Abstract | AIMS/HYPOTHESIS:
Gestational diabetes mellitus (GDM) affects up to 20% of pregnancies, and almost half of the women affected progress to type 2 diabetes later in life, making GDM the most significant risk factor for the development of future type 2 diabetes. An accurate prediction of future type 2 diabetes risk in the early postpartum period after GDM would allow for timely interventions to prevent or delay type 2 diabetes. In addition, new targets for interventions may be revealed by understanding the underlying pathophysiology of the transition from GDM to type 2 diabetes. The aim of this study is to identify both a predictive signature and early-stage pathophysiology of the transition from GDM to type 2 diabetes. METHODS: We used a well-characterised prospective cohort of women with a history of GDM pregnancy, all of whom were enrolled at 6-9 weeks postpartum (baseline), were confirmed not to have diabetes via 2 h 75 g OGTT and tested anually for type 2 diabetes on an ongoing basis (2 years of follow-up). A large-scale targeted lipidomic study was implemented to analyse ~1100 lipid metabolites in baseline plasma samples using a nested pair-matched case-control design, with 55 incident cases matched to 85 non-case control participants. The relationships between the concentrations of baseline plasma lipids and respective follow-up status (either type 2 diabetes or no type 2 diabetes) were employed to discover both a predictive signature and the underlying pathophysiology of the transition from GDM to type 2 diabetes. In addition, the underlying pathophysiology was examined in vivo and in vitro. RESULTS: Machine learning optimisation in a decision tree format revealed a seven- lipid metabolite type 2 diabetes predictive signature with a discriminating power (AUC) of 0.92 (87% sensitivity, 93% specificity and 91% accuracy). The signature was highly robust as it includes 45-fold cross-validation under a high confidence threshold (1.0) and binary output, which together minimise the chance of data overfitting and bias selection. Concurrent analysis of differentially expressed lipid metabolite pathways uncovered the upregulation of α-linolenic/ linoleic acid metabolism (false discovery rate [FDR] 0.002) and fatty acid biosynthesis (FDR 0.005) and the downregulation of sphingolipid metabolism (FDR 0.009) as being strongly associated with the risk of developing future type 2 diabetes. Focusing specifically on sphingolipids, the downregulation of sphingolipid metabolism using the pharmacological inhibitors fumonisin B1 (FB1) and myriocin in mouse islets and Min6 K8 cells (a pancreatic beta-cell like cell line) significantly impaired glucose-stimulated insulin secretion but had no significant impact on whole-body glucose homeostasis or insulin sensitivity. CONCLUSIONS/INTERPRETATION:
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Authors | Saifur R Khan, Haneesha Mohan, Ying Liu, Battsetseg Batchuluun, Himaben Gohil, Dana Al Rijjal, Yousef Manialawy, Brian J Cox, Erica P Gunderson, Michael B Wheeler |
Journal | Diabetologia
(Diabetologia)
Vol. 62
Issue 4
Pg. 687-703
(04 2019)
ISSN: 1432-0428 [Electronic] Germany |
PMID | 30645667
(Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
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Chemical References |
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Topics |
- Adult
- Animals
- Area Under Curve
- Asian
- Biomarkers
(blood)
- Case-Control Studies
- Decision Trees
- Diabetes Mellitus, Type 2
(blood, diagnosis, ethnology)
- Diabetes, Gestational
(blood, ethnology)
- Disease Progression
- Female
- Glucose Tolerance Test
- Hispanic or Latino
- Humans
- Islets of Langerhans
(metabolism)
- Machine Learning
- Male
- Mice
- Mice, Inbred C57BL
- Postpartum Period
- Pregnancy
- Prospective Studies
- Risk Factors
- Sphingolipids
(metabolism)
- United States
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