Journal
DIABETOLOGIA
Volume 57, Issue 1, Pages 16-29Publisher
SPRINGER
DOI: 10.1007/s00125-013-3061-3
Keywords
Biomarkers; Calibration; Discrimination; Genetic variants; Metabolomics; Prediction models; Reclassification; Repeated measures; Review; Risk Scores; Type 2 diabetes
Categories
Funding
- German Federal Ministry of Health (Berlin, Germany)
- Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia (Dusseldorf, Germany)
- German Federal Ministry of Education and Research (BMBF)
- Greifswald
- Federal Ministry of Education and Research
- Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania
- TAMOP [4.2.4.A/1-11-1-2012-0001]
- European Union
- European Social Fund
Ask authors/readers for more resources
The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available