Gestational diabetes & diabetes in pregnancy
Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus
Authors' 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.
Study context
- Population
- Asian Indian pregnant women from the STRiDE study; early pregnancy (< 16 weeks) plasma samplesfrom 2115 in STRiDE
- Participants
- 100 women
- Comparison
- Gestational diabetes mellitus (GDM) vs normal by oral glucose tolerance test (OGTT) at 24-28 weeks
- GDMn = 50
- Normaln = 50reference
- Outcome
- Early pregnancy metabolomic signatures predictive of GDM
- Follow-up
- < 16 weeks
Methodological detailsuntargeted metabolomic profiling (UPLC-MS/MS), machine learning (logistic regression, random forest)
- Untargeted metabolomic profiling (UPLC-MS/MS)
- Machine learning (logistic regression, random forest)
Main finding
As reported in the abstract
Prediction of GDM by early-pregnancy metabolites
Metabolites significantly associated with GDM, mainly phosphatidylcholines, sphingomyelins, triacylglycerols
49
RF eight-metabolite panel AUC
0.880
95% CI 0.809–0.951
Panel combined with clinical risk factors AUC (95% CI 0.810-0.952), comparable
0.88
Observational data: associations, not effects.
Further findings
Pathway enrichment (KEGG)
- Dysregulated glycerophospholipid and sphingolipid metabolism, autophagy and insulin resistance pathways
Original publication
Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus. Cardiovasc Diabetol. Published 14 November 2025. doi:10.
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