Innovative Methodologic Advances for Mixtures Research in Epidemiology

摘要 人体对化学物质暴露的生物监测产生了大量数据。对这些数据的分析 向流行病学家和生物统计学家提出了一个具有挑战性的问题。这些产品的一个显著特点是 环境数据是，接触总是化学品的混合物，而混合物中的化学品是 通常是中度或高度相关的。一种化学品对任何健康后果的不利影响是 由于暴露水平较低，通常较小。然而，接触混合物中的化学物质的影响可能 积累健康成果并采取协同行动。这个项目的总目标是更好地发展 了解接触化学品混合物的有害健康影响的统计方法。至 为了实现这一目标，我们提出了对现有全基因组复杂性状分析的改进 使接触化学混合物的累积效应和总的相互作用效应 可以用最小的偏差进行估计。我们进一步建议将个别化学影响估计为 通过倾向性分数调整的平均因果效应。这些估计数将作为以下方面的基础 化学品的毒性评估。最后，我们提出了一种灵活的网络分析方法来理解 从接触混合物到健康结果的潜在因果路径。这些方法将应用于 研究团队一直致力于回答重要科学问题的数据集的数量 持久性有机污染物暴露与内分泌和心脏代谢的关系 结果，以及与这些联系相关的生物途径和营养物质。这些数据集还可用作 用于开发和使用实施所述方法的软件包的测试格式。这个 软件包将免费提供给环境研究社区。这样做的结果 项目将极大地提高我们理解众多 在人群中发现的化学物质暴露和有害的健康后果。我们的发展 创新的方法将潜在地促进对调节这些变化的生物途径的研究 并加强我们对营养和其他因素的理解，这些因素可能会部分改善 毒物的不良反应。

Abstract Human biomonitoring for chemical exposures has generated large amounts of data. Analysis of those data presents a challenging problem to epidemiologists and biostatisticians. One prominent characteristic of these environmental data is that exposures are always mixtures of chemicals and the chemicals in a mixture are often moderately or highly correlated. The adverse effect of an individual chemical on any health outcome is usually small due to the low exposure level. However, effects of exposure to chemicals in mixtures can accumulate and act synergistically on health outcomes. The overarching goal of this project is to develop better statistical methods for understanding the detrimental health impacts of exposure to mixtures of chemicals. To accomplish this goal, we propose improvements over the existing genome-wide complex trait analysis approach so that the accumulative effects and the total interaction effects of exposure to chemical mixtures can be estimated with minimal bias. We further propose to estimate the individual chemical effects as the average causal effect through the propensity score adjustment. The estimates will serve as the basis for toxicity assessment of chemicals. Lastly, we propose a flexible network analysis approach to understand the potential causal pathways from exposure to mixtures to health outcomes. The methods will be applied to a number of datasets on which the research team has been working to answer important scientific questions with regards to the associations of persistent organic pollutant exposures with endocrine and cardio-metabolic outcomes, and biological pathways and nutrients relevant to these associations. The datasets also serve as testing formats for developing and using the software package implementing the proposed methods. The software package will be made freely available to environmental research community. The results of this project are expected to substantially improve our ability to understand complex relationships among the many chemical exposures found in human populations and detrimental health outcomes. Our development of innovative methods will potentially facilitate the investigation of biological pathways mediating these relationships and enhance our understanding of nutritional and other factors that may in part ameliorate adverse effects of toxicants.