Alterations of gut microbiota-derived metabolites in gestational diabetes mellitus and clinical significance

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Abstract

Background: The change in the characteristics of the gut microbiota is linked to gestational diabetes mellitus (GDM). However, whether and how the gut microbiota-derived metabolites change in GDM is uncertain. Here, we aimed to determine associations between the gut microbiota-derived metabolites and GDM. Methods: Using targeted metabolomics approaches, 7 types of short-chain fatty acids (SCFAs), 38 types of bile acids (BAs), and 5 types of trimethylamine N-oxide (TMAO), and its derivatives of serum samples were obtained from pregnant women with GDM (n = 24), and healthy pregnant controls (HC, n = 28) were detected to identify the metabolic signature of GDM to investigate the potential biomarkers. Moreover, we assessed the associations between gut microbiota-derived metabolites and clinical parameters. Results: In our study, the gut microbiota-derived metabolites signatures were significantly different between GDM and HC. Quantitative results showed the levels of isobutyric acid, isovaleric acid, valeric acid, caproic acid, GUDCA, THDCA + TUDCA, and LCA-3S were significantly higher in GDM, but the level of TMAO and its derivatives did not change significantly. Some altered gut microbiota-derived metabolites were significantly correlated with glucose and lipid levels. Receiver-operating characteristic (ROC) analysis of generalized linear models showed that gut microbiota-derived metabolites may be potential biomarkers of GDM. Conclusion: This study highlights gut microbiota-derived metabolites alterations in GDM and correlation of the clinical indicators, which provides a new direction for future studies aiming to novel serum biomarker for early detection or target of drug therapy of GDM.

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Gao, Y., Chen, H., Li, J., Ren, S., Yang, Z., Zhou, Y., & Xuan, R. (2022). Alterations of gut microbiota-derived metabolites in gestational diabetes mellitus and clinical significance. Journal of Clinical Laboratory Analysis, 36(4). https://doi.org/10.1002/jcla.24333

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