Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development
Complex Mixtures of Endocrine Disrupting Chemicals in Relation to Cognitive Development
批准号:
9893708
负责人:
Elizabeth Atkeson Gibson
金额:
$3.21万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-03-15 至 2022-03-14
关键词:
AddressAgeBig DataBiologicalBiometryBirthCenters for Disease Control and Prevention (U.S.)Chemical ExposureChemicalsChildCognitionCognitiveCognitive deficitsComplexComplex MixturesCosmeticsDataData ScienceDevelopmentDimensionsDiseaseEndocrineEndocrine DisruptorsEngineeringEnrollmentEnvironmental EpidemiologyEnvironmental HealthEnvironmental Risk FactorEpidemiologyExposure toFetal DevelopmentFlame RetardantsFundingFutureGoalsHealthHouseholdIndividualInfrastructureIntelligenceJointsLaboratory ResearchLinkMachine LearningMeasurementMentorsMethodsModelingMothersNational Institute of Environmental Health SciencesNeurodevelopmental DisorderNeurologicNeurotoxinsNewborn InfantOutcomePaintParticipantPathway interactionsPatternPattern RecognitionPhenolsPlacentaPlasticsPolicy MakerPolychlorinated BiphenylsPrevalencePublic HealthReportingReproducibilityResearchResearch DesignResearch PersonnelResearch TrainingRiskRisk FactorsScientistSocietiesSourceStatistical Data InterpretationStructureSupervisionTechniquesTo specifyToxicologyTrainingUmbilical Cord BloodUnited States National Academy of SciencesUrineVulnerable PopulationsWorkbasebisphenol Acareercognitive abilitycognitive developmentcognitive testingcohortconsumer productdata managementdesignepidemiology studyexperiencefundamental researchhealth of the motherhigh dimensionalityin uteroinnovationinterdisciplinary collaborationinterestmodifiable riskmultiple datasetsneurotoxicitynovelphthalatespollutantpolybrominated diphenyl etherprenatal exposurepublic health interventionskillsstatisticstooluser-friendly
中文摘要
项目总结/文摘
英文摘要
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.
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