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Updated: Jun 28, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Blood-based cardiometabolic phenotypes in atrial fibrillation and their associated risk: EAST-AFNET 4 biomolecule
Larissa Fabritz1,2,3,4,5, Winnie Chua5, Victor R Cardoso5
1Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany.
Aims:
Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events, such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. The aim of this study was to test whether biomolecule combinations can define phenotypes in patients with AF.
Methods And Results:
This pre-specified analysis of the EAST-AFNET 4 biomolecule study assigned patients to clusters using polytomous variable latent-class analysis based on baseline concentrations of 13 precisely quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow-up 2.9 years).Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest risk cluster was dominated by elevated bone morphogenetic protein 10, insulin-like growth factor-binding protein 7, N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and growth differentiation factor 15. Patients in the lowest risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of C-reactive protein, interleukin-6, and D-dimer. Patients in the highest risk cluster had a five-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (Pinteraction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients.
Conclusion:
Biomolecule concentrations identify cardiometabolic subphenotypes in patients with AF at high and low cardiovascular risk.
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