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

Peddinti, Gopal, Cobb, Jeff, Yengo, Loic, Froguel, Philippe, Kravic, Jasmina, Balkau, Beverley, Tuomi, Tiinamaija, Aittokallio, Tero and Groop, Leif (2017) Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia, 60 9: 1740-1750. doi:10.1007/s00125-017-4325-0

Author Peddinti, Gopal
Cobb, Jeff
Yengo, Loic
Froguel, Philippe
Kravic, Jasmina
Balkau, Beverley
Tuomi, Tiinamaija
Aittokallio, Tero
Groop, Leif
Title Early metabolic markers identify potential targets for the prevention of type 2 diabetes
Journal name Diabetologia   Check publisher's open access policy
ISSN 1432-0428
Publication date 2017-06-08
Sub-type Article (original research)
DOI 10.1007/s00125-017-4325-0
Open Access Status DOI
Volume 60
Issue 9
Start page 1740
End page 1750
Total pages 11
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Formatted abstract
Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.

Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.

Results: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong’s p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.

Conclusions/interpretation: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
Keyword Biomarkers
Early prediction
Kallikrein–kinin system
Machine learning
Multivariate models
Risk classification
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
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