ReviewDiabetologia2014
The potential of novel biomarkers to improve risk prediction of type 2 diabetes.
Review in Diabetologia, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Physical Activity and Metabolic Alterations in Children and Adolescents Across Different Weight Groups: A Systematic Review.European journal of sport science · 2026Pooled it
- Dysregulated expression of long noncoding RNAs serves as diagnostic biomarkers of type 2 diabetes mellitus.Endocrine · 2019Pooled it
- Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease.Nature genetics · 2017Pooled it
- Gender disparities in random blood glucose levels among Pakistani adults with type 2 diabetes: a cross-sectional analysis.Scientific reports · 2026Article
- Validation of the Finnish Diabetes Risk Score (FINDRISC) in a Central-European Population for the Prediction of Cumulative Incidence of Type 2 Diabetes Over 8-Years-Follow-Up of the Budakalász Health Examination Survey (BHES).Diabetes/metabolism research and reviews · 2025Article
- Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors: a nested case-control study.Diabetologia · 2024Article
- Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.PLOS digital health · 2024Article
- Artificial intelligence with temporal features outperforms machine learning in predicting diabetes.PLOS digital health · 2023Article
- Integrating (Nutri-)Metabolomics into the One Health Tendency-The Key for Personalized Medicine Advancement.Metabolites · 2023Review
- Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study.BMC medicine · 2022Article
- Longitudinal changes in blood biomarkers and their ability to predict type 2 diabetes mellitus-The Tromsø study.Endocrinology, diabetes & metabolism · 2022Article
- Metabolomics and health: from nutritional crops and plant-based pharmaceuticals to profiling of human biofluids.Cellular and molecular life sciences : CMLS · 2021Review
- A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population.The Journal of clinical endocrinology and metabolism · 2021Article
- Association of Coronary Artery Disease and Metabolic Syndrome: Usefulness of Serum Metabolomics Approach.Frontiers in endocrinology · 2021Article
- Associations of cardiac stress biomarkers with incident type 2 diabetes and changes in glucose metabolism: KORA F4/FF4 study.Cardiovascular diabetology · 2020Article
- Higher circulating omentin is associated with increased risk of primary cardiovascular events in individuals with diabetes.Diabetologia · 2020Article
- Article
- Regulation of Glucose and Lipid Metabolism by Long Non-coding RNAs: Facts and Research Progress.Frontiers in endocrinology · 2020Review
- Genome-wide enrichment of mThe pharmacogenomics journal · 2019Article
- Diabetes risk assessment with imaging: a radiomics study of abdominal CT.European radiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.