Literature DB >> 33674279

Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort.

Mohammad L Rahman1,2, Yen-Chen A Feng3,4, Oliver Fiehn5, Paul S Albert6, Michael Y Tsai7, Yeyi Zhu8, Xiaobin Wang9, Fasil Tekola-Ayele2, Liming Liang10, Cuilin Zhang11.   

Abstract

INTRODUCTION: Disruption of lipid metabolism is implicated in gestational diabetes (GDM). However, prospective studies on lipidomics and GDM risk in race/ethnically diverse populations are sparse. Here, we aimed to (1) identify lipid networks in early pregnancy to mid-pregnancy that are associated with subsequent GDM risk and (2) examine the associations of lipid networks with glycemic biomarkers to understand the underlying mechanisms. RESEARCH DESIGN AND METHODS: This study included 107 GDM cases confirmed using the Carpenter and Coustan criteria and 214 non-GDM matched controls from the National Institute of Child Health and Human Development Fetal Growth Studies-Singleton cohort, untargeted lipidomics data of 420 metabolites (328 annotated and 92 unannotated), and information on glycemic biomarkers in maternal plasma at visit 0 (10-14 weeks) and visit 1 (15-26 weeks). We constructed lipid networks using weighted correlation network analysis technique. We examined prospective associations of lipid networks and individual lipids with GDM risk using linear mixed effect models. Furthermore, we calculated Pearson's partial correlation for GDM-related lipid networks and individual lipids with plasma glucose, insulin, C-peptide and glycated hemoglobin at both study visits.
RESULTS: Lipid networks primarily characterized by elevated plasma diglycerides and short, saturated/low unsaturated triglycerides and lower plasma cholesteryl esters, sphingomyelins and phosphatidylcholines were associated with higher risk of developing GDM (false discovery rate (FDR) <0.05). Among individual lipids, 58 metabolites at visit 0 and 96 metabolites at visit 1 (40 metabolites at both time points) significantly differed between women who developed GDM and who did not (FDR <0.05). Furthermore, GDM-related lipid networks and individual lipids showed consistent correlations with maternal glycemic markers particularly in early pregnancy at visit 0.
CONCLUSIONS: Plasma lipid metabolites in early pregnancy both individually and interactively in distinct networks were associated with subsequent GDM risk in race/ethnically diverse US women. Future research is warranted to assess lipid metabolites as etiologic markers of GDM. © Where applicable, author(s) (or their employer(s)) 2021. Re-use permitted under [CC BY]. Published by BMJ.

Entities:  

Keywords:  diabetes; gestational; lipids; metabolism; pregnancy

Year:  2021        PMID: 33674279      PMCID: PMC7939004          DOI: 10.1136/bmjdrc-2020-001551

Source DB:  PubMed          Journal:  BMJ Open Diabetes Res Care        ISSN: 2052-4897


  46 in total

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10.  Integrated lipidomics and transcriptomic analysis of peripheral blood reveals significantly enriched pathways in type 2 diabetes mellitus.

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1.  Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma.

Authors:  Lauren E McMichael; Hannah Heath; Catherine M Johnson; Rob Fanter; Noemi Alarcon; Adilene Quintana-Diaz; Kari Pilolla; Andrew Schaffner; Elissa Jelalian; Rena R Wing; Alex Brito; Suzanne Phelan; Michael R La Frano
Journal:  Metabolomics       Date:  2021-11-27       Impact factor: 4.290

2.  A mouse model of gestational diabetes shows dysregulated lipid metabolism post-weaning, after return to euglycaemia.

Authors:  Samuel Furse; Denise S Fernandez-Twinn; Jessica H Beeson; Davide Chiarugi; Susan E Ozanne; Albert Koulman
Journal:  Nutr Diabetes       Date:  2022-02-15       Impact factor: 5.097

3.  Population-based plasma lipidomics reveals developmental changes in metabolism and signatures of obesity risk: a mother-offspring cohort study.

Authors:  Sartaj Ahmad Mir; Li Chen; Peter J Meikle; Markus R Wenk; Neerja Karnani; Satvika Burugupalli; Bo Burla; Shanshan Ji; Adam Alexander T Smith; Kothandaraman Narasimhan; Adaikalavan Ramasamy; Karen Mei-Ling Tan; Kevin Huynh; Corey Giles; Ding Mei; Gerard Wong; Fabian Yap; Kok Hian Tan; Fiona Collier; Richard Saffery; Peter Vuillermin; Anne K Bendt; David Burgner; Anne-Louise Ponsonby; Yung Seng Lee; Yap Seng Chong; Peter D Gluckman; Johan G Eriksson
Journal:  BMC Med       Date:  2022-07-25       Impact factor: 11.150

  3 in total

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