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Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy

Background: Dysbiosis of human gut microbiota is associated with a wide range of metabolic disorders, including gestational diabetes mellitus (GDM). Yet whether gut microbiota dysbiosis participates in the etiology of GDM remains largely unknown. Objectives: Our study was initiated to determine whet...

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Autores principales: Ma, Shujuan, You, Yiping, Huang, Lingting, Long, Sisi, Zhang, Jiayue, Guo, Chuhao, Zhang, Na, Wu, Xinrui, Xiao, Yanni, Tan, Hongzhuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056672/
https://www.ncbi.nlm.nih.gov/pubmed/32175285
http://dx.doi.org/10.3389/fcimb.2020.00058
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author Ma, Shujuan
You, Yiping
Huang, Lingting
Long, Sisi
Zhang, Jiayue
Guo, Chuhao
Zhang, Na
Wu, Xinrui
Xiao, Yanni
Tan, Hongzhuan
author_facet Ma, Shujuan
You, Yiping
Huang, Lingting
Long, Sisi
Zhang, Jiayue
Guo, Chuhao
Zhang, Na
Wu, Xinrui
Xiao, Yanni
Tan, Hongzhuan
author_sort Ma, Shujuan
collection PubMed
description Background: Dysbiosis of human gut microbiota is associated with a wide range of metabolic disorders, including gestational diabetes mellitus (GDM). Yet whether gut microbiota dysbiosis participates in the etiology of GDM remains largely unknown. Objectives: Our study was initiated to determine whether the alternations in gut microbial composition during early pregnancy linked to the later development of GDM, and explore the feasibility of microbial biomarkers for the early prediction of GDM. Study design: This nested case-control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Gut microbiota profiles of 98 subjects with GDM and 98 matched healthy controls during the early pregnancy (10–15 weeks) were assessed via 16S rRNA gene amplicon sequencing of V4 region. The data set was randomly split into a discovery set and a validation set, the former was used to analyze the differences between GDM cases and controls in gut microbial composition and functional annotation, and to establish an early identification model of GDM, then the performance of the model was verified by the external validation set. Results: Bioinformatic analyses revealed changes to gut microbial composition with significant differences in relative abundance between the groups. Specifically, Eisenbergiella, Tyzzerella 4, and Lachnospiraceae NK4A136 were enriched in the GDM group, whereas Parabacteroides, Megasphaera, Eubacterium eligens group, etc. remained dominant in the controls. Correlation analysis revealed that GDM-enriched genera Eisenbergiella and Tyzzerella 4 were positively correlated with fasting blood glucose levels, while three control-enriched genera (Parabacteroides, Parasutterella, and Ruminococcaceae UCG 002) were the opposite. Further, GDM functional annotation modules revealed enrichment of modules for sphingolipid metabolism, starch and sucrose metabolism, etc., while lysine biosynthesis and nitrogen metabolism were reduced. Finally, five genera and two clinical indices were included in the linear discriminant analysis model for the prediction of GDM; the areas under receiver operating characteristic curves of the training and validation sets were 0.736 (95% confidence interval: 0.663–0.808) and 0.696 (0.575–0.818), respectively. Conclusions: Gut bacterial dysbiosis in early pregnancy was found to be associated with the later development of GDM, and gut microbiota-targeted biomarkers might be utilized as potential predictors of GDM.
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spelling pubmed-70566722020-03-13 Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy Ma, Shujuan You, Yiping Huang, Lingting Long, Sisi Zhang, Jiayue Guo, Chuhao Zhang, Na Wu, Xinrui Xiao, Yanni Tan, Hongzhuan Front Cell Infect Microbiol Cellular and Infection Microbiology Background: Dysbiosis of human gut microbiota is associated with a wide range of metabolic disorders, including gestational diabetes mellitus (GDM). Yet whether gut microbiota dysbiosis participates in the etiology of GDM remains largely unknown. Objectives: Our study was initiated to determine whether the alternations in gut microbial composition during early pregnancy linked to the later development of GDM, and explore the feasibility of microbial biomarkers for the early prediction of GDM. Study design: This nested case-control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Gut microbiota profiles of 98 subjects with GDM and 98 matched healthy controls during the early pregnancy (10–15 weeks) were assessed via 16S rRNA gene amplicon sequencing of V4 region. The data set was randomly split into a discovery set and a validation set, the former was used to analyze the differences between GDM cases and controls in gut microbial composition and functional annotation, and to establish an early identification model of GDM, then the performance of the model was verified by the external validation set. Results: Bioinformatic analyses revealed changes to gut microbial composition with significant differences in relative abundance between the groups. Specifically, Eisenbergiella, Tyzzerella 4, and Lachnospiraceae NK4A136 were enriched in the GDM group, whereas Parabacteroides, Megasphaera, Eubacterium eligens group, etc. remained dominant in the controls. Correlation analysis revealed that GDM-enriched genera Eisenbergiella and Tyzzerella 4 were positively correlated with fasting blood glucose levels, while three control-enriched genera (Parabacteroides, Parasutterella, and Ruminococcaceae UCG 002) were the opposite. Further, GDM functional annotation modules revealed enrichment of modules for sphingolipid metabolism, starch and sucrose metabolism, etc., while lysine biosynthesis and nitrogen metabolism were reduced. Finally, five genera and two clinical indices were included in the linear discriminant analysis model for the prediction of GDM; the areas under receiver operating characteristic curves of the training and validation sets were 0.736 (95% confidence interval: 0.663–0.808) and 0.696 (0.575–0.818), respectively. Conclusions: Gut bacterial dysbiosis in early pregnancy was found to be associated with the later development of GDM, and gut microbiota-targeted biomarkers might be utilized as potential predictors of GDM. Frontiers Media S.A. 2020-02-27 /pmc/articles/PMC7056672/ /pubmed/32175285 http://dx.doi.org/10.3389/fcimb.2020.00058 Text en Copyright © 2020 Ma, You, Huang, Long, Zhang, Guo, Zhang, Wu, Xiao and Tan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Ma, Shujuan
You, Yiping
Huang, Lingting
Long, Sisi
Zhang, Jiayue
Guo, Chuhao
Zhang, Na
Wu, Xinrui
Xiao, Yanni
Tan, Hongzhuan
Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title_full Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title_fullStr Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title_full_unstemmed Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title_short Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy
title_sort alterations in gut microbiota of gestational diabetes patients during the first trimester of pregnancy
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056672/
https://www.ncbi.nlm.nih.gov/pubmed/32175285
http://dx.doi.org/10.3389/fcimb.2020.00058
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