Abstract
Background
Previous studies have suggested only modest benefits of adding genetic information to conventional risk factors for prediction of atrial fibrillation (AF). However, these studies have been based on limited numbers of AF cases and pre-date recent AF genetic discoveries.
Purpose
To examine the independent relevance of common genetic risk factors over and above established non-genetic risk factors for predicting AF amongst 270,000 participants from UK Biobank, and to determine potential clinical utility.
Methods
UK Biobank (UKB) is a large prospective study of over 500,000 British individuals aged 40 to 69 years at recruitment. Incident AF was ascertained using hospital episode statistics and death registry data. The CHARGE-AF score, which combines the relevance of age, height, weight, blood pressure, use of antihypertensives, diabetes, heart failure, and myocardial infarction (MI) was used to estimate 5-year risk of AF at baseline. A polygenic risk score (PRS) was constructed based on 142 independent variants previously associated with AF in a genome-wide meta-analysis of 60,620 AF cases from the AFGen Consortium, weighted by their published effect sizes. A total of 270,254 individuals were analysed after exclusions for genetic QC, non-White British ancestry, and prevalent AF. Cox proportional hazard models were used to estimate associations between risk scores (based on standard deviation [SD] units) and incident AF. Standard methods were used to assess predictive value.
Results
During a median follow-up of 8.1 years, 12,407 incident AF cases were identified. The CHARGE-AF risk score strongly predicted incident AF in UK Biobank, and was associated with a ∼3-fold higher risk of AF per SD (Hazard ratio [HR]=2.88; 95% CI: 2.82–2.94). The PRS was associated with a 54% higher risk of AF per SD (HR=1.54; 95% CI: 1.51–1.57). The independent impact of the PRS, after adjusting for the CHARGE-AF score, was unchanged and remained strongly predictive (HR=1.57, 95% CI: 1.54–1.60), with participants in the upper tertile of the PRS having more than a 2.5-fold higher risk (HR=2.59, 95% CI: 2.47–2.71) when compared with those in the lower tertile. The addition of the PRS improved the C-statistic from 0.758 (CHARGE-AF alone) to 0.783 (Δ=0.025) and correctly reclassified 8.7% of cases and 2.6% of controls at 5 years. Both non-genetic and genetic risk scores were well-calibrated in the UK Biobank participants, and sensitivity of the results to alternative PRS selection approaches and age at risk were also examined.
Conclusion
In a large prospective cohort, genetic determinants of AF were independent of conventional risk factors and significantly improved prediction over a well-validated clinical risk algorithm. This illustrates the potential added benefit of genetic information to identify higher-risk individuals who may benefit from earlier monitoring and personalised risk management strategies.
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): British Heart Foundation
Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores
