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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.
Insights
Biomolecule patterns identify distinct patient groups with atrial fibrillation (AF), revealing varying cardiovascular risks. These cardiometabolic subphenotypes help stratify patients for better risk assessment and management.
Area of Science:
- Cardiology and Precision Medicine
- Biomarker Discovery
- Atrial Fibrillation Pathophysiology
Background:
- Atrial fibrillation (AF) frequently coexists with cardiometabolic diseases, increasing risks of stroke, heart failure, and cardiovascular death.
- Circulating biomolecules serve as quantifiable indicators of underlying cardiometabolic processes.
- Identifying distinct patient subgroups within AF is crucial for personalized risk stratification.
Purpose of the Study:
- To investigate whether combinations of circulating biomolecules can define distinct cardiometabolic phenotypes in patients with atrial fibrillation (AF).
- To assess the cardiovascular event rates associated with these identified AF subphenotypes.
- To validate the identified phenotypes and their associated risks in an independent cohort.
Main Methods:
- Latent-class analysis was employed using baseline concentrations of 13 biomolecules in 1586 patients from the EAST-AFNET 4 study.
- Biomolecules reflected processes including ageing, cardiac fibrosis, metabolic dysfunction, inflammation, and cardiac load.
- Cardiovascular event rates were compared across identified clusters, with validation in the prospective BBC-AF cohort.
Main Results:
- Unsupervised analysis identified four distinct clusters (subphenotypes) of AF patients based on biomolecule profiles.
- The highest-risk cluster exhibited elevated levels of specific biomolecules (e.g., BMP10, IGFBP7, NT-proBNP, Ang-2, GDF-15) and had a five-fold higher cardiovascular event rate compared to the lowest-risk cluster.
- Intermediate-risk clusters were differentiated by inflammatory markers (CRP, IL-6) and coagulation markers (D-dimer); early rhythm control was effective across all identified subphenotypes.
Conclusions:
- Circulating biomolecule concentrations effectively identify distinct cardiometabolic subphenotypes in patients with atrial fibrillation (AF).
- These subphenotypes correlate with significant differences in cardiovascular risk, enabling better patient stratification.
- The findings support the use of biomolecular profiling for personalized risk assessment in AF management.
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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