Elsevier

Molecular Metabolism

Volume 5, Issue 10, October 2016, Pages 918-925
Molecular Metabolism

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

https://doi.org/10.1016/j.molmet.2016.08.011Get rights and content
Under a Creative Commons license
open access

Highlights

  • Metabolites Risk Scores improve the prediction of type 2 diabetes on top of clinical and biological risk factors in both high and low-risk sub-populations.

  • Two predictive metabolites (1,5-anhydroglucitol and Dehydroisoandrosterone sulfate) were well conserved over 9 years.

  • Comparing two statistical approaches revealed that lipid metabolism distinguishes baseline risk from that of fast converters.

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.

Keywords

Type 2 diabetes
Metabolomics
Risk prediction
High dimensional regression
LASSO

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