{"title":"The cardiac blood transcriptome predicts de novo onset of atrial fibrillation in heart failure","authors":"Guillaume Lamirault , Imen Fellah-Hebia , Catherine Chevalier , Isabelle Guisle , Béatrice Guyomarc'h , Aude Solnon , Jean-Baptiste Gourraud , Laurent Desprets , Selim Abbey , Christophe Leclercq , Paul Bru , Antoine Milhem , Olivier Billon , Frederic Anselme , Arnaud Savouré , Jean-Noël Trochu , Rémi Houlgatte , Gilles Lande , Marja Steenman","doi":"10.1016/j.jmccpl.2024.100077","DOIUrl":null,"url":null,"abstract":"<div><p>Heart failure (HF) increases the risk of developing atrial fibrillation (AF), leading to increased morbidity and mortality. Therefore, better prediction of this risk may improve treatment strategies. Although several predictors based on clinical data have been developed, the establishment of a transcriptome-based predictor of AF incidence in HF has proven to be more problematic. We hypothesized that the transcriptome profile of coronary sinus blood samples of HF patients is associated with AF incidence. We therefore enrolled 192 HF patients who were selected for biventricular cardioverter defibrillator implantation. Both coronary sinus and peripheral blood samples were obtained during the procedure. Patients were followed-up during two years and AF occurrence was based on interrogation of the defibrillator. A total of 96 patients stayed in sinus rhythm (SR) during follow-up, 13 patients developed AF within 1 year and 10 patients developed AF during the second year of follow up. Gene expression profiling of coronary sinus samples led to the identification of 321 AF predictor genes based on their differential expression between patients developing AF within 1 year of blood sampling and patients remaining in SR. The expression levels of these genes were combined to obtain a molecular atrial fibrillation prediction score for each patient which was significantly different between both patient groups (Mann-Whitney, <em>p</em> = 0.00018). We conclude that the cardiac blood transcriptome of HF patients should be further investigated as a potential AF risk prediction tool.</p></div>","PeriodicalId":73835,"journal":{"name":"Journal of molecular and cellular cardiology plus","volume":"8 ","pages":"Article 100077"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772976124000175/pdfft?md5=b124ea86f7f6347dacfa628812a1629e&pid=1-s2.0-S2772976124000175-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of molecular and cellular cardiology plus","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772976124000175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Heart failure (HF) increases the risk of developing atrial fibrillation (AF), leading to increased morbidity and mortality. Therefore, better prediction of this risk may improve treatment strategies. Although several predictors based on clinical data have been developed, the establishment of a transcriptome-based predictor of AF incidence in HF has proven to be more problematic. We hypothesized that the transcriptome profile of coronary sinus blood samples of HF patients is associated with AF incidence. We therefore enrolled 192 HF patients who were selected for biventricular cardioverter defibrillator implantation. Both coronary sinus and peripheral blood samples were obtained during the procedure. Patients were followed-up during two years and AF occurrence was based on interrogation of the defibrillator. A total of 96 patients stayed in sinus rhythm (SR) during follow-up, 13 patients developed AF within 1 year and 10 patients developed AF during the second year of follow up. Gene expression profiling of coronary sinus samples led to the identification of 321 AF predictor genes based on their differential expression between patients developing AF within 1 year of blood sampling and patients remaining in SR. The expression levels of these genes were combined to obtain a molecular atrial fibrillation prediction score for each patient which was significantly different between both patient groups (Mann-Whitney, p = 0.00018). We conclude that the cardiac blood transcriptome of HF patients should be further investigated as a potential AF risk prediction tool.