Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development

内分泌干​​扰化学物质的复杂混合物与认知发展的关系

• 批准号: 9893708
• 负责人: Elizabeth Atkeson Gibson
• 金额: $ 3.21万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2022-03-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9893708
• 关键词: Address; Age; Big Data; Biological; Biometry; Birth; Centers for Disease Control and Prevention (U.S.); Chemical Exposure; Chemicals; Child; Cognition; Cognitive; Cognitive deficits; Complex; Complex Mixtures; Cosmetics; Data; Data Science; Development; Dimensions; Disease; Endocrine; Endocrine Disruptors; Engineering; Enrollment; Environmental Epidemiology; Environmental Health; Environmental Risk Factor; Epidemiology; Exposure to; Fetal Development; Flame Retardants; Funding; Future; Goals; Health; Household; Individual; Infrastructure; Intelligence; Joints; Laboratory Research; Link; Machine Learning; Measurement; Mentors; Methods; Modeling; Mothers; National Institute of Environmental Health Sciences; Neurodevelopmental Disorder; Neurologic; Neurotoxins; Newborn Infant; Outcome; Paint; Participant; Pathway interactions; Pattern; Pattern Recognition; Phenols; Placenta; Plastics; Policy Maker; Polychlorinated Biphenyls; Prevalence; Public Health; Reporting; Reproducibility; Research; Research Design; Research Personnel; Research Training; Risk; Risk Factors; Scientist; Societies; Source; Statistical Data Interpretation; Structure; Supervision; Techniques; To specify; Toxicology; Training; Umbilical Cord Blood; United States National Academy of Sciences; Urine; Vulnerable Populations; Work; base; bisphenol A; career; cognitive ability; cognitive development; cognitive testing; cohort; consumer product; data management; design; epidemiology study; experience; fundamental research; health of the mother; high dimensionality; in utero; innovation; interdisciplinary collaboration; interest; modifiable risk; multiple datasets; neurotoxicity; novel; phthalates; pollutant; polybrominated diphenyl ether; prenatal exposure; public health intervention; skills; statistics; tool; user-friendly

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.

项目总结/摘要 内分泌干扰化学品（EDCs）包括已被广泛使用的多类化学品 在消费品中。越来越多的毒理学和流行病学研究证据表明，内分泌干扰物是 发育神经毒物和EDC暴露在子宫内的关键时期与不良 儿童认知发展不幸的是，目前的研究集中在个别的内分泌干扰物，并在很大程度上忽视了 EDC的联合和交互作用以及EDC混合物的总体效果。为了评估暴露于多种 同时，必须考虑暴露矩阵的高维性和复杂性。 统计分析中化学品之间的相关性结构。为了解决现有方法的局限性，我们 我建议采用一种稳健的技术，这种技术在模式识别和维度方面已经得到很好的确立 减少机器学习。我们的具体目标是使用潜在狄利克雷分配（LDA），一种强大的 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， 已知可穿过胎盘的内分泌干扰物-多溴联苯醚（PBDEs）、多氯联苯 （多氯联苯）、酚类（例如，双酚A）和邻苯二甲酸酯-并表征这些之间的关系 暴露模式和认知发展。LDA是计算机驱动的，因此研究人员不需要 先验地指定模式的数量，并且非负性约束增强了模式的可解释性。 模式识别。对于我们的健康模型，我们将使用监督方法， 为LDA解决方案提供信息，从而能够识别与结果最相关的模式。我们将 利用哥伦比亚儿童环境健康中心的现有基础设施开展这项工作 母亲和新生儿研究，一个纵向出生队列的母子二人组。我们还将建立 通过创建一个用户友好的R包，使其他研究人员可以很容易地应用该方法的可重复性 LDA在环境流行病学中的应用，我们将验证该方法的可移植性和功能性， 单独的队列。这将是第一项评估多种内分泌干扰物相互作用和总体影响的研究。 儿童认知发展，介绍LDA作为一个简单的工具，分析复杂的混合物， 流行病学如果成功，这种方法对环境流行病学有更广泛的影响，因为它可以 很容易应用于其他感兴趣的环境混合物。本提案所涵盖的活动 （研究设计，数据管理，高级统计学，机器学习，数据科学和演示） 调查结果）涵盖了一个科学家进入跨学科领域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。应用经验 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， 开始了作为环境流行病学独立调查员的职业生涯。