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Background and hypothesis <p>Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder in pregnant women, marked by hyperglycemia and glucose intolerance that initially appears during pregnancy. It presents serious health concerns to both the mother and the foetus, increasing the likelihood of complications such as preterm birth, preeclampsia, and fetal macrosomia. Early detection and timely intervention are crucial for minimizing these risks. However, current screening methods for GDM, such as the oral glucose tolerance test (OGTT), are typically administered between 24 and 28 weeks of gestation, leaving a substantial window during which metabolic changes may already be affecting maternal and fetal outcomes. We hypothesized that the serum metabolomics analysis may serve as a potential method for the predictive screening of GDM during the first trimester of pregnancy. Within this framework, the present study has been designed to identify distinctive metabolic patterns in the serum samples of pregnant women during the first trimester that are predictive of GDM development later in pregnancy.</p> Methods <p>The study involved a cohort of hundred (<i>N</i> = 100) pregnant women in their first trimester were recruited and grouped based on their subsequent development of GDM (referred here as pre-GDM group, <i>N</i> = 29) and non-GDM (i.e. normoglycemic, <i>N</i> = 71). The serum metabolic profiles were measured using 800&#xa0;MHz NMR spectroscopy and compared using sparse PLS-DA (Partial Least Squares Discriminant Analysis).</p> Results <p>The 3D score plot revealed exquisite clustering of pre-GDM subjects and separation from non-GDM group. The performance of sPLS-DA model is validated through cross-validation and the VIP score plot highlighted several key metabolites contributing to the discrimination. Further, the receiver operating characteristic (ROC) curve analysis highlighted their diagnostic potential in distinguishing between the pre-GDM and non-GDM groups. Overall, 15 metabolic features provided a robust foundation for distinguishing between pre-GDM and non-GDM groups, with alanine, myo-inositol, valine and glucose showing the highest diagnostic potential.</p> Conclusion <p>The study demonstrated that NMR-based serum metabolomics can identify distinctive metabolic signatures during the first trimester that are predictive of subsequent GDM development. These findings suggest the potential of this approach as a supportive tool for early screening and risk stratification in pregnant women, warranting validation in larger, multi-center cohorts.</p>

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NMR-based clinical metabolomics for predictive screening of gestational diabetes mellitus (GDM) during the first trimester: a pilot study on North Indian population

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Pragati Gupta,
  • Gurvinder Singh,
  • Indu Lata,
  • Dinesh Kumar

摘要

Background and hypothesis

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder in pregnant women, marked by hyperglycemia and glucose intolerance that initially appears during pregnancy. It presents serious health concerns to both the mother and the foetus, increasing the likelihood of complications such as preterm birth, preeclampsia, and fetal macrosomia. Early detection and timely intervention are crucial for minimizing these risks. However, current screening methods for GDM, such as the oral glucose tolerance test (OGTT), are typically administered between 24 and 28 weeks of gestation, leaving a substantial window during which metabolic changes may already be affecting maternal and fetal outcomes. We hypothesized that the serum metabolomics analysis may serve as a potential method for the predictive screening of GDM during the first trimester of pregnancy. Within this framework, the present study has been designed to identify distinctive metabolic patterns in the serum samples of pregnant women during the first trimester that are predictive of GDM development later in pregnancy.

Methods

The study involved a cohort of hundred (N = 100) pregnant women in their first trimester were recruited and grouped based on their subsequent development of GDM (referred here as pre-GDM group, N = 29) and non-GDM (i.e. normoglycemic, N = 71). The serum metabolic profiles were measured using 800 MHz NMR spectroscopy and compared using sparse PLS-DA (Partial Least Squares Discriminant Analysis).

Results

The 3D score plot revealed exquisite clustering of pre-GDM subjects and separation from non-GDM group. The performance of sPLS-DA model is validated through cross-validation and the VIP score plot highlighted several key metabolites contributing to the discrimination. Further, the receiver operating characteristic (ROC) curve analysis highlighted their diagnostic potential in distinguishing between the pre-GDM and non-GDM groups. Overall, 15 metabolic features provided a robust foundation for distinguishing between pre-GDM and non-GDM groups, with alanine, myo-inositol, valine and glucose showing the highest diagnostic potential.

Conclusion

The study demonstrated that NMR-based serum metabolomics can identify distinctive metabolic signatures during the first trimester that are predictive of subsequent GDM development. These findings suggest the potential of this approach as a supportive tool for early screening and risk stratification in pregnant women, warranting validation in larger, multi-center cohorts.