Frontiers in molecular biosciences · 2026
Comprehensive first-trimester targeted metabolomics for early prediction and understanding of GDM pathophysiology.
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Mojsak Patrycja, Godlewski Adrian, Sołowiej Krzysztof, Kulczynska-Przybik Agieszka, Chmielewska Sandra, Mroczko Barbara, Szelachowska Małgorzata, Krętowski Adam, Ciborowski Michał
Аннотация
INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: First-trimester metabolomic signatures hold significant promise for early prediction, diagnosis, and understanding of GDM, enabling personalised risk assessment and timely intervention during pregnancy.
METHODS: 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.
RESULTS: 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.
CONCLUSION: First-trimester metabolomic signatures hold significant promise for early prediction, diagnosis, and understanding of GDM, enabling personalised risk assessment and timely intervention during pregnancy.