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.

Machine learning for screening active metabolites with metabolomics in environmental science

“…performed the quantitative analyses of multiple endocrine-disrupting chemicals and metabolites for a longitudinal cohort with 2317 pregnant women, a random forest model with recursive feature elimination was successfully used to predict gestational age with high accuracy, and interpret the mixture effect of endocrine-disrupting chemicals on pregnancy 56 . BKMR based on the kernel machine framework was recently developed to estimate the health effects of multi-pollutant mixtures.…”

“…58 Luan et al performed the quantitative analyses of multiple endocrine-disrupting chemicals and metabolites for a longitudinal cohort with 2317 pregnant women, and a random forest model with recursive feature elimination was successfully used to predict the gestational age with high accuracy, and interpret the mixture effect of endocrine-disrupting chemicals on pregnancy. 59 BKMR based on the kernel machine framework was recently developed to estimate the health effects of multi-pollutant mixtures. Matta et al studied the association between endometriosis and persistent organic pollutants, and BKMR was used to examine the joint effects of complex multi-pollutant mixtures and interactions between chemicals and metabolites.…”

Precise Lipidomics Decipher Circulating Ceramide and Sphingomyelin Cycle Associated with the Progression of Rheumatoid Arthritis

“…For example, certain genetic variants have been linked to an increased risk of developing RA, suggesting that genetic changes can cause the synovial cells to become activated . Additionally, environmental factors, such as smoking or exposure to certain chemicals, can also trigger the activation of synovial cells in individuals with RA. − Synovial cells are the cells that line the joint capsule and play an important role in the development and progression of RA. , Synovial cells are a type of mesenchymal stem cells that line the joint capsule and produce synovial fluid that lubricates the joint. In healthy individuals, these cells secrete proteins and other molecules that help to maintain the joint’s normal function.…”

Editorial: Frontiers in Cardiovascular Medicine: Rising Stars 2021

“…Use of Multi-modal Data and Machine Learning to Improve Cardiovascular Disease Care , by Amal et al . With the rapid development of digital health and machine learning technologies ( 5 , 6 ), data fusion can integrate multiple sources of information to improve the prediction of health risk factors. This paper reviews the state-of-the-art research on the latest techniques in data fusion in the field of cardiovascular medicine.…”