A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
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 we...
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Published in | Diabetes (New York, N.Y.) Vol. 65; no. 9; pp. 2529 - 2539 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Diabetes Association
01.09.2016
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Subjects | |
Online Access | Get full text |
ISSN | 0012-1797 1939-327X 1939-327X |
DOI | 10.2337/db15-1720 |
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Abstract | 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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AbstractList | 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.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. 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. |
Author | Ning, Xian Wheeler, Michael B. Nalla, Amarnadh Liu, Ying Cox, Brian J. Dai, Feihan F. Osborne, Lucy R. Prentice, Kacey J. Gunderson, Erica P. Allalou, Amina Zhang, Ming |
Author_xml | – sequence: 1 givenname: Amina surname: Allalou fullname: Allalou, Amina organization: Department of Medicine, University of Toronto, Ontario, Canada – sequence: 2 givenname: Amarnadh surname: Nalla fullname: Nalla, Amarnadh organization: Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark, Department of Physiology, University of Toronto, Ontario, Canada – sequence: 3 givenname: Kacey J. surname: Prentice fullname: Prentice, Kacey J. organization: Department of Physiology, University of Toronto, Ontario, Canada – sequence: 4 givenname: Ying surname: Liu fullname: Liu, Ying organization: Department of Physiology, University of Toronto, Ontario, Canada – sequence: 5 givenname: Ming surname: Zhang fullname: Zhang, Ming organization: Department of Physiology, University of Toronto, Ontario, Canada – sequence: 6 givenname: Feihan F. surname: Dai fullname: Dai, Feihan F. organization: Department of Physiology, University of Toronto, Ontario, Canada – sequence: 7 givenname: Xian surname: Ning fullname: Ning, Xian organization: Kaiser Permanente Northern California, Division of Research, Oakland, CA – sequence: 8 givenname: Lucy R. surname: Osborne fullname: Osborne, Lucy R. organization: Department of Medicine, University of Toronto, Ontario, Canada, Department of Molecular Genetics, University of Toronto, Ontario, Canada – sequence: 9 givenname: Brian J. surname: Cox fullname: Cox, Brian J. organization: Department of Physiology, University of Toronto, Ontario, Canada, Department of Obstetrics and Gynaecology, University of Toronto, Ontario, Canada – sequence: 10 givenname: Erica P. surname: Gunderson fullname: Gunderson, Erica P. organization: Kaiser Permanente Northern California, Division of Research, Oakland, CA – sequence: 11 givenname: Michael B. surname: Wheeler fullname: Wheeler, Michael B. organization: Department of Medicine, University of Toronto, Ontario, Canada, Department of Physiology, University of Toronto, Ontario, Canada |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27338739$$D View this record in MEDLINE/PubMed |
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Copyright | 2016 by the American Diabetes Association. Copyright American Diabetes Association Sep 1, 2016 2016 by the American Diabetes Association. 2016 |
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Snippet | 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... 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... |
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SubjectTerms | Adult Blood Glucose - metabolism Body mass index Case-Control Studies Diabetes Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - epidemiology Diabetes, Gestational - blood Diabetes, Gestational - epidemiology Female Glucose Tolerance Test Humans Incidence Metabolism Middle Aged Postpartum Period - blood Pregnancy Prospective Studies Women Young Adult |
Title | A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes |
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