Abstract
The presence of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in various everyday products has raised concerns about their potential impact on prostate health. This study aimed to investigate the effects of different types of PFAS on prostate health, including PFDeA, PFOA, PFOS, PFHxS, and PFNA. To assess the relationship between PFAS exposure and prostate injury, machine learning algorithms were employed to analyze prostate-specific antigen (PSA) metrics. The analysis revealed a linear and positive dose-dependent association between PFOS and the ratio of free PSA to total PSA (f/tPSA). Non-linear dose-response relationships were observed between the other four types of PFAS and the f/tPSA ratio. Additionally, the analysis showed a positive association between the mixture of PFAS and prostate hyperplasia, with PFNA having the highest impact followed by PFOS. These findings suggest that elevated serum levels of PFDeA, PFOA, PFOS, and PFNA are linked to prostate hyperplasia. Therefore, this study utilized advanced machine learning techniques to uncover potential hazardous effects of PFAS exposure on prostate health, specifically the positive association between PFAS and prostate hyperplasia.
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