AF frequently goes clinically unrecognized and undiagnosed,1,2 and AF may first present as a stroke.3 Subclinical AF associates with an increased risk of clinical4 and silent5 strokes. Furthermore, appreciation has increased that monitoring after ischemic stroke may reveal previously undiagnosed AF.6-8 In a systematic review of studies of strokes of unknown origin, also known as cryptogenic strokes, the average rate of AF detection was 10.7% (95% CI, 7.9-14.3%), with the detection rate increasing with longer length of monitoring.9 Hence, the ability to predict incident AF has major public health import.
Several risk prediction scores for AF have been published including from the Framingham Heart Study (FHS),10-11 the Atherosclerosis Risk in Communities Study (ARIC),12 and the Women’s Health Study.13 The CHARGE-AF group took individual-level data from 3 U.S. cohort studies (FHS, ARIC, and the Cardiovascular Health Study) and developed a simple clinical model to predict 5-year risk of new-onset AF, and validated the model in 2 European cohorts (Age, Gene/Environment Susceptibility-Reykjavik Study and Rotterdam Study).14 A model including age, race, height, weight, systolic and diastolic blood pressure, current smoking, diabetes, use of antihypertensive medication, and history of myocardial infarction and heart failure had good discrimination (C-statistic, 0.765; 95% CI, 0.748 to 0.781) in the discovery cohorts, and adequate discrimination in the validation cohorts (AGES C-statistic, 0.664; 95% CI, 0.632 to 0.697 and RS C-statistic, 0.705; 95% CI, 0.664 to 0.747). Risk prediction models may serve as benchmarks to evaluate putative novel risk markers and to risk stratify individuals for inclusion in trials of screening or preventive therapies for AF.
Investigators have reported numerous novel biomarkers that may enhance risk stratification for new-onset or recurrent AF, and predict prognosis in individuals with established AF.15 The American Heart Association has issued a Scientific Statement outlining a framework for the evaluation of novel biomarkers.16 To be clinically useful biomarkers should not only associate with disease, but should improve discrimination, provide incremental information over and above clinical factors, improve clinical outcomes, and be cost-effective.16 Among the numerous biomarkers proposed, only the natriuretic peptides consistently improve risk reclassification for incident AF.17-18
Whereas AF in rare families with dense pedigree patterns of inheritance has long been appreciated,19-20 more recent work has established the heritability of AF in the community.21-23 Accounting for AF risk factors, a history of familial AF associates with a 40% higher risk of developing AF. Furthermore, a family history of AF modestly enhanced reclassification of risk.22
Genome-wide association studies have revealed 9 loci associated with an increased risk of AF.24-28 Using a multi-marker genetic risk score, which included the 12 non-redundant SNPs at the 9 AF risk loci (4 SNPs at PITX2), there was a 5-fold higher risk in individuals of European ancestry with a high score compared with those with a low score; findings that were replicated in a Japanese cohort.29 Investigators from the Malmö Diet and Cancer Prevention Study did not observe enhanced discrimination for the prediction of AF when analyzing 2 common SNPs.30 Using a 12 SNP genetic risk score investigators from the Women’s Genome Health Study observed that the risk score enhanced discrimination, but not risk reclassification beyond clinical risk factors.13
In summary, clinical risk factors are reasonably good at risk stratifying the onset of AF. While numerous biological and genetic markers associate with increased risk of AF, with the exception of natriuretic peptides, they have added little to our ability to reclassify risk of AF. Potential future directions for biomarker and genetic research in AF risk stratification include:
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