Prenatal Exposure to Endocrine Disrupting Chemical Mixtures and ASD Risk

68名美国儿童中就有1名患有ASD，因此确定可改变的风险因素至关重要。的最新证据 双胞胎和家庭研究支持了起源于子宫内的环境因素的重要作用， 诱发遗传因素产前大脑发育受激素机制的影响很大， 内分泌干扰物（EDCs）穿过胎盘到达没有完全能力的胎儿， 代谢和清除异生物质。暴露于内分泌干扰物是普遍存在的，这种暴露已被链接到 广泛的不良神经发育结果。目前，EDCs作为ASD风险因素的证据 更有限，主要是考虑到在关键时期需要足够的样本量和暴露评估， Windows.在研究内分泌干扰物对ASD的潜在影响时，考虑内分泌干扰物也可能特别重要。 作为混合物。这是因为低剂量的暴露仍然会影响激素水平，激素水平的微小变化 已知具有生物学上重要的后果，EDC混合物的综合作用超过了这些 预期来自可比水平的单次EDC暴露或来自假设简单累加效应的模型 在混合物组分中。拟议的项目独特而有效地利用了EDC的可用性 两个设计相似的妊娠队列（HOME和EARLI队列）中的暴露生物标志物， 474对母子产前EDC混合物暴露与ASD相关表型研究校长 结果将是社会反应量表（SRS），一个有效的家长报告的定量测量 自闭症特征我们将使用一种新的两步统计方法，结合贝叶斯核机器回归 （BKMR）与弹性网络正则化，以评估EDC混合物的累积效应，并突出特定的 化学品驱动混合物协会-了解这些环境影响的关键优先事项 暴露。该方法将应用于一组73种内分泌干扰物的可用生物标志物。我们还将重新运行 这种建模方法在由性别、认知状态和是否存在老年人定义的亚组中进行， 患有ASD的兄弟姐妹（EARLI中的所有受试者都影响了年长的兄弟姐妹），以探索效果修改。 最后，由于产前母体甲状腺激素紊乱与神经发育有关， 结果，我们将应用我们的建模方法来估计复杂的EDC混合物与孕产妇 产前甲状腺激素水平的探索性分析。将对亚组进行敏感性分析 符合ASD诊断标准的儿童。这项研究将调查一个普遍的，可修改的一类候选人 通过生物标志物评估的环境ASD风险因素。我们的工作将是最大的前瞻性研究 迄今为止，EDC混合物对ASD相关结局的影响，并且是第一批采用最新复杂 分析方法承认混合物暴露的复杂性;因此，这里的发现有可能 实质性地推进我们对内分泌干扰物在不良神经发育中的作用的理解。

With 1 in 68 US children affected by ASD, it is critical to identify modifiable risk factors. Recent evidence from twin and family studies supports a substantive role for environmental factors originating in utero in addition to predisposing genetic factors. Prenatal brain development is heavily influenced by hormonal mechanisms, and endocrine disrupting chemicals (EDCs) cross the placenta to reach a fetus that is without full capacity to metabolize and clear xenobiotics. Exposure to EDCs is ubiquitous, and such exposure has been linked to a broad range of adverse neurodevelopmental outcomes. The evidence for EDCs as an ASD risk factor is currently more limited, largely given the need for sufficient sample sizes and exposure assessments during critical windows. In studying potential impacts of EDCs on ASD, it may also be particularly important to consider EDCs as mixtures. This is because low-dose exposure still affects hormone levels, small changes in hormone levels are known to have biologically important consequences, and combined effects of EDC mixtures exceed those expected from single EDC exposures at comparable levels or from models assuming simple additive effects among mixture components. The proposed project uniquely and efficiently leverages the availability of EDC exposure biomarkers in two similarly designed pregnancy cohorts (the HOME and EARLI cohorts) in order to study prenatal EDC mixture exposure and ASD-related phenotype in 474 maternal child dyads. The principal outcome will be the Social Responsiveness Scale (SRS), a validated parent-report measure of quantitative autism traits. We will use a novel two-step statistical approach combining Bayesian Kernel Machine Regression (BKMR) with elastic net regularization to assess the cumulative effect of EDC mixtures and to highlight specific chemicals driving the mixture association - key priorities for understanding the impact of these environmental exposures. This approach will be applied to available biomarkers for a group of 73 EDCs. We will also re-run this modeling approach in subgroups defined by sex, cognitive status, and presence or absence of an older sibling with an ASD (all subjects in EARLI have affected older siblings) in order to explore effect modification. Finally, because prenatal maternal thyroid hormone disruption has been linked to neurodevelopmental outcomes, we will apply our modeling approach to estimate complex EDC mixture associations with maternal prenatal thyroid hormone levels in exploratory analyses. Sensitivity analyses will be performed on the subgroup of children meeting ASD diagnostic criteria. This study will investigate a prevalent, modifiable class of candidate environmental ASD risk factors as assessed through biomarkers. Our work will be the largest prospective study to date of EDC mixture effects on ASD-related outcomes and among the first to employ the latest sophisticated analytic methods that acknowledge the complexity of mixture exposure; thus, findings here have the potential to substantially advance our understanding of the role of EDCs in adverse neurodevelopment.