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Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

BACKGROUND: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for provi...

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Autores principales: Lu, Wenqian, Luo, Mingjuan, Fang, Xiangnan, Zhang, Rong, Li, Shanshan, Tang, Mengyang, Yu, Xiangtian, Hu, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379750/
https://www.ncbi.nlm.nih.gov/pubmed/34419103
http://dx.doi.org/10.1186/s12986-021-00606-8
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author Lu, Wenqian
Luo, Mingjuan
Fang, Xiangnan
Zhang, Rong
Li, Shanshan
Tang, Mengyang
Yu, Xiangtian
Hu, Cheng
author_facet Lu, Wenqian
Luo, Mingjuan
Fang, Xiangnan
Zhang, Rong
Li, Shanshan
Tang, Mengyang
Yu, Xiangtian
Hu, Cheng
author_sort Lu, Wenqian
collection PubMed
description BACKGROUND: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. METHODS: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. RESULTS: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. CONCLUSIONS: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12986-021-00606-8.
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spelling pubmed-83797502021-08-23 Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population Lu, Wenqian Luo, Mingjuan Fang, Xiangnan Zhang, Rong Li, Shanshan Tang, Mengyang Yu, Xiangtian Hu, Cheng Nutr Metab (Lond) Research BACKGROUND: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. METHODS: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. RESULTS: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. CONCLUSIONS: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12986-021-00606-8. BioMed Central 2021-08-21 /pmc/articles/PMC8379750/ /pubmed/34419103 http://dx.doi.org/10.1186/s12986-021-00606-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Lu, Wenqian
Luo, Mingjuan
Fang, Xiangnan
Zhang, Rong
Li, Shanshan
Tang, Mengyang
Yu, Xiangtian
Hu, Cheng
Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title_full Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title_fullStr Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title_full_unstemmed Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title_short Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population
title_sort discovery of metabolic biomarkers for gestational diabetes mellitus in a chinese population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379750/
https://www.ncbi.nlm.nih.gov/pubmed/34419103
http://dx.doi.org/10.1186/s12986-021-00606-8
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