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

Luan, Hemi1,2; Zhao, Hongzhi2,3; Li, Jiufeng2; Zhou, Yanqiu2; Fang, Jing2; Liu, Hongxiu4,5,6; Li, Yuanyuan4,5,6; Xia, Wei4,5,6; Xu, Shunqing4,5,6; Cai, Zongwei2

2021-10-18

10.34133/2021/9873135

RESEARCH   影响因子和分区

2639-5274

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.

SCI ; EI

英语

第一

National Natural Science Foundation of China[21904058] ; National Key Research and Development Program of China[2019YFC1804602] ; Department of Education of Guangdong Province[2020KZDZX1183]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:000709831200001

AMER ASSOC ADVANCEMENT SCIENCE

20214411085160

Chemicals ; Endocrine disrupters ; Machine learning

Medicine and Pharmacology:461.6 ; Health Care:461.7 ; Chemical Agents and Basic Industrial Chemicals:803 ; Chemical Products Generally:804

Web of Science

1.Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Sch Med, Shenzhen, Peoples R China
2.Hong Kong Baptist Univ, Dept Chem, State Key Lab Environm & Biol Anal, Hong Kong, Peoples R China
3.Nankai Univ, Coll Environm Sci & Engn, Key Lab Pollut Proc & Environm Criteria, Minist Educ, Tianjin 300350, Peoples R China
4.Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, Key Lab Environm & Hlth,Minist Educ, Wuhan 430030, Hubei, Peoples R China
5.Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, Minist Environm Protect, Wuhan 430030, Hubei, Peoples R China
6.Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, State Key Lab Environm Hlth, Wuhan 430030, Hubei, Peoples R China

Luan, Hemi,Zhao, Hongzhi,Li, Jiufeng,et al. Research Article Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort[J]. RESEARCH,2021,2021.

Luan, Hemi.,Zhao, Hongzhi.,Li, Jiufeng.,Zhou, Yanqiu.,Fang, Jing.,...&Cai, Zongwei.(2021).Research Article Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.RESEARCH,2021.

Luan, Hemi,et al."Research Article Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort".RESEARCH 2021(2021).