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Background <p>Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, leading to adverse maternal and neonatal outcomes. Exosomal microRNAs (exo-miRNAs) have emerged as promising noninvasive biomarkers due to their stability and regulatory roles in glucose metabolism. However, robust diagnostic models integrating exo-miRNAs profiles for early prediction of GDM remain lacking.</p> Methods <p>In this study, we used the GSE192813 dataset as a discovery cohort to identify differentially expressed exo-miRNAs (DE-exo-miRNAs) in exosomes between GDM and normal glucose tolerance (NGT) pregnancies. After differential expression analysis, five machine learning (ML) feature selection algorithms (LASSO, Random Forest, SVM-RFE, XGBoost, and Boruta) were applied to identify robust predictive DE-exo-miRNAs features. Subsequently, ten classification algorithms (including Logistic Regression, Random Forest, SVM, XGBoost, LightGBM, CatBoost, KNN, Naïve Bayes, Neural Network, and Decision Tree) were combined with the five feature-selection methods, generating 50 distinct ML models. Model performance was evaluated through repeated 7:3 train-test splits, and the best-performing classifier was externally validated using GSE114860.</p> Results <p>A total of 12 DEmiRNAs were identified in GSE192813, of which a subset of key exo-miRNAs (including miR-423-5p, miR-99a-5p, miR-148a-3p, miR-192-5p, and miR-122-5p) were consistently selected across multiple algorithms. Among the 50 ML combinations, the XGBoost + Boruta model achieved the highest diagnostic accuracy, with an AUC exceeding 0.90 and an overall accuracy greater than 90% in the discovery dataset. External validation in GSE114860 demonstrated stable performance, achieving an accuracy above 80% and good calibration. Functional enrichment analysis of target genes indicated significant involvement in insulin signaling, lipid metabolism, and inflammatory pathways.</p> Conclusion <p>This integrative machine learning framework successfully identified a robust exo-miRNAs-based predictive signature for GDM. The model exhibited high diagnostic accuracy and generalizability across independent cohorts, highlighting its potential for early, noninvasive screening and precision management of gestational diabetes mellitus.</p>

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Identification of exosomal miRNA-based predictive signatures for gestational diabetes mellitus via multi-algorithm machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Peihan Jiang,
  • Jie Huang,
  • Shuxun Wang,
  • Chunxiu Dai

摘要

Background

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, leading to adverse maternal and neonatal outcomes. Exosomal microRNAs (exo-miRNAs) have emerged as promising noninvasive biomarkers due to their stability and regulatory roles in glucose metabolism. However, robust diagnostic models integrating exo-miRNAs profiles for early prediction of GDM remain lacking.

Methods

In this study, we used the GSE192813 dataset as a discovery cohort to identify differentially expressed exo-miRNAs (DE-exo-miRNAs) in exosomes between GDM and normal glucose tolerance (NGT) pregnancies. After differential expression analysis, five machine learning (ML) feature selection algorithms (LASSO, Random Forest, SVM-RFE, XGBoost, and Boruta) were applied to identify robust predictive DE-exo-miRNAs features. Subsequently, ten classification algorithms (including Logistic Regression, Random Forest, SVM, XGBoost, LightGBM, CatBoost, KNN, Naïve Bayes, Neural Network, and Decision Tree) were combined with the five feature-selection methods, generating 50 distinct ML models. Model performance was evaluated through repeated 7:3 train-test splits, and the best-performing classifier was externally validated using GSE114860.

Results

A total of 12 DEmiRNAs were identified in GSE192813, of which a subset of key exo-miRNAs (including miR-423-5p, miR-99a-5p, miR-148a-3p, miR-192-5p, and miR-122-5p) were consistently selected across multiple algorithms. Among the 50 ML combinations, the XGBoost + Boruta model achieved the highest diagnostic accuracy, with an AUC exceeding 0.90 and an overall accuracy greater than 90% in the discovery dataset. External validation in GSE114860 demonstrated stable performance, achieving an accuracy above 80% and good calibration. Functional enrichment analysis of target genes indicated significant involvement in insulin signaling, lipid metabolism, and inflammatory pathways.

Conclusion

This integrative machine learning framework successfully identified a robust exo-miRNAs-based predictive signature for GDM. The model exhibited high diagnostic accuracy and generalizability across independent cohorts, highlighting its potential for early, noninvasive screening and precision management of gestational diabetes mellitus.