Estimated glucose disposal rate and cardiovascular disease risk: a meta-analysis of cohort studies
Year: 2025
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: Background Insulin resistance (IR) is a key cardiovascular disease (CVD) risk factor. The estimated glucose disposal rate (eGDR) is a reliable IR marker linked to CVD risk. This study is the first extensive meta-analysis of this correlation in a general population free from baseline CVD. Methods We searched electronic databases such as PubMed, Web of Science and Embase for cohort studies reporting eGDR and CVD risk. Studies included adults without baseline CVD, measured eGDR at baseline, and reported hazard ratio (HR) [95% confidence interval (CI)]. The combined HR and its 95% CI were determined through the application of random or fixed effects models. Meta-regression with robust error was utilized to depict the nonlinear dose-response relationship. Results Twelve cohort studies with 547,287 subjects were included, with follow-up durations ranging from 5.6 to 14.1 years. Participants with the highest eGDR category had a lower risk of CVD (HR: 0.58, 95% CI 0.53–0.63), stroke (HR: 0.62, 95% CI: 0.56–0.69), and coronary heart disease (HR: 0.46, 95% CI: 0.25–0.83) compared with the lowest eGDR category. This aligns with the meta-analysis results, where eGDR as a continuous variable had HRs of 0.88 (95% CI: 0.85–0.91) for CVD, 0.84 (95% CI: 0.76–0.93) for stroke, and 0.85 (95% CI: 0.83–0.87) for coronary heart disease. Subgroup analyses revealed that sex, sample size, follow-up duration, and prediabetes/diabetes status did not significantly affect the results. Dose–response analysis indicated that there was a linear negative association of the eGDR with the risk of CVD (P nonlinear =0.120) or stroke (P nonlinear =0.084). Conclusions The higher eGDR is associated with lower risk of CVD, stroke, and coronary heart disease in individuals without baseline CVD. However, the observational design and high heterogeneity across studies prevent causal inference.
Source: Frontiers in Endocrinology
Authors: Zhijun Zhang, Beibei Wang, Lijuan Qu, Boping Huang
Institutions: Shanxi Medical University, Shanxi Academy of Medical Sciences
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4417263170