{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:41:19Z","timestamp":1792384879053,"version":"4.4.0"},"reference-count":58,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024QH289"],"award-info":[{"award-number":["ZR2024QH289"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82404261"],"award-info":[{"award-number":["82404261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) is a common pregnancy complication, yet its molecular mechanisms remain incompletely understood. This study applied a multi\u2010omics strategy combined with machine learning to identify candidate biomarkers and dysregulated molecular networks at the maternal\u2013fetal interface.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Umbilical cord plasma from 61 pregnant women, including 34 with GDM and 27 normoglycemic controls, was analysed using high\u2010resolution 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\u2010validation. Proteome\u2013metabolome integration was performed with mixOmics. Immunohistochemistry (IHC) of placental tissues and RT\u2010qPCR in HTR8\/SVneo cells provided tissue\u2010level and transcriptional validation, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Proteomics identified 37 differentially expressed proteins and selected five hub proteins\u2014AFP, ORM1, PRSS2, LACRT and LCN1. The composite protein score demonstrated strong discriminatory performance, with an AUC of 0.984 (95% CI: 0.958\u20131.000). IHC confirmed the dysregulation of these hub proteins in GDM placentas and RT\u2010qPCR revealed that the mRNA levels of\n                      <jats:italic>AFP<\/jats:italic>\n                      ,\n                      <jats:italic>ORM1<\/jats:italic>\n                      and\n                      <jats:italic>PRSS2<\/jats:italic>\n                      were consistently upregulated under high\u2010glucose conditions. Metabolomics identified 185 differential metabolites and three hub metabolites, including C\n                      <jats:sub>30<\/jats:sub>\n                      H\n                      <jats:sub>38<\/jats:sub>\n                      O\n                      <jats:sub>7<\/jats:sub>\n                      , nicotine N\u2010oxide and 7(1)\u2010hydroxychlorophyll. The composite metabolite score showed an AUC of 0.991 (95% CI: 0.977\u20131.000). Cross\u2010omics integration revealed an exploratory proteome\u2013metabolome network associated with cornified envelope formation, keratinisation, humoral immunity and vitamin\/nucleoside transport.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Integrative proteomic\u2013metabolomic profiling of umbilical cord plasma revealed coordinated immune, barrier and metabolic dysregulation in GDM, supporting exploratory multi\u2010omics biomarker development and future mechanistic studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71241","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:04:45Z","timestamp":1787018685000},"page":"10261-10277","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Proteomics\u2013Metabolomics Profiling of Umbilical Cord Plasma With Machine Learning Reveals Candidate Biomarkers for Gestational Diabetes Mellitus"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2842-2721","authenticated-orcid":false,"given":"Rui","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Clinical Nutrition The First Affiliated Hospital of Shandong First Medical University &amp; Shandong Provincial Qianfoshan Hospital  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yidan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Nursing Shandong First Medical University  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Nursing Shandong First Medical University  Jinan Shandong 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