Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes
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 SCORE...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
24 April 2025
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| In: |
European journal of preventive cardiology
Year: 2025, Pages: 1-10 |
| ISSN: | 2047-4881 |
| DOI: | 10.1093/eurjpc/zwaf254 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/eurjpc/zwaf254
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| Author Notes: | Ruijie Xie, Sha Sha, Lei Peng, Bernd Holleczek, Hermann Brenner, and Ben Schöttker |
| Summary: | 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.Data from 187 039 and 5578 participants from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation and internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Least Absolute Shrinkage and Selection Operator (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. Thirteen metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, six male-specific metabolite, 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.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.This study demonstrated that adding the concentrations of 13 metabolites measured in blood samples to an established cardiovascular risk prediction model, named SCORE2, substantially enhanced the prediction accuracy of the SCORE2 model for major cardiovascular events (MACE), such as heart attacks and strokes, over the next 10 years in individuals without a history of cardiovascular disease or diabetes.Although the SCORE2 risk prediction model includes important risk factors for MACE, such as age, sex, smoking, high blood pressure, and high cholesterol, and predicts MACE well, the 13 metabolites led to an even more accurate risk prediction, which is important when identifying patients at a high risk of MACE and choosing the most appropriate intervention(s) to prevent MACE in the next 10 years.The improved model was developed and tested in population samples from the UK and Germany and could only be used in these countries so far because adaptions for further countries would need to be done by additional studies. |
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| Item Description: | Gesehen am 25.08.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2047-4881 |
| DOI: | 10.1093/eurjpc/zwaf254 |