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

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 w...

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Autores principales: Rahman, Mohammad L, Feng, Yen-Chen A, Fiehn, Oliver, Albert, Paul S, Tsai, Michael Y, Zhu, Yeyi, Wang, Xiaobin, Tekola-Ayele, Fasil, Liang, Liming, Zhang, Cuilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939004/
https://www.ncbi.nlm.nih.gov/pubmed/33674279
http://dx.doi.org/10.1136/bmjdrc-2020-001551
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author Rahman, Mohammad L
Feng, Yen-Chen A
Fiehn, Oliver
Albert, Paul S
Tsai, Michael Y
Zhu, Yeyi
Wang, Xiaobin
Tekola-Ayele, Fasil
Liang, Liming
Zhang, Cuilin
author_facet Rahman, Mohammad L
Feng, Yen-Chen A
Fiehn, Oliver
Albert, Paul S
Tsai, Michael Y
Zhu, Yeyi
Wang, Xiaobin
Tekola-Ayele, Fasil
Liang, Liming
Zhang, Cuilin
author_sort Rahman, Mohammad L
collection PubMed
description 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.
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spelling pubmed-79390042021-03-21 Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort Rahman, Mohammad L Feng, Yen-Chen A Fiehn, Oliver Albert, Paul S Tsai, Michael Y Zhu, Yeyi Wang, Xiaobin Tekola-Ayele, Fasil Liang, Liming Zhang, Cuilin BMJ Open Diabetes Res Care Epidemiology/Health services research 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. BMJ Publishing Group 2021-03-05 /pmc/articles/PMC7939004/ /pubmed/33674279 http://dx.doi.org/10.1136/bmjdrc-2020-001551 Text en © Where applicable, author(s) (or their employer(s)) 2021. Re-use permitted under [CC BY]. Published by BMJ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology/Health services research
Rahman, Mohammad L
Feng, Yen-Chen A
Fiehn, Oliver
Albert, Paul S
Tsai, Michael Y
Zhu, Yeyi
Wang, Xiaobin
Tekola-Ayele, Fasil
Liang, Liming
Zhang, Cuilin
Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title_full Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title_fullStr Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title_full_unstemmed Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title_short Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
title_sort plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort
topic Epidemiology/Health services research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939004/
https://www.ncbi.nlm.nih.gov/pubmed/33674279
http://dx.doi.org/10.1136/bmjdrc-2020-001551
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