Sex Differences in the Association Between Polygenic Risk Score and Atrial Fibrillation Incidence: A Prospective Cohort Study.

Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction. We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF. We constructed a (i) clinical risk score using CHARGE-AF clinical variables, (ii) polygenic risk score using pre-determined weights, (iii) protein risk score using regularized logistic regression, and (iv) ECG risk score from a convolutional neural network. Risk prediction performance was measured using regularized logistic regression. After a median follow-up of 15.1 years, 1910 AF events occurred since Visit 3 and 229 participants had prevalent AF at Visit 5. The area under curve (AUC) improved from 0.660 to 0.752 (95% CI, 0.741-0.763) and from 0.737 to 0.854 (95% CI, 0.828-0.880) after addition of the polygenic risk score to the CHARGE-AF clinical variables for predicting incident and prevalent AF, respectively. Further addition of ECG and protein risk scores improved the AUC to 0.763 (95% CI, 0.753-0.772) and 0.875 (95% CI, 0.851-0.899) for predicting incident and prevalent AF, respectively. A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.

Genetic Risk Prediction for CKD: A Journey of a Thousand Miles

BackgroundAtrial myopathy is increasingly recognized as a distinct clinical entity, and an important underlying component in atrial fibrillation (AF). While histological examination of atrial biopsies is the gold-standard for diagnosis of atrial myopathy, it is often impractical in a clinical setting.PurposeThis study aimed to examine the prevalence and inter-relationships of indirect atrial myopathy markers, identify associations with incident AF, and explore clinical or genetic risk scores for risk-stratification in individuals with markers of atrial myopathy.MethodsThe study cohort consisted of European ancestry individuals from the UK Biobank with cardiac magnetic resonance imaging and electrocardiographic information, and no prior history of AF, heart failure, or cardiac conduction disorders. Three markers of atrial myopathy were examined: Left atrial (LA) dilation (indexed LA volume >60 ml/m2), mechanical dysfunction (LA emptying fraction <45%), and electrical dysfunction (P-wave duration >120 ms). Hazard ratios (HR) for AF were examined in a multivariate Cox regression adjusted for sex, age, body-mass index, hypertension, coronary artery disease and diabetes. The cohort was stratified by LA myopathy markers, genetic risk of AF (using a previously validated polygenic risk score [PRS]), and clinical risk of AF (using the HARMS2-AF score). Genetic risk was stratified by PRS tertiles, and clinical risk was stratified by HARMS2-AF score ≤2 (low risk), HARMS2-AF score between 3-6 (intermediate risk), and HARMS2-AF score ≥7 (high risk). Five-year incidences of AF were calculated using the Aalen-Johansen estimator, with all-cause mortality as a competing risk.ResultsAmong 26,026 individuals, 2,447 (9.7%) had at least one marker of atrial myopathy, while 244 (0.9%) had two or more markers. The largest overlap was seen between LA dilation and mechanical dysfunction. During a median follow-up of 5 years, 583 (2.2%) were diagnosed with incident AF. Having one LA myopathy marker conferred a HR of 2.53 (95% confidence interval [CI]: 2.06-3.11; P<0.001) for AF. Higher rates were observed in individuals with two or more markers (HR for AF: 6.34; 95% CI: 4.56-8.81; P<0.001). Individuals with one or more LA myopathy marker, and high clinical and genetic risk, had a 11.7% (95% CI: 7.5-16.0%) 5-year risk of incident AF, compared with a 0.7% (95% CI: 0.4-0.9%) risk in those with no LA myopathy markers, and low clinical and genetic risk.ConclusionsIn a cohort without prior history of AF, almost one in ten individuals had at least one marker of atrial myopathy. Although overlap between different markers was modest, a dose-response-like relationship was observed between amount of atrial myopathy markers and rates of AF. Integration of clinical and genetic risk scores showed a several-fold risk gradient, highlighting that PRS and clinical risk scores may be useful aids in risk stratification of individuals with markers of LA myopathy.Co-occurence of atrial myopathy markers Rates of incident atrial fibrillation

Effect of Dietary Fish Oil on Atrial Fibrillation After Cardiac Surgery

Sex differences have the potential to impact diagnostic and therapeutic interventions in a wide variety of medical conditions, and cardiac arrhythmias are no exception.1 Studies evaluating pathophysiology, disease course, and therapeutic options for cardiac arrhythmias have been performed predominantly in male patients. However, catheter and device-based therapies coupled with landmark clinical trials have contributed to an improved understanding of this important aspect. The objective of this review is to present the current state of knowledge on sex differences in cardiac arrhythmias with a focus on clinical management, while highlighting gaps in knowledge that would benefit from future investigation. ### Atrial Fibrillation and Atrial Flutter #### Disease Burden Atrial fibrillation (AF) and atrial flutter (AFL) are the most commonly encountered tachyarrhythmias in clinical practice, with significant implications for public health and healthcare costs. Stroke, hospitalization, and loss of productivity are the major consequences of AF.2 The incidence of AF (per 1000 person-years) is reported to be between 1.6 and 2.7 in women and between 3.8 and 4.7 in men.2 The age-adjusted incidence and prevalence of AF is lower in women compared with that in men, and accordingly, the lifetime risk of AF from the Framingham Heart Study at 40 years of age was higher in men (26.0% for men versus 23.0% for women).3 Another analysis from the Framingham Heart Study demonstrated no significant sex differences in the risk of developing AFL.4 The prevalence of AF continues to rise among both men and women. In a study investigating the global burden of disease from 1980 to 2010, there was not only an increase in overall burden, incidence, and prevalence of AF, but most importantly an increase in AF-associated mortality in both men and women (Figure 1).5 The age-adjusted mortality for women was consistently higher compared with that for men from 1990 to 2010 (Figure …

BackgroundNew-onset postoperative atrial fibrillation (POAF) complicates 1 in 3 cardiac surgeries and is associated with morbidity, mortality and clinical AF in long-term follow-up. Clinical risk scores have modest performance for predicting POAF. Polygenic risk scores are derived from the summation of up to millions of genetic variants and have shown good predictive ability for incident AF in the community. The ability of polygenic risk scores to predict POAF and subsequent recurrence of clinical AF in cardiac surgery patients is unclear.MethodsWe performed a prospective cohort study of patients from 4 regions (Canada, Hong Kong, Malaysia, United Kingdom) without a pre-operative history of atrial fibrillation (AF) who underwent cardiac surgery and were followed for 1 year. From pre-operative blood samples, we extracted DNA and calculated each participant’s polygenic risk score for AF using a penalized regression method (lassosum) to combine the effects of 5,000,621 genetic variants, weighted by their association with AF status from a previous genome-wide association study by Miyazawa (Nature Genetics, 2023). We estimated the association of this polygenic risk score for AF with the incidence of new-onset POAF using analyses adjusted for genetic ancestry. We assessed the ability of the polygenic risk score to predict POAF when added to common clinical risk scores. As a secondary objective, among patients who developed POAF, we estimated the association of the polygenic risk score with AF recurrence in follow-up beyond 30 post-operative days.ResultsAmong 3031 patients (63.5% isolated coronary artery bypass grafting), 1282 patients (42.3%) developed new-onset POAF. The polygenic risk score for AF was strongly associated with the risk for POAF (odds ratio 1.3 per standard deviation increase in polygenic risk score [95% CI 1.2-1.4]). The 10% of participants with highest polygenic risk had a risk of POAF of 50.5% as compared to 41.4% for the bottom 90% (odds ratio 1.4 [95% CI 1.1-1.8]).When the polygenic risk score was added to the clinical risk scores, it improved the model fit for all scores, significantly improved the C-statistic for the CHA2DS2-VASc, POAF and HATCH Scores and improved measures of risk classification for all scores (Table).Follow-up data on AF status beyond 30 days were available for 902 patients; 71 patients (7.9%) had AF recurrence detected beyond 30 days post-operatively. The polygenic risk score was not significantly associated with a higher risk for AF recurrence (odds ratio, 1.1 per standard deviation increase in polygenic risk score [95% CI, 0.9-1.5]).ConclusionsA higher polygenic risk score for AF is associated with the development of new-onset POAF following cardiac surgery and improves risk classification compared with clinical risk scores alone. However, this study failed to demonstrate an association of the polygenic risk score with AF recurrence in patients who develop POAF.

POSTER PRESENTATIONS

Life's Crucial 9, Genetic Susceptibility, and the Risk of Atrial Fibrillation: A Prospective Study in the UK Biobank Cohort.

Introduction: Prior studies demonstrate correlation between atrial fibrillation (AF) polygenic risk score (PRS) and ischemic stroke, especially cardioembolic (CE) stroke, suggesting shared genetic components. In this study, we hypothesized that an AF PRS can discriminate CE from non-CE strokes. Methods: We evaluated AF and stroke risk in 26,145 individuals of European descent from the NINDS Stroke Genetics Network (SiGN) with study-adjudicated TOAST subtypes. AF genetic risk was estimated using two newly derived PRS (LDpred-funct and sBayesR) and two previously validated PRS (pruning and thresholding and LDpred). A clinical risk score (CRS) was derived within separate individuals in UK Biobank utilizing components of the Cohorts for Aging and Genomic Research in Epidemiology (CHARGE-AF) model available in SiGN. We regressed each AF PRS on AF status and separately CE stroke in logistic regression models adjusted for CRS, imputation group, and the first 10 principal components. We calculated discrimination of AF and CE stroke, and compared across models using 2000-iteration bootstrapping and pairwise Z-testing. We also assessed category-free reclassification of CE stroke risk with the addition of PRS to a) CRS, and b) a modified CHA 2 DS 2 -VASc score including all available score components. Results: Each AF PRS was significantly associated with AF and CE stroke after adjustment for the CRS. Addition of any AF PRS significantly improved model discrimination as compared to the CRS alone, and LDpred discriminated both AF and CE better than other PRSs ( Figure ; P &lt;0.005). Adding LDpred PRS to CRS resulted in appropriate reclassification of CE stroke risk compared to the CRS or the modified CHA 2 DS 2 -VASc score alone ( Figure ). Conclusions: Addition of AF polygenic risk to clinical risk factors improves discrimination of CE from non-CE strokes, as well as reclassification of stroke subtype. AF polygenic risk may be a useful biomarker for identifying AF-related strokes.

Combining polygenic and clinical risk scores in atrial fibrillation risk prediction: Implications for population screening.

A MET a Day Keeps Arrhythmia at Bay: The Association Between Exercise or Cardiorespiratory Fitness and Atrial Fibrillation

Taiwan Atrial Fibrillation Score: A New Clinical Tool for Predicting New Onset Atrial Fibrillation in Asian Populations.

Improving prediction of atrial fibrillation: the impact of polygenic risk scores over conventional risk factors amongst 270,000 individuals in UK Biobank

Background: Atrial fibrillation (AF) poses substantial morbidity and mortality burdens globally. It is critical to identify individuals at high-risk for AF to implement both lifestyle modifications and close monitoring for initiation of oral anticoagulants. Currently, risk prediction in AF relies on clinical scores such as CHARGE-AF. Small molecule metabolites are thought to play a role in AF pathogenesis, but the additional predictive value of metabolomics over clinical and genetic risk factors is unknown. Using the UK Biobank (UKBB), we sought to perform this evaluation. Methods: We included all UKBB participants with complete 1H-NMR metabolite measurements. We excluded participants with prior history of AF or with incomplete AF Polygenic Risk Score (AF-PRS) or CHARGE-AF components. The final cohort included 240,628 patients. In-patient records, primary care notes, and death registries were queried to identify patients who had developed incident AF within 5 years of enrollment. The cohort was divided into a 80/20:Train/Test split. We compared the performance of an Elastic-Net regularized Cox Proportional Hazards (EN-CPH) model trained on CHARGE-AF, AF-PRS, and the full 170 metabolite panel to a CPH model trained only on CHARGE-AF and AF-PRS. Results: Within 5 years, 4,174 (1.7%) patients developed AF. Compared to the control population, the incident AF cases were more likely to be older (62 vs. 56, p&lt;0.001), male (65% vs 45%, p&lt;0.001), and have higher CHARGE-AF and AF-PRS scores (p&lt;0.001). After training the EN-CPH model on CHARGE-AF, AF-PRS, and 170 metabolites, the final model included 8 metabolites on top of CHARGE-AF (HR: 1.28) and AF-PRS (HR: 1.11). After adjusting for CHARGE-AF and AF-PRS, creatinine level was associated with increased risk of AF (HR: 1.01) while linoleic acid level (HR: 0.985) was associated with decreased risk. Furthermore, total cholesterol, esterified cholesterol, free cholesterol, cholesteryl esters in IDL, omega-6 fatty acids, and cholesterol in large LDL were associated with HR 0.993-0.999. The EN-CPH model out-performed the CPH model without metabolomics on the test set (AUC 0.786 vs 0.753, p &lt; 0.001). Conclusions: The addition of metabolomics to clinical and genomic risk scores improves prediction of 5-year incident AF within a general population. This study highlights the significance of the metabolome as an independent prognosticator of AF risk. Further study of the mechanisms by which selected metabolites alter AF risk is needed.

Background: Early detection of atrial fibrillation (AF) could permit earlier anticoagulation and thus potentially reduce risk of embolic stroke. A validated genetic risk score (GRS) has been shown to predict AF risk but its clinical utility beyond established AF risk factors is unclear. Methods: We performed a prospective cohort analysis in pts w/o prior AF from 4 TIMI trials [SOLID-TIMI 52, SAVOR-TIMI 53, PEGASUS-TIMI 54, FOURIER (TIMI 59)]. Pts were divided into genetic risk quintiles using a validated 1,018 SNP GRS for AF. Clinical risk for AF was also calculated using the validated CHARGE-AF model (age, ht, wt, SBP, DBP, anti-HTN med, DM, CHF, MI, smoking). 3-year KM rates and adjusted HRs were calculated across clinical and genetic risk groups. C-index was used to determine if the addition of GRS improved AF prediction compared with clinical risk and NT-proBNP. Results: In 36,663 pts followed for a median of 2.3 yrs, 1,018 new AF cases (2.8%) were identified. AF GRS predicted a significant risk gradient for AF with a 33% increased risk per 1-SD increase in GRS (HR 1.33 [95%CI 1.26-1.41]; p&lt;0.001). Those in the top quintile of AF GRS were more than 2-fold more likely to develop AF (HR 2.26 [1.84-2.78], p&lt;0.001) compared with the bottom quintile. This risk prediction was on par with age and stronger than any other clinical risk factor. Further, GRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, such that pts with high GRS and CHARGE-AF score had an 8.1% 3-year AF risk (Fig) . The C-index for CHARGE-AF was 0.64 (0.63-0.66); addition of NT-proBNP raised the C-index to 0.67 (0.65-0.69), and inclusion of GRS increased it further to 0.69 (0.67-0.71) (p&lt;0.001). Conclusion: In pts with CV disease, AF GRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score. AF GRS may be a clinically useful tool to identify very high-risk pts for consideration of AF screening.