2229-P: Association between C-Reactive Protein–Triglyceride–Glucose Index and New-Onset Chronic Disease Incidence and Mortality: Evidence from Multinational Cohorts

Introduction and Objective: The C-reactive protein-triglyceride-glucose index (CTI) is a composite biomarker reflecting insulin resistance and systemic inflammation. Large-scale evidence linking CTI to multiple chronic diseases and cause-specific mortality remains limited. Methods: We conducted a multicenter cohort study of middle-aged and older adults using the China Health and Retirement Longitudinal Study (CHARLS, n=5366) and US National Health and Nutrition Examination Survey (NHANES, n=2921) as primary cohorts, with validation in UK Biobank and a hospital cohort. In CHARLS, we assessed incident chronic diseases using Cox models with baseline CTI, cumulative CTI (cumCTI), and K-means clustering. In NHANES, we evaluated mortality using Cox models. Restricted cubic splines and area under the curve (AUC) assessed non-linear associations and predictive performance. Results: Elevated CTI was consistently associated with adverse outcomes. In CHARLS, higher cumCTI increased risks of hypertension (HR=1.18, 95% CI:1.11-1.26), diabetes (HR=1.60, 95% CI:1.47-1.74), dyslipidemia (HR=1.80, 95% CI:1.69-1.92), stroke (HR=1.15, 95% CI:1.03-1.29), and asthma (HR=1.25, 95% CI:1.08-1.45), with three distinct patterns. In NHANES, higher CTI was associated with all-cause mortality (HR=1.19, 95% CI:1.14-1.25), cancer (HR=1.16, 95% CI:1.05-1.28), heart disease (HR=1.20, 95% CI:1.09-1.32), lung disease (HR=1.36, 95% CI:1.15-1.60), and diabetes mortality (HR=1.45, 95% CI:1.18-1.79). CTI 9.0 represented a critical threshold for elevated mortality. CTI showed strong predictive performance (AUC: 0.74 for dyslipidemia, 0.85 for mortality). Findings were validated across independent cohorts. Conclusion: CTI is a valuable biomarker consistently associated with chronic disease and mortality across diverse populations. Integrating metabolic and inflammatory pathways, CTI may enhance risk assessment and identification of high-risk individuals for targeted interventions. Disclosure X. Li: None. H. Zhang: None. J. Zhang: None.