Exploring Clinico-Metabolomics Approach for Predictive Screening of Gestational Diabetes Mellitus (GDM) During the First Trimester

Pragati Gupta (Centre of Biomedical Research Institute Lucknow, India)

LinkedIn: Pragati Gupta, X: @praggupta856

Abstract: Gestational diabetes mellitus (GDM) is a major pregnancy-associated metabolic disorder characterized by glucose intolerance first recognized during pregnancy. Conventional diagnosis using oral glucose tolerance test (OGTT) is typically performed during the second trimester, limiting opportunities for early intervention . Our study aimed to investigate whether first-trimester serum metabolomics combined with clinical profiling could enable early predictive screening of GDM. In this prospective observational study, pregnant women recruited during the first trimester (7–13+6 weeks) were followed until routine OGTT screening at 24–28 weeks of gestation. Based on subsequent diagnosis, subjects were categorized into pre-GDM and non-GDM groups. Serum metabolic profiling was performed using high-field 800 MHz 1H NMR spectroscopy followed by multivariate statistical analysis including sparse Partial Least Squares Discriminant Analysis (sPLS-DA), receiver operating characteristic (ROC) analysis, and pathway interpretation. Distinct metabolic clustering between pre-GDM and non-GDM subjects was observed in the sPLS-DA model, indicating early metabolic perturbations preceding clinical GDM diagnosis. Several metabolites associated with glucose metabolism, branched-chain amino acid metabolism, energy metabolism, and gluconeogenesis exhibited significant alterations in pre-GDM subjects. Key discriminatory metabolites included glucose, alanine, valine, glutamine, histidine, and myo-inositol, with ROC analysis demonstrating strong diagnostic performance. Additionally, reduced alanine-to-glucose ratio suggested potential dysregulation of the hepatic alanine–glucose cycle and altered gluconeogenic metabolism during early pregnancy.
Overall, the study demonstrates that NMR-based clinico-metabolomics can identify early metabolic signatures predictive of GDM before conventional clinical diagnosis. These findings highlight the potential utility of metabolomics-assisted risk stratification as a non-invasive approach for early screening and timely intervention in high-risk pregnancies.