Bridging environmental endocrine-disrupting chemicals and AMI: identification of MERTK as a potential diagnostic candidate for AMI via integrated bioinformatics and clinical validation

Background: Increasing attention has been drawn to the close association between exposure to environmental endocrine-disrupting chemicals (EDCs) and the risk of acute myocardial infarction (AMI). This study aimed to utilize multi-omics approaches to identify EDCs-related diagnostic candidate and elucidate their potential roles in AMI. Methods: This study integrated toxicological targets of 13 representative EDCs with transcriptomic datasets from AMI cohorts. Machine learning algorithms (LASSO and SVM-RFE) were employed to screen feature genes, and model interpretability was achieved via SHAP analysis. Additionally, Summary-data-based Mendelian Randomization (SMR) and clinical validation in plasma samples were utilized to verify the candidate. Results: levels were significantly elevated in AMI patients compared to controls. Conclusion: as a key diagnostic candidate potentially mediating EDC-induced AMI. These findings provide novel molecular targets for environmental risk assessment and precision diagnosis.