Dataset Information

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

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 (? = -3.44 years per MRS2 SD in the training population, p = 1.56 × 10(-7); ? = -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.

SUBMITTER: Yengo L

PROVIDER: S-EPMC5034686 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Yengo Loic L Arredouani Abdelilah A Marre Michel M Roussel Ronan R Vaxillaire Martine M Falchi Mario M Haoudi Abdelali A Tichet Jean J Balkau Beverley B Bonnefond Amélie A Froguel Philippe P

Molecular metabolism 20160823 10

<h4>Objective</h4>Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions.<h4>Research design and methods</h4>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 as ...[more]

PMID: 27689004

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Dataset Information

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

Objective

Research design and methods

Results

Conclusions

Publications

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

Similar Datasets

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