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Background <p>Gestational diabetes mellitus (GDM) is a metabolic disorder characterized by impaired glucose tolerance during pregnancy. The incidence of GDM is rising annually, with the potential to elicit a multitude of adverse pregnancy outcomes. However, the diagnostic approaches for GDM are limited, lacking early diagnostic markers, thereby narrowing the treatment window and restricting therapeutic options.</p> Methods <p>In a nested case-control study, 34 pregnant women diagnosed with GDM were matched with 34 non-GDM women based on similar age, gestational age, gravidity, and parity. A retrospective analysis was conducted using untargeted metabolomics to explore serum metabolic differences between the two groups during early pregnancy (9–13 weeks). Additionally, targeted quantification of biomarker levels was performed, and a predictive model was established.</p> Results <p>Fifty-six differential metabolites were identified between the GDM and non-GDM groups in early pregnancy, primarily encompassing amino acids and their derivatives, as well as lipids and lipid-like molecules. Enriched metabolic pathway analysis revealed significant changes in arginine biosynthesis, nitrogen metabolism, glutamate metabolism, and glycerophospholipid metabolism, among others, in the GDM group during early pregnancy. Specifically, glycerophospholipid metabolism was upregulated, while glutamate metabolism was downregulated. Furthermore, ROC curve analysis revealed that a combination of three metabolites—L-phenylalanine, uracil, and pyroglutamic acid—displayed robust predictive power for GDM (AUC = 0.920, sensitivity = 0.941, and specificity = 0.794). Following absolute quantification of the combined biomarkers, a predictive equation based on binary logistic regression was formulated as follows: <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text{y}=0.0579A+0.5971B-0.0394C+0.0609\)</EquationSource> </InlineEquation>. This equation exhibited an AUC of 0.860, a sensitivity of 0.714, and a specificity of 0.857, demonstrating exceptional predictive capability for GDM in early pregnancy.</p> Conclusions <p>This study identified significant serum metabolic changes during early pregnancy in GDM women, unveiling potential mechanisms underlying the early development of GDM and prognostic biomarkers. These findings not only deepen our understanding of metabolic disturbances in GDM but also provide crucial theoretical foundations for early screening and precision interventions in clinical practice.</p> Graphical abstract <p></p>

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Metabolomics reveals early pregnancy serum metabolic changes and predictive biomarkers in gestational diabetes mellitus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Feng Wang,
  • Xiaodie Li,
  • Xue Gao,
  • Jinping Liang,
  • Xuemei Guo,
  • Jingzhong Ou,
  • Ya Yuan,
  • Chuchu Zhao,
  • Ting Zhang,
  • Juan Dai

摘要

Background

Gestational diabetes mellitus (GDM) is a metabolic disorder characterized by impaired glucose tolerance during pregnancy. The incidence of GDM is rising annually, with the potential to elicit a multitude of adverse pregnancy outcomes. However, the diagnostic approaches for GDM are limited, lacking early diagnostic markers, thereby narrowing the treatment window and restricting therapeutic options.

Methods

In a nested case-control study, 34 pregnant women diagnosed with GDM were matched with 34 non-GDM women based on similar age, gestational age, gravidity, and parity. A retrospective analysis was conducted using untargeted metabolomics to explore serum metabolic differences between the two groups during early pregnancy (9–13 weeks). Additionally, targeted quantification of biomarker levels was performed, and a predictive model was established.

Results

Fifty-six differential metabolites were identified between the GDM and non-GDM groups in early pregnancy, primarily encompassing amino acids and their derivatives, as well as lipids and lipid-like molecules. Enriched metabolic pathway analysis revealed significant changes in arginine biosynthesis, nitrogen metabolism, glutamate metabolism, and glycerophospholipid metabolism, among others, in the GDM group during early pregnancy. Specifically, glycerophospholipid metabolism was upregulated, while glutamate metabolism was downregulated. Furthermore, ROC curve analysis revealed that a combination of three metabolites—L-phenylalanine, uracil, and pyroglutamic acid—displayed robust predictive power for GDM (AUC = 0.920, sensitivity = 0.941, and specificity = 0.794). Following absolute quantification of the combined biomarkers, a predictive equation based on binary logistic regression was formulated as follows: \(\text{y}=0.0579A+0.5971B-0.0394C+0.0609\) . This equation exhibited an AUC of 0.860, a sensitivity of 0.714, and a specificity of 0.857, demonstrating exceptional predictive capability for GDM in early pregnancy.

Conclusions

This study identified significant serum metabolic changes during early pregnancy in GDM women, unveiling potential mechanisms underlying the early development of GDM and prognostic biomarkers. These findings not only deepen our understanding of metabolic disturbances in GDM but also provide crucial theoretical foundations for early screening and precision interventions in clinical practice.

Graphical abstract