Applying and advancing modern approaches for studying the joint impacts of environmental chemicals on pregnancy outcomes

早产是一个重大的公共卫生挑战，因为随着时间的推移，早产率不断上升， 对婴儿死亡率、儿童发病率和社会经济成本的影响。的条件 导致早产的原因尚不清楚，尽管环境化学品暴露的影响是 怀疑，但了解甚少。与常见的环境化学品建立联系可能会产生巨大的 公共健康影响，因为许多接触可以通过补救、政策或其他措施加以改变。 干预措施。低出生体重和胎儿生长受限也是一个巨大的公共卫生挑战， 近几十年来，它们也有所增加，可能受到环境中可改变的暴露的影响。 化学品本项目建议利用已建立的波士顿生命密码队列研究（N~ 4，000）， 早产和其他不良妊娠结果的因素，重点是接触以下混合物： 常见的化学物质。我们建议从Lifecodes中选择1，000名单胎分娩， 在怀孕期间多次收集的信息和样本。然后我们将利用最先进的 评估接触化学混合物（全氟和多氟物质）的生物标志物的方法 [PFAS]、邻苯二甲酸酯和邻苯二甲酸酯替代化学品、多环芳烃[PAH]，以及 金属/类金属），以及胎儿的中间效应生物标志物和重复超声测量。 生长，以便提供急需的关于怀孕的环境和其他预测因素的人类数据 结果和有关生物学途径的见解。我们的初步工作结果表明， 氧化应激可能是暴露和结果之间的重要联系，需要更多的研究来探索。 使用所提出的途径特异性生物标志物的深度。为了实现我们的目标，我们将开发创新的 用于分析高维混合物和调解的统计和机器学习方法 中介集，目的是提高我们发现和定义这些关系的能力。最后，一项研究 次级目标是确定导致高暴露的条件/活动，从而为减少暴露提供信息 战略布局这项研究的预期成果是新的和急需的信息的规模， 接触常见化学品的影响，包括单独接触和混合接触， 以及确定相关生物途径和评估 接触混合物对健康的影响。我们的发现将对公共卫生产生重大影响， 广泛接触目标化学品，越来越需要确定环境因素， 影响怀孕，以及需要发现美国早产率高的原因， 超过这一点是可以避免的。我们的研究也将提供新的信息，氧化应激的作用， 不良妊娠结局可能为未来的治疗或预防干预提供信息， 新的统计和机器学习方法，用于调查混合物，调解和出生结果。

Preterm birth is a significant public health challenge due to increasing rates over time, as well as serious consequences for infant mortality, childhood morbidity, and economic costs to society. Conditions that contribute to preterm birth remain unclear, though an influence by environmental chemical exposures is suspected but poorly understood. Establishing links with common environmental chemicals could have huge public health impact since many exposures could be modifiable through remediation, policies or other interventions. Low birth weight and fetal growth restriction also represent a great public health challenge, as they too have increased in recent decades and may be influenced by modifiable exposures to environmental chemicals. This project proposes to leverage the established Boston Lifecodes cohort study (N~4,000) of risk factors for preterm birth and other adverse pregnancy outcomes, with a focus on exposure to mixtures of commonly-encountered chemicals. We propose to select 1,000 singleton births from Lifecodes with detailed information and samples collected at multiple times during pregnancy. We will then utilize state-of-the-art methods to estimate biomarkers of exposure to chemical mixtures (per- and polyfluorinated substances [PFAS], phthalates and phthalate replacement chemicals, polycyclic aromatic hydrocarbons [PAH], and metals/metalloids), in addition to intermediate biomarkers of effect and repeated ultrasound measures of fetal growth, in order to provide much needed human data on environmental and other predictors of pregnancy outcomes and insights on the biological pathways involved. Results from our preliminary work show that oxidative stress may be an important link between exposure and outcome that needs to be explored in more depth using the proposed pathway-specific biomarkers. To accomplish our aims we will develop innovative statistical and machine learning approaches for analyzing mixtures and mediation with high-dimensional mediator sets, with the goal of improving our ability to discover and define these relationships. Finally, a study sub-aim is to identify conditions/activities contributing to high exposures that can inform exposure reduction strategies. The expected outcomes of this study are new and much needed information on the magnitude, sources, and impacts of exposure to commonly encountered chemicals, both individually and in combination, among pregnant women, and innovative methods for identifying relevant biological pathways and assessing health impacts from exposure to mixtures. Our findings will have a significant impact on public health given widespread exposure to the target chemicals, the growing need to identify environmental agents that adversely impact pregnancy, and the need to discover contributors to the high rates of preterm birth in the U.S. and beyond that could be prevented. Our study will also provide new information on the role of oxidative stress in adverse pregnancy outcomes which may inform future therapeutic or preventative interventions, and contribute new statistical and machine learning methods for investigating mixtures, mediation, and birth outcomes.