Heart Failure, Female Sex, and Atrial Fibrillation Are the Main Drivers of Human Atrial Cardiomyopathy: Results From the CATCH ME Consortium
Year: 2023
Type: article
Study design: Observational Study
Abstract: Background Atrial cardiomyopathy (AtCM) is emerging as an independent prognostic factor in cardiovascular disease. Fibrotic remodeling, cardiomyocyte hypertrophy, and capillary density are histological hallmarks of atCM. However, the contribution of various etiological factors and atrial fibrillation (AF) to the development of differential atCM phenotypes has not been robustly quantified. We aimed to evaluate the association between histological features of atCM and the clinical phenotype. Methods We examined left (LA, n=95) and right (RA, n=76) atrial appendages sampled from a European cohort of patients undergoing cardiac surgery. Quantification of histological atCM features was performed using the JavaCyte algorithm, following staining with agglutinin (WGA), CD31 and vimentin. The contributions of AF, heart failure (HF), sex and age to histological characteristics were determined in a multivariate model. K-means clustering of 6 histological features was performed to identify different types of atCM. Results In both LA and RA, persistent AF was associated with increased endomysial fibrosis (LA:+1.07±0.41µm,p=0.01; RA:+0.89±0.43µm,p=0.032), whereas total extracellular matrix (ECM) content was unchanged in AF. Men had larger cardiomyocytes (LA:+1.87±0.72μm,p=0.012), while women had a higher degree of endomysial fibrosis (LA:+0.99±0.51µm,p=0.048). Heart failure patients showed more endomysial fibrosis (LA:+1.79±0.41µm,p<0.001) and ECM content (LA:+2.93±1.15%, p=0.014), and a higher capillary density (LA:+0.14±0.06,p=0.032) and size (LA:+0.48±0.23µm,p=0.041; RA:+0.31±0.16µm,p=0.047). Clustering of samples based on structural features identified 2 distinct atCM phenotypes; one characterized by enhanced endomysial fibrosis (LA:+3.35µm,p<0.001; RA:+1.88μm,p<0.001), ECM content (LA:+5.68%,p<0.001; RA:+7.78%,p<0.001), and a higher fibroblast density (LA:+4.79%,p<0,001) and one characterized by cardiomyocyte hypertrophy (LA:+1.20µm,p=0.009; RA:+2.95µm, p<0.001). Patients with fibrotic atCM were more often female (LA:OR=1.31,p=0.003; RA:OR=1.55,p=0.003), had more often persistent AF (LA:OR=1.23,p=0.031) or heart failure (LA:OR=1.62,p<0.001) whereas hypertrophic features were more common in men (LA:OR=1.31,p=0.031; RA:OR= 1.55,p=0.003). Conclusions AtCM phenotypes vary with patient characteristics. Fibrotic atCM is associated with female sex, persistent AF and heart failure, while hypertrophic features are more common in men.
Authors: J. Winters, A. Isaacs, S. Zeemering, M. Kawczynski, B. Maesen, J. Maessen, E. Bidar, B. Boukens, B. Hermans, A van Hunnik, B. Casadei, L. Fabritz, W. Chua, L.C. Sommerfeld, E. Guasch, L. Mont, M. Batlle, S. Hatem, P. Kirchhof, R. Wakili, M.F. Sinner, S. Kääb, M. Stoll, A. Goette, S. Verheule, U. Schotten
Institutions: Maastricht University, Maastricht University Medical Centre, University of Oxford, University Medical Center Hamburg-Eppendorf, German Centre for Cardiovascular Research, University of Birmingham, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Universitat de Barcelona, Centro de Investigación en Red en Enfermedades Cardiovasculares, Centro de Investigación Biomédica en Red, Sorbonne Université, Goethe University Frankfurt, Essen University Hospital, University of Münster
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,700
Awards: Personalized Therapies for Atrial Fibrillation. A Translational Approach, G1357640399, DIGITAL, RISK-BASED SCREENING FOR ATRIAL FIBRILLATION IN THE EUROPEAN COMMUNITY, RESTORING CARDIAC MECHANICAL FUNCTION BY POLYMERIC ARTIFICIAL MUSCULAR TISSUE, G4638203960, G5276883691, G5858538993, The BHF Chair of Cardiovascular Medicine, Accelerator Award, Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly, Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, G7880718483
OpenAlex ID: W4388833419
