Blood-based cardiometabolic phenotypes in atrial fibrillation and their associated risk: EAST-AFNET 4 biomolecule study.

BACKGROUND: Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. Their role in defining subphenotypes of AF is not known. METHODS AND RESULTS: This prespecified analysis of the EAST-AFNET4 biomolecule study assigned patients to clusters using polytomous variable latent class analysis (poLCA) based on baseline concentrations of thirteen precisely-quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow up 2.9 years).Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest-risk cluster was dominated by elevated BMP10, IGFBP7, NT-proBNP, ANGPT2 and GDF15. Patients in the lowest-risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of hsCRP, IL-6, and D-dimer. Patients in the highest-risk cluster had a 5-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (pinteraction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients. CONCLUSIONS: Biomolecule concentrations identify cardiometabolic subphenotypes in patients with atrial fibrillation at high and low cardiovascular risk.

Reduced plakoglobin increases the risk of sodium current defects and atrial conduction abnormalities in response to androgenic anabolic steroid abuse.

Androgenic anabolic steroids (AAS) are commonly abused by young men. Male sex and increased AAS levels are associated with earlier and more severe manifestation of common cardiac conditions, such as atrial fibrillation, and rare ones, such as arrhythmogenic right ventricular cardiomyopathy (ARVC). Clinical observations suggest a potential atrial involvement in ARVC. Arrhythmogenic right ventricular cardiomyopathy is caused by desmosomal gene defects, including reduced plakoglobin expression. Here, we analysed clinical records from 146 ARVC patients to identify that ARVC is more common in males than females. Patients with ARVC also had an increased incidence of atrial arrhythmias and P wave changes. To study desmosomal vulnerability and the effects of AAS on the atria, young adult male mice, heterozygously deficient for plakoglobin (Plako+/- ), and wild type (WT) littermates were chronically exposed to 5α-dihydrotestosterone (DHT) or placebo. The DHT increased atrial expression of pro-hypertrophic, fibrotic and inflammatory transcripts. In mice with reduced plakoglobin, DHT exaggerated P wave abnormalities, atrial conduction slowing, sodium current depletion, action potential amplitude reduction and the fall in action potential depolarization rate. Super-resolution microscopy revealed a decrease in NaV 1.5 membrane clustering in Plako+/- atrial cardiomyocytes after DHT exposure. In summary, AAS combined with plakoglobin deficiency cause pathological atrial electrical remodelling in young male hearts. Male sex is likely to increase the risk of atrial arrhythmia, particularly in those with desmosomal gene variants. This risk is likely to be exaggerated further by AAS use. KEY POINTS: Androgenic male sex hormones, such as testosterone, might increase the risk of atrial fibrillation in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), which is often caused by desmosomal gene defects (e.g. reduced plakoglobin expression). In this study, we observed a significantly higher proportion of males who had ARVC compared with females, and atrial arrhythmias and P wave changes represented a common observation in advanced ARVC stages. In mice with reduced plakoglobin expression, chronic administration of 5α-dihydrotestosterone led to P wave abnormalities, atrial conduction slowing, sodium current depletion and a decrease in membrane-localized NaV 1.5 clusters. 5α-Dihydrotestosterone, therefore, represents a stimulus aggravating the pro-arrhythmic phenotype in carriers of desmosomal mutations and can affect atrial electrical function.

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions.

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation.

Quantification of fibroblast growth factor 23 and N-terminal pro-B-type natriuretic peptide to identify patients with atrial fibrillation using a high-throughput platform: A validation study.

BACKGROUND: Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. METHODS AND FINDINGS: For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p < 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p < 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. CONCLUSIONS: Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. TRIAL REGISTRATION: Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom.

Reduced left atrial cardiomyocyte PITX2 and elevated circulating BMP10 predict atrial fibrillation after ablation.

BACKGROUNDGenomic and experimental studies suggest a role for PITX2 in atrial fibrillation (AF). To assess if this association is relevant for recurrent AF in patients, we tested whether left atrial PITX2 affects recurrent AF after AF ablation.METHODSmRNA concentrations of PITX2 and its cardiac isoform, PITX2c, were quantified in left atrial appendages (LAAs) from patients undergoing thoracoscopic AF ablation, either in whole LAA tissue (n = 83) or in LAA cardiomyocytes (n = 52), and combined with clinical parameters to predict AF recurrence. Literature suggests that BMP10 is a PITX2-repressed, atrial-specific, secreted protein. BMP10 plasma concentrations were combined with 11 cardiovascular biomarkers and clinical parameters to predict recurrent AF after catheter ablation in 359 patients.RESULTSReduced concentrations of cardiomyocyte PITX2, but not whole LAA tissue PITX2, were associated with AF recurrence after thoracoscopic AF ablation (16% decreased recurrence per 2-(ΔΔCt) increase in PITX2). RNA sequencing, quantitative PCR, and Western blotting confirmed that BMP10 is one of the most PITX2-repressed atrial genes. Left atrial size (HR per mm increase [95% CI], 1.055 [1.028, 1.082]); nonparoxysmal AF (HR 1.672 [1.206, 2.318]), and elevated BMP10 (HR 1.339 [CI 1.159, 1.546] per quartile increase) were predictive of recurrent AF. BMP10 outperformed 11 other cardiovascular biomarkers in predicting recurrent AF.CONCLUSIONSReduced left atrial cardiomyocyte PITX2 and elevated plasma concentrations of the PITX2-repressed, secreted atrial protein BMP10 identify patients at risk of recurrent AF after ablation.TRIAL REGISTRATIONClinicalTrials.gov NCT01091389, NL50069.018.14, Dutch National Registry of Clinical Research Projects EK494-16.FUNDINGBritish Heart Foundation, European Union (H2020), Leducq Foundation.

Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation.

AIMS: Undetected atrial fibrillation (AF) is a major health concern. Blood biomarkers associated with AF could simplify patient selection for screening and further inform ongoing research towards stratified prevention and treatment of AF. METHODS AND RESULTS: Forty common cardiovascular biomarkers were quantified in 638 consecutive patients referred to hospital [mean ± standard deviation age 70 ± 12 years, 398 (62%) male, 294 (46%) with AF] with known AF or ≥2 CHA2DS2-VASc risk factors. Paroxysmal or silent AF was ruled out by 7-day ECG monitoring. Logistic regression with forward selection and machine learning algorithms were used to determine clinical risk factors, imaging parameters, and biomarkers associated with AF. Atrial fibrillation was significantly associated with age [bootstrapped odds ratio (OR) per year = 1.060, 95% confidence interval (1.04-1.10); P = 0.001], male sex [OR = 2.022 (1.28-3.56); P = 0.008], body mass index [BMI, OR per unit = 1.060 (1.02-1.12); P = 0.003], elevated brain natriuretic peptide [BNP, OR per fold change = 1.293 (1.11-1.63); P = 0.002], elevated fibroblast growth factor-23 [FGF-23, OR = 1.667 (1.36-2.34); P = 0.001], and reduced TNF-related apoptosis-induced ligand-receptor 2 [TRAIL-R2, OR = 0.242 (0.14-0.32); P = 0.001], but not other biomarkers. Biomarkers improved the prediction of AF compared with clinical risk factors alone (net reclassification improvement = 0.178; P < 0.001). Both logistic regression and machine learning predicted AF well during validation [area under the receiver-operator curve = 0.684 (0.62-0.75) and 0.697 (0.63-0.76), respectively]. CONCLUSION: Three simple clinical risk factors (age, sex, and BMI) and two biomarkers (elevated BNP and elevated FGF-23) identify patients with AF. Further research is warranted to elucidate FGF-23 dependent mechanisms of AF.