Frontiers in Cardiovascular Medicine (Sep 2024)
Causal relationship between novel antidiabetic drugs and ischemic stroke: a drug-targeted Mendelian randomization study
Abstract
BackgroundThe escalating global economic burden of ischemic stroke poses a significant public health challenge amid global aging trends. The broad therapeutic efficacy of new antidiabetic drugs may offer new options in the prevention and treatment of ischemic stroke. Consistent conclusions regarding the relationship between novel antidiabetic agents and the risk of ischemic stroke remain elusive, and the causal relationship deserves further investigation.Materials and methodsThree novel antidiabetic drug targets were selected, and cis-expression quantitative trait loci (cis-eQTL) were screened as instrumental variables. Genetic association data for ischemic stroke were obtained from the Genome-wide Association Study (GWAS) database. Mendelian randomization (MR) analysis, facilitated by R software, calculated MR estimates for each single nucleotide polymorphism (SNP), and meta-analysis was performed using five methods. To ensure robustness, sensitivity analyses, heterogeneity analyses, horizontal pleiotropy analyses, and co-localization analyses were conducted for significant MR associations.ResultsThree eQTLs for antidiabetic drug genes served as instrumental variables, utilizing a GWAS dataset comprising 34,217 cases and 406,111 controls for ischemic stroke. Genetic variants in glucagon-like peptide-1 receptor agonists (GLP-1 RA) targets exhibited a positive correlation with ischemic stroke risk (OR 1.06, 95% CI 1.04–1.08, P = 0.000), while genetic variation in dipeptidyl peptidase 4 inhibitors (DPP-4i) targets showed a negative association with ischemic stroke risk (OR 0.93, 95% CI 0.89–0.97, P = 0.003). Sensitivity analyses supported robust conclusions, revealing no heterogeneity or horizontal pleiotropy.ConclusionThis study found that GLP-1 RA and DPP-4i were associated with an increased risk of ischemic stroke by MR analysis. Although sensitivity analyses provide support for this result, it contradicts previous knowledge. Therefore, the results of this study still need to treated with caution. Updated and more in-depth GWAS data and high-quality real-world data are expected to validate the results.
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