PeerJ · 2026
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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Lin Zhenguo, Li Lei, Fan Yuhang, Chen Hao, Cai Yanhua, Yang Rongrong, Chen Mei, Hong Guolin
Аннотация
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.
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.