Atrial fibrillation (AF) affects over 33 million individuals worldwide1 and has a complex heritability.2 We conducted the largest meta-analysis of genome-wide association studies for AF to date, consisting of over half a million individuals including 65,446 with AF. In total, we identified 97 loci significantly associated with AF including 67 of which were novel in a combined-ancestry analysis, and 3 in a European specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait loci (eQTL) analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

Background Atrial fibrillation (AF) is common and associated with adverse health outcomes. Timely detection of AF can be challenging using traditional diagnostic tools. Smartphone use is increasing and may provide an inexpensive and user-friendly means to diagnose AF. Objective To test the hypothesis that a smartphone-based application could detect an irregular pulse from AF. Methods 76 adults with persistent AF were consented for participation in our study. We obtained pulsatile time series recordings before and after cardioversion using an iPhone 4S camera. A novel smartphone application conducted real-time pulse analysis using 2 statistical methods [Root Mean Square of Successive RR Differences (RMSSD/mean); Shannon Entropy (ShE)]. We examined the sensitivity, specificity, and predictive accuracy of both algorithms using the 12-lead electrocardiogram as the gold standard. Results RMSDD/mean and ShE were higher in participants in AF compared with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (β coefficients per SD-increment in RMSDD/mean and ShE were −0.20 and −0.35; p<0.001). An algorithm combining the 2 statistical methods demonstrated excellent sensitivity (0.962), specificity (0.975), and accuracy (0.968) for beat-to-beat discrimination of an irregular pulse during AF from sinus rhythm. Conclusions In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, we found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm. Data are needed to explore the performance and acceptability of smartphone-based applications for AF detection.

Background Atrial fibrillation (AF) is a common and dangerous paroxysmal rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. Objectives To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs) and premature ventricular contractions (PVCs). Methods We analyzed 219 2-minute pulse recordings from 121 participants with AF (n=98), PACs (n=15), or PVCs (n=15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from pre-existing AF. The PULSESMART app conducted pulse analysis using 3 methods [Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot]. We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n=65) of app users. Results The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was “useful” and “not complex” to use. Conclusions A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.

Transcripts in platelets are largely produced in precursor megakaryocytes but remain physiologically-active as platelets translate RNAs and regulate protein/RNA levels. Recent studies using transcriptome sequencing (RNA-seq) characterized the platelet transcriptome in limited numbers of non-diseased individuals. Here, we expand upon these RNA-seq studies by completing RNA-seq in platelets from 32 patients with acute myocardial infarction (MI). Our goals were to characterize the platelet transcriptome using a population of patients with acute MI and relate gene expression to platelet aggregation measures and ST-segment elevation MI (STEMI) (n=16) versus non-STEMI (NSTEMI) (n=16) subtypes. Similar to other studies, we detected 9,565 expressed transcripts, including several known platelet-enriched markers (e.g., PPBP, OST4). Our RNA-seq data strongly correlated with independently ascertained platelet expression data and showed enrichment for platelet-related pathways (e.g., wound response, hemostasis, and platelet activation), as well as actin-related and post-transcriptional processes. Several transcripts displayed suggestively higher (FBXL4, ECHDC3, KCNE1, TAOK2, AURKB, ERG, and FKBP5) and lower (MIAT, PVRL3and PZP) expression in STEMI platelets compared to NSTEMI. We also identified transcripts correlated with platelet aggregation to TRAP (ATP6V1G2, SLC2A3), collagen (CEACAM1, ITGA2), and ADP (PDGFB, PDGFC, ST3GAL6). Our study adds to current platelet gene expression resources by providing transcriptome-wide analyses in platelets isolated from patients with acute MI. In concert with prior studies, we identify various genes for further study in regards to platelet function and acute MI. Future platelet RNA-seq studies examining more diverse sets of healthy and diseased samples will add to our understanding of platelet thrombotic and non-thrombotic functions.

Background MicroRNAs (miRNAs) are associated with cardiovascular disease (CVD), control gene expression, and are detectable in the circulation. Objective To test the hypothesis that circulating miRNAs would be associated with atrial fibrillation (AF). Methods Using a prospective study design powered to detect subtle differences in miRNAs, we quantified plasma expression of 86 miRNAs by high-throughput quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) in 112 participants with AF and 99 without AF. To examine parallels between cardiac and plasma miRNA profiles, we quantified atrial tissue and plasma miRNA expression using qRT-PCR in 31 participants undergoing surgery. We also explored the hypothesis that lower AF burden after ablation would be reflected in the circulating blood pool by examining change in plasma miRNAs after AF ablation (n=47). Results The mean age of the cohort was 59 years. 58% of participants were men. Plasma miRs-21 and 150 were 2-fold lower in participants with AF than in those without AF after adjustment (p ≤ 0.0006). Plasma levels of miRs-21 and 150 were also lower in participants with paroxysmal AF than in those with persistent AF (p <0.05). Expression of miR-21, but not miR-150, was lower in atrial tissue from patients with AF compared to no AF (p<0.05). Plasma levels of miRs-21 and 150 increased 3-fold after AF ablation (p ≤ 0.0006). Conclusions Cardiac miRs-21 and 150 are known to regulate genes implicated in atrial remodeling. Our findings show associations between plasma miRs-21 and 150 and AF, suggesting that circulating miRNAs provide insights into cardiac gene regulation.