Dataset Information

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.

ABSTRACT: Endocrine-disrupting chemicals (EDCs) are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy. However, the adverse effects of EDCs in the body of pregnant women were underestimated. The interaction between dynamic concentration of EDCs and endogenous hormones (EHs) on gestational age and delivery time remains unclear. To define a temporal interaction between the EDCs and EHs during pregnancy, comprehensive, unbiased, and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women. We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women. The optimal combination of EHs and EDCs can identify when labor occurs (time to delivery within two and four weeks, AUROC of 0.82). Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time. This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs' mixture effect on pregnancy with potential clinical utilities.

SUBMITTER: Luan H

PROVIDER: S-EPMC8548981 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.

Luan Hemi H Zhao Hongzhi H Li Jiufeng J Zhou Yanqiu Y Fang Jing J Liu Hongxiu H Li Yuanyuan Y Xia Wei W Xu Shunqing S Cai Zongwei Z

Research (Washington, D.C.) 20211018

Endocrine-disrupting chemicals (EDCs) are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy. However, the adverse effects of EDCs in the body of pregnant women were underestimated. The interaction between dynamic concentration of EDCs and endogenous hormones (EHs) on gestational age and delivery time remains unclear. To define a temporal interaction between the EDCs and EHs during pregnancy, comprehensive ...[more]

PMID: 34755115

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Project description:BackgroundPrenatal endocrine disrupting chemical (EDC) exposure has been associated with increased risk of preterm birth. Non-Hispanic Black women have higher incidence of preterm birth compared to other racial/ethnic groups and may be disproportionately exposed to EDCs through EDC-containing hair products. However, research on the use of EDC-associated hair products during pregnancy and risk of preterm birth is lacking. Therefore, the objective of this pilot study was to estimate associations of prenatal hair product use with gestational age at delivery in a Boston, Massachusetts area pregnancy cohort.MethodsThe study population consisted of a subset of participants enrolled in the Environmental Reproductive and Glucose Outcomes (ERGO) Study between 2018 and 2020. We collected self-reported data on demographics and hair product use using a previously validated questionnaire at four prenatal visits (median: 12, 19, 26, 36 weeks' gestation) and abstracted gestational age at delivery from medical records. We compared gestational age and hair product use by race/ethnicity and used linear regression to estimate covariate-adjusted associations of product use and frequency of use at each study visit with gestational age at delivery. Primary models were adjusted for maternal age at enrollment and delivery method.ResultsOf the 154 study participants, 7% delivered preterm. Non-Hispanic Black participants had lower mean gestational age at delivery compared to non-Hispanic White participants (38.2 vs. 39.2 weeks) and were more likely to report ever and more frequent use of hair products. In regression models, participants reporting daily use of hair oils at visit 4 had lower mean gestational age at delivery compared to non-users (β: -8.3 days; 95% confidence interval: -14.9, -1.6). We did not find evidence of associations at earlier visits or with other products.ConclusionsFrequent use of hair oils during late pregnancy may be associated with shorter gestational duration. As hair oils are more commonly used by non-Hispanic Black women and represent potentially modifiable EDC exposure sources, this may have important implications for the known racial disparity in preterm birth.

Project description:ObjectiveTo conduct a comprehensive systematic review and meta-analysis to estimate the relationship between endocrine-disrupting chemicals (EDCs), including polychlorinated biphenyls (PCBs), poly-brominated diphenyl ethers (PBDEs), phthalates (PAEs), and per- and polyfluoroalkyl substances (PFAS) exposure and risk of gestational diabetes mellitus (GDM).MethodsRelevant studies from their inception to November 2021 were identified by searching EMBASE, PubMed, and Web of Science. The cohort and case-control studies that reported effect size with 95% confidence intervals (CIs) of EDC exposure and GDM were selected. The heterogeneity among the included studies was quantified by I2 statistic. Publication bias was evaluated through the Begg and Egger tests.ResultsTwenty-five articles with a total of 23,796 participants were found. Results indicated that exposure to PCBs has a significant influence on the incidence of GDM (OR = 1.14; 95% CI = 1.00--1.31; n = 8). The risk of GDM was found to be associated with PBDE exposure (OR = 1.32; 95% CI = 1.15-1.53; n = 4). PAEs and PFASs exposure were also positively associated with the risk of GDM, with summary ORs of 1.10 (95% CI = 1.03-1.16; n = 7 for PAEs) and 1.09 (95% CI = 1.02-1.16; n = 11 for PFASs), respectively. When only cohort studies were considered, the summary OR between PCBs exposure and the risk of GDM was 0.99 (95% CI = 0.91-1.09; n = 5). Meanwhile, the summary ORs from cohort studies for PBDEs, PAEs, and PFASs exposure were 1.12 (95% CI = 1.00-1.26; n = 2), 1.08 (95% CI = 1.02-1.15; n = 5), and 1.06 (95% CI = 1.00-1.12; n = 8), respectively. The Beggs and Egger tests did not show publication bias, and the sensitivity analyses did not change the results in this meta-analysis.ConclusionThese results support that exposure to certain EDCs, including PCBs, PBDEs, PAEs, and PFAS, increase the risk of GDM. Further large-sample epidemiologic researches and mechanistic studies are needed to verify the potential relationship and biological mechanisms. These results are of public health significance because the daily EDC exposure is expected to increase the risk of GDM development.

Dataset Information

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.

Publications

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.

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