Diabetes, obesity & metabolism · 2026
Integrative Proteomics-Metabolomics Profiling of Umbilical Cord Plasma With Machine Learning Reveals Candidate Biomarkers for Gestational Diabetes Mellitus.
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Zhao Rui, Zhu Yidan, Liu Yuanjie, Yuan Zhaoqi, Lu Haozheng, Jiang Aimin, Chai Tingting, Guo Meiying, Xu Cuiping
Аннотация
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.
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.