| Title: | Circulating inflammation-related proteome improves cardiovascular risk prediction. Results from two large European cohort studies |
| Journal: | European Journal of Epidemiology |
| Published: | 13 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40801988/ |
| DOI: | https://doi.org/10.1007/s10654-025-01285-y |
Publication 16563
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Abstract
BackgroundInflammation plays a crucial role in cardiovascular disease (CVD), but the value of inflammation-related proteins in predicting major adverse cardiovascular events (MACE) is unclear. This study evaluated whether incorporating inflammation-related proteins into the SCORE2 model improves 10-year MACE risk prediction.MethodsThis study included 47,382 participants from the UK Biobank and 4,135 participants from the German ESTHER study without prior CVD or diabetes. We tested C-reactive protein (CRP) and 73 inflammation-related proteins measured with Olink® panels. Biomarker selection was performed using least absolute shrinkage and selection operator (LASSO) regression with bootstrapping separately for males and females. Selected proteins were added to the SCORE2 model variables. Model performance was evaluated using Harrell's C-index, net reclassification index (NRI), and integrated discrimination index (IDI).ResultsSeven inflammation-related proteins but not CRP were selected, including two for both sexes, three specifically for males, and two specifically for females. Incorporating these proteins significantly improved the C-index (95% confidence interval (95%CI)) of the refitted SCORE2 model from 0.716 (0.698, 0.734) to 0.750 (0.732, 0.768) in internal validation in the UK Biobank and from 0.677 (0.644, 0.710) to 0.713 (0.681, 0.745) in external validation in the ESTHER study. The NRI with 95%CI was 12.4% (5.2%, 16.3%) in internal validation and 4.2% (0.5%, 23.6%) in external validation. The IDI also improved significantly.ConclusionIncorporating inflammation-related proteins into the SCORE2 model significantly improves the prediction of 10-year MACE risk among individuals without prior CVD or diabetes. Measuring these proteins may enhance risk stratification in clinical practice.</p>
4 Authors
- Ruijie Xie
- Sha Sha
- 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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