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

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 ?-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.

SUBMITTER: Khan SR

PROVIDER: S-EPMC7237273 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Khan Saifur R SR Mohan Haneesha H Liu Ying Y Batchuluun Battsetseg B Gohil Himaben H Al Rijjal Dana D Manialawy Yousef Y Cox Brian J BJ Gunderson Erica P EP Wheeler Michael B MB

Diabetologia 20190115 4

<h4>Aims/hypothesis</h4>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 und ...[more]

PMID: 30645667

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Project description:Gestational diabetes mellitus (GDM) can be considered a silent risk for out-of-pregnancy diabetes mellitus (DM) and cardiovascular disease (CVD) later in life. We aimed to assess the predictive role of 3rd trimester lipid profile during pregnancy for the susceptibility to markers of subclinical atherosclerosis (CVD susceptibility) at 3 years in a cohort of women with history of GDM. A secondary aim is to evaluate the usefulness of novel nutrigenetic markers, in addition to traditional parameters, for predicting early subclinical atherosclerosis in such women in order to plan adequate early prevention interventions. We assessed 28 consecutive GDM women in whom we collected socio-demographic characteristics and clinical and anthropometric parameters at the 3rd trimester of pregnancy. In a single blood sample, from each patient, we assessed 9 single nucleotide polymorphisms (SNPs) from 9 genes related to nutrients and metabolism, which were genotyped by High Resolution Melting analysis. All women then attended a 3-year-postpartum follow-up and on that occasion performed an oral glucose tolerance test (OGTT, with 75 g oral glucose), the measurement of carotid artery intima-media thickness (cIMT), and analyses of metabolic parameters. In addition, we evaluated the physical activity level and the adherence to Mediterranean diet (MedDiet) using the International Physical Activity Questionnaire (IPAQ-short version) and PREDIMED questionnaires. We found an association between 3rd trimester triglycerides and cIMT (p = 0.014). We also found significant associations between the APOA5 CC genotype and cIMT after adjustments for age and body mass index (p = 0.045) and between the interaction CC APOA5/CC LDLR and cIMT (p = 0.010). At the follow-up, the cohort also featured a mean BMI in the overweight range and a high mean waist circumference. We found no difference in the MedDiet adherence, physical activity, and smoking but an inverse correlation between the PREDIMED and the IPAQ scores with the IMT. In conclusion, this preliminary study provides insight into the predictive role of lipid profile during pregnancy and of some genetic variants on cIMT taken as a parameter of subclinical CVD susceptibility in GDM.

Project description:BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Dataset Information

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

Publications

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

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