dc.contributor.author | Yengo, Loic | en_US |
dc.contributor.author | Arredouani, Abdelilah | en_US |
dc.contributor.author | Marre, Michel | en_US |
dc.contributor.author | Roussel, Ronan | en_US |
dc.contributor.author | Vaxillaire, Martine | en_US |
dc.contributor.author | Falchi, Mario | en_US |
dc.contributor.author | Haoudi, Abdelali | en_US |
dc.contributor.author | Tichet, Jean | en_US |
dc.contributor.author | Balkau, Beverley | en_US |
dc.contributor.author | Bonnefond, Amélie | en_US |
dc.contributor.author | Froguel, Philippe | en_US |
dc.date.accessioned | 2016-11-18T20:06:06Z | |
dc.date.issued | 2016 | en_US |
dc.identifier.citation | Yengo, L., A. Arredouani, M. Marre, R. Roussel, M. Vaxillaire, M. Falchi, A. Haoudi, et al. 2016. “Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling.” Molecular Metabolism 5 (10): 918-925. doi:10.1016/j.molmet.2016.08.011. http://dx.doi.org/10.1016/j.molmet.2016.08.011. | en |
dc.identifier.issn | 2212-8778 | en |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:29407756 | |
dc.description.abstract | Objective: Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions. Research design and methods We developed predictive models of type 2 diabetes using two strategies. One strategy focused on the probability of incidence only and was based on logistic regression (MRS1); the other strategy accounted for the age at diagnosis of diabetes and was based on Cox regression (MRS2). We assessed 293 metabolites using non-targeted metabolomics in fasting plasma samples of 1,044 participants (including 231 incident cases over 9 years) used as training population; and fasting serum samples of 128 participants (64 incident cases versus 64 controls) used as validation population. We applied a LASSO-based variable selection aiming at maximizing the out-of-sample area under the receiver operating characteristic curve (AROC) and integrated AROC. Results: Sixteen and 17 metabolites were selected for MRS1 and MRS2, respectively, with AROC = 90% and 73% in the training and validation populations, respectively for MRS1. MRS2 had a similar performance and was significantly associated with a younger age of onset of type 2 diabetes (β = −3.44 years per MRS2 SD in the training population, p = 1.56 × 10−7; β = −4.73 years per MRS2 SD in the validation population, p = 4.04 × 10−3). Conclusions: Overall, this study illustrates that metabolomics improves prediction of type 2 diabetes incidence of 4.5% on top of known clinical and biological markers, reaching 90% in total AROC, which is considered the threshold for clinical validity, suggesting it may be used in targeting interventions to prevent type 2 diabetes. | en |
dc.language.iso | en_US | en |
dc.publisher | Elsevier | en |
dc.relation.isversionof | doi:10.1016/j.molmet.2016.08.011 | en |
dc.relation.hasversion | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034686/pdf/ | en |
dash.license | LAA | en_US |
dc.subject | Type 2 diabetes | en |
dc.subject | Metabolomics | en |
dc.subject | Risk prediction | en |
dc.subject | High dimensional regression | en |
dc.subject | LASSO | en |
dc.title | Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling | en |
dc.type | Journal Article | en_US |
dc.description.version | Version of Record | en |
dc.relation.journal | Molecular Metabolism | en |
dc.date.available | 2016-11-18T20:06:06Z | |
dc.identifier.doi | 10.1016/j.molmet.2016.08.011 | * |
dash.authorsordered | false | |