Association Between Exposure to Organophosphorus Pesticides and the Risk of Diabetes Among US Adults: Cross-Sectional Findings From the National Health and Nutrition Examination Survey

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Objective: Organophosphorus pesticides (OPPs) are commonly used pesticides across the world, however there is little epidemiological evidence linking their exposure to diabetes. Hence, this study aimed at investigating the effect of OPP exposure on the prevalence of diabetes in American adults.

Methods: Adults (≥20 years old) were eligible for this study from the National Health and Nutrition Examination Survey (NHANES). Multivariate logistic regression model was employed to explore the associations of six main urinary OPPs metabolites with diabetes. Subgroup analyses were performed by age and gender. Combined effect of OPPs metabolites on the overall association with diabetes was evaluated by weighted quantile sum regression (WQS). Furthermore, Bayesian kernel machine regression (BKMR) model was implemented to explore joint effect of multiple OPPs metabolites on diabetes.

Results: Ultimately, 6,593 adults were included in our analysis. Of them, 1,044 participants were determined as diabetes patients. The results of logistic regression shown that urinary OPPs metabolites concentrations, whether taken as continuous variables or quantiles, were in positive correlation with diabetes. Notably, the p for trend of diethylphosphate (DEP), a kind of OPPs metabolites, was less than 0.05 indicated that a linear trend may exist between levels of DEP and prevalence of diabetes among adults while this trend was not obversed in other OPPs metabolites. In the WQS model, combined exposure of OPPs metabolites had a significantly positive association with diabetes (OR: 1.057; 95% CI: 1.002, 1.114) and diethylphosphate (36.84%) made the largest contributor to the WQS index. The result of BKMR also suggested a positive trend of association between mixed OPPs metabolites and diabetes.

Conclusion: Our results add credibility to the argument that OPP exposure might trigger diabetes. Certainly, prospective data are required to corroborate our findings.



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Bayesian kernel machine regression, diabetes, joint effect, organophosphorus pesticides, weighted quantile sum regression