Association of miR polymorphisms with coronary artery disease risk: a comprehensive meta-analysis

Abstract Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide, with coronary artery disease (CAD) being the most prevalent type caused by atherosclerosis. Genetic factors contribute to 40–60% of CAD susceptibility. miRNAs have emerged as potential biomarkers due to their regulatory roles in cardiovascular pathways. miR-146a, miR-149, miR-196a2, and miR-499 are linked to CAD, influencing inflammation, endothelial dysfunction, and lipid metabolism. Additionally, miR-21, miR-126, and miR-155 have been studied in myocardial infarction and heart failure, highlighting their diagnostic relevance. Identifying miRNA polymorphisms associated with CAD may improve prognostic indicators and treatment strategies. Methods This study aimed to investigate the association between miRNA polymorphisms and CAD through a comprehensive meta-analysis using data from PubMed, Scopus, Web of Science, and Embase. Inclusion criteria involved case–control studies with genotyping data on CAD/ Acute Coronary Syndrome (ACS) risk. Heterogeneity was evaluated with the I 2 , tau 2 , Q score and H values, and the Newcastle–Ottawa Scale assessed study quality. For statistical analysis, odds ratio of miRNA SNPs reported in three or more studies were calculated. Results A total of 276 studies were identified, of which 10 met the inclusion criteria for meta-analysis. Among the 13 miRNA SNPs reported, only three (rs2910164, rs11614913, and rs3746444) were included in the meta-analysis, as they were examined in three or more studies. rs2910164 (miR-146a) showed a significant association with CAD risk in the homozygous (OR: 0.79, 95% CI 0.63–1.00), heterozygous (OR: 0.88, 95% CI 0.79–0.99), and dominant models (OR: 0.86, 95% CI 0.75–0.99), suggesting a potential protective role. However, rs11614913 (miR-196a2) and rs3746444 (miR-499) did not show significant associations with CAD risk. Publication bias analysis revealed potential bias in the allele, homozygote, and recessive models of rs11614913. In-silico analysis identified weakly validated common targets among miR-146a, miR-196a, and miR-499, with KEGG pathway analysis highlighting key pathways, including cell cycle regulation and adherens junctions, involved in CAD pathogenesis. Conclusion The results obtained suggests that rs2910164 maybe be associated with increased risk of CAD. Given the limitations listed above, further research with bigger sample sizes are required to definitely identify the connection between miRNA variations and the risk of CAD.