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From traditional to novel: a national cohort study on the predictive value of C-reactive protein–triglyceride–glucose-obesity composite parameters for cardiovascular disease

Aug 2026 · Therapeutic Advances in Endocrinology and Metabolism · Vol 17 · 0 citations · 51 references
Medicine

TL;DR

CTI combined with obesity indices, particularly CTI-CVAI, was associated with modestly improved predictive ability for CVD risk compared with CTI alone, and this parameter may offer incremental information for CVD risk stratification by integrating metabolic inflammation and visceral fat markers.

Abstract

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. The C-reactive protein–triglyceride–glucose (CTI) index captures metabolic inflammation and insulin resistance, while obesity indices reflect adiposity-related cardiometabolic risk. However, the predictive value of combining CTI with obesity indices for CVD remains unclear. Objectives: To examine the prognostic utility of CTI combined with established and novel obesity indicators for CVD, and to benchmark their predictive efficacy. Design: Nationally representative prospective cohort study. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort, 7847 adults aged 45 years and above without baseline CVD were enrolled. CTI was calculated from C-reactive protein, fasting blood glucose, and triglycerides (TG); each obesity index was combined with CTI to form composite parameters (CTI-body mass index (CTI-BMI), CTI-waist circumference (CTI-WC), CTI-waist-to-height ratio (CTI-WHtR), CTI-body roundness index (CTI-BRI), CTI-weight-adjusted waist index (CTI-WWI), CTI-Chinese visceral adiposity index (CTI-CVAI), CTI-a body shape index (CTI-ABSI)). We employed Cox proportional hazard models, restricted cubic spline analyses, Kaplan–Meier survival plots, ROC curve evaluations, and weighted quantile sum (WQS) regression to examine the links and prognostic utility of each metric with incident CVD across baseline and cumulative exposure tiers. Results: Over a 9-year follow-up period, 1938 new-onset CVD cases were documented. Following multivariable adjustment, each CTI-obesity composite metric showed a significant positive link to CVD risk (p < 0.05). Moreover, all CTI‑obesity composite parameters showed stronger associations with CVD than CTI alone. CTI‑CVAI demonstrated the relatively best predictive accuracy among the evaluated indices, though absolute discriminatory ability remained modest (baseline area under the ROC curve (AUC): 0.596; cumulative AUC: 0.600). Trajectory clustering found that, versus the well-managed group, the poorly managed subgroup had 21%–71% higher CVD risk (max 71% for CTI-WC). WQS regression indicated that CRP and obesity indices (especially CVAI) were the major contributors in the mixed exposure; cumulative exposure WQS analysis showed that cumulative TG and cumulative obesity parameters contributed the most. Conclusion: CTI combined with obesity indices, particularly CTI-CVAI, was associated with modestly improved predictive ability for CVD risk compared with CTI alone. This parameter may offer incremental information for CVD risk stratification by integrating metabolic inflammation and visceral fat markers.

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