4.7 Article

Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women

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SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-021-95903-w

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  1. Singapore National Medical Research Council [NMRC/CNIG/1114/2013]
  2. Singapore National Medical Council Transition Award [NMRC/TA/0027/2014]
  3. Singapore National Research Foundation (NRF)
  4. Singapore National Medical Research Council (NMRC) [NMRC/TCR/004-NUS/2008]

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In this study, serum metabolites associated with GDM and postpartum AGM were identified among women in Singapore. These metabolites combined with traditional risk factors showed higher indicative value in predicting AGM compared to traditional models, shedding light on the pathophysiology of the transition from GDM to AGM.
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 R-2 (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.

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