The Triglyceride Glucose–Conicity Index as a Novel Predictor for Stroke Risk: A Nationwide Prospective Cohort Study

TL;DR

This study finds that combining two health markers (TyG and CI) improves prediction of stroke risk better than using either alone.

Contribution

The novel composite TyG–conicity index (TyG-CI) is introduced as a superior predictor of stroke risk compared to its individual components.

Findings

Participants in the highest TyG-CI quartile had a 69% higher stroke risk compared to the lowest quartile.

TyG-CI outperformed TyG and CI alone in predicting stroke risk with a higher AUC (0.594 vs. 0.575).

The relationship between TyG-CI and stroke risk was linear, with no evidence of nonlinearity.

Abstract

Background/Objectives: The triglyceride–glucose index (TyG) and conicity index (CI) are markers of insulin resistance and abdominal obesity, respectively. However, their joint impact on stroke remains unclear. This study aimed to assess the association between the novel composite TyG–conicity index (TyG-CI = TyG × CI) and stroke risk. Methods: This prospective cohort study enrolled 8011 participants aged 45 years or older with no history of stroke at baseline, from the China Health and Retirement Longitudinal Study. Cox proportional hazards regression models were used to estimate the impact of TyG-CI on the risk of incident stroke. Restricted cubic spline regressions were applied to estimate possible nonlinear associations. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and time-dependent Harrell ‘s C-index. Results: Over a mean follow-up of 8.36 years, 753 participants (9.4%) experienced stroke. After adjusting for potential confounders, compared with the lowest quartile, the adjusted HRs of incident stroke for participants with the highest quartile of TyG, CI, and TyG-CI were 1.36 (95%CI 1.05–1.76), 1.43 (95%CI 1.15–1.78), and 1.69 (95%CI 1.31–2.17), respectively. Restricted cubic spline analysis confirmed a linear relationship between TyG-CI and stroke risk (p for nonlinearity = 0.90). The AUC was highest for TyG-CI (0.594), exceeding TyG (0.575) and CI (0.575) with statistically significant differences (all p < 0.05). Conclusions: Elevated TyG-CI indicates a higher stroke risk, with superior predictive value compared to its components alone, providing a promising indicator that is simple, reliable, and inexpensive for identifying stroke risk in clinical practice.

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