98%
921
2 minutes
20
Background: Early detection and management of atrial fibrillation (AF) are crucial. Combined models incorporating genetic risks and clinical risks have been developed to improve predictive ability. Although sex differences have been reported in many aspects of AF, sex differences in genetic risk have not been studied.
Objective: The purpose of this study was to assess the sex difference in the effect of polygenic risk score for AF (AF-PRS) on AF prevalence using cross-sectional data from the Tohoku Medical Megabank Project Community-Based Cohort Study in Japan.
Methods: AF-PRS and Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score were used for genetic AF risks and clinical AF risks, respectively. Sex differences in the association of AF-PRS with the prevalence of AF were evaluated.
Results: Among 16,853 participants (mean age 63.4 years; 5182, 30.7% men), the prevalence of AF was 255 (4.9%) in men and 130 (1.1%) in women. In the group with high AF-PRS and high CHARGE-AF score, the odds ratio for AF was highest in men and women (8.2 in men and 9.4 in women), compared with that in the group with low AF-PRS and low CHARGE-AF score. Integrating AF-PRS into the CHARGE-AF score significantly enhanced the area under the receiver operating characteristic curve for AF in men (from 0.639 to 0.749) but not in women (from 0.710 to 0.733).
Conclusion: Our study is the first to show a sex difference in the association of AF-PRS and AF prevalence. AF-PRS is more closely associated with the prevalence of AF in men than in women.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.hrthm.2025.03.1974 | DOI Listing |
Heart Rhythm
August 2025
National Heart and Lung Institute, Imperial College London, United Kingdom; Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom; Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom. Electronic address: f.ng@imper
Background: Multiple risk scores and biomarkers have been proposed for the prediction of atrial fibrillation (AF), but it is unknown how these compare to each other and if they could be combined.
Objective: Evaluate and compare approaches for incident AF prediction METHODS: The artificial intelligence-enhanced electrocardiogram (AI-ECG) Risk Estimator-AF (AIRE-AF), a convolutional neural network with a discrete-time survival loss function, was developed to predict incident AF. It was trained using a dataset of 1,163,401 ECGs from 189,539 patients from the Beth Israel Deaconess Medical Center (BIDMC) and externally validated in the UK Biobank (UKB, n = 38,892).
Circ Genom Precis Med
August 2025
Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).
Background: Clinical factors discriminate incident atrial fibrillation (AF) risk with moderate accuracy, with only modest improvement after incorporation of polygenic risk scores. Whether emerging large-scale proteomic profiling can augment AF risk estimation is unknown.
Methods: In the UK Biobank cohort, we derived and validated a machine learning model to predict incident AF risk using serum proteins (Pro-AF).
Heart Rhythm
May 2025
Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland. Electronic address:
Background: Atrial fibrillation (AF) is the most common sustained arrhythmia in patients with hypertrophic cardiomyopathy (HCM). The 2024 American Heart Association/American College of Cardiology guidelines recommend validated clinical tools such as the HCM-AF score for individualized assessment of AF risk. To date, these tools have been validated only in predominantly white HCM patient populations.
View Article and Find Full Text PDFCirculation
July 2025
Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea (H.P., D.K., E.J., H.T.Y., T.-H.K., J.-S.U., H.-N.P., M.-H.L., B.J.).
Background: Proteomic signatures might improve disease prediction and enable targeted disease prevention and management. We explored whether a protein risk score derived from large-scale proteomics data improves risk prediction of atrial fibrillation (AF).
Methods: A total of 51 680 individuals with 1459 unique plasma protein measurements and without a history of AF were included from the UKB-PPP (UK Biobank Pharma Proteomics Project).
COPD is associated with an increased AFib-related morbidity and mortality. There are several AFib risk prediction models available, but none have been validated in the COPD population. Our study aims to (1) identify spirometric and radiographic variables that are associated with an increased risk of AFib and (2) determine if these associated variables improve the risk discrimination of established AFib risk prediction models in individuals with COPD.
View Article and Find Full Text PDF