Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes
Authors' conclusion
The integration of metabolomic biomarkers into the SCORE2 model markedly improves the prediction of 10-year cardiovascular risk.
Study context
- Population
- General population without previous CVD or DM (UK Biobank and German ESTHER cohort)UK Biobank (UKB) 187,039
- Comparison
- SCORE2 plus metabolomic biomarkers vs SCORE2 alone
- Outcome
- Predictive performance (Harrell's C-index) for major adverse cardiovascular events (MACE)
- Follow-up
- 10 years
Methodological detailspopulation-based cohorts, internal and external validation, sex-specific analyses, 249 metabolites measured by nuclear magnetic resonance (NMR) spectroscopy; LASSO regression with bootstrapping
- Population-based cohorts
- Internal and external validation
- Sex-specific analyses
- 249 metabolites measured by NMR spectroscopy; LASSO regression with bootstrapping
Performance
As reported in the abstract
C-index, internal validation (UKB)
SCORE2 + metabolites
0.710
C-index
SCORE2
0.691
C-index
P < 0.001
Further findings
C-index, external validation (ESTHER)
- C-index increase similar to internal validation, from 0.673 to 0.688
- P = 0.042
Contribution of individual metabolites
- Glycoprotein acetyls (inflammation biomarker) contributed most to the C-index increase in both men and women
Original publication
Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes. Eur J Prev Cardiol. Published 24 April 2025. doi:10.
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