Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus

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: 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.