Joint Association of Inflammatory-Metabolic Indices With Risk of Incident Stroke Among Middle-Aged and Older Chinese Adults.

Authors

Binbin Wang, Shuke Liu, Na Meng, Quan Li, Yan Chen, Xiaoxiao Peng, Haohao Wu, Shanwen Zhang, Gangzhen Qian

Published in

Journal of obesity. Volume 2026. Issue 1. Pages e8894079.

Abstract

To investigate the independent and joint associations of the C-reactive protein-triglyceride-glucose index (CTI) and the Chinese visceral adiposity index (CVAI) with incident stroke. Additionally, we evaluated whether a novel combined index, CTI-CVAI, outperforms the established TyG-CVAI in predicting stroke risk.
This cohort study included 8267 stroke-free participants from the China Health and Retirement Longitudinal Study (CHARLS). Cox proportional hazards models, Kaplan-Meier curves, and restricted cubic splines were utilized to analyze associations with incident stroke. Additive and multiplicative interactions between CTI and CVAI were examined. Predictive performance was assessed via receiver operating characteristic (ROC) analysis, comparing CTI-CVAI against TyG-CVAI using DeLong's test and the Boruta algorithm for feature importance.
During a mean follow-up of 8.37 years, 816 incident stroke cases were identified. Elevated CTI and CVAI were independently associated with increased stroke risk in a linear dose-response manner, without significant interaction. The novel CTI-CVAI index exhibited a robust association with stroke; participants in the highest tertile had a 2.14-fold higher risk (HR 2.14, 95% CI 1.75-2.62) compared to the lowest. Crucially, CTI-CVAI demonstrated superior predictive accuracy (AUC 0.627) over the established TyG-CVAI index (P = 0.002), a finding supported by consistent results across subgroups.
CTI and CVAI are independent risk factors for stroke, and their combination (CTI-CVAI) offers enhanced predictive value, outperforming the TyG-CVAI index. Monitoring CTI-CVAI may serve as a superior strategy for early risk stratification and targeted stroke prevention in middle-aged and elderly populations.

PMID:
42803652
Bibliographic data and abstract were imported from PubMed on 28 Sep 2026.

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