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

Loic Yengo, Abdelilah Arredouani, Michel Marre, Ronan Roussel, Martine Vaxillaire, Mario Falchi, Abdelali Haoudi, Jean Tichet, Beverley Balkau, Amélie Bonnefond, Philippe Froguel

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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.

Original languageEnglish
Pages (from-to)918-925
Number of pages8
JournalMolecular Metabolism
Volume5
Issue number10
DOIs
Publication statusPublished - 1 Oct 2016

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Keywords

  • High dimensional regression
  • LASSO
  • Metabolomics
  • Risk prediction
  • Type 2 diabetes

ASJC Scopus subject areas

  • Molecular Biology
  • Cell Biology

Cite this

Yengo, L., Arredouani, A., Marre, M., Roussel, R., Vaxillaire, M., Falchi, M., Haoudi, A., Tichet, J., Balkau, B., Bonnefond, A., & Froguel, P. (2016). Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling. Molecular Metabolism, 5(10), 918-925. https://doi.org/10.1016/j.molmet.2016.08.011