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Peer-reviewedCC BY 4.0Communications Medicine

Lipidomic profiling reveals a distinct lipidomic signature of early gestational diabetes

  • Saeede Saadati1
  • Rasoul Godini1,2
  • Eveline Jona1
  • Christopher K. Barlow3
  • Adriana CH Neven1
  • Christopher J. Nolan4,5
  • David Simmons6
  • Helena Teede1
  • Aya Mousa1
6 affiliations
  1. 1Monash Centre for Health Research and Implementation (MCHRI), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC Australia
  2. 2Development and Stem Cells Program, Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Melbourne, VIC Australia
  3. 3Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC Australia
  4. 4Department of Diabetes and Endocrinology, The Canberra Hospital, Canberra, ACT Australia
  5. 5School of Medicine and Psychology, Australian National University, Canberra, ACT Australia
  6. 6Macarthur Clinical School, Western Sydney University, Campbelltown, NSW Australia
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Abstract

Background: Gestational diabetes mellitus (GDM) diagnosed early in pregnancy (before 20 weeks’ gestation) is associated with increased metabolic risk, yet its molecular profile remains poorly defined. While disrupted lipid metabolism is an important feature of GDM, no previous study has characterized the lipidomic profile of women with early GDM (eGDM).

Methods: We performed untargeted liquid chromatography mass spectrometry on maternal plasma samples from a cohort of 180 women at risk of GDM, enrolled in the Treatment of Booking GDM (TOBOGM) multicenter randomized controlled trial. Oral glucose tolerance tests (OGTT) were conducted before 20 weeks’ gestation to diagnose eGDM using World Health Organization (2013) criteria. Lipidomic data were analyzed using multivariable linear regression, unsupervised clustering, weighted gene co-expression network analysis (WGCNA), and logistic regression-based risk modeling.

Results: In 89 eGDM and 91 non-GDM controls, we quantify 543 lipid species across 18 lipid classes. We identify a distinct lipidomic signature of eGDM, comprising elevated concentrations of glycerolipids (diacylglycerols), fatty acids, and ethanolamine-containing glycerophospholipids (phosphatidylethanolamine and lysophosphatidylethanolamine), alongside lower concentrations of choline-containing glycerophospholipids (lysophosphatidylcholine and ether-linked phosphatidylcholine) and glycosphingolipids (hexosylceramides). Fatty acid and diacylglycerol species are the strongest and most consistent lipid predictors of eGDM and glycemic indices, independent of clinical risk factors. Lipid ontology-based enrichment analysis reveals perturbations in pathways related to lipid storage, membrane remodeling, and signaling.

Conclusion: To our knowledge, this is the first study to characterize the lipidomic profile of women with eGDM. We identify a distinct lipidomic signature associated with eGDM, offering molecular insights into pathophysiology and highlighting candidate lipid biomarkers for future investigation and validation in this context.

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Cite

BibTeXRIS
BibTeX
@article{saadati2026lipidomic,
  title = {{Lipidomic profiling reveals a distinct lipidomic signature of early gestational diabetes}},
  author = {Saeede Saadati and Rasoul Godini and Eveline Jona and Christopher K. Barlow and Adriana CH Neven and Christopher J. Nolan and David Simmons and Helena Teede and Aya Mousa},
  journal = {Communications Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s43856-026-01765-6},
  url = {https://doi.org/10.1038/s43856-026-01765-6},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Lipidomic profiling reveals a distinct lipidomic signature of early gestational diabetes
AU  - Saeede Saadati
AU  - Rasoul Godini
AU  - Eveline Jona
AU  - Christopher K. Barlow
AU  - Adriana CH Neven
AU  - Christopher J. Nolan
AU  - David Simmons
AU  - Helena Teede
AU  - Aya Mousa
PY  - 2026
DA  - 2026/07/04
JO  - Communications Medicine
DO  - 10.1038/s43856-026-01765-6
UR  - https://doi.org/10.1038/s43856-026-01765-6
AB  - Background: Gestational diabetes mellitus (GDM) diagnosed early in pregnancy (before 20 weeks’ gestation) is associated with increased metabolic risk, yet its molecular profile remains poorly defined. While disrupted lipid metabolism is an important feature of GDM, no previous study has characterized the lipidomic profile of women with early GDM (eGDM).  Methods: We performed untargeted liquid chromatography mass spectrometry on maternal plasma samples from a cohort of 180 women at risk of GDM, enrolled in the Treatment of Booking GDM (TOBOGM) multicenter randomized controlled trial. Oral glucose tolerance tests (OGTT) were conducted before 20 weeks’ gestation to diagnose eGDM using World Health Organization (2013) criteria. Lipidomic data were analyzed using multivariable linear regression, unsupervised clustering, weighted gene co-expression network analysis (WGCNA), and logistic regression-based risk modeling.  Results: In 89 eGDM and 91 non-GDM controls, we quantify 543 lipid species across 18 lipid classes. We identify a distinct lipidomic signature of eGDM, comprising elevated concentrations of glycerolipids (diacylglycerols), fatty acids, and ethanolamine-containing glycerophospholipids (phosphatidylethanolamine and lysophosphatidylethanolamine), alongside lower concentrations of choline-containing glycerophospholipids (lysophosphatidylcholine and ether-linked phosphatidylcholine) and glycosphingolipids (hexosylceramides). Fatty acid and diacylglycerol species are the strongest and most consistent lipid predictors of eGDM and glycemic indices, independent of clinical risk factors. Lipid ontology-based enrichment analysis reveals perturbations in pathways related to lipid storage, membrane remodeling, and signaling.  Conclusion: To our knowledge, this is the first study to characterize the lipidomic profile of women with eGDM. We identify a distinct lipidomic signature associated with eGDM, offering molecular insights into pathophysiology and highlighting candidate lipid biomarkers for future investigation and validation in this context.
SN  - 2730-664X
ER  -