Key result
A 165-protein risk score improves the incident AF prediction C-index by ~0.04 over baseline models.
Why the study?
Proteomic signatures may improve disease prediction, leading the authors to explore whether a protein risk score derived from large-scale proteomics data improves AF risk prediction.
Does a protein risk score improve risk prediction of incident atrial fibrillation compared to standard clinical, biomarker, and genetic risk models in individuals without a history of AF?
Cohort (n=62,692)
Yes
Does a protein risk score improve risk prediction of incident atrial fibrillation compared to standard clinical, biomarker, and genetic risk models in individuals without a history of AF?
A novel protein risk score derived from a single plasma sample significantly improves the prediction of incident atrial fibrillation when added to standard clinical, biomarker, and genetic risk models.
Protein score enhances AF prediction beyond clinical-biomarker-genetic models; hypothesis-generating and requires prospective validation before adoption.
BACKGROUND: Proteomic signatures might improve disease prediction and enable targeted disease prevention and management. We explored whether a protein risk score derived from large-scale proteomics data improves risk prediction of atrial fibrillation (AF). METHODS: A total of 51 680 individuals with 1459 unique plasma protein measurements and without a history of AF were included from the UKB-PPP (UK Biobank Pharma Proteomics Project). A protein risk score was developed with lasso-penalized Cox regression from a random subset of 70% (36 176 individuals, 54.4% women, 2155 events) and was tested on the remaining 30% (15 504 individuals, 54.4% women, 910 events). The protein risk score was externally replicated with the ARIC study (Atherosclerosis Risk in Communities; 11 012 individuals, 54.8% women, 1260 events). RESULTS: The protein risk score formula developed from the UKB-PPP derivation set was composed of 165 unique plasma proteins, and 15 of them were associated with atrial remodeling. In the UKB-PPP test set, a 1-SD increase in protein risk score was associated with a hazard ratio of 2.20 (95% CI, 2.05-2.41) for incident AF. The C index for a model including CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation), NT-proBNP (N-terminal B-type natriuretic peptide), polygenic risk score, and protein risk score was 0.816 (95% CI, 0.802-0.829) compared with 0.771 (95% CI, 0.755-0.787) for a model including CHARGE-AF, NT-proBNP, and polygenic risk score (C-index change, 0.044 [95% CI, 0.039-0.055]). Protein risk score added to CHARGE-AF, NT-proBNP, and polygenic risk score resulted in a risk reclassification of 5.4% (95% CI, 2.9%-7.9%) with a 5-year risk threshold of 5%. In the decision curve, the predicted net benefit before and after the addition of protein risk score to a model including CHARGE-AF, NT-proBNP, and polygenic risk score was 3.8 and 5.4 per 1000 people, respectively, at a 5-year risk threshold of 5%. External replication of a protein risk score in the ARIC study showed consistent improvement in risk stratification of AF. CONCLUSIONS: Protein risk score derived from a single plasma sample improved risk prediction of AF. Further research using proteomic signatures in AF screening and prevention is needed.
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Park et al. (2025) conducted a cohort in Atrial Fibrillation (n=62,692). Protein risk score (165 unique plasma proteins) vs. Model including CHARGE-AF, NT-proBNP, and polygenic risk score was evaluated on Incident atrial fibrillation (C index) (C-index change 0.044, 95% CI 0.039-0.055). Adding a 165-protein risk score to a model including CHARGE-AF, NT-proBNP, and polygenic risk score improved the C index for incident atrial fibrillation prediction by 0.044 (95% CI, 0.039-0.055).
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