Sökning: onr:"swepub:oai:lup.lub.lu.se:5e1d884a-03b6-49c9-bebc-026eaef3f322" >
Early metabolic mar...
-
Peddinti, GopalUniversity of Helsinki
(författare)
Early metabolic markers identify potential targets for the prevention of type 2 diabetes
- Artikel/kapitelEngelska2017
Förlag, utgivningsår, omfång ...
-
2017-06-08
-
Springer Science and Business Media LLC,2017
-
11 s.
Nummerbeteckningar
-
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
Kompletterande språkuppgifter
-
Språk:engelska
-
Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
-
Ämneskategori:art swepub-publicationtype
-
Ämneskategori:ref swepub-contenttype
Anmärkningar
-
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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
-
Cobb, JeffMetabolon Inc.
(författare)
-
Yengo, LoicUniversity of Queensland,Pasteur Institute of Lille,University of Lille
(författare)
-
Froguel, PhilippeHammersmith Hospital,Pasteur Institute of Lille,University of Lille
(författare)
-
Kravic, JasminaLund University,Lunds universitet,Translationell Muskel Forskning,Forskargrupper vid Lunds universitet,Translational Muscle Research,Lund University Research Groups(Swepub:lu)med-jik
(författare)
-
Balkau, BeverleyCentre for Research in Epidemiology and Population Health (CESP)
(författare)
-
Tuomi, TiinamaijaUniversity of Helsinki,Institute for Molecular Medicine Finland (FIMM),Helsinki University Central Hospital(Swepub:lu)ti8736tu
(författare)
-
Aittokallio, TeroUniversity of Turku,University of Helsinki
(författare)
-
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
(författare)
-
University of HelsinkiMetabolon Inc.
(creator_code:org_t)
Sammanhörande titlar
-
Ingår i:Diabetologia: Springer Science and Business Media LLC, s. 1-110012-186X1432-0428
Internetlänk
Hitta via bibliotek
Till lärosätets databas