Comprehensive first-trimester targeted metabolomics for early prediction and understanding of GDM pathophysiology

Gestational diabetes mellitus (GDM) is among the most common metabolic disorders during pregnancy, and early detection is key to reducing complications for both mother and child. Mass spectrometry-based metabolomics enables detailed metabolite profiling, offering opportunities not only for early diagnosis and risk prediction but also for understanding the pathophysiological mechanisms that drive the development of GDM.

For the first time, an analysis of such a large number of metabolites was conducted: over 1,000 metabolites across 39 biochemical classes, including 912 lipids and 107 small molecules, were measured in first-trimester plasma from women with abnormal or normal fasting plasma glucose who later developed GDM, as well as from controls with normal glucose tolerance. Statistical analyses included Kruskal-Wallis ANOVA with Conover-Iman post hoc tests, Wilcoxon signed-rank tests for longitudinal changes, and ROC analysis to assess predictive and diagnostic performance. Spearman's rank correlations were used to examine relationships between metabolites and clinical parameters.

Distinct metabolic signatures in the first trimester were associated with later GDM development. A prognostic panel, including TG (18:1_36:6), Hex2Cer(d18:1/14:0), valine, PS(36:1), TG (17:2_36:3), p-cresol sulfate, and PC(O-42:4), accurately predicted GDM (AUC = 0.934). A diagnostic panel comprising PE (P-18:0/22:4), glycine-conjugated cholic acid, LPC (20:3), carnitine esters, and arginine detected early signs of carbohydrate metabolism issues (AUC = 0.821). Women with normal fasting glucose who later developed GDM exhibited significant lipid alterations, whereas those with early fasting irregularities showed a partially GDM-like profile. Correlation analyses revealed distinct inflammatory and hormonal networks, with TNF-α-induced lipid remodelling linked to early dysglycaemia.

First-trimester metabolomic signatures hold significant promise for early prediction, diagnosis, and understanding of GDM, enabling personalised risk assessment and timely intervention during pregnancy.