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      A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes

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          Gestational diabetes mellitus (GDM) affects 3–14% of pregnancies, with 20–50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6–9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non–case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions.

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          Most cited references 43

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          Standards of medical care in diabetes--2014.

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            Metabolite profiles and the risk of developing diabetes.

            Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.
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              A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance.

              Metabolomic profiling of obese versus lean humans reveals a branched-chain amino acid (BCAA)-related metabolite signature that is suggestive of increased catabolism of BCAA and correlated with insulin resistance. To test its impact on metabolic homeostasis, we fed rats on high-fat (HF), HF with supplemented BCAA (HF/BCAA), or standard chow (SC) diets. Despite having reduced food intake and a low rate of weight gain equivalent to the SC group, HF/BCAA rats were as insulin resistant as HF rats. Pair-feeding of HF diet to match the HF/BCAA animals or BCAA addition to SC diet did not cause insulin resistance. Insulin resistance induced by HF/BCAA feeding was accompanied by chronic phosphorylation of mTOR, JNK, and IRS1Ser307 and by accumulation of multiple acylcarnitines in muscle, and it was reversed by the mTOR inhibitor, rapamycin. Our findings show that in the context of a dietary pattern that includes high fat consumption, BCAA contributes to development of obesity-associated insulin resistance.

                Author and article information

                American Diabetes Association
                September 2016
                20 June 2016
                : 65
                : 9
                : 2529-2539
                1Department of Medicine, University of Toronto, Ontario, Canada
                2Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
                3Department of Physiology, University of Toronto, Ontario, Canada
                4Kaiser Permanente Northern California, Division of Research, Oakland, CA
                5Department of Molecular Genetics, University of Toronto, Ontario, Canada
                6Department of Obstetrics and Gynaecology, University of Toronto, Ontario, Canada
                Author notes
                Corresponding authors: Erica P. Gunderson, erica.gunderson@ , and Michael B. Wheeler, michael.wheeler@ .

                E.P.G. and M.B.W. are co-senior authors.

                © 2016 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at

                Page count
                Figures: 3, Tables: 4, Equations: 0, References: 43, Pages: 11
                Funded by: National Institute of Child Health and Human Development
                Award ID: R01-HD-050625
                Award ID: R01-HD-050625-03S1
                Award ID: R01-HD-050625-05S
                Funded by: National Center for Research Resources
                Award ID: UCSF-CTSI UL1-RR-024131
                Funded by: Kaiser Permanente Community Benefit Program
                Funded by: W.K. Kellogg Foundation
                Funded by: Canadian Institutes of Health Research
                Award ID: MOP-136810
                Funded by: Canadian Diabetes Association
                Award ID: CG-3-12-37
                Funded by: Banting and Best Diabetes Centre (BBDC), University of Toronto
                Funded by: Danish Diabetes Academy

                Endocrinology & Diabetes


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