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Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus
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DOI：10.7717/peerj.20841.png)
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Abstract
En 中文
Objective Emerging evidence links lipid metabolism to the pathogenesis of gestational diabetes mellitus (GDM). This study aimed to identify lipidomic biomarkers and explore their clinical significance for GDM and related fetal growth and development through serum lipid profiling. Methods Lipidomic profiles of pregnant women with and without GDM were analyzed using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), Uniform Manifold Approximation and Projection (UMAP), volcano plots, and heatmaps. Carbon chain length and unsaturation effects on fold change (FC) were evaluated. Pathway analysis was performed via the Lipid Ontology (LION) platform, while lipid networks were constructed using Debiased Sparse Partial Correlation (DSPC). Hub lipids were identified through topological analysis and visualized with UpsetR. A GDM detection model was developed using Boruta and LogitBoost algorithms, assessed by receiver operating characteristic (ROC) curve analysis, and interpreted via Local Interpretable Model-agnostic Explanations (LIME). Results Twelve serum lipid metabolites were significantly associated with GDM risk. Phosphatidylglycerol (PG)(O-27:1) and triacylglycerol (TG)(35.5) were identified as hub lipids. The GDM detection model, incorporating TG(35:5), PG(O-27:1), total protein (TP), and red blood cell distribution width (RDW), achieved high accuracy. Conclusion This study preliminarily characterized lipid metabolic pathway disturbances in patients with GDM, highlighting the potential of integrating lipidomics with interpretable machine learning techniques for biomarker discovery and mechanistic insight.
Keywords:
Gestational diabetes mellitus
Lipidomics
Biomarkers
LogitBoost
Fetal growth
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