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Prenatal Exposure to Endocrine Disrupting Chemical Mixtures and ASD Risk

产前接触内分泌干扰化学混合物和自闭症谱系障碍 (ASD) 风险

基本信息

批准号：
10020188
负责人：
Craig J Newschaffer
金额：
$ 36.34万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10020188
关键词：
Adverse effects Affect Automobile Driving Behavior Behavior Disorders Biological Biological Markers Blood Brain Characteristics Chemical Exposure Chemicals Child Cognitive Complex Complex Mixtures Data Development Diagnostic Diffuse Dimensions Dose Educational workshop Endocrine Disruptors Environment Environmental Exposure Environmental Impact Environmental Risk Factor Epidemiology Exposure to Family Study Fetus Genetic Health Heavy Metals Hormonal Hormones Hypothalamic structure Individual Investigation Link Mass Spectrum Analysis Measurable Measures Modeling Modification Mothers National Health and Nutrition Examination Survey National Institute of Environmental Health Sciences Nature Outcome Outcome Measure Parents Pathway interactions Pesticides Phenols Phenotype Pituitary Gland Placenta Polychlorinated Biphenyls Pregnancy Pregnant Women Prospective Studies Proxy Reporting Research Risk Risk Factors Role Running Sample Size Sampling Science Serum Siblings Strategic Planning Subgroup Techniques Thyroid Hormones Toxicology Twin Studies Urine Woman Work Xenobiotics analytical method autism spectrum disorder cohort design disorder risk epidemiology study in utero innovation insight meetings method development modifiable risk neurodevelopment novel offspring phthalates pregnant prenatal prenatal exposure primary outcome proband prospective sex social thyroid disruption trait

项目摘要

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.

美国每 68 名儿童中就有 1 人受到自闭症谱系障碍 (ASD) 的影响，因此识别可改变的风险因素至关重要。最近的证据来自双胞胎和家庭研究支持除了以下因素外，源自子宫的环境因素也发挥着实质性作用：诱发遗传因素。产前大脑发育很大程度上受到荷尔蒙机制的影响，内分泌干扰物 (EDC) 会穿过胎盘到达尚不具备完全代谢能力的胎儿。代谢和清除异生素。接触 EDC 是普遍存在的，并且这种接触与广泛的不良神经发育结果。目前有证据表明 EDC 是 ASD 风险因素更加有限，主要是考虑到在关键时期需要足够的样本量和暴露评估视窗。在研究 EDC 对 ASD 的潜在影响时，考虑 EDC 也可能尤为重要作为混合物。这是因为低剂量暴露仍然会影响激素水平，激素水平的微小变化已知具有重要的生物学后果，并且 EDC 混合物的综合效应超过了这些效应预期来自可比较水平的单次 EDC 暴露或来自假设简单累加效应的模型混合物组分之间。拟议的项目独特且有效地利用了 EDC 的可用性在两个类似设计的妊娠队列（HOME 和 EARLI 队列）中暴露生物标志物，以便研究了 474 名母婴的产前 EDC 混合物暴露和 ASD 相关表型。校长结果将是社会反应量表（SRS），这是一种经过验证的家长报告定量衡量标准自闭症特征。我们将使用一种新颖的两步统计方法，结合贝叶斯核机器回归 (BKMR) 具有弹性网络正则化，以评估 EDC 混合物的累积效应并突出显示特定的推动混合物关联的化学品 - 了解这些环境影响的关键优先事项曝光。该方法将应用于 73 种 EDC 的可用生物标志物。我们也将重新运行这种建模方法是根据性别、认知状态以及是否存在老年人来定义的亚组患有 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，由于产前母亲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 混合物与母体的关联探索性分析中的产前甲状腺激素水平。将对子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存在的、可修改的候选人类别通过生物标志物评估环境 ASD 风险因素。我们的工作将是最大规模的前瞻性研究迄今为止，EDC 混合物对自闭症谱系障碍 (ASD) 相关结果的影响是最先采用最新复杂技术的承认混合物暴露的复杂性的分析方法；因此，这里的发现有可能大大增进了我们对 EDC 在不良神经发育中的作用的理解。