Atrial fibrillation: current knowledge and future directions in epidemiology and genomics

Abstract

Atrial fibrillation (AF) is of public health importance and profoundly increases morbidity, mortality and health-related expenditures. Morbidities include the increased risks of cardiovascular outcomes such as heart failure and stroke, and the deleterious effects on quality of life, functional status and cognition. The clinical epidemiology of AF, its risk factors and outcomes, have been extensively investigated. Genetic advances over the last decade have facilitated the identification of mutations and common polymorphisms associated with AF. Metabolomics, proteomics and other “omics” technologies have only recently been applied to the study of AF, and have not yet been systematically investigated. Systems biology approaches, while still in their infancy, offer the promise of providing novel insights into pathways influencing AF risk. In the present review, we address the current state of the epidemiology and genomics of AF. We seek to emphasize how epidemiology and “omic” advances will contribute towards a systems biology approach that will help to unravel the pathogenesis, risk stratification, and novel targets for AF therapies. Our purpose is to articulate questions and challenges that hinge on integrating novel scientific advances in the epidemiology and genomics of AF. As a reference we have provided a glossary in the inset box.

Figure 1

The estimated U.S. prevalence of atrial fibrillation (AF) in the year 2050 ranges from 5.6 million to as high as 15.9 million individuals. The blue line shows estimates derived from the AnTicoagulation and Risk Factors in Atrial Fibrillation Study, comprised of cross-sectional data from a large California health maintenance organization. The orange line shows data estimating the number of individuals with AF in the US through 2050. The investigators identified cases of ECG-confirmed incident AF in Olmsted County Minnesota, from 1980–2000, and employed U.S. Census Bureau data to estimate that the number of individuals with AF will reach 12.1 million by 2050. However, an increase in age-adjusted incidence of AF in Olmsted County was identified from 1980–2000. The red line shows the projected number of individuals with AF in the U.S. should this increase in age-adjusted incidence of AF persist. Figure used with permission and modified from Miyasaka et al.

Figure 2

Lifetime risk for developing atrial fibrillation (AF) from the Framingham Heart Study. Men and women without AF at age 40 were determined to have a 26% and 23% likelihood of developing incident AF by age 80. Reproduced from Lloyd-Jones et al.

Figure 3

The population attributable risk of atrial fibrillation in men and women determined from a community-based longitudinal study. For both men and women a substantial portion of atrial fibrillation risk remains unexplained. MI, myocardial infarction; HTN, hypertension; HF, heart failure; VHD, valvular heart disease; DM, diabetes mellitus; LVH, electrocardiographic left ventricular hypertrophy.

Figure 4

The figure displays the chromosomal positions for each of the three loci associated with atrial fibrillation (AF) in meta-analyses. Such a representation is designated a “Manhattan plot,” as it suggests the New York City skyline. While the x-axis displays the chromosomal position, the y-axis indicates the statistical significance for the association between the single-nucleotide polymorphism (SNP) and the phenotype of interest. The dotted line shows the threshold for statistical significance, adjusted for multiple testing to P<10⁻⁸. SNPs with P >0.05 are not displayed on the plot. The initial SNPs identified were on chromosome 4q25 located adjacent to PITX2 and had an effect size as high as 2-fold (Odds ratio [OR] 2.12; 95% confidence interval [CI], 1.77 to 2.54, P=6.3×10⁻¹⁸) in Icelandic individuals diagnosed with AF at ≤60 years. The second locus, associated with ZFHX3, identified 3 SNPs with relative risk ranging from 1.26–1.65. The third and most recently published SNP, identified on the 4q25 locus with OR 2.03, 95% CI, 1.79–2.30, P=2.5×10^−28. Figure modified with permission from Ellinor PT, et al.

Figure 5

Circos plot representing the genetic variants found by genome-wide association study for atrial fibrillation (AF) and AF risk factors. The outer ring represents the chromosomes and the inner rings detail the location of different single nucleotide polymorphisms related to AF and AF risk factors. The colors represent each phenotype or AF risk factor. AF, atrial fibrillation; BMI, body mass index; CHD, coronary heart disease; CRP, C-reactive protein; CSF, cardiac structure and function; DBP, diastolic blood pressure; DM, diabetes mellitus; ECG, electrocardiographic traits; echo, echocardiographic traits; HF, heart failure; HTN, hypertension; MI, myocardial infarction; PR, PR interval; SBP, systolic blood pressure. See electronic supplement for methods.

Figure 6

The figure summarizes the identified genetic associations of atrial fibrillation (AF) and future areas of genetic study. Investigators have identified Mendelian families with AF genes having large effect size but rare frequency. Both candidate gene studies and genome-wide association studies (GWAS) have identified variants and loci with low-to-modest effect sizes. Among the two study types, candidate gene studies have tended to report larger effect sizes. Such an overestimation of effect size has been described in a phrase borrowed from economics as the “winner’s curse.” In contrast GWAS have identified three distinct loci with low-to-modest effect sizes. Sequencing efforts may fill a substantive genomic gap by identifying unrecognized low-frequency variants with moderate effect sizes. As a prominent component of the population attributable risk (see Figure 3) remains unknown, further genetic study may contribute additional insights into the etiology of AF. Figure modified from Manolio et al.

Figure 7

The figure shows the informational flow which comprises the systems biology approach. Genetic loci provide targets for epigenomic, transcriptomic, proteomic and metabolomic investigations. Epidemiologic investigations identify risk factors. In turn, systems biology translates epidemiologic and genomic pathways, providing insight into electrical and structural substrates. Systems biology in turn may yield novel hypotheses for risk factors, target intermediate endophenotypes for genetic dissection, or elucidate novel up-stream targets for therapeutic intervention. HTN, hypertension; HF, heart failure; SDB, sleep disordered breathing; LA, left atrial.