The C-reactive protein-triglyceride glucose index (CTI) predicts mortality in cardiovascular-kidney-metabolic...

Cardiova scular-kidney-metabolic (CKM) syndrome urgently requires accessible biomarkers for stratification of death risk. This study validated the predictive value of a novel inflammatory metabolic biomarker, the C-reactive protein-triglyceride-glucose index (CTI), for all-cause and cardiovascular mortality in dual U.S. and Chinese cohorts and developed a survival analysis machine learning (ML) model.

We integrated data from the National Health and Nutrition Examination Survey (NHANES, n = 8784) and China Health and Retirement Longitudinal Study (CHARLS, n = 7745). Multivariate Cox regression was used to evaluate the associations between CTI (formula: 0.412 × Ln(C-reactive protein) + Ln[triglycerides × fasting blood glucose/2]) and mortality. Seven ML models were built using the NHANES data, with CHARLS as the external validation set. SHapley Additive exPlanations (SHAP) clarified the prediction mechanisms.

Per 1-standard deviation increase in CTI, all-cause mortality risk increased significantly (NHANES: hazard ratios (HRs) = 1.31, 95% confidence interval (CI): 1.19-1.44; CHARLS: HR = 1.67, 95% CI: 1.44-1.93), and cardiovascular mortality increased by 35% in NHANES (HR = 1.35, P < 0.001). The Random Survival Forest (RSF) model performed best: internal validation area under the curve (AUC) = 0.866 (NHANES) with the highest time-dependent Concordance Index, and external validation in CHARLS yielded AUCs of 0.811 (3-year), 0.804 (5-year), and 0.775 (9/12-year), outperforming other models. SHAP analysis identified age (42.2% contribution) and CTI (10.1%) as key predictors, with age, CTI, and systolic blood pressure acting via independent main effects, whereas estimated glomerular filtration rate exerted an influence primarily through synergistic interactions.

CTI, a novel inflammatory metabolic biomarker, reliably predicts all-cause and cardiovascular mortality in CKM syndrome, with consistent validation across NHANES and CHARLS. The NHANES-derived RSF model (AUC > 0.86) combines high accuracy and clinical utility, and is supported by stable external validation in CHARLS and sensitivity analyses. SHAP-based mechanistic insights further enable personalized risk assessments.