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Early metabolic mar...
Early metabolic markers identify potential targets for the prevention of type 2 diabetes
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- Peddinti, Gopal (author)
- University of Helsinki
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- Cobb, Jeff (author)
- Metabolon Inc.
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- Yengo, Loic (author)
- University of Lille,Pasteur Institute of Lille,University of Queensland
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- Froguel, Philippe (author)
- Hammersmith Hospital,University of Lille,Pasteur Institute of Lille
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- Kravic, Jasmina (author)
- Lund University,Lunds universitet,Translationell muskelforskning,Forskargrupper vid Lunds universitet,Translational Muscle Research,Lund University Research Groups
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- Balkau, Beverley (author)
- Centre for Research in Epidemiology and Population Health (CESP)
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- Tuomi, Tiinamaija (author)
- University of Helsinki,Institute for Molecular Medicine Finland (FIMM),Helsinki University Central Hospital
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- Aittokallio, Tero (author)
- University of Turku,University of Helsinki
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- Groop, Leif (author)
- Lund University,Lunds universitet,Translationell muskelforskning,Forskargrupper vid Lunds universitet,Translational Muscle Research,Lund University Research Groups,University of Helsinki,Institute for Molecular Medicine Finland (FIMM)
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University of Helsinki Metabolon Inc (creator_code:org_t)
- 2017-06-08
- 2017
- English 11 s.
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; , s. 1-11
- Related links:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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Abstract
Subject headings
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)
Keyword
- Biomarkers
- Early prediction
- Kallikrein–kinin system
- Machine learning
- Metabolomics
- Multivariate models
- Prevention
- Risk classification
Publication and Content Type
- art (subject category)
- ref (subject category)
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