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.

有68名美国儿童受ASD影响的人，至关重要的是识别可修改的风险因素。最近的证据双胞胎和家庭研究还支持源自子宫内环境因素的实质性作用，此外易于遗传因素。产前大脑发育受到激素机制的严重影响，并且内分泌破坏化学物质（EDC）越过胎盘，到达没有满足的胎儿代谢并清除异生元。接触EDC的无处不在，这种暴露已与广泛的不良神经发育结果。 EDC作为ASD风险因素的证据目前是更有限的，很大程度上需要在关键期间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也可能特别重要作为混合物。这是因为低剂量暴露仍然会影响激素水平，激素水平的较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从单一EDC暴露于可比级别或假设简单添加效果的模型中的预期在混合组件中。拟议的项目唯一有效地利用了EDC的可用性在两个类似设计的怀孕人群中的暴露生物标志物（家庭和Earli队列），以便研究产前EDC混合物的暴露和与ASD相关的表型474个孕产妇二元组中。校长结果将是社会响应量表（SRS），这是经过验证的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，结合了贝叶斯内核机器回归（BKMR）具有弹性净正则化，以评估EDC混合物的累积效应并突出特定驱动混合物协会的化学品 - 了解这些环境的影响的关键优先事项暴露。该方法将应用于一组73个EDC的可用生物标志物。我们还将重新运行通过性别，认知状况以及较旧的存在或不存在的亚组中的这种建模方法为了探索效果修改，带有ASD的兄弟姐妹（Earli中的所有受试者都影响了较老的兄弟姐妹）。最后，因为产前母体甲状腺激素破坏与神经发育有关结果，我们将采用建模方法来估计与母体的复杂EDC混合物关联探索性分析中的甲状腺激素水平。敏感性分析将在亚组上进行符合ASD诊断标准的儿童。这项研究将研究一系列普遍的，可修改的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混合物对与ASD相关的结果的影响，以及最早采用最新复杂的结果确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促进了我们对EDC在不良神经发育中的作用的理解。