Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling

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Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling

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Title: Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling
Author: Yengo, Loic; Arredouani, Abdelilah; Marre, Michel; Roussel, Ronan; Vaxillaire, Martine; Falchi, Mario; Haoudi, Abdelali; Tichet, Jean; Balkau, Beverley; Bonnefond, Amélie; Froguel, Philippe

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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.
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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.
Published Version: doi:10.1016/j.molmet.2016.08.011
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034686/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:29407756
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