GEO series
Multi-Omics Profiling Reveals Distinct Lipidomic and Transcriptomic Alterations in Gestational Diabetes Mellitus, Preeclampsia, and Coexistence of Both: Implications for Maternal and Neonatal Outcomes
GSE284329
Homo sapiens
Expression profiling by high throughput sequencing
17 samples
2025/07/30
GPL28038
Summary
Gestational diabetes mellitus (GDM) and preeclampsia (PE) are major contributors to maternal and perinatal morbidity and mortality, characterized by dysregulated lipid metabolism, chronic inflammation, and immune dysfunction, disrupting maternal-fetal homeostasis. The coexistence of GDM and PE (PG) presents a uniquely complex clinical scenario with molecular mechanisms that remain poorly understood. Peripheral blood samples from 42 pregnant women were categorized into CTL (n=10), GDM (n=12), PE (n=10), and PG (n=10) groups. Lipidomic profiling via LC-MS and transcriptomic analysis through RNA sequencing were performed. Multi-omics integration correlated molecular signatures with clinical and neonatal outcomes, and a predictive model for PE was constructed using differentially expressed lipids. Additionally, UK Biobank data was analyzed to explore associations between pregnancy complications and maternal postpartum depression (PD).PE exhibited the most pronounced lipidomic alterations, correlating with adverse neonatal outcomes, including low birth weight, while GDM showed the most significant transcriptomic changes. PG demonstrated unique transcriptomic signatures associated with neuronal and immune dysfunction, suggesting a potential link to maternal PD. Seven lipid biomarkers were identified, significantly correlating with neonatal outcomes, and a lipid-based predictive model for PE showed robust performance across PE and PG cohorts. GDM and PE were significantly associated with PD, though PG’s association remains inconclusive due to limited data.This study highlights the utility of multi-omics in unraveling shared and disease-specific mechanisms underlying pregnancy complications. The identified biomarkers and PE predictive model offer promising avenues for advancing diagnosis, prognosis, and personalized maternal-fetal care.
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Paper (PMID 40715462) ↗
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