Key result
Five-protein plasma proteomics model improves stroke risk discrimination over clinical models in AF with ~0.73 C-index.
Why the study?
The accuracy of stroke risk stratification in AF patients remains limited due to a lack of reliable molecular biomarkers. This study aimed to identify circulating proteins associated with stroke risk in AF and develop an integrated proteomics-based prediction model.
Does a plasma proteomics-based model incorporating five core proteins improve stroke risk prediction compared to clinical models in patients with atrial fibrillation?
Cohort (n=709)
Does a plasma proteomics-based model incorporating five core proteins improve stroke risk prediction compared to clinical models in patients with atrial fibrillation?
A plasma proteomics model incorporating five core circulating proteins (EGFR, ATP6V1D, NTF4, AMN, and DCBLD2) significantly improves stroke risk stratification over clinical models in patients with atrial fibrillation, with Mendelian randomization supporting causal roles for AMN, EGFR, and DCBLD2.
Background The accuracy of stroke risk stratification in atrial fibrillation (AF) patients remains limited due to the lack of reliable molecular biomarkers. This study aimed to identify circulating proteins associated with stroke risk in AF patients and develop an integrated proteomics-based prediction model. Methods We included 709 AF participants from the UK Biobank Pharma Proteomics Project (UKB-PPP) (median age 64 years, 30.5% female), with a median follow-up of 15 years. Stroke-related proteins were screened using multivariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression models, and machine learning modeling. Mendelian randomization (MR) analysis further validated the potential causal association between key proteins and stroke. Finally, a protein model was constructed, and its discrimination against the clinical model was assessed by C-index and time-dependent area under the receiver operating characteristic curve (AUC), with advantages verified through model performance comparison. Results Five core proteins for post-AF stroke were identified: EGFR, ATP6V1D, NTF4, AMN, and DCBLD2. MR validation showed that genetically higher levels of AMN and EGFR were associated with reduced stroke risk (both P< 0.05), while DCBLD2 was linked to increased risk (P< 0.05). The protein-based model achieved a C-index of 0.734 (95% CI: 0.675–0.789), outperforming the reference model (age and sex: 0.610 [95% CI: 0.550–0.671]) and clinical model (0.663 [95% CI: 0.600–0.725]). Time-dependent AUC values at 3, 5, and 8 years were 0.765, 0.722, and 0.753, respectively—all higher than the clinical model. The model demonstrated significant discrimination improvement (8-year integrated discrimination improvement [IDI] = 0.073, continuous net reclassification improvement [NRI] = 0.356). Subgroup analysis revealed strong performance in male and older age subgroups. Conclusion This study identified five core proteins for stroke in AF. The protein-based model outperformed clinical models, and key proteins showed causal associations with stroke risk—providing new targets and tools for optimizing precision stroke prevention in AF patients.stroke
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Huang et al. (2026) conducted a cohort in Atrial fibrillation (n=709). Five-protein plasma proteomics prediction model (EGFR, ATP6V1D, NTF4, AMN, DCBLD2) vs. Clinical prediction model and reference model (age and sex) was evaluated on Post-atrial fibrillation stroke risk discrimination (C-index 0.734, 95% CI 0.675-0.789). A 5-protein plasma proteomics model outperformed the clinical model for predicting stroke in atrial fibrillation (C-index 0.734 [95% CI 0.675-0.789] vs 0.663 [95% CI 0.600-0.725]).
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