Heart failure & cardiomyopathies
Evaluation of large-scale plasma proteomics for prediction of heart failure in individuals with a full range of glucose metabolism profiles
Performance · HF prediction in validation cohortResult — go to the resultAUC improvement 0.05Authors' conclusion
This study identified plasma protein biomarkers linked to HF development in abnormal glucose metabolism population and established the predictive models.
Study context
- Population
- UK Biobank participants with abnormal glucose metabolism and no prior heart failure (HF)discovery 6517, validation 2783
- Comparison
- Protein-panel-clinical-factors (PPCF) model vs clinical factors-only model
- Outcome
- Incident HF
- Follow-up
- Median 13.90 years
Methodological detailsUK Biobank, discovery and validation cohorts
- UK Biobank
- Discovery and validation cohorts
Performance
HF prediction in validation cohort
AUC improvement
0.05
P < 0.001
By group
PPCF
0.823
95% CI 0.785–0.860
AUC
As reported in the abstract
Further findings
| Outcome | PPCF |
|---|---|
| HF prediction in general population (n = 23 107) | |
| AUC | 0.807 (95% CI: 0.786-0.829) |
Net benefit (decision curve)
- Protein panel model and PPCF model superior net benefits over clinical factors model in abnormal glucose metabolism population
Original publication
Evaluation of large-scale plasma proteomics for prediction of heart failure in individuals with a full range of glucose metabolism profiles. Eur J Prev Cardiol. Published 25 June 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →