Using Machine Learning to Construct the Blood–Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

作者：Guohuan Zhang, Weinan Lin, Ning Gao, Changxin Lan, Mengyuan Ren, Lailai Yan, Bo Pan, Jia Xu, Bin Han, Ligang Hu, Yuanchen Chen, Tianxiang Wu, Lili Zhuang, Qun Lu, Bin Wang, Mingliang Fang · 发表于：Environmental Science & Technology · 年份：2024 · DOI：10.1021/acs.est.3c10904 · 被引用次数：12 · 研究领域：Pregnancy and preeclampsia studies、Birth, Development, and Health、Ovarian function and disorders

Permeabilities of various trace elements (TEs) through the blood–follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood–follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19–4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076–0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was ...