Improving inferences on health effects of chemical exposures

改进对化学品暴露对健康影响的推断

• 批准号: 10753010
• 负责人: David Brian Dunson
• 金额: $ 42.7万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10753010
• 关键词: Adverse effects; Air; Assessment tool; Biological; Breathing; Chemical Exposure; Chemical Structure; Chemicals; Child; Childhood; Complex Mixtures; Computer software; Data; Data Set; Data Sources; Databases; Detection; Dimensions; Eating; Environment; Environmental Health; Environmental Pollutants; Epidemiology; Exposure to; Food; Funding; Goals; Health; Human; In Vitro; Individual; Industrial Product; Journals; Life; Literature; Methods; Modeling; Molecular Structure; National Health and Nutrition Examination Survey; National Institute of Environmental Health Sciences; Outcome; Output; Performance; Public Health; Publishing; Reproducibility; Research; Research Project Grants; Risk; Statistical Models; Testing; Time; Toxic effect; Toxicology; Variant; Water; Weight; adverse outcome; early childhood; epidemiologic data; epidemiology study; exposed human population; flexibility; high throughput screening; improved; in vivo; innovation; insight; neurodevelopment; programs; response; screening; simulation; software development; tool; user-friendly

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.

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于不同化学污染物的复杂混合物， 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 与暴露于化学混合物有关的人类的不良健康影响。挑战包括巨大 不同可能的混合物的数量，多元非参数中的维度曲线 回归和中等至高相关性。基于引人注目的初步 来自非常成功的NIEHS PRIME计划R01的结果，我们开发了一个变革性统计数据 在高通量筛选中推断化学暴露对健康影响的推断工具箱 在流行病学研究中，环境和更好地解散化学混合物的健康效应。 研究通过以下目标进行。 AIM 1开发方法用于推断协同作用和 来自流行病学数据的拮抗相互作用，包括收集的纵向动机的数据 通过研究对儿童神经发育的影响。我们比当前大大改善 可解释性和能力检测相互作用的非参数回归方法；协同作用 化学物质扩大彼此影响的相互作用尤为重要。 AIM 2发展 聚类方法，以提高对健康暴露变化的理解。这些 与当前模型相比 基于聚类的方法。此外，还提供了易于解释的结果，增加了其他 关于基于最新回归的方法的见解。 AIM 3开发了推断的新方法 化学分子结构与生物学活性之间的关系。给定数字 在缺乏任何直接体内或体外数据的化学物质中，使用分子结构至关重要 预测生物活性。利用Toxcast/Tox21和其他数据源，我们开发了改进 将化学结构与活动相关的静态模型，用于推断 基于分子结构的化学活性，以及​​最佳选择要测试的下一种化学物质。 这些方法可用于预测缺少任何直接体内或体外数据的化学物质的影响 通过针对数据库中相关化学物质的信息的有针对性借款。 AIM 4发展 用户友好且可重现的软件，同时使用这些方法来彻底分析来自 激励流行病学研究，特别关注西奈山儿童的环境 健康研究和UNC早期生命因素研究，两者都集中于评估对暴露的影响 童年时期的神经发育。我们希望我们的方法会带来重要的新发现。