ArticleDiabetology & metabolic syndrome2025
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.
Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Sex-Specific Associations of Triglyceride-Glucose Index and a Body Shape Index with Cardiometabolic Multimorbidity Risk: A Prospective Cohort Study.Journal of clinical medicine · 2026Article
- Associations of the combined C-reactive protein-triglyceride glucose index and frailty index in different dimensions with incident stroke among individuals with stages 0-3 cardiovascular-kidney-metabolic syndrome.Cardiovascular diabetology · 2026Article
- Cardiovascular-kidney-metabolic syndrome: a comprehensive review of pathophysiology, epidemiology, diagnosis, and management.Cardiovascular diabetology · 2026Review
- Association of CTI and its obesity-related derivatives with incident depression among middle-aged and older adults across CKM stages 0-4: a nationwide prospective cohort study and external clinical validation.Frontiers in endocrinology · 2026Article
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Authors and funding
11 authors.
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Abstract
backgroundInsulin 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.
methodsThis 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.
resultsAfter 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.
conclusionTyG-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.
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