Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss

The assessment of how environmental mixture exposures affect reproductive health faces difficulties. While knowledge graph networks offer valuable advantages in biological interpretation and prediction, their application in epidemiological studies, particularly in a small sample size setting, remains scarce. We recruited 116 women undergoing in vitro fertilization and embryo transfer (IVF-ET) treatment in Beijing and Yantai City, China. Among them, 55 women were diagnosed with early pregnancy loss (EPL), while 61 achieved clinical pregnancy. Clinical records, and paired hair, serum, and follicular samples were collected, with 16 per- and polyfluoroalkyl substances (PFAS) and 41 metal(loid)s measured. We developed a framework coupled with biological knowledge graph-based networks (BKGNs) and machine learning (ML) to predict EPL. Our BKGNs integrate chemical-specific biological pathways, i.e., Gene Ontology (GO) and protein, with individual-level mixture exposure data. The GO-integrated model, with an area under the curve (AUC) of 0.876, outperformed others (AUC = 0.819), even when the sample size decreased to 60% of the total. Additionally, this framework deciphered critical exposures (e.g., serum selenium and chromium) and biological perturbations (e.g., cell population proliferation and apoptotic nuclear changes), linking mixture exposure to EPL. Our proposed novel framework is both robust and cost-effective, offering a mechanistic lens for predicting exposure-associated health outcomes.

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Publication Details

Journal
Environmental Science & Technology
Published
2025-08-29
DOI
https://doi.org/10.1021/acs.est.5c05389
Citations
7
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
14.40

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article

Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss

Michael S. Bloom, Mengyuan Ren, Han Zhang, Yuanchen Chen et al.
7 citations
Environmental Science & Technology
Pregnancy and preeclampsia studies
14.40
article

Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss

Michael S. Bloom, Mengyuan Ren, Han Zhang, Yuanchen Chen, Bin Wang, Mingliang Fang, Jingchuan Xue, Xikun Han, Shuo Yang, Lili Zhuang, Bo Pan, Tiantian Li, Lu Zhao, Tao Wu
article en
7 citations

Abstract

The assessment of how environmental mixture exposures affect reproductive health faces difficulties. While knowledge graph networks offer valuable advantages in biological interpretation and prediction, their application in epidemiological studies, particularly in a small sample size setting, remains scarce. We recruited 116 women undergoing in vitro fertilization and embryo transfer (IVF-ET) treatment in Beijing and Yantai City, China. Among them, 55 women were diagnosed with early pregnancy loss (EPL), while 61 achieved clinical pregnancy. Clinical records, and paired hair, serum, and follicular samples were collected, with 16 per- and polyfluoroalkyl substances (PFAS) and 41 metal(loid)s measured. We developed a framework coupled with biological knowledge graph-based networks (BKGNs) and machine learning (ML) to predict EPL. Our BKGNs integrate chemical-specific biological pathways, i.e., Gene Ontology (GO) and protein, with individual-level mixture exposure data. The GO-integrated model, with an area under the curve (AUC) of 0.876, outperformed others (AUC = 0.819), even when the sample size decreased to 60% of the total. Additionally, this framework deciphered critical exposures (e.g., serum selenium and chromium) and biological perturbations (e.g., cell population proliferation and apoptotic nuclear changes), linking mixture exposure to EPL. Our proposed novel framework is both robust and cost-effective, offering a mechanistic lens for predicting exposure-associated health outcomes.

Environmental Science & TechnologyVol. 59(37)
Kunming University of Science and Technology (CN), Qingdao University (CN), King University (US), Guangdong University of Technology (CN), Capital University (US), George Mason University (US), Capital Medical University (CN), Chinese Center For Disease Control and Prevention (CN), Peking University (CN), Fudan University (CN), National Health and Family Planning Commission (CN), Beijing Chao-Yang Hospital, Capital Medical University (CN), Health Forecasting (US), Yuhuangding Hospital (CN), Ministry of Education (RO), Global College (CY), Ministry of Education (BN), Affiliated Hospital of Qingdao University (CN), Orszagos Kornyezetegeszsegugyi Intezet (HU), Peking University People's Hospital (CN), Zhejiang Energy Group (China) (CN), Reproductive Science Medical Center (US), Taiwan Centers for Disease Control (TW), Institute for Reproductive Health (US), Zhejiang University of Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Pregnancy and preeclampsia studies
14.40
DOISourceOpenAlex
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