Early metabolic markers identify potential targets for the prevention of type 2 diabetes

Early metabolic markers identify potential targets for the prevention of type 2 diabetes Diabetologia (2017) 60:1740–1750 DOI 10.1007/s00125-017-4325-0 ARTICLE Early metabolic markers identify potential targets for the prevention of type 2 diabetes 1,2 3 4,5,6,7 4,5,6,8 Gopal Peddinti & Jeff Cobb & Loic Yengo & Philippe Froguel & 9 10 1,11,12 1,13 Jasmina Kravić & Beverley Balkau & Tiinamaija Tuomi & Tero Aittokallio & 1,9 Leif Groop Received: 17 February 2017 /Accepted: 11 May 2017 /Published online: 8 June 2017 The Author(s) 2017. This article is an open access publication Abstract regularised least-squares modelling was used to perform ma- Aims/hypothesis The aims of this study were to evaluate sys- chine learning-based risk classification and marker selection. tematically the predictive power of comprehensive metabolo- The predictive performance of the machine learning models mics profiles in predicting the future risk of type 2 diabetes, and marker panels was evaluated using repeated nested cross- and to identify a panel of the most predictive metabolic validation, and replicated in an independent French cohort of markers. 1044 individuals including 231 participants who progressed to Methods We applied an unbiased systems medicine approach type 2 diabetes during a 9 year follow-up period in the DESIR to mine metabolite combinations that provide added value in (Data from an Epidemiological http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Diabetologia Springer Journals

Early metabolic markers identify potential targets for the prevention of type 2 diabetes

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by The Author(s)
Subject
Medicine & Public Health; Internal Medicine; Metabolic Diseases; Human Physiology
ISSN
0012-186X
eISSN
1432-0428
D.O.I.
10.1007/s00125-017-4325-0
Publisher site
See Article on Publisher Site

Abstract

Diabetologia (2017) 60:1740–1750 DOI 10.1007/s00125-017-4325-0 ARTICLE Early metabolic markers identify potential targets for the prevention of type 2 diabetes 1,2 3 4,5,6,7 4,5,6,8 Gopal Peddinti & Jeff Cobb & Loic Yengo & Philippe Froguel & 9 10 1,11,12 1,13 Jasmina Kravić & Beverley Balkau & Tiinamaija Tuomi & Tero Aittokallio & 1,9 Leif Groop Received: 17 February 2017 /Accepted: 11 May 2017 /Published online: 8 June 2017 The Author(s) 2017. This article is an open access publication Abstract regularised least-squares modelling was used to perform ma- Aims/hypothesis The aims of this study were to evaluate sys- chine learning-based risk classification and marker selection. tematically the predictive power of comprehensive metabolo- The predictive performance of the machine learning models mics profiles in predicting the future risk of type 2 diabetes, and marker panels was evaluated using repeated nested cross- and to identify a panel of the most predictive metabolic validation, and replicated in an independent French cohort of markers. 1044 individuals including 231 participants who progressed to Methods We applied an unbiased systems medicine approach type 2 diabetes during a 9 year follow-up period in the DESIR to mine metabolite combinations that provide added value in (Data from an Epidemiological

Journal

DiabetologiaSpringer Journals

Published: Jun 8, 2017

References

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