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

journal contribution
submitted on 2024-06-30, 09:42 and posted on 2024-06-30, 09:48 authored by Loic Yengo, Abdelilah Arredouani, Michel Marre, Ronan Roussel, Martine Vaxillaire, Mario Falchi, Abdelali Haoudi, Jean Tichet, Beverley Balkau, Amélie Bonnefond, Philippe Froguel

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.

Other Information

Published in: Molecular Metabolism
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.molmet.2016.08.011

Funding

Centre National de la Recherche Scientifique (N/A).

Fédération Française de Cardiologie (N/A).

Association Diabète Risque Vasculaire (N/A).

Novo Nordisk (N/A).

Qatar Foundation (N/A).

Institut National de la Santé et de la Recherche Médicale (N/A).

Fondation de France (N/A).

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2016

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Biomedical Research Institute - HBKU

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    Qatar Biomedical Research Institute - HBKU

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