Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation

Pascal B. Meyre (), Stefanie Aeschbacher, Steffen Blum, Tobias Reichlin, Moa Haller, Nicolas Rodondi, Andreas S. Müller, Alain Bernheim, Jürg Hans Beer, Giorgio Moschovitis, André Ziegler, Bianca Wahrenberger, Elia Rigamonti, Giulio Conte, Philipp Krisai, Leo H. Bonati, Stefan Osswald, Michael Kühne and David Conen
Additional contact information
Pascal B. Meyre: University Hospital Basel
Stefanie Aeschbacher: University Hospital Basel
Steffen Blum: University Hospital Basel
Tobias Reichlin: Bern University Hospital
Moa Haller: University of Bern
Nicolas Rodondi: University of Bern
Andreas S. Müller: Triemli Hospital Zurich
Alain Bernheim: Triemli Hospital Zurich
Jürg Hans Beer: University of Zürich
Giorgio Moschovitis: Ente Ospedaliero Cantonale (EOS)
André Ziegler: Roche Diagnostics International
Bianca Wahrenberger: University Hospital Basel
Elia Rigamonti: Ente Ospedaliero Cantonale (EOS)
Giulio Conte: Ente Ospedaliero Cantonale (EOC)
Philipp Krisai: University Hospital Basel
Leo H. Bonati: Rheinfelden Rehabilitation Clinic
Stefan Osswald: University Hospital Basel
Michael Kühne: University Hospital Basel
David Conen: McMaster University

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Atrial fibrillation (AF) increases the risk of adverse cardiovascular events, yet the underlying biological mechanisms remain unclear. We evaluate a panel of 12 circulating biomarkers representing diverse pathophysiological pathways in 3817 AF patients to assess their association with adverse cardiovascular outcomes. We identify 5 biomarkers including D-dimer, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTropT) that independently predict cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy. Additionally, GDF-15, insulin-like growth factor-binding protein-7 (IGFBP-7), NT-proBNP, and hsTropT predict heart failure hospitalization, while GDF-15 and IL-6 are associated with major bleeding events. A biomarker model improves predictive accuracy for stroke and major bleeding compared to established clinical risk scores. Machine learning models incorporating these biomarkers demonstrate consistent improvements in risk stratification across most outcomes. In this work, we show that integrating biomarkers related to myocardial injury, inflammation, oxidative stress, and coagulation into both conventional and machine learning-based models refine prognosis and guide clinical decision-making in AF patients.

Date: 2025
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DOI: 10.1038/s41467-025-62218-7

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