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

ABSTRACT: 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 119 women during first-trimester screening: 40 cases and 79 controls for GDM prediction and 45 cases and 74 controls for macrosomia prediction (newborn weight more than 90 percentile). Lipidomic 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

SUBMITTER: Tokareva A 

PROVIDER: S-EPMC12733181 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature