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Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS

OBJECTIVE: Through metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM....

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Detalles Bibliográficos
Autores principales: Li, Genxia, Gao, Wanli, Xu, Yajuan, Xie, Mingkun, Tang, Suhua, Yin, Pan, Guo, Shuhua, Chu, Shuhui, Sultana, Shaima, Cui, Shihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923470/
https://www.ncbi.nlm.nih.gov/pubmed/31889794
http://dx.doi.org/10.1016/j.sjbs.2019.09.016
Descripción
Sumario:OBJECTIVE: Through metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM. METHODS: The serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database. RESULTS: Multivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism. CONCLUSION: The discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.