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。 我们都通过呼吸的空气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，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风险，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 不同可能混合物的数量，多元非参数中的维数灾难， 回归和中度至高度相关。基于令人信服的初步 从一个非常成功的NIEHS PRIME程序R 01的结果，我们开发了一个变革性的统计 用于推断化学品接触对健康影响的工具箱， 这是为了更好地解决流行病学研究中化学混合物对健康的影响。的 本研究的目的如下。目的1发展的方法推断协同和 来自流行病学数据的拮抗相互作用，包括纵向收集的数据 通过研究暴露对儿童神经发育的影响。我们大大改善了当前 非参数回归方法在可解释性和检测相互作用的能力方面的作用;协同作用 化学品之间的相互作用放大了彼此的影响，这一点尤其重要。目标2开发 聚类方法，以更好地了解与健康有关的接触变化。这些 方法将在显著提高当前模型的实际性能方面产生广泛的影响， 基于聚类的方法。此外，还提供了易于解释的结果，增加了额外的 对最先进的基于回归的方法的见解。Aim 3开发了新的推理方法 化学分子结构与生物活性之间的关系。考虑到 缺乏任何直接的体内或体外数据的化学物质，使用分子结构来 预测生物活性。利用ToxCast/Tox 21和其他数据源，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 基于分子结构的化学活性，并用于最佳地选择下一个要测试的化学品。 这些方法可用于预测缺乏任何直接体内或体外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 用户友好和可重复的软件，同时使用的方法，彻底分析数据， 激励流行病学研究，特别侧重于西奈山儿童的环境 健康研究和早期生命因素研究，这两项研究都侧重于评估暴露对 儿童早期的神经发育。我们希望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.