Integrating multiple machine learning approaches with Mendelian randomization to unveil the effects of BMI-mediated matrix metalloproteinase 3 levels on hearing

This study aimed to investigate the genetic mechanism of the levels of matrix metalloproteinase 3 (MMP-3)-induced hearing impairment and whether BMI mediates their causal relationship by using Mendelian randomization (MR) and machine learning. This research employed aggregated GWAS data from the IEU (Integrative Epidemiology Unit) database for MR and Bayesian Weighted Mendelian Randomization (BWMR) analysis, to uncover the causal association between MMP-3 levels and hearing, along with a potential mediating factor BMI. Inverse variance weighted (IVW), MR Egger, Weighted median, simple mode, and Weighted mode approaches were utilized, and multi-effect testing and heterogeneity testing were conducted. Finally, unsupervised and supervised machine learning were used to verify the robustness of MR Results, as long as with statistical power. In our study, it was revealed that MMP-3 levels exerted an inhibitory effect on normal hearing, without demonstrating pleiotropy or heterogeneity in the IVW analysis (p = 0.019, OR with 95%CI = 0.995 [0.992–0.999). Mediating analysis indicated that BMI served as the mediating factor, suggesting that the MMP-3 level might lead to hearing issues via BMI, and the BWMR proved to be dependable. Machine learning and statistical power results verify the robustness of Mendelian randomization. Our discoveries imply that the levels of MMP-3 exert a perilous impact on hearing via BMI, offering a potential route for prevention and intercession in populations at risk. This finding emphasizes the significance of regulating MMP-3 levels and BMI as tactics to diminish the likelihood of incurring hearing impairments.