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

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Peddinti, GopalUniversity of Helsinki (author)

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

Article/chapterEnglish2017

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2017-06-08
Springer Science and Business Media LLC,2017
11 s.

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LIBRIS-ID:oai:lup.lub.lu.se:5e1d884a-03b6-49c9-bebc-026eaef3f322
https://lup.lub.lu.se/record/5e1d884a-03b6-49c9-bebc-026eaef3f322URI
https://doi.org/10.1007/s00125-017-4325-0DOI

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Language:English
Summary in:English

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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 and genre

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Endokrinologi och diabetes hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Endocrinology and Diabetes hsv//eng
Biomarkers
Early prediction
Kallikrein–kinin system
Machine learning
Metabolomics
Multivariate models
Prevention
Risk classification

Added entries (persons, corporate bodies, meetings, titles ...)

Cobb, JeffMetabolon Inc. (author)
Yengo, LoicUniversity of Queensland,Pasteur Institute of Lille,University of Lille (author)
Froguel, PhilippeHammersmith Hospital,Pasteur Institute of Lille,University of Lille (author)
Kravic, JasminaLund University,Lunds universitet,Translationell Muskel Forskning,Forskargrupper vid Lunds universitet,Translational Muscle Research,Lund University Research Groups(Swepub:lu)med-jik (author)
Balkau, BeverleyCentre for Research in Epidemiology and Population Health (CESP) (author)
Tuomi, TiinamaijaUniversity of Helsinki,Institute for Molecular Medicine Finland (FIMM),Helsinki University Central Hospital(Swepub:lu)ti8736tu (author)
Aittokallio, TeroUniversity of Turku,University of Helsinki (author)
Groop, LeifLund University,Lunds universitet,Translationell Muskel Forskning,Forskargrupper vid Lunds universitet,Translational Muscle Research,Lund University Research Groups,University of Helsinki,Institute for Molecular Medicine Finland (FIMM)(Swepub:lu)endo-lgr (author)
University of HelsinkiMetabolon Inc. (creator_code:org_t)

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In:Diabetologia: Springer Science and Business Media LLC, s. 1-110012-186X1432-0428

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