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Diabetologia

pp 1–17 | Cite as

The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes

  • Saifur R. Khan
  • Haneesha Mohan
  • Ying Liu
  • Battsetseg Batchuluun
  • Himaben Gohil
  • Dana Al Rijjal
  • Yousef Manialawy
  • Brian J. CoxEmail author
  • Erica P. GundersonEmail author
  • Michael B. WheelerEmail author
Article

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

We reveal a novel predictive signature and associate reduced sphingolipids with the pathophysiology of transition from GDM to type 2 diabetes. Attenuating sphingolipid metabolism in islets impairs glucose-stimulated insulin secretion.

Keywords

Gestational diabetes mellitus Glucose-stimulated insulin secretion Lipidomic study Machine learning Multiple logistic regression Pathophysiology Predictive biomarker Prospective cohort Sphingolipid metabolism Type 2 diabetes 

Abbreviations

BCAA

Branched-chain amino acid

CE

Cholesteryl ester

Cer

Ceramide

FB1

Fumonisin B1

FC

Filtered classifier

FDR

False discovery rate

FPG

Fasting plasma glucose

GDM

Gestational diabetes

GPI

Glycosylphosphatidylinositol

GSIS

Glucose-stimulated insulin secretion

IQR

Interquartile range

KEGG

Kyoto Encyclopedia of Genes and Genomes

LCer

Lactosylceramide

LPC

Lysophosphatidylcholine

LPE

Lysophosphatidylethanolamine

MLR

Multiple logistic regression

PC

Phosphatidylcholine

PCA

Principal component analysis

PE

Phosphatidylethanolamine

PLS-DA

Partial least squares-discriminant analysis

ROC

Receiver operating characteristic

SM

Sphingomyelin

So

Sphingosine

SWIFT

Study of Women, Infant Feeding and Type 2 Diabetes after GDM

TAG

Triacylglycerol

Notes

Contribution statement

SRK, MBW, EPG, BJC, HM and YL designed the research work. All predictive analytics and bioinformatics were performed by SRK and supervised by MBW and BJC. All in vivo studies were conducted by SRK, HG, HM, YL, DAR, BB and YM. All in vitro studies were conducted by HM, BB and SRK. The manuscript was written by SRK and edited by MBW, EPG, BJC, HM and YL. All authors assisted in reviewing the manuscript and gave final approval of the version to be published. MBW is the guarantor of this work.

Funding

These studies are supported by Canadian Institutes of Health Research (CIHR), FRN 143219 (MBW). and National Institute of Child Health and Human Development (NICHD) R01 HD050625 (EPG). SRK is supported by a Diabetes Canada post-doctoral fellowship.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2018_4800_MOESM1_ESM.pdf (1.2 mb)
ESM 1 (PDF 1.20 MB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Saifur R. Khan
    • 1
    • 2
  • Haneesha Mohan
    • 1
    • 2
  • Ying Liu
    • 1
    • 2
  • Battsetseg Batchuluun
    • 1
    • 2
  • Himaben Gohil
    • 1
    • 2
  • Dana Al Rijjal
    • 1
    • 2
  • Yousef Manialawy
    • 1
    • 2
  • Brian J. Cox
    • 3
    • 4
    Email author
  • Erica P. Gunderson
    • 5
    Email author
  • Michael B. Wheeler
    • 1
    • 2
    Email author
  1. 1.Endocrine and Diabetes Platform, Department of PhysiologyUniversity of TorontoTorontoCanada
  2. 2.Advanced Diagnostics, MetabolismToronto General Hospital Research InstituteTorontoCanada
  3. 3.Reproduction and Development Platform, Department of PhysiologyUniversity of TorontoTorontoCanada
  4. 4.Department of Obstetrics and GynecologyUniversity of TorontoTorontoCanada
  5. 5.Kaiser Permanente Northern California, Division of ResearchOaklandUSA

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