Predicting cardiovascular risk across cardiovascular-kidney-metabolic syndrome stages in middle-aged and older Chinese adults: An interpretable machine learning analysis.

Objective: To develop and internally validate interpretable machine learning (ML) models for predicting incident cardiovascular disease (CVD) among middle-aged and older Chinese adults across cardiovascular-kidney-metabolic (CKM) stages 0-3, excluding CKM stage 4 (established CVD) by design, and to characterize stage-specific risk patterns using SHapley Additive exPlanations (SHAP). Methods: Using data from 6049 adults aged ≥45 years without baseline CVD in the China Health and Retirement Longitudinal Study, we developed interpretable ML models to predict incident, self-reported CVD during follow-up across CKM stages 0-3. Feature selection combining the Boruta algorithm with recursive feature elimination identified 11 key predictors. Five ML algorithms-logistic regression, random forest (RF), Extreme Gradient Boosting, Light Gradient Boosting Machine, and multilayer perceptron-were trained and evaluated using a stratified 70/30 train-test split. SHAP analysis enhanced model interpretability and characterized stage-specific risk profiles. Results: During a median 6-year follow-up, 1373 participants (22.7%) developed CVD, with incidence increasing progressively from 11.9% in Stage 0 to 24.4% in Stage 3 ( Conclusions: This interpretable ML framework provides a stage-specific CVD risk stratification approach across the CKM spectrum and may inform future risk assessment research in middle-aged and older Chinese adults. However, external validation and clinical utility evaluation are required before clinical translation.