Cheng et al. investigated the potential association between triclosan (TCS), a common antimicrobial agent, and Alzheimer’s disease (AD). Machine learning models (LASSO, SVM-RFE, RF) were used to screen and validate key TCS-AD genes. Three genes—APP, SLC6A3, and DRD2—emerged as central to this relationship, with DRD2 identified as the most robust core gene. Functional analyses suggested that TCS downregulates these genes, which are critical for neuroplasticity, cognition, and dopaminergic signaling. A logistic regression model incorporating these genes was developed to predict AD risk. However, the study was limited to computational predictions using public datasets and did not account for complex human exposure scenarios. The authors recommend future integration of epigenomic and proteomic data to strengthen biological plausibility.