Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development

项目摘要/摘要 内分泌干扰物(EDCs)包括被广泛使用的多种化学物质 在消费品方面。来自毒理学和流行病学研究的越来越多的证据表明，内分泌细胞 发育神经毒物和宫内关键时期的EDC暴露与不良反应相关 儿童认知发展。不幸的是，目前的研究集中在单个的EDC上，而在很大程度上忽视了 EDC的联合和相互作用效应以及EDC混合物的整体效应。评估暴露于多个 EDCS的同时，必须考虑曝光矩阵的高维和复数 统计分析中跨化学品的相关性结构。为了解决现有方法的局限性，我们 建议采用一种在模式识别和维度方面成熟的可靠技术 减少了机器学习。我们的具体目标是使用潜在狄利克雷分配(LDA)，这是一种健壮性 贝叶斯非负矩阵分解，以确定暴露于四个普遍存在的类别的模式 已知的EDCs与胎盘-多溴联苯醚(PBDEs)、多氯联苯 (多氯联苯)、苯酚(如双酚A)和邻苯二甲酸盐--以及它们之间的关系 暴露模式和认知发展。LDA是经验驱动的，因此研究人员不需要 为了先验地指定模式的数量，非负性约束增强了 识别出了模式。对于我们的健康模型，我们将使用一种监督方法，允许儿童认知评分 向LDA解决方案提供信息，从而能够确定与结果最相关的模式。我们会 利用哥伦比亚儿童环境健康中心现有的基础设施开展这项工作 母亲和新生儿研究，母子双胞胎的纵向出生队列。我们还将建立 通过创建用户友好的R包来实现方法的重复性，以便其他研究人员可以轻松地应用 LDA在环境流行病学中的应用，我们将在一个 单独的队列。这将是第一项评估多个电子数据中心的相互作用和整体影响的研究 儿童认知发展，引入LDA作为分析复杂混合物的直接工具 流行病学。如果成功，这种方法将对环境流行病学产生更广泛的影响。 可轻松应用于其他感兴趣的环境混合物。这项提案所涵盖的活动 (研究设计、数据管理、高级统计、机器学习、数据科学和演示 调查结果)涵盖进入跨学科领域的科学家所需的一套基本研究技能 大数据和精准公共卫生时代的环境流行病学。应用体验 通过开展这项研究，结合教学培训和个人跨学科 指导，包括一个全面的研究培训计划，将作为一个平台，从 从事环境流行病学方面的独立调查员工作。

Project Summary/Abstract Endocrine disrupting chemicals (EDCs) include multiple classes of chemicals that have been used extensively in consumer products. Mounting evidence from toxicological and epidemiological studies suggest EDCs are developmental neurotoxicants, and EDC exposure during the critical in utero period is associated with adverse child cognitive development. Unfortunately, current research focuses on individual EDCs and largely ignores joint and interactive effects of EDCs and the overall effect of the EDC mixture. To assess exposure to multiple EDCs simultaneously, one must consider the high dimensionality of the exposure matrix and the complex correlation structures across chemicals in statistical analyses. To address limitations of existing methods, we propose to adapt a robust technique that is well-established for pattern recognition and dimensionality reduction in machine learning. We specifically aim to use Latent Dirichlet Allocation (LDA), a type of robust Bayesian non-negative matrix factorization, to determine the patterns of exposure to four ubiquitous classes of EDCs known to cross the placenta—polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), phenols (e.g., bisphenol A), and phthalates—and to characterize the relationship between these exposure patterns and cognitive development. LDA is empirically-driven so that the researcher does not need to specify a priori the number of patterns, and the non-negativity constraint enhances the interpretability of the patterns identified. For our health model, we will use a supervised approach that allows child cognitive scores to inform the LDA solution, thereby enabling identification of patterns most relevant to the outcome. We will conduct this work using the existing infrastructure of the Columbia Center for Children’s Environmental Health Mothers and Newborns Study, a longitudinal birth cohort of mother-child dyads. We will also establish reproducibility of the method by creating a user-friendly R package so that other researchers can easily apply LDA in environmental epidemiology, and we will verify transferability and functionality of the method on a separate cohort. This will be the first study to assess the interacting and overall effects of multiple EDCs on child cognitive development, introducing LDA as a straight-forward tool for the analysis of complex mixtures in epidemiology. If successful, this method has broader implications for environmental epidemiology, as it can easily be applied to other environmental mixtures of interest. The activities encompassed by this proposal (study design, data management, advanced statistics, machine learning, data science, and presentation of findings) cover the set of fundamental research skills required by a scientist entering the interdisciplinary field of environmental epidemiology in the era of Big Data and Precision Public Health. The applied experience gained from carrying out this research, in combination with didactic training and individual cross-disciplinary mentoring, comprises a comprehensive research training plan that will serve as a platform from which to launch a career as an independent investigator in environmental epidemiology.