Triclosan exposure potentiates ischemic stroke risk: Multi-omics integration and molecular docking unveil neurotoxic mechanisms

Abstract

This study applied network toxicology and multimodal biological approaches integrated with machine learning to systematically identify four TCS-IS-related genes, providing a comprehensive understanding of the pathophysiological relationship between triclosan exposure and ischemic stroke (IS). A predictive model for TCS-associated IS risk was developed, complemented by mechanistic insights through the construction of miRNA-TF-mRNA regulatory networks and GeneMANIA functional prediction maps. SHAP analysis clarified feature contributions, confirming the model's robustness. Molecular docking analysis further validated the heightened genetic vulnerability to TCS exposure in IS pathogenesis. The research offers two key contributions: 1) A quantifiable framework for assessing environmental toxicant risks in cerebrovascular diseases, and 2) Strong evidence advocating for urgent innovation in biocompatible material and enhanced regulatory oversight of TCS use. These findings fill critical gaps in environmental neurotoxicology and establish new paradigms for predictive toxicological modeling. SYNOPSIS: Widespread triclosan exposure exacerbates human stroke risk, urging prioritized regulation of this persistent environmental toxicant in consumer and medical products.

Keywords: Ischemic stroke; Machine learning; Molecular docking; SHAP; TCS; Triclosan.