Triglyceride-glucose-body mass index and the incidence of cardiovascular diseases: a meta-analysis of cohort studies.

Background: Numerous studies have shown that insulin resistance (IR) is closely related to the pathogenesis of cardiovascular disease (CVD). This study aims to summarize the correlation between the triglyceride-glucose-body mass index (TyG-BMI index), a novel surrogate indicator of insulin resistance, and the incidence of CVD in patients without CVD at baseline through meta-analysis.

Method: Cohort studies assessing multivariate-corrected hazard ratios (HRs) for associations between the TyG-BMI index and cardiovascular disease (CVD) were obtained by searching PubMed, Cochrane Library, EMBASE, and Web of Science. Results were combined using a random-effects model to account for heterogeneity among the included studies. Robust error meta-regression was used to fit the nonlinear dose-response relationship. Statistical analysis was performed using Review Manager 5.4 and STATA 18.0.

Result: Ten cohort studies involving a total of 871,728 subjects were included. The results indicated that Compared with the lowest TyG-BMI index category, the highest TyG-BMI index was related to a higher incidence of cardiovascular diseases (CVD) (HR = 1.62; 95% confidence interval (CI): 1.35-1.95; I = 94%),coronary artery disease (CAD) (HR = 1.69; 95% (CI): 1.23-2.31; I = 94%). stroke(HR = 1.57; 95% (CI): 1.11-2.23; I = 94%).In the dose-response analysis, there was a linear association of the TyG-BMI index with the risk of CVD (P = 0.223), CAD (P = 0.693), and stroke (P = 0.122)No significant effects were observed regarding participants' gender, length of follow-up, sample size or mean age(P > 0.05).

Conclusion: Higher TyG-BMI may be independently associated with an increased risk of CVD in individuals without CVD at baseline. Numerous cohort studies are needed to further validate and elucidate the pathologic role between Tyg-BMI and CVD and to determine whether it can be incorporated into CVD risk prediction tools to enhance predictive accuracy.