Jared W. Magnani

Myocarditis Current Trends in Diagnosis and Treatment

Myocarditis is clinically and pathologically defined as “inflammation of the myocardium.” Despite its rather clear-cut definition, the classification, diagnosis, and treatment of myocarditis continue to prompt considerable debate. The more routine use of endomyocardial biopsy has helped to better define the natural history of human myocarditis and to clarify clinicopathological correlations. Clinical presentations of the disease range from nonspecific systemic symptoms (fever, myalgias, palpitations, or exertional dyspnea) to fulminant hemodynamic collapse and sudden death. The extreme diversity of clinical manifestations has made the true incidence of myocarditis difficult to determine. Recent prospective postmortem data have implicated myocarditis in sudden cardiac death of young adults at rates of 8.6% to 12%.1,2 Furthermore, it has been identified as a cause of dilated cardiomyopathy in 9% of cases in a large prospective series.3 Recent molecular techniques have facilitated new insights into inflammatory autoimmune processes that affect the myocardium and ultimately result in acute or chronic dilated cardiomyopathy. Despite the well-established morbidity and mortality associated with myocarditis,4–7 clinical practice guidelines with regard to its evaluation and treatment are lacking.8 The wide variety of etiologies implicated in myocarditis and its heterogeneous clinical presentations5,7,9 have impeded patient identification and consensus on the most appropriate diagnostic criteria. The Dallas pathological criteria, published in 1986, served as the first attempt to develop standardized diagnostic guidelines for the histopathological classification of myocarditis.10 Active myocarditis is characterized by an inflammatory cellular infiltrate with evidence of myocyte necrosis (Figure 1), whereas borderline myocarditis demonstrates an inflammatory cellular infiltrate without evidence of myocyte injury (Figure 2). The inflammatory infiltrate should be further described as lymphocytic, eosinophilic, or granulomatous (Figure 3). The amount of inflammation may be mild, moderate, or severe, and its distribution may be focal, confluent, or diffuse, respectively. A retrospective study of 112 consecutive …

Meta-analysis identifies six new susceptibility loci for atrial fibrillation

Atrial fibrillation is a highly prevalent arrhythmia and a major risk factor for stroke, heart failure and death. We conducted a genome-wide association study (GWAS) in individuals of European ancestry, including 6,707 with and 52,426 without atrial fibrillation. Six new atrial fibrillation susceptibility loci were identified and replicated in an additional sample of individuals of European ancestry, including 5,381 subjects with and 10,030 subjects without atrial fibrillation (P < 5 × 10−8). Four of the loci identified in Europeans were further replicated in silico in a GWAS of Japanese individuals, including 843 individuals with and 3,350 individuals without atrial fibrillation. The identified loci implicate candidate genes that encode transcription factors related to cardiopulmonary development, cardiac-expressed ion channels and cell signaling molecules.

Genome-wide association study of PR interval

The electrocardiographic PR interval (or PQ interval) reflects atrial and atrioventricular nodal conduction, disturbances of which increase risk of atrial fibrillation. We report a meta-analysis of genome-wide association studies for PR interval from seven population-based European studies in the CHARGE Consortium: AGES, ARIC, CHS, FHS, KORA, Rotterdam Study, and SardiNIA (N = 28,517). We identified nine loci associated with PR interval at P < 5 × 10−8. At the 3p22.2 locus, we observed two independent associations in voltage-gated sodium channel genes, SCN10A and SCN5A. Six of the loci were near cardiac developmental genes, including CAV1-CAV2, NKX2-5 (CSX1), SOX5, WNT11, MEIS1, and TBX5-TBX3, providing pathophysiologically interesting candidate genes. Five of the loci, SCN5A, SCN10A, NKX2-5, CAV1-CAV2, and SOX5, were also associated with atrial fibrillation (N = 5,741 cases, P < 0.0056). This suggests a role for common variation in ion channel and developmental genes in atrial and atrioventricular conduction as well as in susceptibility to atrial fibrillation.

50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study

BACKGROUND Comprehensive long-term data on atrial fibrillation trends in men and women are scant. We aimed to provide such data through analysis of the Framingham cohort over 50 years. METHODS We investigated trends in incidence, prevalence, and risk factors for atrial fibrillation and its association with stroke and mortality after onset in 9511 participants enrolled in the Framingham Heart Study between 1958 and 2007. We analysed trends within 10 year groups (1958-67, 1968-77, 1978-87, 1988-97, and 1998-2007), stratified by sex. FINDINGS During 50 years of observation (202,417 person-years), 1544 cases of new-onset atrial fibrillation occurred (of whom 723 [47%] were women). Between 1958-67 and 1998-2007, age-adjusted prevalence of atrial fibrillation quadrupled from 20·4 to 96·2 cases per 1000 person-years in men and from 13·7 to 49·4 cases per 1000 person-years in women; age-adjusted incidence increased from 3·7 to 13·4 new cases per 1000 person-years in men and from 2·5 to 8·6 new cases per 1000 person-years in women (ptrend<0·0001 for all comparisons). For atrial fibrillation diagnosed by electrocardiograph (ECG) during routine Framingham examinations, age-adjusted prevalence per 1000 person-years increased (12·6 in 1958-67 to 25·7 in 1998-2007 in men, ptrend=0·0007; 8·1 to 11·8 in women, ptrend=0·009). However, age-adjusted incidence of atrial fibrillation by Framingham Heart Study ECGs did not change significantly with time. Although the prevalence of most risk factors changed over time, their associated hazards for atrial fibrillation changed little. Multivariable-adjusted proportional hazards models revealed a 74% (95% CI 50-86%) decrease in stroke (hazards ratio [HR] 3·77, 95% CI 1·98-7·20 in 1958-1967 compared with 1998-2007; ptrend=0·0001) and a 25% (95% CI -3-46%) decrease in mortality (HR 1·34, 95% CI 0·97-1·86 in 1958-1967 compared with 1998-2007; ptrend=0·003) in 20 years following atrial fibrillation onset. INTERPRETATION Trends of increased incidence and prevalence of atrial fibrillation in the community were probably partly due to enhanced surveillance. Measures are needed to enhance early detection of atrial fibrillation, through increased awareness coupled with targeted screening programmes and risk factor-specific prevention. FUNDING NIH, NHLBI, NINDS, Deutsche Forschungsgemeinschaft.

Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium

Background Tools for the prediction of atrial fibrillation (AF) may identify high‐risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. Methods and Results Individual‐level data from 3 large cohorts in the United States (Atherosclerosis Risk in Communities [ARIC] study, the Cardiovascular Health Study [CHS], and the Framingham Heart Study [FHS]), including 18 556 men and women aged 46 to 94 years (19% African Americans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment—Reykjavik study (AGES) and the Rotterdam Study (RS). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5‐year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C‐statistic, 0.765; 95% CI, 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C‐statistic, 0.767; 95% CI, 0.750 to 0.783; categorical net reclassification improvement, −0.0032; 95% CI, −0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable (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) and calibration was adequate. Conclusion A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the United States and Europe.

Association Between Familial Atrial Fibrillation and Risk of New-Onset Atrial Fibrillation

CONTEXT Although the heritability of atrial fibrillation (AF) is established, the contribution of familial AF to predicting new-onset AF remains unknown. OBJECTIVE To determine whether familial occurrence of AF is associated with new-onset AF beyond established risk factors. DESIGN, SETTING, AND PARTICIPANTS The Framingham Heart Study, a prospective community-based cohort study started in 1948. Original and Offspring Cohort participants were aged at least 30 years, were free of AF at the baseline examination, and had at least 1 parent or sibling enrolled in the study. The 4421 participants in this analysis (mean age, 54 [SD, 13] years; 54% women) were followed up through December 31, 2007. MAIN OUTCOME MEASURES Incremental predictive value of incorporating different features of familial AF (any familial AF, premature familial AF [onset ≤65 years old], number of affected relatives, and youngest age of onset in a relative) into a risk model for new-onset AF. RESULTS Across 11,971 examinations during the period 1968-2007, 440 participants developed AF. Familial AF occurred among 1185 participants (26.8%) and premature familial AF occurred among 351 participants (7.9%). Atrial fibrillation occurred more frequently among participants with familial AF than without familial AF (unadjusted absolute event rates of 5.8% and 3.1%, respectively). The association was not attenuated by adjustment for AF risk factors (multivariable-adjusted hazard ratio, 1.40; 95% confidence interval [CI], 1.13-1.74) or reported AF-related genetic variants. Among the different features of familial AF examined, premature familial AF was associated with improved discrimination beyond traditional risk factors to the greatest extent (traditional risk factors, C statistic, 0.842 [95% CI, 0.826-0.858]; premature familial AF, C statistic, 0.846 [95% CI, 0.831-0.862]; P = .004). Modest changes in integrated discrimination improvement were observed with premature familial AF (2.1%). Net reclassification improvement (assessed using 8-year risk thresholds of <5%, 5%-10%, and >10%) did not change significantly with premature familial AF (index statistic, 0.011; 95% CI, -0.021 to 0.042; P = .51), although categoryless net reclassification was improved (index statistic, 0.127; 95% CI, 0.064-0.189; P = .009). CONCLUSIONS In this cohort, familial AF was associated with an increased risk of AF that was not attenuated by adjustment for AF risk factors including genetic variants. Assessment of premature familial AF was associated with a very slight increase in predictive accuracy compared with traditional risk factors.

Relations of Biomarkers of Distinct Pathophysiological Pathways and Atrial Fibrillation Incidence in the Community

Background— Biomarkers of multiple pathophysiological pathways have been related to incident atrial fibrillation (AF), but their predictive ability remains controversial. Methods and Results— In 3120 Framingham cohort participants (mean age 58.4±9.7 years, 54% women), we related 10 biomarkers that represented inflammation (C-reactive protein and fibrinogen), neurohormonal activation (B-type natriuretic peptide [BNP] and N-terminal proatrial natriuretic peptide), oxidative stress (homocysteine), the renin-angiotensin-aldosterone system (renin and aldosterone), thrombosis and endothelial function (D-dimer and plasminogen activator inhibitor type 1), and microvascular damage (urinary albumin excretion; n=2673) to incident AF (n=209, 40% women) over a median follow-up of 9.7 years (range 0.05 to 12.8 years). In multivariable-adjusted analyses, the biomarker panel was associated with incident AF (P<0.0001). In stepwise-selection models (P<0.01 for entry and retention), log-transformed BNP (hazard ratio per SD 1.62, 95% confidence interval 1.41 to 1.85, P<0.0001) and C-reactive protein (hazard ratio 1.25, 95% confidence interval 1.07 to 1.45, P=0.004) were chosen. The addition of BNP to variables recently combined in a risk score for AF increased the C-statistic from 0.78 (95% confidence interval 0.75 to 0.81) to 0.80 (95% confidence interval 0.78 to 0.83) and showed an integrated discrimination improvement of 0.03 (95% confidence interval 0.02 to 0.04, P<0.0001), with 34.9% relative improvement in reclassification analysis. The combined analysis of BNP and C-reactive protein did not appreciably improve risk prediction over the model that incorporated BNP in addition to the risk factors. Conclusions— BNP is a predictor of incident AF and improves risk stratification based on well-established clinical risk factors. Whether knowledge of BNP concentrations may be used to target individuals at risk of AF for more intensive monitoring or primary prevention requires further investigation.

Atrial Fibrillation Current Knowledge and Future Directions in Epidemiology and Genomics

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.

P wave duration and risk of longitudinal atrial fibrillation in persons ≥ 60 years old (from the Framingham Heart Study).

Long-term risk prediction is a priority for the prevention of atrial fibrillation (AF). P wave indices are electrocardiographic measurements describing atrial conduction. The role of P wave indices in the prospective determination of AF and mortality risk has had limited assessment. We quantified by digital caliper the P wave indices of maximum duration and dispersion in 1,550 Framingham Heart Study participants ≥ 60 years old (58% women) from single-channel electrocardiograms recorded from 1968 through 1971. We examined the association of selected P wave indices and long-term outcomes using Cox proportional hazards regression incorporating age, gender, body mass index, systolic blood pressure, treatment for hypertension, significant murmur, heart failure, and PR interval. Over a median follow-up of 15.8 years (range 0 to 38.7), 359 participants developed AF and 1,525 died. Multivariable-adjusted hazard ratios (HRs) per SD increase in maximum P wave duration were 1.15 (95% confidence interval [CI] 0.90 to 1.47, p = 0.27) for AF and 1.02 (95% CI 0.96 to 1.08, p = 0.18) for mortality. The upper 5% of P wave maximum duration had a multivariable-adjusted HR of 2.51 (95% CI 1.13 to 5.57, p = 0.024) for AF and an HR of 1.11 (95% CI 0.87 to 1.40, p = 0.20) for mortality. We found no significant associations between P wave dispersion with incidence of AF or mortality. In conclusion, maximum P wave duration at the upper fifth percentile was associated with long-term AF risk in an elderly community-based cohort. P wave duration is an electrocardiographic endophenotype for AF.

Integrating Genetic, Transcriptional, and Functional Analyses to Identify 5 Novel Genes for Atrial Fibrillation

Background— Atrial fibrillation (AF) affects >30 million individuals worldwide and is associated with an increased risk of stroke, heart failure, and death. AF is highly heritable, yet the genetic basis for the arrhythmia remains incompletely understood. Methods and Results— To identify new AF-related genes, we used a multifaceted approach, combining large-scale genotyping in 2 ethnically distinct populations, cis-eQTL (expression quantitative trait loci) mapping, and functional validation. Four novel loci were identified in individuals of European descent near the genes NEURL (rs12415501; relative risk [RR]=1.18; 95% confidence interval [CI], 1.13–1.23; P=6.5×10−16), GJA1 (rs13216675; RR=1.10; 95% CI, 1.06–1.14; P=2.2×10−8), TBX5 (rs10507248; RR=1.12; 95% CI, 1.08–1.16; P=5.7×10−11), and CAND2 (rs4642101; RR=1.10; 95% CI, 1.06–1.14; P=9.8×10−9). In Japanese, novel loci were identified near NEURL (rs6584555; RR=1.32; 95% CI, 1.26–1.39; P=2.0×10−25) and CUX2 (rs6490029; RR=1.12; 95% CI, 1.08–1.16; P=3.9×10−9). The top single-nucleotide polymorphisms or their proxies were identified as cis-eQTLs for the genes CAND2 (P=2.6×10−19), GJA1 (P=2.66×10−6), and TBX5 (P=1.36×10−5). Knockdown of the zebrafish orthologs of NEURL and CAND2 resulted in prolongation of the atrial action potential duration (17% and 45%, respectively). Conclusions— We have identified 5 novel loci for AF. Our results expand the diversity of genetic pathways implicated in AF and provide novel molecular targets for future biological and pharmacological investigation.