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Sharma, S. ; Subrahmanyam, Y.V.* ; Gupta, P.* ; Vadivel, S.* ; Deepa, M.* ; Tandon, A.* ; Sreedevi, S.* ; Ram, U.* ; Narad, P.* ; Parmar, D.* ; Anjana, R.M.* ; Raghunathan, A.* ; Balasubramanyam, M.* ; Mohan, V.* ; Sengupta, A.* ; Adamski, J. ; Saravanan, P.* ; Panchagnula, V.* ; Usharani, D.* ; Gokulakrishnan, K.*

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus.

Cardiovasc. Diabetol. 24:434 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND AND AIM: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. METHODS: Among 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (< 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. RESULTS: A total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance. CONCLUSION: This study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter First Trimester ; Gestational Diabetes Mellitus ; Indian Women ; Mass Spectrometry ; Metabolomics ; Prediction; Mitochondrial Dysfunction; Risks
ISSN (print) / ISBN 1475-2840
e-ISSN 1475-2840
Zeitschrift Cardiovascular Diabetology
Quellenangaben Band: 24, Heft: 1, Seiten: , Artikelnummer: 434 Supplement: ,
Verlag Bmc
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Institute of Experimental Genetics (IEG)
Förderungen
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)