Improving inferences on health effects of chemical exposures

环境污染物对人类健康的不利影响是一个主要的公共卫生问题。 通过我们呼吸的空气，我们都暴露在不同化学污染物的复杂混合物中， 我们喝的水，我们吃的食物，以及我们使用的产品。随着新的工业产品的生产， 导致新的直接和间接暴露，迫切需要新的工具来评估 与接触化学混合物有关的对人类健康的不利影响。挑战包括巨大的 不同可能混合的个数，多元非参数中的维度灾难 不同暴露条件下的回归和中高度相关性。建立在令人信服的初步基础上 结果从一个非常成功的NIEHS Prime程序R01，我们开发了一个变革性的统计 高通量筛查中化学品暴露对健康影响的推断工具箱 为了在流行病学研究中更好地理清化学混合物对健康的影响。这个 研究通过以下目标进行。目标1开发了推断协同作用和 来自流行病学数据的拮抗相互作用，包括纵向动机收集的数据 通过研究暴露对儿童神经发育的影响。我们大大改善了目前的状况 可解释性和检测相互作用能力的非参数回归方法；协同性 化学物质放大彼此影响的相互作用尤为重要。AIM 2开发 分组方法，以提高对暴露与健康相关的变化的理解。这些 方法将对显著提高当前模型的实际性能产生广泛影响- 基于集群的方法。此外，还提供了易于解释的结果，增加了 对最先进的基于回归的方法的见解。目标3开发新的推理方法 化学分子结构与生物活性的关系。鉴于这个庞大的数字 对于缺乏任何直接体内或体外数据的化学物质，利用分子结构来 预测生物活动。利用ToxCast/Tox21等数据源，我们开发了改进的 用于将化学结构与活性相关联的统计模型，用于推断低维总结 基于分子结构的化学活性，并用于优化选择下一步要测试的化学物质。 这些方法可以用来预测缺乏体内或体外直接数据的化学物质的影响。 通过有针对性地借用数据库中相关化学品的信息。AIM 4开发 用户友好和可重现的软件，同时使用这些方法彻底分析来自 推动流行病学研究，特别关注西奈山儿童的环境 健康研究和北卡罗来纳大学早期生活因素研究，两者都侧重于评估暴露对 儿童早期的神经发育。我们期待我们的方法能带来重要的新发现。

Adverse effects of environmental contaminants on human health are a major public health concern. We are all exposed to a complex mixture of different chemical contaminants through the air we breathe, the water we drink, the food we eat, and the products we use. As new industrial products are produced, leading to new direct and indirect exposures, there is a pressing need for new tools for assessing the adverse health effects in humans associated with exposure to chemical mixtures. Challenges include huge numbers of different possible mixtures, the curse of dimensionality in multivariate nonparametric regression and moderate to high correlation in different exposures. Building on compelling preliminary results from a highly successful NIEHS PRIME program R01, we develop a transformative statistical toolbox for inferences on health effects of chemical exposures, both in the high throughput screening context and for better disentangling health effects of chemical mixtures in epidemiology studies. The research proceeds through the following Aims. Aim 1 develops methods for inferring synergistic and antagonistic interactions from epidemiologic data, including for data collected longitudinally motivated by studies of exposure effects on childhood neurodevelopment. We improve substantially over current nonparametric regression approaches in interpretability and power to detect interactions; synergistic interactions in which chemicals amplify each other’s effects are particularly important. Aim 2 develops clustering methods to improve understanding of variation in exposure in relation to health. These methods will have broad impact in dramatically improving practical performance over current model- based clustering approaches. In addition, easily interpretable results are provided, adding additional insights over state-of-the-art regression-based methods. Aim 3 develops new methods for inferring relationships between chemical molecular structure and biologic activity. Given the sheer number of chemicals lacking any direct in vivo or in vitro data, it becomes crucial to use molecular structure to predict biologic activity. Leveraging on ToxCast/Tox21 and other data sources, we develop improved statistical models for relating chemical structure to activity, for inferring low-dimensional summaries of chemical activity based on molecular structure, and for optimally choosing the next chemicals to be tested. These methods can be used to predict effects of chemicals lacking any direct in vivo or in vitro data through targeted borrowing of information across related chemicals in the database. Aim 4 develops user-friendly and reproducible software, while using the methods to thoroughly analyze data from the motivating epidemiology studies, with a particular focus on the Mount Sinai Children’s Environmental Health Study and the UNC Early Life Factors Study, which both focus on assessing exposure effects on neurodevelopment in early childhood. We expect our methods to lead to important new findings.