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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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Autores principales: Allalou, Amina, Nalla, Amarnadh, Prentice, Kacey J., Liu, Ying, Zhang, Ming, Dai, Feihan F., Ning, Xian, Osborne, Lucy R., Cox, Brian J., Gunderson, Erica P., Wheeler, Michael B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Diabetes Association 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001181/
https://www.ncbi.nlm.nih.gov/pubmed/27338739
http://dx.doi.org/10.2337/db15-1720
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author Allalou, Amina
Nalla, Amarnadh
Prentice, Kacey J.
Liu, Ying
Zhang, Ming
Dai, Feihan F.
Ning, Xian
Osborne, Lucy R.
Cox, Brian J.
Gunderson, Erica P.
Wheeler, Michael B.
author_facet Allalou, Amina
Nalla, Amarnadh
Prentice, Kacey J.
Liu, Ying
Zhang, Ming
Dai, Feihan F.
Ning, Xian
Osborne, Lucy R.
Cox, Brian J.
Gunderson, Erica P.
Wheeler, Michael B.
author_sort Allalou, Amina
collection PubMed
description 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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spelling pubmed-50011812017-09-01 A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes Allalou, Amina Nalla, Amarnadh Prentice, Kacey J. Liu, Ying Zhang, Ming Dai, Feihan F. Ning, Xian Osborne, Lucy R. Cox, Brian J. Gunderson, Erica P. Wheeler, Michael B. Diabetes Metabolism 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. American Diabetes Association 2016-09 2016-06-20 /pmc/articles/PMC5001181/ /pubmed/27338739 http://dx.doi.org/10.2337/db15-1720 Text en © 2016 by the American Diabetes Association. http://diabetesjournals.org/site/licenseReaders 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 http://diabetesjournals.org/site/license.
spellingShingle Metabolism
Allalou, Amina
Nalla, Amarnadh
Prentice, Kacey J.
Liu, Ying
Zhang, Ming
Dai, Feihan F.
Ning, Xian
Osborne, Lucy R.
Cox, Brian J.
Gunderson, Erica P.
Wheeler, Michael B.
A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title_full A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title_fullStr A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title_full_unstemmed A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title_short A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes
title_sort predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes
topic Metabolism
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001181/
https://www.ncbi.nlm.nih.gov/pubmed/27338739
http://dx.doi.org/10.2337/db15-1720
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