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Genetic Risk Prediction of Atrial Fibrillation.

ABSTRACT: BACKGROUND:Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. METHODS:To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18?919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (?5%) associated with AF at P values ranging from <1×10-3 to <1×10-8 in a prior independent genetic association study. RESULTS:Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13-1.46; P=1.5×10-4) to 1.67 (25 variants; 95% confidence interval, 1.47-1.90; P=9.3×10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ?C statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39-4.58; P=2.7×10-3). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20-4.40; P=0.01). CONCLUSIONS:Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

SUBMITTER: Lubitz SA

PROVIDER: S-EPMC5380586 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Genetic Risk Prediction of Atrial Fibrillation.

Lubitz Steven A SA Yin Xiaoyan X Lin Henry J HJ Kolek Matthew M Smith J Gustav JG Trompet Stella S Rienstra Michiel M Rost Natalia S NS Teixeira Pedro L PL Almgren Peter P Anderson Christopher D CD Chen Lin Y LY Engström Gunnar G Ford Ian I Furie Karen L KL Guo Xiuqing X Larson Martin G MG Lunetta Kathryn L KL Macfarlane Peter W PW Psaty Bruce M BM Soliman Elsayed Z EZ Sotoodehnia Nona N Stott David J DJ Taylor Kent D KD Weng Lu-Chen LC Yao Jie J Geelhoed Bastiaan B Verweij Niek N Siland Joylene E JE Kathiresan Sekar S Roselli Carolina C Roden Dan M DM van der Harst Pim P Darbar Dawood D Jukema J Wouter JW Melander Olle O Rosand Jonathan J Rotter Jerome I JI Heckbert Susan R SR Ellinor Patrick T PT Alonso Alvaro A Benjamin Emelia J EJ

Circulation 20161028 14

<h4>Background</h4>Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke.<h4>Methods</h4>To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a ...[more]

PMID: 27793994

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Project description:Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF. Methods: We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort. Results: A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort. Conclusions: We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Dataset Information

Genetic Risk Prediction of Atrial Fibrillation.

Publications

Genetic Risk Prediction of Atrial Fibrillation.

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