Combined effect of triglyceride-glucose index and glucose disposal rate on cardio-cerebrovascular disease

by Hongfei Yang, Chao Sun, Ya Li, You Zhou, Rui Wang, Yingxue Li

The triglyceride-glucose index and estimated glucose disposal rate serve as notable surrogate markers of insulin resistance, demonstrating established links to cardio-cerebrovascular disease. However, their combined prognostic value in predicting cardio-cerebrovascular disease outcomes remains unexplored. The current investigation examined the interaction between the TyG (triglyceride–glucose index) index and eGDR (estimated glucose disposal rate) concerning the danger of cardiovascular disease within a clinical population.

This investigation employed data sourced from the China Health and Retirement Longitudinal Study (CHARLS). The median TyG index and eGDR scores were used to stratify the participants into 4 categories: low TyG/high eGDR, high TyG/high eGDR, low TyG/low eGDR, and high TyG/low eGDR. Clinical characteristics across groups were systematically compared. Cox proportional hazards regression models evaluated the distinct and interconnected associations of the TyG index and eGDR with the risk of cardio-cerebrovascular disease, with multiplicative and additive interaction effects subsequently assessed through formal interaction analysis.

The final study cohort comprised 7,330 participants, with 1,336 individuals (18.2%) developing cardio-cerebrovascular disease during the 9-year follow-up. Stratification using median thresholds (TyG: 8.59; eGDR: 10.55 mg/kg/min) yielded four groups: low TyG/high eGDR (n = 2,991), high TyG/high eGDR (n = 1,375), low TyG/low eGDR (n = 1,372), and high TyG/low eGDR (n = 2,292). Multivariable-adjusted Cox regression analyses revealed markedly increased risks of cardio-cerebrovascular disease among the various exposure groups when contrasted with the low TyG/high eGDR reference: high TyG/high eGDR (HR: 1.31, 95%CI: 1.10–1.57, ppp Conclusion

The TyG index and eGDR demonstrate independent associations with cardio-cerebrovascular disease risk, while their combined assessment reveals synergistic predictive capacity. Combined assessment of the two allows for further accurate stratification of the population based on insulin resistance and improved prediction of cardio-cerebrovascular disease.