Revealing environmental chemical exposome in a diverse prenatal population and relationships to maternal and perinatal health (REVEAL)

揭示不同产前人群的环境化学暴露及其与孕产妇和围产期健康的关系 (REVEAL)

• 批准号: 10587408
• 负责人: Tracey J. Woodruff
• 金额: $ 59.41万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587408
• 关键词: Acceleration; Address; Affect; Analytical Chemistry; Binding; Biological; Biological Markers; Biological Monitoring; Birth Weight; Blood; Blood specimen; Chemical Exposure; Chemicals; Complex; Computational Technique; Computer software; Computing Methodologies; Concentration measurement; Data; Data Analyses; Databases; Development; Disease; Environmental Exposure; Evaluation; Exposure to; Gestational Age; Gestational Diabetes; Goals; Health; Human; Late pregnancy; Left; Link; Low Birth Weight Infant; Mass Spectrum Analysis; Maternal Health; Measurement; Measures; Methods; Modeling; Newborn Infant; Outcome; Output; Participant; Pathway interactions; Pattern; Persons; Phenotype; Plastics; Poly-fluoroalkyl substances; Population; Population Heterogeneity; Predisposition; Pregnancy; Pregnancy Outcome; Pregnancy Trimesters; Premature Birth; Prevalence; Production; Regulation; Research; Resolution; Risk; Rubber; Sampling; Second Pregnancy Trimester; Serum; Shotguns; Siloxanes; Software Tools; Source; Stains; Supercomputing; Techniques; Technology; Third Pregnancy Trimester; advanced analytics; adverse maternal outcomes; adverse pregnancy outcome; analytical method; analytical tool; chemical group; computerized tools; dashboard; data integration; data tools; environmental chemical; fetal; improved; lipid metabolism; machine learning algorithm; maternal serum; metabolome; novel; novel strategies; open source; perinatal health; perinatal outcomes; personal care products; pregnant; prenatal; prenatal exposure; prevent; public health intervention; study population; surfactant

ABSTRACT - Revealing environmental chemical exposome in a diverse prenatal population and relationships to maternal and perinatal health (REVEAL) Our renewal application builds on our original R01 by advancing analytical chemistry and computational techniques to characterize more fully the prenatal chemical exposome and its links to adverse maternal and perinatal health outcomes (e.g., gestational diabetes, preterm birth, and low birthweight). Prevalence of these outcomes has increased and is linked to chemical exposures in the US population. Prenatal exposures to a broad range of environmental chemicals, including plastic related chemicals and per- and polyfluoroalkyl substances (PFAS), have been shown to adversely affect health, but technological challenges have left significant data gaps hindering our ability to characterize environmental exposures and drivers of disease. Over 350,000 chemicals and compound mixtures are registered for production worldwide, and thousands of high–production volume chemicals are used extensively in the US, yet human biomonitoring capacity covers a very small fraction of the chemical exposome. In our previous R01 we pioneered a nontargeted analysis (NTA) workflow, with significantly improved computational and analytical methods using high-resolution mass spectrometry (HRMS) in a demographically diverse population of maternal-newborn pairs. We were the first to identify multiple chemicals in maternal and newborn sample used in high volumes in the US not previously detected in human blood samples including chemicals used in personal care products and fragrances, production of plastics, surfactants, and stain treatment. We also identified over 50 novel chemicals with limited to no information on their uses or sources and found significant associations between exogenous and endogenous compounds, several chemical groups (siloxanes, PFAS, and chemicals used in plastics and rubber) and biomarkers of lipid metabolism and regulation, supporting a link to gestational diabetes. For our renewal project, we propose to expand our study population by analyzing an additional 750 2nd trimester pregnancy samples, 300 of the samples matched to our original 3rd trimester samples. We will contribute to improved understanding of the prenatal chemical exposome by integrating NTA with improved computational methods including a chemical suspect database ~1,000,000 exogenous and endogenous chemical features in combination with upgraded software for supercomputing to identify ~500 chemical prenatal exposures and estimate their concentrations using new machine learning algorithms. We will use this data to characterize complex relationships between multiple exogenous chemical exposures, endogenous effect biomarkers, and maternal and perinatal health outcomes. The overarching goal of this renewal is to develop an integrated package of analytic and computational tools that allow expanded capacity to identify and calculate chemical concentrations and their link to adverse maternal and pregnancy outcomes, critical to information public health interventions to prevent harmful chemical exposures.

摘要-揭示环境化学暴露在不同的产前人群和

项目成果

Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites.

• DOI: 10.1038/s41598-021-87070-9
• 发表时间: 2021-04-07
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Bessonneau V;Gerona RR;Trowbridge J;Grashow R;Lin T;Buren H;Morello-Frosch R;Rudel RA
• 通讯作者: Rudel RA

Translating community-based participatory research into broadscale sociopolitical change: insights from a coalition of women firefighters, scientists, and environmental health advocates.

• DOI: 10.1186/s12940-023-01005-7
• 发表时间: 2023-08-30
• 期刊: ENVIRONMENTAL HEALTH
• 影响因子: 6
• 作者: Ohayon, Jennifer Liss;Rasanayagam, Sharima;Rudel, Ruthann A.;Patton, Sharyle;Buren, Heather;Stefani, Tony;Trowbridge, Jessica;Clarity, Cassidy;Brody, Julia Green;Morello-Frosch, Rachel
• 通讯作者: Morello-Frosch, Rachel