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Frailty is an age-related clinical state characterized by reduced physiological reserve and multisystem dysfunction. Because a complete Fried frailty phenotype could not be reconstructed from the available CHARLS variables, this study used \u201cfrailty risk factors\u201d to refer to available physical performance and biomarker indicators related to frailty or high frailty risk, including handgrip strength (HGS)-derived variables and cystatin C (Cys C). This study explored the association between these indicators and CVDs.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    Data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Participants were grouped by prevalent CVDs, defined as self-reported physician-diagnosed hypertension, heart disease, or stroke. LASSO regression followed by simple and multiple logistic regression was used to screen variables. The dataset was randomly split 6:2:2 into training, testing, and validation sets to develop six models (LR, XGBoost, LightGBM, SVM, RF, and DT), and model performance was evaluated by discrimination, calibration, decision curve analysis, and SHAP interpretability analyses.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    A total of 10,657 participants were included (76% female), of whom 2,650 had CVDs (84% female). The XGBoost model showed the best overall performance. SHAP analysis identified age, HbA1c, UA, and Cys C as variables increasing the predicted risk of CVDs, whereas higher left HGS\/weight ratio and lower blood pressure values were associated with lower predicted risk.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion<\/jats:bold>\n                    These findings suggest that frailty risk factors, especially HGS\/weight ratio and Cys C, may provide additional information for CVD risk stratification beyond conventional factors. The results support incorporating simple physical performance assessment and frailty-risk evaluation into CVD prevention strategies.\n                  <\/jats:p>","DOI":"10.1186\/s12872-026-06213-w","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:44:48Z","timestamp":1783475088000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Association of frailty-risk indicators with cardiovascular diseases: evidence from the CHARLS"],"prefix":"10.1186","volume":"26","author":[{"given":"Fangfang","family":"Zhuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao\u2019 xing","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Run\u2019 ze","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang\u2019 rong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"6213_CR1","unstructured":"Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. 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