Integrative Inflammation–Metabolism Indicator for Cardiovascular–Kidney–Metabolic Syndrome: Evaluating the C‐Reactive Protein–Triglyceride Glucose Index for Risk Stratification and Progression Across Three National Cohorts

Abstract

Background Inflammation plays a critical role in the onset and progression of cardiovascular–kidney–metabolic (CKM) syndrome. However, the optimal inflammatory biomarker that simultaneously reflects disease severity and predicts future progression of CKM remains unexplored. Methods This study utilized data from three nationally representative cohorts: National Health and Nutrition Examination Survey (NHANES), UK Biobank (UKB), and China Health and Retirement Longitudinal Study (CHARLS). In NHANES, three machine learning approaches were applied to identify the inflammatory biomarker most strongly associated with CKM stages. The association between this biomarker and CKM severity was validated in UKB and CHARLS. Its predictive value for CKM progression was examined in UKB and CHARLS with Cox proportional hazard models. Multiple sensitivity analyses were conducted to ensure the robustness of the findings. Results Among 15 circulating inflammatory biomarkers, the C‐reactive protein–triglyceride glucose index (CTI) was identified as the most informative indicator of advanced CKM risk. In the cross‐sectional analyses, 8959 participants from NHANES, 208,625 from UKB, and 8550 from CHARLS were included. After adjustment for potential confounders, higher CTI levels were consistently associated with advanced CKM across the three cohorts (NHANES: odds ratio [OR] = 1.35, 95% confidence interval (CI): 1.20–1.48; UKB: OR = 1.55, 95% CI: 1.46–1.64; CHARLS: OR = 1.42, 95% CI: 1.28–1.60). In longitudinal analyses including 186,753 participants from UKB and 3673 from CHARLS, elevated CTI levels predicted an increased risk of incident advanced CKM (UKB: HR = 1.22, 95% CI: 1.17–1.30; CHARLS: HR = 1.21, 95% CI: 1.01–1.46). Conclusions CTI emerged as the most robust inflammatory biomarker for assessing disease severity and predicting CKM progression among multiple candidates. Its reproducible associations across national cohorts support its utility for early detection and risk stratification, offering a basis to refine CKM management.