Integrative Proteomics-Metabolomics Profiling of Umbilical Cord Plasma With Machine Learning Reveals Candidate Biomarkers for Gestational Diabetes Mellitus.

IF 6.1 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Diabetes, Obesity & Metabolism Pub Date : 2026-11-01 Epub Date: 2026-08-17 DOI:10.1111/dom.71241
Rui Zhao, Yidan Zhu, Yuanjie Liu, Zhaoqi Yuan, Haozheng Lu, Aimin Jiang, Tingting Chai, Meiying Guo, Cuiping Xu
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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.

利用机器学习对脐带血浆进行综合蛋白质组学-代谢组学分析,揭示妊娠糖尿病的候选生物标志物。
背景:妊娠期糖尿病(GDM)是一种常见的妊娠并发症,但其分子机制尚不完全清楚。本研究采用多组学策略结合机器学习来识别母胎界面的候选生物标志物和失调分子网络。方法:采用高分辨率蛋白质组学和非靶向代谢组学对61例孕妇的脐带血浆进行分析,其中34例为GDM, 27例为血糖控制正常的孕妇。使用LASSO、random forest和XGBoost对Hub候选生物标志物进行优先排序;采用ROC分析评估其歧视性表现,并通过自举重采样和重复交叉验证进行内部验证。使用mixOmics进行蛋白质组学-代谢组学整合。胎盘组织免疫组化(IHC)和HTR8/SVneo细胞的RT-qPCR分别提供了组织水平和转录水平的验证。结果:蛋白质组学鉴定出37个差异表达蛋白,筛选出afp、ORM1、PRSS2、LACRT和LCN1 5个枢纽蛋白。复合蛋白评分具有很强的歧视性,AUC为0.984 (95% CI: 0.958-1.000)。免疫组化证实了GDM胎盘中这些中枢蛋白的失调,RT-qPCR显示,高糖条件下,AFP、ORM1和PRSS2的mRNA水平持续上调。代谢组学鉴定出185种差异代谢物和3种枢纽代谢物,包括C30H38O7、尼古丁n -氧化物和7(1)-羟叶绿素。复合代谢物评分的AUC为0.991 (95% CI: 0.977 ~ 1.000)。交叉组学整合揭示了一个探索性的蛋白质组-代谢组网络,与锥形包膜形成、角化、体液免疫和维生素/核苷运输相关。结论:脐带血浆的综合蛋白质组学-代谢组学分析揭示了GDM中协调的免疫、屏障和代谢失调,支持探索性的多组学生物标志物开发和未来的机制研究。
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来源期刊
Diabetes, Obesity & Metabolism
Diabetes, Obesity & Metabolism 医学-内分泌学与代谢
CiteScore
10.90
自引率
6.90%
发文量
319
审稿时长
3-8 weeks
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.
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