Associations of single and multiple organophosphate pesticide exposure with female infertility in the USA: data from the 2015–2018 National Health and Nutrition Examination Survey

Environmental science and pollution research international(2022)

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摘要
Substantial evidence has shown that organophosphate pesticide (OPP) exposure altered the reproductive system functions, such as prolonged menstrual cycles, sexual hormone imbalance, and changes in ovarian weight. However, the association of OPP exposure with female infertility is unclear. We explored the relationships of four single OPP metabolites and their mixed exposure with self-reported infertility among women aged 20–50 in the USA using the data from two cycles (2015–2016 and 2017–2018) of the National Health and Nutrition Examination Survey (NHANES) by multiple logistic regression, Bayesian kernel machine regression (BKMR), and quantile g-computation (QGC). Eventually, 659 females were included in our study. Among these females, 77 participants were ever infertile. Multiple logistic regression showed that the odds ratios (ORs) in the second and third tertiles of dimethylphosphate (DMP) for female infertility were 2.53 (95% confidence interval (95%CI): 1.20–5.32, P value = 0.016) and 2.96 (95%CI: 1.18–7.47, P value = 0.023) compared to the lowest tertile after adjusting for all covariates ( P for trend = 0.022). A significantly positive association between the mixed OPP metabolites and infertility was observed in the BKMR model, in which DMP had the highest posterior inclusion probability ( PIP = 0.741). The QGC model showed similar results, in which OPP metabolite mixtures increased the risk of female infertility, with DMP as a significantly positive contributor to the outcome. This study revealed the potential harm of OPP mixtures for female infertility in the USA, and DMP played the most critical role in female infertility risk among all OPP metabolites.
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关键词
Association,Bayesian kernel machine regression (BKMR),Infertility,NHANES,Organophosphate pesticide,Quantile g-computation (QGC)
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