An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Scientific Reports · 23 authors, 15 centres

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This cross-sectional, observational study investigated whether a combination of circulating biomarkers and simple clinical characteristics could identify prevalent atrial fibrillation (AF) in unselected patients with cardiovascular conditions presenting to a hospital. From 12 reviewed biomarkers, three were identified—ANGPT2, BMP10, and FGF23—which, when combined with age, sex, and BMI, were associated with AF in a discovery cohort (AUC 0.743). The model was validated in a separate cohort, yielding an AUC of 0.719 with regression and 0.784 with neural networks. The findings suggest these biomarkers reflect distinct biological pathways (endothelial dysfunction, atrial-specific cardiomyocyte signaling, and phosphate homeostasis) linked to AF. Limitations include the single-center, acute-care setting, which may affect biomarker concentrations and patient selection, and the need for external validation in prospective and population-based cohorts. The study demonstrates potential for enhancing AF screening strategies but does not assess risk for future incident AF.

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cohort

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cardiology

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ORIGINAL PAPER

PMC10556075

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Scientific Reports

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ABSTRACT

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation.

CITATION EXPORT

Winnie Chua, Victor R. Cardoso, Eduard Guasch et al.. An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions. Scientific Reports. (2023). https://doi.org/10.1038/s41598-023-42331-7

AUTHORS

23 · 15 CENTRES

Winnie Chua, Victor R. Cardoso, Eduard Guasch, Moritz F. Sinner, Christoph Al-Taie, Paul Brady, Barbara Casadei, Harry J. G. M. Crijns, Elton A. M. P. Dudink, Stéphane N. Hatem, Stefan Kääb, Peter Kastner, Lluis Mont, Frantisek Nehaj, Yanish Purmah, Jasmeet S. Reyat, Ulrich Schotten, Laura C. Sommerfeld, Stef Zeemering, André Ziegler, Georgios V. Gkoutos, Paulus Kirchhof, Larissa Fabritz

1https://ror.org/03angcq70grid.6572.60000 0004 1936 7486Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
2grid.507332.00000 0004 9548 940XMRC Health Data Research UK (HDR), Midlands Site, London, UK
3https://ror.org/03angcq70grid.6572.60000 0004 1936 7486Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
4https://ror.org/02a2kzf50grid.410458.c0000 0000 9635 9413Hospital Clinic de Barcelona, Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Barcelona, Spain
5grid.5252.00000 0004 1936 973XDepartment of Medicine I, University Hospital, LMU, Munich, Germany
6https://ror.org/031t5w623grid.452396.f0000 0004 5937 5237German Centre for Cardiovascular Research (DZHK), Partner Site: Munich Heart Alliance, Munich, Germany
7grid.13648.380000 0001 2180 3484University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, UKE Martinistrasse 52, 20246 Hamburg, Germany
8https://ror.org/031t5w623grid.452396.f0000 0004 5937 5237German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
9grid.13648.380000 0001 2180 3484Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
10https://ror.org/05mzf3276grid.412919.6Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK
11https://ror.org/052gg0110grid.4991.50000 0004 1936 8948University of Oxford, Oxford, UK
12https://ror.org/02jz4aj89grid.5012.60000 0001 0481 6099Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
13https://ror.org/050c3pq49grid.477396.8IHU-ICAN Institute of Cardiometabolism and Nutrition, Paris, France
14grid.424277.0Roche Diagnostics GmbH, Penzberg, Germany
15grid.417570.00000 0004 0374 1269Roche Diagnostics International AG, Rotkreuz, Switzerland