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The Future of Risk Stratification–Biomarkers and Genetics

Development of AF

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:

 

  • Other races/ethnicities.The vast majority of studies have included individuals of European ancestry. Future studies should include other races/ethnicities.
  • Biomarker / genetic study quality.16Future biomarker studies should adhere to AHA recommendations and be adequate in size, and report absolute and relative risk estimates, CIs, and P-value, discrimination, reclassification, and accuracy metrics in models with standard AF risk factors compared with the bio/genetic marker added.
  • Reclassification.Bio/genetic markers may provide potential pathophysiological insights, but only natriuretic peptides reliably reclassify risk for incident AF. Ideally, the discovery and validation of additional more specific markers that mechanistically link to AF risk will enhance reclassification.
  • Test whether bio/genetic markers are clinically useful and cost effective.16 Novel risk prediction models that incorporate clinical and bio/genetic markers for incident AF will require rigorous evaluation of whether they clinically and cost-effectively improve the risk stratification of individuals for more intensive screening2 or for trials to prevent AF.16,31

 

REFERENCES

 

1.         Friberg L, Engdahl J, Frykman V, Svennberg E, Levin LA, Rosenqvist M. Population screening of 75- and 76-year-old men and women for silent atrial fibrillation (STROKESTOP).Europace.2013;15(1):135-140.

2.         Lowres N, Neubeck L, Redfern J, Freedman SB. Screening to identify unknown atrial fibrillation. A systematic review.Thromb Haemost.2013;110(2):213-222.

3.         Lin HJ, Wolf PA, Benjamin EJ, Belanger AJ, D'Agostino RB. Newly diagnosed atrial fibrillation and acute stroke. The Framingham Study.Stroke.1995;26(9):1527-1530.

4.         Healey JS, Connolly SJ, Gold MR, et al. Subclinical atrial fibrillation and the risk of stroke.N Engl J Med.2012;366(2):120-129.

5.         Marfella R, Sasso FC, Siniscalchi M, et al. Brief episodes of silent atrial fibrillation predict clinical vascular brain disease in type 2 diabetic patients.J Am Coll Cardiol.2013;62(6):525-530.

6.         Kishore A, Vail A, Majid A, et al. Detection of atrial fibrillation after ischemic stroke or transient ischemic attack: a systematic review and meta-analysis.Stroke.2014;45(2):520-526.

7.         Flint AC, Banki NM, Ren X, Rao VA, Go AS. Detection of paroxysmal atrial fibrillation by 30-day event monitoring in cryptogenic ischemic stroke: the Stroke and Monitoring for PAF in Real Time (SMART) Registry.Stroke.2012;43(10):2788-2790.

8.         Cotter PE, Martin PJ, Ring L, Warburton EA, Belham M, Pugh PJ. Incidence of atrial fibrillation detected by implantable loop recorders in unexplained stroke.Neurology.2013;80(17):1546-1550.

9.         Culebras A, Messe SR, Chaturvedi S, Kase CS, Gronseth G. Summary of evidence-based guideline update: prevention of stroke in nonvalvular atrial fibrillation: report of the Guideline Development Subcommittee of the American Academy of Neurology.Neurology.2014;82(8):716-724.

10.       Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study.Lancet.2009;373(9665):739-745.

11.       Schnabel RB, Aspelund T, Li G, et al. Validation of an atrial fibrillation risk algorithm in whites and African Americans.Arch Intern Med.2010;170(21):1909-1917.

12.       Chamberlain AM, Agarwal SK, Folsom AR, et al. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study).Am J Cardiol.2011;107(1):85-91.

13.       Everett BM, Cook NR, Conen D, Chasman DI, Ridker PM, Albert CM. Novel genetic markers improve measures of atrial fibrillation risk prediction.Eur Heart J.2013;34(29):2243-2251.

14.       Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.J Am Heart Assoc.2013;2(2):e000102.

15.       Hijazi Z, Oldgren J, Siegbahn A, Granger CB, Wallentin L. Biomarkers in atrial fibrillation: a clinical review.Eur Heart J.2013;34(20):1475-1480.

16.       Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association.Circulation.2009;119(17):2408-2416.

17.       Schnabel RB, Larson MG, Yamamoto JF, et al. Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community.Circulation.2010;121(2):200-207.

18.       Smith JG, Newton-Cheh C, Almgren P, et al. Assessment of conventional cardiovascular risk factors and multiple biomarkers for the prediction of incident heart failure and atrial fibrillation.J Am Coll Cardiol.2010;56(21):1712-1719.

19.       Brugada R, Tapscott T, Czernuszewicz GZ, et al. Identification of a genetic locus for familial atrial fibrillation.N Engl J Med.1997;336(13):905-911.

20.       Ellinor PT, Yoerger DM, Ruskin JN, MacRae CA. Familial aggregation in lone atrial fibrillation.Hum Genet.2005;118(2):179-184.

21.       Fox CS, Parise H, D'Agostino RB, Sr., et al. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring.JAMA.2004;291(23):2851-2855.

22.       Lubitz SA, Yin X, Fontes JD, et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation.JAMA.2010;304(20):2263-2269.

23.       Arnar DO, Thorvaldsson S, Manolio TA, et al. Familial aggregation of atrial fibrillation in Iceland.Eur Heart J.2006;27(6):708-712.

24.       Gudbjartsson DF, Arnar DO, Helgadottir A, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25.Nature.2007;448(7151):353-357.

25.       Benjamin EJ, Rice KM, Arking DE, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry.Nat Genet.2009;41(8):879-881.

26.       Gudbjartsson DF, Holm H, Gretarsdottir S, et al. A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke.Nat Genet.2009;41(8):876-878.

27.       Ellinor PT, Lunetta KL, Glazer NL, et al. Common variants in KCNN3 are associated with lone atrial fibrillation.Nat Genet.2010;42(3):240-244.

28.       Ellinor PT, Lunetta KL, Albert CM, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation.Nat Genet.2012;44(6):670-675.

29.       Lubitz SA, Lunetta KL, Lin H, et al. Novel Genetic Markers Associate with Atrial Fibrillation Risk in Europeans and Japanese.J Am Coll Cardiol.2014.

30.       Smith JG, Newton-Cheh C, Almgren P, Melander O, Platonov PG. Genetic polymorphisms for estimating risk of atrial fibrillation in the general population: a prospective study.Arch Intern Med.2012;172(9):742-744.

31.       Benjamin EJ, Chen PS, Bild DE, et al. Prevention of atrial fibrillation: report from a national heart, lung, and blood institute workshop.Circulation.2009;119(4):606-618.

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