Danuta Dudzik ^1* Vangeliya Atanasova ² Coral Barbas ³ Jose Luis Bartha ^2*

• ¹ Department of Biopharmaceutics and Pharmacodynamics, Pharmacy Faculty, Medical University of Gdańsk, Gdańsk, Poland
• ² Division of Maternal and Fetal Medicine, University Hospital La Paz, Madrid, Spain
• ³ Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain

Gestational diabetes mellitus (GDM) is a global health consern with significant short and long-term complications for both mother and baby. Early prediction of GDM, particularly late-onset, is crucial for implementing timely interventions to mitigate adverse outcomes. In this study, we conducted a comprehensive metabolomic analysis to explore potential biomarkers for early GDM prediction. Plasma samples were collected during the first trimester from 60 women: 20 with early-onset GDM, 20 with late-onset GDM, and 20 with normal glucose tolerance. Using advanced analytical techniques, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatographymass spectrometry (GC-MS), we profiled over 150 lipid species and central carbon metabolism intermediates. Significant metabolic alterations were observed in both early-and late-onset GDM groups compared to healthy controls, with a specific focus on glycerolipids, fatty acids, and glucose metabolism. Key findings revealed a 4.0-fold increase in TG(44:0), TG(46:0), TG(46:1) with p-values < 0.001 and TG(46:2) with 4.7-fold increase and p-value < 0.0001 as well as changes in several phospholipids as PC(38:3), PC(40:4) with 1.4-fold increase, p < 0.001 and PE(34:1), PE(34:2) and PE(36:2) with 1.5-fold change, p < 0.001 in late-onset GDM. Observed lipid changes highlight disruptions in energy metabolism and inflammatory pathways. It is suggested that lipid profiles with distinct fatty acid chain lengths and degrees of unsaturation can serve as early biomarkers of GDM risk. These findings underline the importance of integrating metabolomic insights with clinical data to develop predictive models for GDM. Such models could enable early risk stratification, allowing for timely dietary, lifestyle, or medical interventions aimed at optimizing glucose regulation and preventing complications such as preeclampsia, macrosomia, and neonatal metabolic disorders. By focusing on metabolic disruptions evident in the first trimester, this approach addresses a critical window for improving maternal and fetal outcomes. Our study demonstrates the value of metabolomics in understanding the metabolic perturbations associated with GDM. Future research is needed to validate these biomarkers in larger cohorts and assess their integration into clinical workflows for personalized pregnancy care.