Background Obstructive sleep apnea (OSA) has shown to be associated with an increased risk of atrial fibrillation in observational studies. Whether this association reflect causal effect is still unclear. The aim of this study was to evaluate the causal effect of OSA on atrial fibrillation. Methods and Results We used a 2-sample Mendelian randomization (MR) method to evaluate the causal effect of OSA on atrial fibrillation. Summary data on genetic variant-OSA association were obtained from a recently published genome-wide association studies with up to 217 955 individuals and data on variant-atrial fibrillation association from another genome-wide association study with up to 1 030 836 individuals. Effect estimates were evaluated using inverse-variance weighted method. Other MR analyses, including penalized inverse-variance weighted, penalized robust inverse-variance weighted, MR-Egger, simple median, weighted median, weighted mode-based estimate and Mendelian Randomization Pleiotropy Residual Sum and Outlier methods were performed in sensitivity analyses. The MR analyses in both the fixed-effect and random-effect inverse-variance weighted models showed that genetically predicted OSA was associated with an increased risk of atrial fibrillation (odds ratio [OR], 1.21; 95% CI, 1.12-1.31, <0.001; OR, 1.21; 95% CI, 1.11-1.32, <0.001) using 5 single nucleotide polymorphisms as the instruments. MR-Egger indicated no evidence of genetic pleiotropy (intercept, -0.014; 95% CI, -0.033 to 0.005, =0.14). Results were robust using other MR methods in sensitivity analyses. Conclusions This MR analysis found that genetically predicted OSA had causal effect on an increased risk of atrial fibrillation.
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http://dx.doi.org/10.1161/JAHA.121.022560 | DOI Listing |
Curr Oncol Rep
January 2025
Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 111 East 210Th Street, Bronx, NY, 10461, USA.
Purpose Of Review: This paper reviewed the current literature on incidence, clinical manifestations, and risk factors of Chimeric Antigen Receptor T-cell (CAR-T) cardiotoxicity.
Recent Findings: CAR-T therapy has emerged as a groundbreaking treatment for hematological malignancies since FDA approval in 2017. CAR-T therapy is however associated with a few side effects, among which cardiotoxicity is of significant concern.
Eur J Clin Invest
January 2025
Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Cureus
December 2024
Internal Medicine, University of Health Sciences, Lahore, PAK.
Acute coronary syndrome (ACS) remains a major global health burden, encompassing a spectrum of conditions from unstable angina to acute myocardial infarction. Despite advancements in early detection and management, ACS is often complicated by the development of heart failure. This systematic review and meta-analysis aimed to identify factors associated with the development of heart failure following acute coronary syndrome.
View Article and Find Full Text PDFCureus
December 2024
Cardiology, Avicenna Military Hospital, Marrakesh, MAR.
Introduction Atrial fibrillation (AF), the most common cardiac arrhythmia, poses challenges in predicting thromboembolic risk. While the CHADS-VASc (congestive heart failure, hypertension, age ≥ 75 years (doubled), type 2 diabetes mellitus, previous stroke, transient ischemic attack, or thromboembolism (doubled), vascular disease, age 65-74 years, and sex category) score remains essential, its limitations include failure to identify left atrial (LA) thrombus in some patients. Transesophageal echocardiography (TEE) provides superior detection of LA thrombi and thrombogenic factors compared to transthoracic echocardiography (TTE), improving risk stratification, especially in intermediate-risk groups.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).
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