A Machine Learning Model Based on First-Trimester Lipidomic Signatures for Predicting Metabolic Pregnancy Complications

Gestational diabetes mellitus (GDM) and macrosomia, are crucial for improving maternal and neonatal outcomes. Molecular dysregulations can manifest long before clinical symptoms appear. This study aimed to leverage first-trimester serum lipidomic signatures to build early predictive models for these complications. A case-control study was conducted using serum samples from women during first-trimester screening. Lip-idomic profiling was performed using shotgun mass spectrometry in both positive and negative electrospray ionization modes. After feature selection based on Shapley values, machine learning models—including Random Forest and XGBoost were constructed and evaluated via 10-fold cross-validation. For GDM, potential early biomarkers included elevated levels of TG 55:7, and decreased levels of 13-Docosenamide, PC P-36:2, and PC 42:7. For macrosomia, PG (i-, a- 29:0), 4-Hydroxybutyric acid, and Pantothenol were sig-nificantly altered. The model for GDM prediction achieved a sensitivity of 87% and spec-ificity of 89%. For macrosomia, the model demonstrated a sensitivity of 87% and speci-ficity of 93%. The risk ratio between the high- and low-risk groups defined by the models was 11.9 for GDM and 11.1 for macrosomia. Our findings demonstrate that first-trimester serum lipidomic profiles, combined with clinical data and interpreted by advanced ma-chine learning, can accurately identify patients at high risk for GDM and macrosomia. This integrated approach holds significant promise for developing a clinical tool for timely intervention and personalized pregnancy management.