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BACKGROUND: While perfluoroalkyl substances (PFASs) exposure has been associated with autoimmune diseases (ADs), the robustness of these associations remains uncertain due to potential confounding and reverse causation in observational studies. This study aims to explore the potential relationship and underlying mechanisms between PFASs exposure and ADs risk. METHODS: We utilized Mendelian randomization (MR) and generalized summary-data-based MR (GSMR) to investigate potential associations between exposure to two major PFASs (perfluorooctanoic acid PFOA and perfluorooctanesulfonic acid PFOS) and the risk of ten ADs, using genetic data exclusively from European-ancestry populations. For significant associations, we subsequently conducted network toxicology analysis, applied machine learning algorithms to systematically screen and prioritize key genes, and assessed immune infiltration analysis to elucidate mechanisms. We then validated these findings through molecular docking and molecular dynamics simulations. RESULTS: MR and GSMR analyses demonstrated a significant association between PFOS exposure and an increased risk of multiple sclerosis (MS). Among 107 intersecting candidate genes, four genes (CASP3, STAT3, ESR1, and GSK3B) were identified as key target genes through machine learning. Molecular docking and molecular dynamics simulations further confirmed stable binding interactions between PFOS and these proteins. Additionally, immune infiltration analysis suggested these genes may contribute to immune microenvironment dysregulation in MS. Pathway analysis indicated that PFOS potentially influences MS pathogenesis through PPAR signaling and efferocytosis. CONCLUSIONS: Our integrated analysis reveals evidence supporting an association between PFOS and MS and uncovers underlying molecular mechanisms. These findings establish a foundation for further investigation into the role of environmental pollutants in autoimmune disorders.
Liu et al. (Wed,) studied this question.
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