Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-021-95903-w
Title: Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women
Authors: Wang X.-M.
Gao Y.
Eriksson J.G. 
Chen W.
Chong Y.S. 
Tan K.H. 
Zhang C.
Zhou L. 
Li L.-J. 
Issue Date: 12-Aug-2021
Publisher: Nature Research
Citation: Wang X.-M., Gao Y., Eriksson J.G., Chen W., Chong Y.S., Tan K.H., Zhang C., Zhou L., Li L.-J. (2021-08-12). Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women. Scientific Reports 11 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-021-95903-w
Abstract: We aimed to identify serum metabolites related to abnormal glucose metabolism (AGM) among women with gestational diabetes mellitus (GDM). The study recruited 50 women diagnosed with GDM during mid-late pregnancy and 50 non-GDM matchees in a Singapore birth cohort. At the 5-year post-partum follow-up, we applied an untargeted approach to investigate the profiles of serum metabolites among all participants. We first employed OPLS-DA and logistic regression to discriminate women with and without follow-up AGM, and then applied area under the curve (AUC) to assess the incremental indicative value of metabolic signatures on AGM. We identified 23 candidate metabolites that were associated with postpartum AGM among all participants. We then narrowed down to five metabolites [p-cresol sulfate, linoleic acid, glycocholic acid, lysoPC(16:1) and lysoPC(20:3)] specifically associating with both GDM and postpartum AGM. The combined metabolites in addition to traditional risks showed a higher indicative value in AUC (0.92–0.94 vs. 0.74 of traditional risks and 0.77 of baseline diagnostic biomarkers) and R2 (0.67–0.70 vs. 0.25 of traditional risks and 0.32 of baseline diagnostic biomarkers) in terms of AGM indication, compared with the traditional risks model and traditional risks and diagnostic biomarkers combined model. These metabolic signatures significantly increased the AUC value of AGM indication in addition to traditional risks, and might shed light on the pathophysiology underlying the transition from GDM to AGM.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/198829
ISSN: 20452322
DOI: 10.1038/s41598-021-95903-w
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