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. 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.
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. 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 language | English |
|---|---|
| Journal | Molecular Metabolism |
| Early online date | 23 Aug 2016 |
| DOIs | |
| Publication status | E-pub ahead of print - 23 Aug 2016 |
Keywords
- type 2 diabetes
- metabolomics
- risk prediction
- high dimensional regression
- LASSO
