Related Experiment Video
Updated: Jul 15, 2026
Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
Published on: April 26, 2018
Interpretable machine learning-based insights into early-life endocrine disruptor exposure and small vulnerable
Luhan Yang1, Yuxian Liu2, Henglin Zhang3
1Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China; School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
None:
Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newborns, including conditions such as being small for gestational age (SGA) and preterm birth (PTB), yet evidence remains limited. This study, which is based on 739 mother-infant pairs in the Chinese Jiashan Birth Cohort (2016-2018), including 39 SGA and 38 PTB cases, employed interpretable machine learning to elucidate the isolated effects of 34 EDCs on SGA and PTB risk and sex interactions in a multi-substance exposure context. Extra Trees and CatBoost classifiers performed best for SGA and PTB, respectively, achieving sensitivities of 0.60 and 0.73 and specificities of 0.82 and 0.97. For SGA, key predictors included bisphenol A (2,3-dihydroxypropyl) glycidyl ether (BADGE-H2O), benzophenone (bZp), bisphenol A bis(2,3-dihydroxypropyl) ether (BADGE-2H2O), propyl paraben (PrP), and 2-methylthio-benzothiazole (2-Me-S-BTH). Lower exposures to BADGE-H2O, bZp, and BADGE-2H2O (concentrations below 0.21, 4.22, and 0.93 μg·g-1 creatinine, respectively) and higher exposure to 2-Me-S-BTH (above 0.15 μg·g-1 creatinine) were both associated with increased SGA risk. Notably, BADGE-H2O, BADGE-2H2O, and PrP showed significant interactions with fetal sex. For PTB, key predictors included ethyl paraben (EtP), methyl paraben (MeP), bZp, BADGE-H2O, and 1H-benzotriazole (1-H-BTR). Lower BADGE-H2O and higher EtP and bZp exposures increased PTB risk (< 0.10 and > 0.01 and 0.60 μg·g-1 creatinine, respectively). Male fetuses appeared more susceptible to EtP and MeP, and female fetuses were more susceptible to 1-H-BTR. Bayesian kernel machine regression was performed to compare the results. This study demonstrated the potential of interpretable machine learning in environmental epidemiology.
