{"title":"Integrative Proteomics-Metabolomics Profiling of Umbilical Cord Plasma With Machine Learning Reveals Candidate Biomarkers for Gestational Diabetes Mellitus.","authors":"Rui Zhao, Yidan Zhu, Yuanjie Liu, Zhaoqi Yuan, Haozheng Lu, Aimin Jiang, Tingting Chai, Meiying Guo, Cuiping Xu","doi":"10.1111/dom.71241","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Gestational diabetes mellitus (GDM) is a common pregnancy complication, yet its molecular mechanisms remain incompletely understood. This study applied a multi-omics strategy combined with machine learning to identify candidate biomarkers and dysregulated molecular networks at the maternal-fetal interface.</p><p><strong>Methods: </strong>Umbilical cord plasma from 61 pregnant women, including 34 with GDM and 27 normoglycemic controls, was analysed using high-resolution proteomics and untargeted metabolomics. Hub candidate biomarkers were prioritised using LASSO, random forest and XGBoost; their discriminatory performance was assessed by ROC analysis and internally validated through bootstrap resampling and repeated cross-validation. Proteome-metabolome integration was performed with mixOmics. Immunohistochemistry (IHC) of placental tissues and RT-qPCR in HTR8/SVneo cells provided tissue-level and transcriptional validation, respectively.</p><p><strong>Results: </strong>Proteomics identified 37 differentially expressed proteins and selected five hub proteins-AFP, ORM1, PRSS2, LACRT and LCN1. The composite protein score demonstrated strong discriminatory performance, with an AUC of 0.984 (95% CI: 0.958-1.000). IHC confirmed the dysregulation of these hub proteins in GDM placentas and RT-qPCR revealed that the mRNA levels of AFP, ORM1 and PRSS2 were consistently upregulated under high-glucose conditions. Metabolomics identified 185 differential metabolites and three hub metabolites, including C<sub>30</sub>H<sub>38</sub>O<sub>7</sub>, nicotine N-oxide and 7(1)-hydroxychlorophyll. The composite metabolite score showed an AUC of 0.991 (95% CI: 0.977-1.000). Cross-omics integration revealed an exploratory proteome-metabolome network associated with cornified envelope formation, keratinisation, humoral immunity and vitamin/nucleoside transport.</p><p><strong>Conclusions: </strong>Integrative proteomic-metabolomic profiling of umbilical cord plasma revealed coordinated immune, barrier and metabolic dysregulation in GDM, supporting exploratory multi-omics biomarker development and future mechanistic studies.</p>","PeriodicalId":158,"journal":{"name":"Diabetes, Obesity & Metabolism","volume":" ","pages":"10261-10277"},"PeriodicalIF":6.1000,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes, Obesity & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/dom.71241","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/8/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Gestational diabetes mellitus (GDM) is a common pregnancy complication, yet its molecular mechanisms remain incompletely understood. This study applied a multi-omics strategy combined with machine learning to identify candidate biomarkers and dysregulated molecular networks at the maternal-fetal interface.
Methods: Umbilical cord plasma from 61 pregnant women, including 34 with GDM and 27 normoglycemic controls, was analysed using high-resolution proteomics and untargeted metabolomics. Hub candidate biomarkers were prioritised using LASSO, random forest and XGBoost; their discriminatory performance was assessed by ROC analysis and internally validated through bootstrap resampling and repeated cross-validation. Proteome-metabolome integration was performed with mixOmics. Immunohistochemistry (IHC) of placental tissues and RT-qPCR in HTR8/SVneo cells provided tissue-level and transcriptional validation, respectively.
Results: Proteomics identified 37 differentially expressed proteins and selected five hub proteins-AFP, ORM1, PRSS2, LACRT and LCN1. The composite protein score demonstrated strong discriminatory performance, with an AUC of 0.984 (95% CI: 0.958-1.000). IHC confirmed the dysregulation of these hub proteins in GDM placentas and RT-qPCR revealed that the mRNA levels of AFP, ORM1 and PRSS2 were consistently upregulated under high-glucose conditions. Metabolomics identified 185 differential metabolites and three hub metabolites, including C30H38O7, nicotine N-oxide and 7(1)-hydroxychlorophyll. The composite metabolite score showed an AUC of 0.991 (95% CI: 0.977-1.000). Cross-omics integration revealed an exploratory proteome-metabolome network associated with cornified envelope formation, keratinisation, humoral immunity and vitamin/nucleoside transport.
Conclusions: Integrative proteomic-metabolomic profiling of umbilical cord plasma revealed coordinated immune, barrier and metabolic dysregulation in GDM, supporting exploratory multi-omics biomarker development and future mechanistic studies.
期刊介绍:
Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.