Abstract

IntroductionPer- and polyfluoroalkyl substances (PFAS) have infiltrated countless everyday products, raising concerns about potential effects on human health, specifically on the cardiovascular system and the development of abdominal aortic calcification (AAC). However, our understanding of this relationship is still limited. ObjectivesThis study aims to investigate the effects of PFAS on AAC using machine learning algorithms. MethodsLeveraging the power of machine learning (ML) technique, extreme gradient boosting (XGBoost), we assessed the relationship between PFAS exposure and AAC risk. We focused on three PFAS compounds, perfluorodecanoic acid (PFDeA), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA) through multiple logistic regression, restricted cubic spline (RCS), and quantile g-computation (QGC) models. To get more insight into the underlying mechanisms, mediation analyses are used to investigate the potential mediating role of fatty acids and blood cell fractions in AAC. ResultsOur findings indicate that elevated serum levels of PFHxS and PFDeA are associated with the increased risk of AAC. The QGC analyses underscore the overall positive association between the PFAS mixture and AAC risk, with PFHxS carrying the greatest weight, followed by PFDeA. The RCS analyses reveal a dose-dependent increase between serum PFHxS concentration and AAC risk in an inverted V-shape way. Moreover, age and PFHxS exposure are identified as the primary factors contributing to abdominal aortic calcification risk in SHapley Additive exPlanation (SHAP) summary plot combined with XGBoost technique. Although PFAS significantly change the profile of fatty acids, we do not find any mediating roles of them in AAC. Despite strong associations between PFAS exposure and hematological indicators, our analysis does not find evidence that these indicators mediate the development of AAC. ConclusionsIn summary, our study highlights the detrimental impact of PFAS on abdominal aortic health and emphasizes the need for further research to understand the underlying mechanisms involved.

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