| Title: | Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes |
| Journal: | European Journal of Preventive Cardiology |
| Published: | 24 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40269530/ |
| DOI: | https://doi.org/10.1093/eurjpc/zwaf254 |
Publication 15759
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Abstract
BACKGROUND AND AIMS: The value of metabolomic biomarkers for cardiovascular risk prediction is unclear. This study aimed to evaluate the potential of improved prediction of the 10-year risk of major adverse cardiovascular events (MACE) in large population-based cohorts by adding metabolomic biomarkers to the novel SCORE2 model, which was introduced in 2021 for the European population without previous cardiovascular disease or diabetes.</p>
METHODS: Data from 187,039 and 5,578 participants from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. LASSO regression with bootstrapping was used to identify metabolites in sex-specific analyses and the predictive performance of metabolites added to the SCORE2 model was primarily evaluated with Harrell's C-index.</p>
RESULTS: Thirteen metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, six male- and four female-specific metabolites) in the UKB derivation set. In internal validation with the UKB, adding the selected metabolites to the SCORE2 model increased the C-index statistically significantly (P<0.001) from 0.691 to 0.710. In external validation with ESTHER, the C-index increase was similar (from 0.673 to 0.688, P=0.042). The inflammation biomarker, glycoprotein acetyls, contributed the most to the increased C-index in both men and women.</p>
CONCLUSIONS: The integration of metabolomic biomarkers into the SCORE2 model markedly improves the prediction of 10-year cardiovascular risk. With recent advancements in reducing costs and standardizing processes, NMR metabolomics holds considerable promise for implementation in clinical practice.</p>
6 Authors
- Ruijie Xie
- Sha Sha
- Lei Peng
- Bernd Holleczek
- Hermann Brenner
- Ben Schöttker
1 Application
| Application ID | Title |
|---|---|
| 101633 | Use of omics data to understand better the etiology of age-related diseases and to improve their risk prediction |
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