Which insulin resistance indices best predict future frailty progression in a cardiovascular-kidney-metabolic syndrome stage 0–3 population: a national prospective cohort and machine learning study

BACKGROUND: Insulin resistance (IR) is implicated in frailty progression within Cardiovascular-Kidney-Metabolic (CKM) syndrome populations. While multiple non-insulin-based IR indices have been proposed, their comparative utility in predicting frailty across early CKM stages (0-3) remains unclear. Identifying reliable, non-insulin-based IR indices for predicting frailty index (FI) across early CKM stages (0-3) remains challenging. METHODS: This prospective cohort study analyzed 4,354 adults (≥ 45 years) from the China Health and Retirement Longitudinal Study (2011-2015). We evaluated and compared 12 IR indices for predicting frailty progression. Associations were assessed using multivariable logistic regression. Machine learning (RFE, Boruta, and LASSO) identified optimal predictors, and a Random Forest (RF) model incorporating key covariates was developed and validated. RESULTS: After full adjustment, CTI (OR = 1.19), TyG-WHtR (OR = 1.12), TyG-WC (OR = 1.00), and eGDR (OR = 0.87) significantly predicted FI (all p < 0.05). Among all indices, TyG-WHtR demonstrated the most stable discriminative performance across CKM stages 0-3 (AUCs: 0.52-0.59, all p < 0.001). Machine learning consistently selected TyG-WHtR as the top predictor. The final 13-variable RF model achieved an AUC of 0.74. SHAP analysis confirmed TyG-WHtR, age, depressive symptoms, and renal biomarkers as key predictors. CONCLUSION: TyG-WHtR is a robust, stable predictor of frailty progression in individuals with CKM syndrome stages 0-3. Its integration into clinical practice, potentially via the developed web tool, could enhance early frailty risk stratification.