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

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 dyn...

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Autores principales: Luan, Hemi, Zhao, Hongzhi, Li, Jiufeng, Zhou, Yanqiu, Fang, Jing, Liu, Hongxiu, Li, Yuanyuan, Xia, Wei, Xu, Shunqing, Cai, Zongwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548981/
https://www.ncbi.nlm.nih.gov/pubmed/34755115
http://dx.doi.org/10.34133/2021/9873135
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author Luan, Hemi
Zhao, Hongzhi
Li, Jiufeng
Zhou, Yanqiu
Fang, Jing
Liu, Hongxiu
Li, Yuanyuan
Xia, Wei
Xu, Shunqing
Cai, Zongwei
author_facet Luan, Hemi
Zhao, Hongzhi
Li, Jiufeng
Zhou, Yanqiu
Fang, Jing
Liu, Hongxiu
Li, Yuanyuan
Xia, Wei
Xu, Shunqing
Cai, Zongwei
author_sort Luan, Hemi
collection PubMed
description 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.
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spelling pubmed-85489812021-11-08 Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort Luan, Hemi Zhao, Hongzhi Li, Jiufeng Zhou, Yanqiu Fang, Jing Liu, Hongxiu Li, Yuanyuan Xia, Wei Xu, Shunqing Cai, Zongwei Research (Wash D C) Research Article 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. AAAS 2021-10-18 /pmc/articles/PMC8548981/ /pubmed/34755115 http://dx.doi.org/10.34133/2021/9873135 Text en Copyright © 2021 Hemi Luan et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Science and Technology Review Publishing House. Distributed under a Creative Commons Attribution License (CC BY 4.0).
spellingShingle Research Article
Luan, Hemi
Zhao, Hongzhi
Li, Jiufeng
Zhou, Yanqiu
Fang, Jing
Liu, Hongxiu
Li, Yuanyuan
Xia, Wei
Xu, Shunqing
Cai, Zongwei
Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title_full Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title_fullStr Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title_full_unstemmed Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title_short Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort
title_sort machine learning for investigation on endocrine-disrupting chemicals with gestational age and delivery time in a longitudinal cohort
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548981/
https://www.ncbi.nlm.nih.gov/pubmed/34755115
http://dx.doi.org/10.34133/2021/9873135
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