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

## TL;DR

This study identifies early pregnancy metabolomic signatures that can predict gestational diabetes in Indian women, offering a potential tool for early detection.

## Contribution

The study introduces a novel eight-metabolite panel for early prediction of GDM in Asian Indian populations.

## Key findings

- 49 metabolites were significantly associated with GDM, including lipid classes like phosphatidylcholines and sphingomyelins.
- An eight-metabolite panel achieved high predictive accuracy (AUC 0.880) for GDM using random forest analysis.
- Enrichment analysis revealed dysregulated pathways such as glycerophospholipid metabolism and insulin resistance.

## Abstract

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.

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.

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.

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.

The online version contains supplementary material available at 10.1186/s12933-025-02978-0.