https://doi.org/10.1007/s00392-025-02625-4
1Universitätsklinikum Würzburg Medizinische Klinik und Poliklinik I Würzburg, Deutschland; 2Universitätsmedizin der Johannes Gutenberg-Universität Mainz Präventive Kardiologie und Medizinische Prävention Mainz, Deutschland; 3Universitätsmedizin der Johannes Gutenberg-Universität Mainz Kardiologie 1, Zentrum für Kardiologie Mainz, Deutschland; 4Universitätsklinikum Würzburg Deutsches Zentrum für Herzinsuffizienz/DZHI Würzburg, Deutschland
Introduction
Despite advances in understanding the molecular pathophysiology of atrial fibrillation (AF), data on the interaction between clinical risk factors and molecular alterations remain limited. Further, biomarkers for AF in heart failure (HF) are lacking. By combining proteomic profiling with bioinformatics, we aimed to i) characterize the protein profile of AF, ii) identify relevant signaling pathways linked to AF, and iii) estimate the diagnostic and prognostic utility of a newly derived AF protein score in a large cohort of HF.
Methods
Data from the MyoVasc Study, a prospective cohort of HF (n=3,289), were analyzed. Subjects with active cancer (n=262) or missing protein data (n=89) were excluded. Baseline and biannual follow-up comprised deep clinical phenotyping and biobanking. All subjects with prior or primary diagnosis of AF at baseline were identified, individuals without diagnosis of AF served as controls. The AF polygenic risk score (AF-PRS) was calculated. Using proximity extension assay technology, 536 proteins were analyzed. Differentially expressed proteins were identified and a novel protein signature for AF was derived by supervised machine-learning. The association of the resulting AF protein score with prevalent AF was validated by multivariable logistic regression analyses with adjustment for age, sex, cardiovascular risk factors (CVRFs), humoral markers, CHA2DS2-VA-score and HF status. The prognostic utility of the AF protein score was analyzed using multivariable Cox proportional hazards models. We performed external validation using a clinically and methodologically comparable subcohort of the Gutenberg Health Study. Bioinformatic methods were applied for pathway analysis.
Results
Prevalence of AF in the study sample was 28% (n=812; 69.9% men; mean age 68.9±9.3 years). Subjects with AF had a higher burden of CVRFs and comorbidities compared to controls. Between the AF subsample and controls, the proportion of symptomatic HF (78% vs 46%) and the AF-PRS were different (both p<0.0001). In total, 145 proteins associated with AF were identified. Fibroblast growth factor 23 (FGF-23), tartrate-resistant acid phosphatase type 5 (TR-AP) and Notch 3 were identified as most important predictors for prevalent AF. Proteins with the most pronounced expression changes (Cohen’s d) showed a notable gradient of effect size from group paroxysmal to group permanent AF. The AF protein score predicted AF independent of the clinical profile (OR 3.09, 95%CI 2.56–3.73, p<0.0001; AUC 0.81). This effect persisted across all HF phenotypes and AHA (American Heart Association) stages of HF. A weak association between protein score and AF-PRS is compatible with a relevant incremental value of the protein score. Worsening of HF (HR 1.27; 1.09–1.47), cardiac death (HR 1.47; 1.22–1.78) and all cause death (HR 1.36; 1.22 – 1.53) were predicted by the AF protein score independent of sex, age, CVRFs and comorbidities. Protein-protein interaction and functional enrichment analyses revealed clustering in immune- and endothelial function-related pathways
Conclusion:
We identified novel protein markers and signaling pathways associated with AF independent of the clinical profile. Combining proteomic profiling with bioinformatic pathway analysis emerges as a promising approach to advance our understanding of the molecular mechanisms underlying AF in HF, thus providing a basis for the development of biomarkers and novel antiarrhythmic therapies.