Clinical Risk Factors For Atrial Fibrillation Research Articles

The association between eicosanoids and incident atrial fibrillation in the Framingham Heart Study

Chronic inflammation is a continuous low-grade activation of the systemic immune response. Whereas downstream inflammatory markers are associated with atrial fibrillation (AF), upstream inflammatory effectors including eicosanoids are less studied. To examine the association between eicosanoids and incident AF. We used a liquid chromatography-mass spectrometry for the non-targeted measurement of 161 eicosanoids and eicosanoid-related metabolites in the Framingham Heart Study. The association of each eicosanoid and incident AF was assessed using Cox proportional hazards models and adjusted for AF risk factors, including age, sex, height, weight, systolic/diastolic blood pressure, current smoking, antihypertensive medication, diabetes, history of myocardial infarction and heart failure. False discovery rate (FDR) was used to adjust for multiple testing. Eicosanoids with FDR < 0.05 were considered significant. In total, 2676 AF-free individuals (mean age 66 ± 9 years, 56% females) were followed for mean 10.8 ± 3.4 years; 351 participants developed incident AF. Six eicosanoids were associated with incident AF after adjusting for multiple testing (FDR < 0.05). A joint score was built from the top eicosanoids weighted by their effect sizes, which was associated with incident AF (HR = 2.72, CI = 1.71–4.31, P = 2.1 × 10–5). In conclusion, six eicosanoids were associated with incident AF after adjusting for clinical risk factors for AF.

Abstract 12922: Electrocardiogram-Based Deep Learning and Clinical Risk Factors to Predict Incident Atrial Fibrillation

Introduction: Deep learning-derived representations of 12-lead electrocardiograms (ECGs) may allow for atrial fibrillation (AF) risk prediction. However, it remains unclear whether ECG-based artificial intelligence improves prediction beyond established clinical risk factors for AF and whether predictions are generalizable. Methods: Within a dataset comprising over 500,000 individuals receiving regular primary care at a multi-institutional network, we trained a convolutional neural network to predict incident AF using 12-lead ECGs (“ECG-AI”). ECG-AI was trained in individuals with ≥1 ECG performed at Massachusetts General Hospital (MGH) within 3 years prior to start of follow-up. We then fit a Cox proportional hazards model with incident AF as the outcome and a) logit-transformed ECG-AI AF probability, and b) the Cohorts for Aging and Genomic Epidemiology AF (CHARGE-AF) score, as covariates (“CH-AI”). We compared the discrimination and calibration of CHARGE-AF versus CH-AI in three independent samples: MGH (n=4,166), Brigham and Women’s Hospital (BWH, n=37,963) and the UK Biobank (n=41,034). Based on available follow-up, AF was evaluated at 5 years in MGH and BWH, and 2 years in the UK Biobank. Results: ECG-AI was trained in 36,081 individuals with an ECG performed at MGH (mean age 55±17, 53% female). CH-AI had substantially better discrimination (area under the receiver operating characteristic curve [AUROC]: MGH 0.838, BWH 0.777, UK Biobank 0.746; average precision [AP] 0.30, 0.21, 0.06) versus CHARGE-AF (AUROC: 0.802, 0.752, 0.732; AP 0.21, 0.17, 0.02, Figure ). CH-AI was well-calibrated in MGH (calibration error 0.012) and BWH (0.019), but overestimated AF risk in the UK Biobank (0.068). Calibration in the UK Biobank was excellent after recalibration to the sample-level 2-year AF hazard (error 7.1x10 -5 ). Conclusions: A model combining clinical AF risk factors with deep learning-derived ECG-based AF risk is favorable for predicting 5-year risk of AF.

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Galectin-3 in Heart Failure with Preserved Ejection Fraction and Persistent Atrial Fibrillation Versus Sinus Rhythm. Correlation with Left Atrial Volume and N-Terminal Pro B-Type Natriuretic Peptide

Background: Galectin-3 (Gal-3) is considered both a profibrotic biomarker in Heart Failure with preserved Ejection Fraction (HFpEF) and a biomarker of atrial remodeling in Atrial Fibrillation (AF). The Left Atrial Volume Index (LAVI) is an echocardiographic parameter considered an index of left atrial remodeling. Aim of this study was to analyse the relation of Gal-3 levels with both LAVI and N-Terminal Pro B-Type Natriuretic Peptide (NT-proBNP) in patients with HFpEF and Persistent AF (HFpEF-PAF). Methods: Serum Gal-3 and NT-proBNP, along with LAVI were measured. A comparison of such parameters between 49 patients with HFpEF-PAF and 53 patients with HFpEF and sinus rhythm (HEpEF-SR) was made. Results: Galectin-3, NT-proBNP and LAVI were significantly higher in patients with HFpEF-PAF compared to HFpEF-SR (23±7 ng/mL vs 19.5±8.5 ng/mL, p=0.027; 3,406.8±2,321.9 pg/mL vs 1,459.6±1,372 pg/mL, p&lt;0.001; 40.1±11mL/m² vs 28.4±7.7 mL/m², p&lt;0.001, respectively). In HFpEF-PAF, Gal- 3 showed a significant correlation with both NT-proBNP (r=0.40, p=0.0038) and LAVI (r=0.28, p=0.044). We found a significant association between patients with higher levels of Gal-3 &gt;17.8 ng/mL and HFpEF-PAF (p=0.002). Finally, a multivariate logistic regression analysis adjusted for age, sex and traditional clinical AF risk factors showed that Gal-3 &gt;17,8 ng/mL (OR 3.862, 95% CI 1.416 to 10.532, p=0.008) was an independent predictor of PAF. Conclusions: In patients with HFpEF-PAF Gal-3 was higher and related with both NT-proBNP and LAVI. The latter correlation may be relevant because LAVI is considered an index of left atrial remodeling. Moreover, higher levels of Gal-3&gt;17,8 ng/mL were an independent predictor of PAF.

Genetic Susceptibility for Atrial Fibrillation in Patients Undergoing Atrial Fibrillation Ablation.

Ablation is a widely used therapy for atrial fibrillation (AF); however, arrhythmia recurrence and repeat procedures are common. Studies examining surrogate markers of genetic susceptibility to AF, such as family history and individual AF susceptibility alleles, suggest these may be associated with recurrence outcomes. Accordingly, the aim of this study was to test the association between AF genetic susceptibility and recurrence after ablation using a comprehensive polygenic risk score for AF. Ten centers from the AF Genetics Consortium identified patients who had undergone de novo AF ablation. AF genetic susceptibility was measured using a previously described polygenic risk score (N=929 single-nucleotide polymorphisms) and tested for an association with clinical characteristics and time-to-recurrence with a 3 month blanking period. Recurrence was defined as >30 seconds of AF, atrial flutter, or atrial tachycardia. Multivariable analysis adjusted for age, sex, height, body mass index, persistent AF, hypertension, coronary disease, left atrial size, left ventricular ejection fraction, and year of ablation. Four thousand two hundred seventy-six patients were eligible for analysis of baseline characteristics and 3259 for recurrence outcomes. The overall arrhythmia recurrence rate between 3 and 12 months was 44% (1443/3259). Patients with higher AF genetic susceptibility were younger (P<0.001) and had fewer clinical risk factors for AF (P=0.001). Persistent AF (hazard ratio [HR], 1.39 [95% CI, 1.22-1.58]; P<0.001), left atrial size (per cm: HR, 1.32 [95% CI, 1.19-1.46]; P<0.001), and left ventricular ejection fraction (per 10%: HR, 0.88 [95% CI, 0.80-0.97]; P=0.008) were associated with increased risk of recurrence. In univariate analysis, higher AF genetic susceptibility trended towards a higher risk of recurrence (HR, 1.08 [95% CI, 0.99-1.18]; P=0.07), which became less significant in multivariable analysis (HR, 1.06 [95% CI, 0.98-1.15]; P=0.13). Higher AF genetic susceptibility was associated with younger age and fewer clinical risk factors but not recurrence. Arrhythmia recurrence after AF ablation may represent a genetically different phenotype compared to AF susceptibility.

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of methods exist for screening for AF, a targeted approach, which requires an efficient method for identifying patients at risk, would be preferred. To examine machine learning approaches applied to electronic health record data that have been harmonized to the Observational Medical Outcomes Partnership Common Data Model for identifying risk of AF. This diagnostic study used data from 2 252 219 individuals cared for in the UCHealth hospital system, which comprises 3 large hospitals in Colorado, from January 1, 2011, to October 1, 2018. Initial analysis was performed in December 2018; follow-up analysis was performed in July 2019. All Observational Medical Outcomes Partnership Common Data Model-harmonized electronic health record features, including diagnoses, procedures, medications, age, and sex. Classification of incident AF in designated 6-month intervals, adjudicated retrospectively, based on area under the receiver operating characteristic curve and F1 statistic. Of 2 252 219 individuals (1 225 533 [54.4%] women; mean [SD] age, 42.9 [22.3] years), 28 036 (1.2%) developed incident AF during a designated 6-month interval. The machine learning model that used the 200 most common electronic health record features, including age and sex, and random oversampling with a single-layer, fully connected neural network provided the optimal prediction of 6-month incident AF, with an area under the receiver operating characteristic curve of 0.800 and an F1 score of 0.110. This model performed only slightly better than a more basic logistic regression model composed of known clinical risk factors for AF, which had an area under the receiver operating characteristic curve of 0.794 and an F1 score of 0.079. Machine learning approaches to electronic health record data offer a promising method for improving risk prediction for incident AF, but more work is needed to show improvement beyond standard risk factors.

Change in left atrial function predicts incident atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis.

AimsLongitudinal change in left atrial (LA) structure and function could be helpful in predicting risk for incident atrial fibrillation (AF). We used cardiac magnetic resonance (CMR) imaging to explore the relationship between change in LA structure and function and incident AF in a multi-ethnic population free of clinical cardiovascular disease at baseline.Methods and resultsIn the Multi-Ethnic Study of Atherosclerosis (MESA), 2338 participants, free at baseline of clinically recognized AF and cardiovascular disease, had LA volume and function assessed with CMR imaging, at baseline (2000–02), and at Exam 4 (2005–07) or 5 (2010–12). Free of AF, 124 participants developed AF over 3.8 ± 0.9 years (2015) following the second imaging. In adjusted Cox regression models, an average annualized change in all LA parameters were significantly associated with an increased risk of AF. An annual decrease of 1-SD unit in total LA emptying fractions (LAEF) was most strongly associated with risk of AF after adjusting for clinical risk factors for AF, baseline LA parameters, and left ventricular mass-to-volume ratio (hazard ratio per SD = 1.91, 95% confidence interval = 1.53–2.38, P < 0.001). The addition of change in total LAEF to an AF risk score improved model discrimination and reclassification (net reclassification improvement = 0.107, P = 0.017; integrative discrimination index = 0.049, P < 0.001).ConclusionIn this multi-ethnic study population free of clinical cardiovascular disease at baseline, a greater increase in LA volumes and decrease in LA function were associated with incident AF. The addition of change in total LAEF to risk prediction models for AF improved model discrimination and reclassification of AF risk.

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

ObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with p < 4.4 × 10−4 in the previous AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio [OR] per SD = 1.40, p = 1.45 × 10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per SD = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1).ConclusionsGenetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.

Nonalcoholic fatty liver disease as a predictor of atrial fibrillation: a systematic review and meta-analysis.

IntroductionNumerous epidemiologic studies have investigated the link between nonalcoholic fatty liver disease (NAFLD) and long-term atrial fibrillation (AF) risk, but the results are surprisingly conflicting.AimTherefore, we systematically reviewed all published studies assessing the risk of AF in patients with NAFLD and conducted a meta-analysis.Material and methodsWe performed a literature search using PubMed, EMBASE and Cochrane Library databases in February 2017 with no restrictions. Two cohort studies and two cross-sectional studies were identified, involving a total of 5150 subjects (NAFLD: 1655; controls: 3495) in this meta-analysis. Data from selected studies were extracted and a meta-analysis was performed using a random effects model.ResultsNonalcoholic fatty liver disease patients had a significantly higher risk of AF compared to controls (relative risk (RR): 2.61; 95% confidence interval (CI): 1.34–5.06, p = 0.00; I2 = 52.5%, p = 0.097). In a further analysis stratified by presence of type 2 diabetes, the increased risk was present predominantly in patients with type 2 diabetes (RR = 5.10; 95% CI: 2.43–10.7, p < 0.001; I2 = 0, p = 0.958). However, subjects without type 2 diabetes were at slightly increased risk of AF but the relative risk did not reach statistical significance (RR = 1.68; 95% CI: 0.99–2.82, p = 0.05; I2 = 0, p = 0.461).ConclusionsOur meta-analysis suggested that ultrasound-diagnosed NAFLD patients have a significantly higher risk for AF after adjustment for numerous important clinical risk factors for AF. These results need to be confirmed in large prospective studies.

Genetic Risk Prediction of Atrial Fibrillation.

Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from <1×10-3 to <1×10-8 in a prior independent genetic association study. Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13-1.46; P=1.5×10-4) to 1.67 (25 variants; 95% confidence interval, 1.47-1.90; P=9.3×10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39-4.58; P=2.7×10-3). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20-4.40; P=0.01). Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

Asymmetric dimethylarginine, related arginine derivatives, and incident atrial fibrillation

Oxidative stress plays an important role in the development of atrial fibrillation (AF). Arginine derivatives including asymmetric dimethylarginine (ADMA) are central to nitric oxide metabolism and nitrosative stress. Whether blood concentrations of arginine derivatives are related to incidence of AF is uncertain. In 3,310 individuals (mean age 58 ± 10 years, 54% women) from the community-based Framingham Study, we prospectively examined the relations of circulating levels of ADMA, l-arginine, symmetric dimethylarginine (SDMA), and the ratio of l-arginine/ADMA to incidence of AF using proportional hazards regression models. Over a median follow-up time of 10 years, 247 AF cases occurred. Using age- and sex-adjusted regression models, ADMA was associated with a hazard ratio of 1.15 per 1-SD increase in loge-biomarker concentration (95% CI 1.02-1.29, P = .02) for AF, which was no longer significant after further risk factor adjustment (hazard ratio 1.09, 95% CI 0.97-1.23, P = .15). Neither l-arginine nor SDMA was related to new-onset AF. A clinical model comprising clinical risk factors for AF (for age, sex, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, diabetes, hypertension treatment, myocardial infarction, and heart failure; c statistic = 0.781; 95% CI 0.753-0.808) was not improved by the addition of ADMA (0.782; 95% CI 0.755-0.809). Asymmetric dimethylarginine and related arginine derivatives were not associated with incident AF in the community after accounting for other clinical risk factors and confounders. Its role in the pathogenesis of AF needs further refinement.

Atrial Fibrillation and Hypertension: Mechanistic, Epidemiologic, and Treatment Parallels.

Atrial fibrillation (AF) is an increasingly prevalent condition and the most common sustained arrhythmia encountered in ambulatory and hospital practice. Several clinical risk factors for AF include age, sex, valvular heart disease, obesity, sleep apnea, heart failure, and hypertension (HTN). Of all the risk factors, HTN is the most commonly encountered condition in patients with incident AF. Hypertension is associated with a 1.8-fold increase in the risk of developing new-onset AF and a 1.5-fold increase in the risk of progression to permanent AF. Hypertension predisposes to cardiac structural changes that influence the development of AF such as atrial remodeling. The renin angiotensin aldosterone system has been demonstrated to be a common mechanistic link in the pathogenesis of HTN and AF. Importantly, HTN is one of the few modifiable AF risk factors, and guideline-directed management of HTN may reduce the incidence of AF.

Multiple Biomarkers and Atrial Fibrillation in the General Population

BackgroundDifferent biological pathways have been related to atrial fibrillation (AF). Novel biomarkers capturing inflammation, oxidative stress, and neurohumoral activation have not been investigated comprehensively in AF.Methods and ResultsIn the population-based Gutenberg Health Study (n = 5000), mean age 56±11 years, 51% males, we measured ten biomarkers representing inflammation (C-reactive protein, fibrinogen), cardiac and vascular function (midregional pro adrenomedullin [MR-proADM], midregional pro atrial natriuretic peptide [MR-proANP], N-terminal pro-B-type natriuretic peptide [Nt-proBNP], sensitive troponin I ultra [TnI ultra], copeptin, and C-terminal pro endothelin-1), and oxidative stress (glutathioneperoxidase-1, myeloperoxidase) in relation to manifest AF (n = 161 cases). Individuals with AF were older, mean age 64.9±8.3, and more often males, 71.4%. In Bonferroni-adjusted multivariable regression analyses strongest associations per standard deviation increase in biomarker concentrations were observed for the natriuretic peptides Nt-proBNP (odds ratio [OR] 2.89, 99.5% confidence interval [CI] 2.14–3.90; P<0.0001), MR-proANP (OR 2.45, 99.5% CI 1.91–3.14; P<0.0001), the vascular function marker MR-proADM (OR 1.54, 99.5% CI 1.20–1.99; P<0.0001), TnI ultra (OR 1.50, 99.5% CI 1.19–1.90; P<0.0001) and. fibrinogen (OR 1.44, 99.5% CI 1.19–1.75; P<0.0001). Based on a model comprising known clinical risk factors for AF, all biomarkers combined resulted in a net reclassification improvement of 0.665 (99.3% CI 0.441–0.888) and an integrated discrimination improvement of >13%.ConclusionsIn conclusion, in our large, population-based study, we identified novel biomarkers reflecting vascular function, MR-proADM, inflammation, and myocardial damage, TnI ultra, as related to AF; the strong association of natriuretic peptides was confirmed. Prospective studies need to examine whether risk prediction of AF can be enhanced beyond clinical risk factors using these biomarkers.

Emerging directions in the genetics of atrial fibrillation.

Atrial fibrillation (AF) is the most common arrhythmia and is associated with increased morbidity. As the population ages and the prevalence of AF continues to rise, the socioeconomic consequences of AF will become increasingly burdensome. Although there are well-defined clinical risk factors for AF, a significant heritable component is also recognized. To identify the molecular basis for the heritability of AF, investigators have used a combination of classical Mendelian genetics, candidate gene screening, and genome-wide association studies. However, these avenues have, as yet, failed to define the majority of the heritability of AF. The goal of this review is to describe the results from both candidate gene and genome-wide studies, as well as to outline potential future avenues for creating a more complete understanding of AF genetics. Ultimately, a more comprehensive view of the genetic underpinnings for AF will lead to the identification of novel molecular pathways and improved risk prediction of this complex arrhythmia.

Galectin 3 and incident atrial fibrillation in the community

Galectin 3 (Gal-3) is a potential mediator of cardiac fibrosis, and Gal-3 concentrations predict incident heart failure. The same mechanisms that lead to cardiac fibrosis in heart failure may influence development of atrial fibrosis and atrial fibrillation (AF). We examined the association of Gal-3 and incident AF in the community. Plasma Gal-3 concentrations were measured in 3,306 participants of the Framingham Offspring cohort who attended the sixth examination cycle (1995-1998, mean age 58 years, 54% women). Cox proportional hazards regression models were used to assess the association of baseline Gal-3 concentrations and incident AF. Over a median follow-up period of 10 years, 250 participants developed incident AF. Crude incidence rates of AF by increasing sex-specific Gal-3 quartiles were 3.7%, 5.9%, 9.1%, and 11.5% (log-rank test P < .0001). In age- and sex-adjusted analyses, each 1-SD increase in loge-Gal-3 was associated with a 19% increased hazard of incident AF (hazard ratio 1.19, 95% CI 1.05-1.36, P = .009). This association was not significant after adjustment for traditional clinical AF risk factors (hazard ratio 1.12, 95% CI 0.98-1.28, P = .10). Higher circulating Gal-3 concentrations were associated with increased risk of developing AF over the subsequent 10 years in age- and sex-adjusted analyses but not after accounting for other traditional clinical AF risk factors. Our results do not support a role for Gal-3 in AF risk prediction. Further studies are needed to evaluate whether Gal-3 plays a role in the development of AF substrate similar to HF.

Relations between circulating microRNAs and atrial fibrillation: data from the Framingham Offspring Study.

MicroRNA (miRNA) expression in atrial tissue has been implicated in pathologic susceptibility to atrial fibrillation (AF). Nevertheless, data on how circulating levels relate to AF are limited. The purpose of this study was to test the hypothesis that circulating miRNAs are associated with AF. Among 2445 Framingham Heart Study Offspring participants, we measured the expression of 385 circulating whole blood miRNAs by high-throughput quantitative reverse transcriptase polymerase chain reaction. We related miRNA levels with prevalent and new-onset AF. Mean age of the cohort was 66.3 ± 8.9 years, and 56% were women; 153 participants had clinically apparent AF at baseline, and 107 developed AF during median follow-up of 5.4 years. miRNA-328 (miR-328) expression was lower among participants with prevalent AF (8.76 cycle threshold) compared to individuals with no AF (7.75 cycle threshold, P <.001). The association between miR-328 and prevalent AF persisted after adjustment for age, sex, and technical covariates (odds ratio 1.21, P = 1.8 × 10(-4)) but was attenuated in analyses adjusting for clinical AF risk factors (odds ratio 1.14, P = .017). In contrast to the associations between miR-328 and prevalent AF, none of the circulating miRNAs were associated with incident AF. Circulating levels of miR-328, a miRNA known to promote atrial electrical remodeling by reducing L-type Ca(2+) channel density, were associated with prevalent AF. Adjustment for risk factors that promote atrial remodeling, including hypertension, attenuated the association between miR-328 and AF, potentially implicating miR-328 as a potential mediator of atrial remodeling and AF vulnerability.

Relation between soluble ST2, growth differentiation factor–15, and high-sensitivity troponin I and incident atrial fibrillation

We investigated whether circulating concentrations of soluble ST2, growth differentiation factor-15 (GDF-15), and high-sensitivity troponin I (hsTnI) are associated with incident atrial fibrillation (AF) and whether these biomarkers improve current risk prediction models including AF risk factors, B-type natriuretic peptide (BNP), and C-reactive protein (CRP). We studied the relation between soluble ST2, GDF-15, and hsTnI and development of AF in Framingham Heart Study participants without prevalent AF. We used Cox proportional hazard regression analysis to examine the relation of incident AF during a 10-year follow-up period with each biomarker. We adjusted for standard AF clinical risk factors, BNP, and CRP. The mean age of the 3,217 participants was 59 ± 10 years, and 54% were women. During a 10-year follow-up, 242 participants developed AF. In age- and sex-adjusted models, GDF-15 and hsTnI were associated with risk of incident AF; however, after including the AF risk factors and BNP and CRP, only hsTnI was significantly associated with AF (hazard ratio per 1 SD of loge hsTnI, 1.12, 95% CI 1.00-1.26, P = .045). The c statistic of the base model including AF risk factors, BNP, and CRP was 0.803 (95% CI 0.777-0.830) and did not improve by adding individual or all 3 biomarkers. None of the discrimination and reclassification statistics were significant compared with the base model. In a community-based cohort, circulating hsTnI concentrations were associated with incident AF. None of the novel biomarkers evaluated improved AF risk discrimination or reclassification beyond standard clinical AF risk factors and biomarkers.

Systematic review of atrial fibrillation and stroke in patients with hypertrophic cardiomyopathy

Background: Hypertrophic Cardiomyopathy (HCM) is a common inherited myocardial disease associated with atrial arrhythmia. Current guidelines for the management of Atrial Fibrillation (AF) do not give detailed advice on management in HCM patients because of a lack of robust clinical prediction tools that can estimate risk of developing AF and an absence of adequately powered studies. Objective: To summarise and critically review literature on AF and stroke in HCM patients and to perform a meta-analysis to determine clinical risk factors for AF and stroke. Data sources: PubMed and Web-of-Science and reference lists of related articles. Study selection: Published articles on clinical studies addressing the topic of HCM, AF and stroke in human subjects. Data extraction: 2 investigators independently extracted data from identified articles. Studies in English language investigating AF and stroke in HCM as primary or secondary endpoint were included. Reviews, case reports, abstracts were excluded. Data synthesis: A random effect meta-regression model was used to determine the pooled overall prevalence and incidence of AF and stroke. The I2 statistic was used to estimate the proportion of total variability in the data attributed to the heterogeneity between the studies. Results: In a total population of 5397 patients in 29 retrospective studies, the overall prevalence of AF was 21.61% (95% CI 19.30-23.92, I2=67.3%, p<0.01). The overall prevalence of thromboembolism in HCM patients with AF was 30.65% (95% CI 23.35-37.95, I2=85%, p<0.01). The overall incidence of AF was 2.99% (95% CI 2.48-3.50, I2=75.4% (p<0.01). The overall incidence of all thromboembolic complications in HCM patients with AF was 4.35% (95% CI 2.91-5.79, I2=62.4%, p<0.05). LA size and age were predictive for the development of AF and stroke in the majority of studies. Meta-analysis revealed an LA diameter of 38.03 mm (95% CI 34.62-41.44) in SR patients and 45.37 mm (95% CI 41.64-49.04) in AF patients. There were no randomized controlled trials of therapy; anticoagulation was associated with a lower incidence of stroke in a small number of studies, but data on the effects of pharmacological therapy and radiofrequency ablation were limited and contradictory. Conclusions: AF is common in HCM and associated with a high risk of thromboembolism. LA size and age are independently associated with AF, but the published literature is insufficient to create a robust tool for the prediction of AF or thromboembolic risk in patients in SR. There are no trials of therapy, but most data suggest that once patients have AF, they should be anticoagulated.

Non-alcoholic fatty liver disease is associated with an increased prevalence of atrial fibrillation in hospitalized patients with Type 2 diabetes

NAFLD (non-alcoholic fatty liver disease) and AF (atrial fibrillation) are two pathological conditions that are highly prevalent in developed countries and share multiple risk factors. The relationship between NAFLD and AF in Type 2 diabetes is currently unknown. We studied a hospital-based sample of 702 patients with Type 2 diabetes discharged from our Division of Endocrinology during 2007-2011. The diagnosis of AF was confirmed in affected participants on the basis of ECGs and medical history by experienced cardiologists. NAFLD was defined by ultrasonographic detection of hepatic steatosis in the absence of other liver diseases. Of the 702 hospitalized patients included in the study, 514 (73.2%) of them had NAFLD and 85 (12.1%) had persistent or permanent AF. NAFLD was associated with an increased risk of prevalent AF {OR (odds ratio), 3.04 [95% CI (confidence interval), 1.54-6.02]; P<0.001}. Adjustments for age, sex, systolic BP (blood pressure), HbA1c, (glycated haemoglobin), estimated GFR (glomerular filtration rate), total cholesterol, electrocardiographic LVH (left ventricular hypertrophy), COPD (chronic obstructive pulmonary disease), and prior history of HF (heart failure), VHD (valvular heart disease) or hyperthyroidism did not attenuate the association between NAFLD and AF [adjusted OR, 5.88 (95% CI, 2.72-12.7); P<0.001]. In conclusion, our results show that ultrasound-diagnosed NAFLD is strongly associated with an increased prevalence of persistent or permanent AF in patients with Type 2 diabetes, independently of several clinical risk factors for AF. The potential impact of NAFLD on AF deserves particular attention, especially with respect to the implications for screening and surveillance strategies in the growing number of patients with NAFLD.

Relation of Elevated Serum Uric Acid Levels to Incidence of Atrial Fibrillation in Patients With Type 2 Diabetes Mellitus

The association between serum uric acid (SUA) levels and atrial fibrillation (AF) is currently poorly known. We examined the association between SUA levels and risk of incident AF in patients with type 2 diabetes mellitus. We followed for 10 years a random sample of 400 type 2 diabetic outpatients, who were free from AF at baseline. A standard 12-lead electrocardiography was undertaken annually and a diagnosis of incident AF was confirmed in affected participants by a single cardiologist. Over 10 years, there were 42 incident AF cases (cumulative incidence of 10.5%). Elevated SUA level was associated with an increased risk of incident AF (odds ratio 2.43, 95% confidence interval 1.8 to 3.4, p <0.0001 for each 1-SD increase in SUA level). Adjustments for age, gender, body mass index, hypertension, chronic kidney disease, electrocardiographic features (left ventricular hypertrophy and PR interval), and use of diuretics and allopurinol did not attenuate the association between SUA and incident AF (adjusted odds ratio 2.44, 95% confidence interval 1.6 to 3.9, p <0.0001). Further adjustment for variables that were included in the 10-year Framingham Heart Study-derived AF risk score did not appreciably weaken this association. Results remained unchanged even when SUA was modeled as a categorical variable (stratifying by either SUA median or hyperuricemia), and when patients with previous coronary heart disease or heart failure were excluded from analysis. In conclusion, our findings suggest that elevated SUA levels are strongly associated with an increased incidence of AF in patients with type 2 diabetes mellitus even after adjustment for multiple clinical risk factors for AF.