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 appli

SUBMITTER: Yengo L 

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

REPOSITORIES: biostudies-literature