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

Summary Gestational diabetes mellitus (GDM) is the top risk factor for future type 2 diabetes (T2D) development. Ethnicity profoundly influences who will transition from GDM to T2D, with high risk observed in Hispanic women. To better understand this risk, a nested 1:1 pair-matched, Hispanic-specific, case-control design was applied to a prospective cohort with GDM history. Women who were non-diabetic 6–9 weeks postpartum (baseline) were monitored for the development of T2D. Metabolomics were performed on baseline plasma to identify metabolic pathways associated with T2D risk. Notably, diminished sphingolipid metabolism was highly associated with future T2D. Defects in sphingolipid metabolism were further implicated by integrating metabolomics and genome-wide association data, which identified two significantly enriched T2D-linked genes, CERS2 and CERS4 . Follow-up experiments in mice and cells demonstrated that inhibiting sphingolipid metabolism impaired pancreatic β cell function. These data suggest early postpartum alterations in sphingolipid biosynthesis contribute to β cell dysfunction and T2D risk.

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