Prenatal Exposure to Pesticide Mixtures and Childhood ADHD

产前有机磷农药（OPs）生物标志物水平与标志物 注意力缺陷多动障碍（ADHD）的症状，包括工作记忆、社交 反应性和ADHD指数1 -4。横断面研究也将同时使用的拟除虫菊酯农药 代谢物与ADHD和儿童ADHD行为的关系5 -7。毒理学研究报告了这些化合物的混合效应 农药对健康结果的影响8 -10。该文献中存在三个主要差距：1）没有研究评估 产前和儿童暴露于这些农药和临床多动症诊断。2)产前 OP/拟除虫菊酯的流行病学研究通常依赖于尿DAP生物标志物， 无毒代谢物和有毒母体农药11。从现场尿液样本的尿液生物标志物也做 不能表征妊娠期广泛性流产12，13，因为OP和拟除虫菊酯代谢迅速14，15。第三章 农药混合物的流行病学研究很少，生物标志物用于此类混合物的分析也很困难。 有问题例如，毒死蜱（一种OP）的施用导致组织中的 氯氰菊酯，同时减少拟除虫菊酯代谢物3-苯氧基苯甲酸的尿排泄16。因此 混合物本身可能会影响生物标志物水平，并增加暴露错误分类。地理空间框架， 农药暴露可以解决尿液生物标志物的一些局限性：农业 农药的应用可以估计为整个怀孕，而不是几天;估计反映了 有毒母体农药，而不是无毒OP代谢物;估计数反映了实际接触情况，而不是 代谢后的水平。然而，地理空间（GIS）方法的农药接触评估， 美国的流行病学研究仅在加利福尼亚进行过17 -20，并且通常依赖于距离到现场的测量。 GIS暴露可能会增加漂移模型，包括热量，湿度，逆温，大气 稳定性，和风21，22，而外部效度可能会增加研究人口以外的加州。 我们建议在亚利桑那州的人口中评估OP，拟除虫菊酯和ADHD之间的关系。到 在确定研究人群时，我们将应用经验证的具有特殊诊断的表型分型算法23， 亚利桑那州医疗补助记录，以确定4,000名儿童多动症病例和16,000名对照。在辅导阶段， 候选人将开发地理空间、表型、暴露评估、混合建模（贝叶斯核 机器回归[BKMR]），以及构建病例对照研究时的机器学习技能。在R 00 在第一阶段，候选人将在频率论中将漂移模型与传统的距离-场测量进行比较。 框架（目标2），以及产前OP和拟除虫菊酯农药混合物与ADHD之间的模型关联 BKMR（目标3）。这些结果将扩大地理信息系统的研究超越加州，有助于稀疏，但关键的 关于农药混合物和神经发育的文献，并成为第一个报告农药混合物和神经发育之间的联系的人。 产前农药暴露和ADHD病例状态的GIS估计。

Prenatal levels of organophosphorus pesticide (OPs) biomarkers have been associated with hallmark symptoms of Attention Deficit Hyperactivity Disorder (ADHD), including deficits in working memory, social responsiveness, and ADHD indices1-4. Cross-sectional studies have also linked concurrent pyrethroid pesticide metabolites with ADHD and ADHD behaviors in children5-7. Toxicology studies report mixture effects for these pesticides on health outcomes8-10. Three major gaps exist in this literature: 1) No studies have evaluated prenatal and childhood exposures to these pesticides and clinical ADHD diagnoses. 2) Prenatal epidemiological studies of OPs/pyrethroids typically rely on urinary DAP biomarkers, which reflect ingestion of both non-toxic metabolites and toxic parent pesticides11. Urinary biomarkers from spot urine samples also do not characterize pregnancy-wide exposures12,13, since OPs and pyrethroids are rapidly metabolized14,15. 3) Studies of pesticide mixtures in epidemiology are scarce, and use of biomarkers for such mixtures analysis is problematic. For instance, administration of chlorpyrifos (an OP) results in increased tissue concentrations of cypermethrin while reducing urinary excretion of the pyrethroid metabolite 3-phenoxbenzoic acid16. Thus, the mixture itself may affect biomarker levels and increase exposure misclassification. A geospatial framework for pesticide exposure can address some of the limitations of urinary biomarkers: exposures from agricultural pesticide applications can be estimated for an entire pregnancy rather than a few days; estimates reflect the toxic parent pesticide rather than non-toxic OP metabolites; and estimates reflect actual exposures rather than a post-metabolism level. However, geospatial (GIS) methods of pesticide exposure assessment for epidemiology in the US have only been done in California17-20, and usually rely on distance-to-field measures. GIS exposure may be enhanced with drift models that incorporate heat, humidity, inversions, atmospheric stability, and wind21,22, while external validity may be increased by studying a population outside of California. We propose to assess the relationship between OPs, pyrethroids, and ADHD in an Arizona population. To identify a study population, we will apply a validated phenotyping algorithm with exceptional diagnostics23 to Arizona Medicaid records to identify 4,000 childhood ADHD cases and 16,000 controls. In the mentored phase, the Candidate will develop geospatial, phenotyping, exposure assessment, mixture modeling (Bayesian Kernel Machine regression [BKMR]), and machine learning skills while constructing the case-control study. In the R00 phase, the Candidate will compare the drift model against traditional distance-to-field measures in a frequentist framework (Aim 2), and model associations between prenatal OP and pyrethroid pesticide mixtures and ADHD with BKMR (Aim 3). These results will expand GIS studies beyond California, contribute to sparse but critical literature on pesticide mixtures and neurodevelopment, and be among the first to report associations between GIS estimates of prenatal pesticide exposures and ADHD case status.