among middle-aged and older adults and their associations with the risk of cardiovascular disease (CVD), and to further examine the mediating role of physical activity in this association.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, all participants enrolled between 2011 and 2015 who were aged≥45 years, had fewer than 5 frailty index items, and without CVD were included. Frailty status was assessed using the Rockwood frailty index and a group-based trajectory model was employed to identify dynamic patterns of frailty change. The primary outcome was self-reported physician-diagnosed CVD at the 2018 follow-up, defined as heart disease or stroke. Cox proportional hazards models were used to assess the associations between frailty trajectories and incident CVD. Furthermore, inverse probability weighting-based marginal structural models were used to evaluate the mediating effect of physical activity on the association between frailty trajectories and CVD risk.

Results

A total of 8,876 participants were included. From 2011 to 2018, transitions among robust, prefrail, and frail states showed substantial dynamism and reversibility. The optimal trajectory model identified three distinct frailty patterns (i.e., trajectory groups 1, 2, and 3), defined asa low-level stable group (n=6 094), a moderate-level increasing group (n=2 299), and a high-level increasing group (n=483). Significant differences were observed across the three trajectory groups in age, sex, educational level, marital status, body mass index, smoking, alcohol consumption, social activity, grip strength, 2.5-meter walk time, and chair stand performance (P < 0.01). Compared with the low-level stable group, the risk of incident CVD was significantly higher in the moderate-level increasing group (HR=2.15, 95%CI: 1.94-2.40, P < 0.01 ) and the high-level increasing group (HR=3.43, 95%CI: 2.92-4.04, P < 0.01), with CVD risk increasing progressively with higher frailty levels and greater frailty progression. Declines in physical activity mediated approximately 21%-23% of the association between frailty trajectories and incident CVD.

Conclusion

Dynamic trajectories of frailty among middle-aged and older adults were significantly associated with the risk of incident CVD, and part of this association was related to declines in physical activity. Among individuals with progressive frailty, maintaining or improving physical activity may help reduce the risk of cardiovascular disease.

Key words: Frailty, Dynamic trajectories, Physical activity, Cardiovascular disease, Mediation analysis, Marginal structural model

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