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 - Diabetology & Metabolic Syndrome
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
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
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