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Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery
BACKGROUND: Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to id...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517617/ https://www.ncbi.nlm.nih.gov/pubmed/32972433 http://dx.doi.org/10.1186/s12967-020-02531-5 |
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author | Diboun, Ilhame Ramanjaneya, Manjunath Majeed, Yasser Ahmed, Lina Bashir, Mohammed Butler, Alexandra E. Abou-Samra, Abdul Badi Atkin, Stephen L. Mazloum, Nayef A. Elrayess, Mohamed A. |
author_facet | Diboun, Ilhame Ramanjaneya, Manjunath Majeed, Yasser Ahmed, Lina Bashir, Mohammed Butler, Alexandra E. Abou-Samra, Abdul Badi Atkin, Stephen L. Mazloum, Nayef A. Elrayess, Mohamed A. |
author_sort | Diboun, Ilhame |
collection | PubMed |
description | BACKGROUND: Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome. METHODS: Sixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP(®) Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor. RESULTS: The gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84). CONCLUSIONS: This study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted. |
format | Online Article Text |
id | pubmed-7517617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75176172020-09-25 Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery Diboun, Ilhame Ramanjaneya, Manjunath Majeed, Yasser Ahmed, Lina Bashir, Mohammed Butler, Alexandra E. Abou-Samra, Abdul Badi Atkin, Stephen L. Mazloum, Nayef A. Elrayess, Mohamed A. J Transl Med Research BACKGROUND: Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome. METHODS: Sixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP(®) Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor. RESULTS: The gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84). CONCLUSIONS: This study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted. BioMed Central 2020-09-24 /pmc/articles/PMC7517617/ /pubmed/32972433 http://dx.doi.org/10.1186/s12967-020-02531-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Diboun, Ilhame Ramanjaneya, Manjunath Majeed, Yasser Ahmed, Lina Bashir, Mohammed Butler, Alexandra E. Abou-Samra, Abdul Badi Atkin, Stephen L. Mazloum, Nayef A. Elrayess, Mohamed A. Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title | Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title_full | Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title_fullStr | Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title_full_unstemmed | Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title_short | Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
title_sort | metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517617/ https://www.ncbi.nlm.nih.gov/pubmed/32972433 http://dx.doi.org/10.1186/s12967-020-02531-5 |
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