Whole Blood Gene Expression Differentiates between Atrial Fibrillation and Sinus Rhythm after Cardioversion

Kripa Raman; Stefanie Aeschbacher; Matthias Bossard; Thomas Hochgruber; Andreas J. Zimmermann; Beat A. Kaufmann; Katrin Pumpol; Peter Rickenbacker; Guillaume Paré; David Conen

doi:10.1371/journal.pone.0157550

Abstract

Background

Treatment to restore sinus rhythm among patients with atrial fibrillation (AF) has limited long-term success rates. Gene expression profiling may provide new insights into AF pathophysiology.

Objective

To identify biomarkers and improve our understanding of AF pathophysiology by comparing whole blood gene expression before and after electrical cardioversion (ECV).

Methods

In 46 patients with persistent AF that underwent ECV, whole blood samples were collected 1–2 hours before and 4 to 6 weeks after successful cardioversion. The paired samples were sent for microarray and plasma biomarker comparison.

Results

Of 13,942 genes tested, expression of SLC25A20 and PDK4 had the strongest associations with AF. Post-cardioversion, SLC25A20 and PDK4 expression decreased by 0.8 (CI 0.7–0.8, p = 2.0x10^-6) and 0.7 (CI 0.6–0.8, p = 3.0x10^-5) fold respectively. Median N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations decreased from 127.7 pg/mL to 44.9 pg/mL (p = 2.3x10^-13) after cardioversion. AF discrimination models combining NT-proBNP and gene expression (NT-proBNP + SLC25A20 area under the curve = 0.88, NT-proBNP + PDK4 AUC = 0.86) had greater discriminative capacity as compared with NT-proBNP alone (AUC = 0.82). Moreover, a model including NT-proBNP, SLC25A20 and PDK4 significantly improved AF discrimination as compared with other models (AUC = 0.87, Net Reclassification Index >0.56, p<5.8x10^-3). We validated the association between SLC25A20 and PDK4 with AF in an independent sample of 17 patients.

Conclusion

This study demonstrates that SLC25A20, PDK4, and NT-proBNP have incremental utility as biomarkers discriminating AF from sinus rhythm. Elevated SLC25A20 and PDK4 expression during AF indicates an important role for energy metabolism in AF.

Citation: Raman K, Aeschbacher S, Bossard M, Hochgruber T, Zimmermann AJ, Kaufmann BA, et al. (2016) Whole Blood Gene Expression Differentiates between Atrial Fibrillation and Sinus Rhythm after Cardioversion. PLoS ONE 11(6): e0157550. https://doi.org/10.1371/journal.pone.0157550

Editor: Alena Talkachova, University of Minnesota, UNITED STATES

Received: February 18, 2016; Accepted: June 1, 2016; Published: June 22, 2016

Copyright: © 2016 Raman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The participant consent form states that all analysis must be approved by the Principal Investigator. Data are from the GAPP-AF study, whose investigators may be contacted through David Conen, University Hospital Basel, Switzerland, david.conen@usb.ch.

Funding: This study was supported by a grant of the Mach Gaensslen Foundation (to David Conen). David Conen and Beat A. Kaufmann have received grants from the Swiss National Science Foundation (PP00P3_133681 and PP00P3_159322 to David Conen, 3232B0_141603 and 310030_149718 to Beat Kaufmann). Matthias Bossard was supported by a grant of the University of Basel and the “Freiwillige Akademische Gesellschaft (FAG)” Basel. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with an increased risk of death, stroke [1,2] and heart failure [2–4]. Its incidence is projected to increase with the ageing of population and rising prevalence of obesity [5–7]. However, treatment strategies aiming to revert AF to sinus rhythm have limited long-term success rates and significant risks [8,9]. While it has been demonstrated that a substantial proportion of AF originates from the pulmonary veins [10], our current understanding of the complex pathophysiology remains incomplete. Improvements in this area may not only help to develop novel treatment strategies for AF patients, but also to anticipate the rhythm stability among patients with AF, and to develop better methods to detect intermittent forms of AF.

We hypothesized that identification of novel biomarkers associated with rhythm changes among AF patients may provide insights into our pathophysiologic understanding of the disease. Assessment of whole blood gene expression is an emerging and promising class of biomarkers. Gene expression levels vary rapidly in response to physiologic changes and may have disease specificity that outreaches conventional biomarkers. Recently, gene expression patterns of left atrial tissue from AF patients has been described [11–13]. In addition, the Framingham group has analyzed peripheral blood gene expression among patients with prevalent AF as compared with a large population of non-affected individuals [14]. However, neither of these studies has assessed changes in peripheral blood gene expression within an individual patient after conversion from AF to sinus rhythm.

We therefore aimed to study AF patients pre- and post-electrical cardioversion (ECV), in order to identify AF specific whole blood RNA biomarkers potentially implicated in AF pathophysiology. We also wanted to assess the discriminative capacity of RNA biomarkers. Finally, novel gene expression biomarkers were validated in an independent group of participants.

Methods

Study population

We prospectively enrolled patients >18 years with persistent AF, defined as a non-self terminating episode lasting >7 days, who were scheduled for non-urgent electrical cardioversion (ECV) at two tertiary hospitals in Switzerland. We excluded patients with untreated severe valvular disease, unstable and acute heart failure, limiting active or chronic major diseases, and a history of open-heart surgery within 3 months of inclusion. Informed written consent was obtained from all patients and the study was approved by Ethics Committee Basel Switzerland.

Study procedures

Study visits were scheduled approximately 24 hours before electrical cardioversion and after 4 ± 1 weeks of follow-up. Information on baseline characteristics, concomitant medication and co-morbidities was collected through study questionnaires both at baseline and follow-up. In addition, conventional blood pressure measurements, standard 12-lead electrocardiogram (ECG), 24-hour Holter ECG monitoring, real time 3-dimensional echocardiography and blood sampling were obtained at both visits. At baseline, all examinations were performed 1–2 hours prior to the cardioversion procedure. ECV was performed according to local standards. After cardioversion, changes in personal medication were strongly discouraged until the follow-up visit. The second blood sampling was obtained directly after the follow-up 24-hour Holter ECG, in order to confirm stable sinus rhythm. Patients who had recurrent AF between the two scheduled visits were excluded from this study.

Blood sampling and biomarker measurements

Prior to ECV and at follow-up, venous blood samples were collected in EDTA tubes and PAXgene™ Blood RNA tubes (PreAnalytiX). EDTA tubes were immediately centrifuged to isolate plasma and all tubes were stored at -80°C. High-sensitivity C-reactive protein (hs-CRP), cystatin C (CYSC), and interleukin-6 (IL6) were measured on a Beckman Coulter Unicel DxC600 Synchron Clinical System (Beckman) according to the manufacturer’s protocol. Myeloperoxidase (MPO) was measured using the ARCHITECT MPO immunoassay on the ARCHITECT Clinical Chemistry Analyzer (Abbott). N-terminal pro B-type natriuretic peptide (NT-proBNP) was measured on the Elecsys 2010 immunoassay analyzer (Roche).

RNA extraction

The PAXgene™ Blood RNA tubes were processed at the Genetic and Molecular Epidemiology Laboratory of PHRI and McMaster University, Hamilton ON. Paired samples were processed using the same RNA extraction and amplification method. Total RNA was isolated from samples using the QIAsymphony PAXgene Blood RNA Kit (QIAGEN) or the MagMAX Stabilized Blood Tube RNA Isolation Kit (LifeTech). RNA was then quantified with RiboGreen® (LifeTech) and Nanodrop (Nanodrop).

Microarray hybridization

200ng of total RNA was amplified and biotinylated according to the manufacturer’s protocol. Samples were amplified with the TotalPrep RNA Amplification Kit (LifeTech) or the lllumina TotalPrep-96 RNA Amplification Kit (LifeTech). The final biotin-labeled cRNA species were then hybridized to the Illumina Human HT-12v4 expression BeadChips (Illumina). Each BeadChip hold 12 samples at a time so paired samples were hybridized on to the same chip. BeadChips were then washed, dried and scanned on the iScan System (Illumina) as per the manufacturer’s protocol.

Microarray pre-processing and quality control

The Illumina Human HT-12v4 BeadChip interrogates expression of 34,694 unique genes using 47,323 probes. The raw BeadChip sample probe profile and control probe profile were exported from GenomeStudio version 1.9.0 (Illumina). All data preprocessing and quality control was performed in R (http://r-project.org). Four samples were found to be outliers. The samples did not pass quality control metrics [15], as the average intensity was significantly different from other arrays. Thus the four samples and their corresponding pairs were excluded from further analysis. Data pre-processing involved background correction using the non-genomic control probes, quantile normalization and log2 transformation [16,17]. Probes with detection P-value <0.05 in >50% of the samples were included for further statistical analysis. The final pre-processed data included expression values for 13,942 RNA probes for each of the 92 samples from 46 individuals. Due to consent form restrictions, expression data cannot be made publicly available.

Quantitative Real-time Polymerase Chain Reaction

Reverse transcription was performed using the QuantiTect Reverse Transcription Kit (Qiagen). SLC25A20 expression was monitored with the Hs00386383_m1 probe (LifeTech), PDK4 with the Hs01037712_m1 probe (LifeTech) and ITGB5 with the Hs00174435_m1 probe (LifeTech) as per the manufacturer protocol. Each qPCR was performed in duplex with the housekeeping gene ACTB, measured using the Hs01060665_g1 probe (LifeTech), to normalize expression. The TaqMan qPCR was conducted on a Viia7 Real-Time System (LifeTech) and cycle threshold (CT) values were calculated automatically with default parameters. Fold change (FC) differences were calculated using the δCT method.

Statistical analysis

All statistical analyses were performed using R. Clinical demographics were grouped according to pre- or post-cardioversion status. Normally distributed variables were compared using paired Student T-tests; otherwise paired Wilcoxon rank sum tests were used. A two-sided p-value<0.05 was considered statistically significant.

Microarrays (and quantitative PCR) measure relative rather than absolute gene expression, or in other words the relative increase or decrease in expression of a gene as compared with global expression (or housekeeping genes). Differential gene expression was thus reported as FC, with 95% confidence intervals (CI). Linear regression models were used to identify differentially expressed RNA transcripts after cardioversion (during sinus rhythm) as compared with pre-cardioversion samples (during AF). Each model tested a single gene’s association with AF while adjusting for sample pairs. To correct for multiple hypothesis testing a conservative Bonferroni correction was applied, setting the significance threshold at 0.05 / 13,942 = 3.6 x 10⁻⁶.

Significant genes and plasma biomarkers were also tested for association with AF risk factors (gender, age, BMI, systolic blood pressure and diastolic blood pressure), Holter ECG (heart rate, heart rate maximum and minimum) and echocardiography (E wave, A wave, deceleration time, left atrial volume, left atrial maximum volume, left ventricular ejection fraction) parameters using linear regression models. An adjusted p-value <0.05/14 = 0.0036 was considered significant. Paired Wilcoxon rank sum test was used to compare biomarker levels pre- and post-cardioversion. Receiver operating characteristic (ROC) curves were constructed, using pROC [18], to determine the discriminative capacity of significant biomarkers for AF status. The area under the ROC curve (AUC) was determined as a measure of sensitivity and specificity. To compare models we calculated the continuous Net Reclassification Index (NRI) [19] using Hmisc [20]. We considered an NRI greater than 0.6 a strong, 0.4 an intermediate, and 0.2 a weak improvement in discriminative capacity. To verify and validate microarray expression results qPCR data was analyzed with linear regression models and adjusted for sample pairs.

Results

Patient characteristics

Between March 2010 to April 2013, 108 consecutive patients with persistent AF were enrolled into the study. 67 patients had successful cardioversion and confirmed sinus rhythm at the follow-up visit; 50 were selected for biomarker discovery and 17 for independent validation. After microarray quality control, the biomarker discovery cohort was reduced to 46 patients. Patient demographics in the discovery cohort are presented in Table 1. Mean age was 65.4 ± 10.6 and 26.1% of participants were female. The post-cardioversion follow-up examination took place 35 days ± 8 days after ECV and there were no significant changes in medication (p>0.05 = NS). Successful cardioversion resulted in a significant decrease in heart rate (pre 87 ± 17 vs. post 60 ± 10, p = 8.7x10^-14) and a non-significant decrease in left atrial maximum volume (pre 91.7mL, post 72.9, p = 0.022). We also observed a decrease in E-wave velocity (pre 0.93 m/s, post 0.77 m/s, p = 2.8x10^-4), an increase in deceleration time (pre 173 ms, post 243 ms, p = 1.0x10^-3) and an increase in left ventricular ejection fraction (pre 44.3%, post 54.8%, p = 1.3x10^-3) following cardioversion.

Download:

Table 1. Participant demographics for biomarker discovery cohort.

https://doi.org/10.1371/journal.pone.0157550.t001

Association between gene expression and AF

Each of the 13,942 RNA probes was tested for association with AF while adjusting for sample pairs. The ten most significant genes from the analysis are presented in Table 2. SLC25A20 expression was most significantly associated with AF, and the only gene that remained significant after adjustment for multiple hypothesis testing (Fig 1). A 0.8 fold decrease in SLC25A20 was observed in post-cardioversion samples as compared with baseline (CI 0.7–0.8, p = 2.0x10^-6, S1 Fig). PDK4 was the second most significant gene and decreased by 0.7 fold post-cardioversion (CI 0.6–0.8, p = 3.0x10^-5, S2 Fig). ITGB5 was the third most significant gene and increased by 1.2 fold post-cardioversion (CI 1.1–1.4, p = 3.1x10^-5, S3 Fig).

Download:

Fig 1. Volcano plot of gene expression changes pre- and post-cardioversion.

Each point represents one of the RNA transcripts tested and the ten most significant genes have been labeled. The x-axis represents the effect of each gene, reported as log2 fold change, and a positive log2 fold change is indicative of increased expression in post-cardioversion samples. The y-axis represents the–log10(P-value). Triangle points represent genes that have significant differential expressed after Bonferroni correction (P-value <3.6x10^-6).

https://doi.org/10.1371/journal.pone.0157550.g001

Download:

Table 2. Top genes associated with stable sinus rhythm after electrical cardioversion.

Fold Change (FC). Confidence Interval (CI).

https://doi.org/10.1371/journal.pone.0157550.t002

Differential expression of the top three genes, SLC25A20, PDK4 and ITGB5, was verified using qPCR. A consistent decrease in SLC25A20 expression was observed in post-cardioversion sinus rhythm samples as compared with baseline AF using qPCR (FC = 0.7, CI 0.6–0.7, p = 1.6x10^-9, S1 Fig). Similarly, a 0.6 fold decrease in PDK4 expression was observed post-cardioversion (CI 0.5–0.7, p = 4.0x10^-5, S2 Fig). Differential expression of ITGB5 could not be verified (p = 0.086, S3 Fig).

SLC25A20 and PDK4 expression are not associated with clinical variables

Restricting the analysis to pre-cardioversion AF samples, we tested SLC25A20 and PDK4 for association with AF risk factors, Holter ECG and echocardiography parameters. We observed no association between pre-cardioversion expression of either gene and measured variables (all p>0.05 = NS). We also restricted the analysis to samples collected post-ECV. After correction for multiple hypotheses testing, we observed a modest association between post-cardioversion SLC25A20 expression and elevated diastolic blood pressure (p = 0.0017). PDK4 had no association with the measured variables.

Association between plasma biomarkers and AF

NT-proBNP levels were significantly decreased in post-cardioversion samples, as compared with baseline (median 127.7 vs. 44.9 pg/mL, p = 2.3x10^-13). Circulating levels of hs-CRP, CYSC, IL6, and MPO did not change between pre and post-cardioversion samples (Table 3). Limiting the analysis to pre-cardioversion or post-cardioversion AF samples, we observed no association between NT-proBNP concentrations and AF risk factors, Holter ECG or echocardiography parameters (all p>0.5 = NS).

Download:

Table 3. Plasma biomarker concentrations in participants pre- and post-cardioversion.

Data are medians (interquartile range).

https://doi.org/10.1371/journal.pone.0157550.t003

Discriminative capacity of NT-proBNP, SLC25A20 and PDK4 for AF

A multivariable logistic regression model for AF including NT-proBNP, SLC25A20 and PDK4, indicated that all three biomarkers were independently associated with AF (S1 Table). The association remained significant after adjustment for left atrial maximum volume (S2 Table). To determine the discriminative capacity of each biomarker we constructed receiver operator characteristics (ROC) curves. In single variable models, the area under the ROC curve discriminating between pre-cardioversion and post-cardioversion samples was 0.82 (CI 0.74–0.91) for NT-proBNP, 0.78 (CI 0.68–0.88) for SLC25A20 and 0.69 (CI 0.58–0.80) for PDK4 (S4 Fig). A two variable model including NT-proBNP and expression of either gene strongly improved discrimination as compared with NT-proBNP alone (NT-proBNP + SLC25A20 AUC = 0.86, CI 0.79–0.94, NRI = 0.65, p = 1.1x10^-3; NT-proBNP + PDK4 AUC = 0.84, CI 0.76–0.92, NRI = 0.61, p = 2.5x10^-3). Moreover a model including all three biomarkers had the greatest discriminative capacity (AUC = 0.87, CI 0.80–0.95). The combination of NT-proBNP, SLC25A20 and PDK4 improved discrimination as compared with two variable models (all vs NT-proBNP+SLC25A20 NRI = 0.61, p = 2.6x10^-3; all vs NT-proBNP+PDK4 NRI = 0.56, p = 5.8x10^-3).

Replication of SLC25A20 and PDK4 in the validation cohort

We validated the decrease in SLC25A20 and PDK4 expression post-cardioversion in an independent sample consisting of 17 individuals. Power to identify an association of similar effect size was estimated at 89% for SLC25A20 and 65% for PDK4, using an alpha of 0.05/2 = 0.025. Patient demographics are shown in S3 Table. After successful cardioversion we observed decreased heart rate (pre = 82.8 bpm, post = 57.45 bpm, p = 1.3x10^-5) and decreased diastolic blood pressure (pre = 84.4, post = 76.5, p = 0.029) similar to the discovery sample. We also detected a 0.8 fold decrease in SLC25A20 (CI 0.7–0.9, p = 2.0x10^-4, S5 Fig) and 0.7 fold decrease in PDK4 (CI 0.5–1.0, p = 0.05, S5 Fig) in post-cardioversion sinus rhythm samples as compared with baseline AF. Restricting the analysis to pre-cardioversion AF samples or post-cardioversion sinus rhythm, we tested both SLC25A20 and PDK4 for association with AF risk factors and observed no association (p = NS for all comparisons).

Discussion

The present study evaluated peripheral blood gene expression and plasma protein biomarkers associated with AF rhythm by comparing paired patient samples pre- and post-ECV. We identified novel associations between whole blood gene expression of SCL25A20 and PDK4 with AF. Expression of both genes was elevated in AF as compared with post-ECV sinus rhythm. Adding either RNA marker to a model with NT-proBNP strongly improved AF discrimination. A model including SLC25A20, PDK4 and NT-proBNP had the greatest ability to discriminate between AF and sinus rhythm. The association between both SLC25A20 and PDK4 with rhythm status was confirmed in an independent validation cohort.

Our results demonstrate that RNA biomarkers can provide independent discriminative information to NT-proBNP. Multiple studies are currently evaluating the clinical utility of NT-proBNP as a marker for cardiac impairment. Such a biomarker may facilitate diagnosis of paroxysmal AF or reclassification of cryptogenic stroke patients. Biomarker panels including NT-proBNP and RNA biomarkers may improve the specificity and sensitivity to detect cardiac dysrhythmias. Rapid point-of-care RNA tests are currently being developed [21], so peripheral blood RNA may potentially be integrated in routine clinical testing in the future.

Using transcriptome-wide expression profiling we identified an association between AF and elevated SLC25A20 expression. SLC25A20 encodes the carnitine-acylcarnitine translocase (CACT), which transports fatty acids into the inner mitochondrial membrane for β-oxidation [22,23]. We also observed increased expression of PDK4 during AF prior to cardioversion. PDK4 encodes pyruvate dehydrogenase kinase lipoamide kinase isozyme 4, which regulates the pyruvate dehydrogenase complex (PDC) [24]. PDC plays a critical role in glucose metabolism, converting pyruvate into acetyl-CoA for the citric acid cycle. Elevated expression of PDK4 inactivates PDC and promotes gluconeogenesis. Thus PDK4 expression is also of interest in diabetes, since its up-regulation can contribute to hyperglycemia [25]. Taken together, the observed decrease in SLC25A20 and PDK4 following cardioversion suggests an adaptive gene expression change in response to the metabolic demands of the heart. Thus, SLC25A20 and PDK4 expression may be associated with AF burden. In this context, recent studies showing that weight reduction was associated with reduced AF burden may also point towards the importance of energy metabolism in the occurrence of AF episodes [26,27].

In support of our results, a recent gene expression study observed decreased expression of SLC25A20 in atrial tissue of patients that had no history of AF as compared with patients that had AF [11]. The researchers also detected a decrease in SLC25A20 and PDK4 in patients currently in sinus rhythm that had a history of AF, as compared with patients currently in AF. Our study confirms the potential importance of these RNA markers in the pathophysiology of AF. In addition, we show that expression changes can be observed not only across different patients, but also in an individual patient, if a sustained change in cardiac rhythm occurs. Considering the potential clinical applicability of these markers, it is of crucial importance that our study detected these expression changes in peripheral blood samples, given that atrial biopsies are not feasible in clinical practice.

The Framingham whole blood expression study [14] did not report an association between AF and SLC25A20 or PDK4. These potential differences are not surprising since our study evaluated expression changes occurring during AF episodes as compared to sinus rhythm within the same individual, while the Framingham study assessed differential expression between individuals with and without AF.

There are limitations of our study, which need to be taken into account. First, we included only patients with persistent AF, and therefore generalizability to other AF populations remains uncertain. Second, all participants were of European origin thus the generalizability to other ethnicities remains uncertain. Third, fasting may have impacted gene expression. Pre-cardioversion samples were mostly collected after several hours of fasting, whereas fasting was not specified prior to post-cardioversion sampling. Studies have shown that free fatty acid concentrations increase with long term fasting [28,29]. Expression profiling of PBMCs after 24-hours of fasting has revealed increased expression of genes involved in fatty acid metabolism, including SLC25A20 and PDK4 [29]. However, studies have not described the time-course of expression changes with respect to the duration of fasting. As such the impact of shorter fasting episodes on gene expression, as in our study, has yet to be published. We have evaluated expression of SLC25A20 and PDK4 in a control population [30] for up to 12 hours of fasting and observed no association between expression and hours since last meal (p>0.5 = NS, S6 Fig). The significance of SLC25A20 and PDK4 in AF is further supported by the atrial tissue study that observed elevated expression of both genes in AF patients as compared with patients in sinus rhythm that had a history of AF [11]. Since surgery is required to collect atrial tissue, all individuals were fasting prior to sampling. Therefore, the results indicate that SLC25A20 and PDK4 are truly associated with AF. Finally, our study populations were relatively small, which may have hindered the detection of subtle gene expression differences during biomarker discovery. In addition, power calculations indicate that a larger sample size is required to validate the significance of PDK4.

In conclusion, the results of this study demonstrate that expression of SLC25A20 and PDK4 are independently associated with rhythm status among patients with persistent AF. These findings indicate that alterations in metabolic pathways are associated with the prevalent cardiac rhythm in an individual AF patient, providing not only novel pathophysiological insights but also new potential intervention targets that can be tested in future studies. In addition, our study demonstrates that NT-proBNP, SLC25A20 and PDK4 have incremental utility as biomarkers discriminating AF from sinus rhythm. Future studies should explore whether these markers may be helpful for predicting AF recurrence in clinical practice.

Supporting Information

S1 Fig. Boxplots of SLC25A20 expression pre- and post- cardioversion.

Boxes extend from the 25^th to the 75^th percentile, with the horizontal line representing the median. Outliers are identified as samples with an expression value 1.5 times more or less than the interquartile range. The CT (cycle threshold) is the number of PCR cycles required for the fluorescent signal to exceed background levels. Unlike microarray values, CT values are inversely proportional to the amount of target nucleic acid in a sample. A) Microarray expression of SLC25A20 decreased following cardioversion. B) qPCR expression of SLC25A20 also decreased following cardioversion. A symbol directly above a bar indicates a significant difference between groups; p <0.0005 (***).

https://doi.org/10.1371/journal.pone.0157550.s001

(TIFF)

S2 Fig. Boxplots of PDK4 expression pre- and post-cardioversion.

Boxes extend from the 25^th to the 75^th percentile, with the horizontal line representing the median. Outliers are identified as samples with an expression value 1.5 times more or less than the interquartile range. The CT (cycle threshold) is the number of PCR cycles required for the fluorescent signal to exceed background levels. Unlike microarray values, CT values are inversely proportional to the amount of target nucleic acid in a sample. A) Microarray expression of PDK4 decreased following cardioversion. B) qPCR expression of PDK4 also decreased following cardioversion. A symbol directly above a bar indicates a significant difference between groups; p <0.0005 (***).

https://doi.org/10.1371/journal.pone.0157550.s002

(TIFF)

S3 Fig. Boxplots of ITGB5 expression pre- and post-cardioversion.

Boxes extend from the 25^th to the 75^th percentile, with the horizontal line representing the median. Outliers are identified as samples with an expression value 1.5 times more or less than the interquartile range. The CT (cycle threshold) is the number of PCR cycles required for the fluorescent signal to exceed background levels. Unlike microarray values, CT values are inversely proportional to the amount of target nucleic acid in a sample. A) Microarray expression of ITGB5 decreased following cardioversion. B) qPCR expression of ITGB5 also decreased following cardioversion. A symbol directly above a bar indicates a significant difference between groups; p <0.0005 (***).

https://doi.org/10.1371/journal.pone.0157550.s003

(TIFF)

S4 Fig. Receiver-operating characteristic curves for the discrimination of pre-cardioversion AF from post-cardioversion sinus rhythm.

https://doi.org/10.1371/journal.pone.0157550.s004

(TIFF)

S5 Fig. qPCR gene expression boxplots in the independent validation cohort.

Boxes extend from the 25^th to the 75^th percentile, with the horizontal line representing the median. Outliers are identified as samples with an expression value 1.5 times more or less than the interquartile range. The CT (cycle threshold) is the number of PCR cycles required for the fluorescent signal to exceed background levels. Unlike microarray values, CT values are inversely proportional to the amount of target nucleic acid in a sample. A) qPCR expression of SLC25A20 pre- and post- cardioversion in the independent validation cohort. B) qPCR expression of PDK4. A symbol directly above a bar indicates a significant difference between groups; p <0.0005 (***), p <0.05(*).

https://doi.org/10.1371/journal.pone.0157550.s005

(TIFF)

S6 Fig. Boxplots of microarray gene expression in control[30] population grouped based on hours from symptom onset.

Boxes extend from the 25^th to the 75^th percentile, with the horizontal line representing the median. Outliers are identified as samples with an expression value 1.5 times more or less than the interquartile range. A) Expression of SLC25A20. B) Expression of PDK4.

https://doi.org/10.1371/journal.pone.0157550.s006

(TIFF)

S1 Table. Results of logistic regression model for AF status including biomarkers.

A logistic regression model was constructed for rhythm status. The model included only NT-proBNP, SLC25A20 and PDK4.

https://doi.org/10.1371/journal.pone.0157550.s007

(DOCX)

S2 Table. Results of logistic regression model for AF status including biomarkers and LA maximum volume.

A logistic regression model was constructed for rhythm status. The model included NT-proBNP, SLC25A20, PDK4 and left atrial maximum volume.

https://doi.org/10.1371/journal.pone.0157550.s008

(DOCX)

S3 Table. Demographics for the independent validation in participants with successful cardioversion.

https://doi.org/10.1371/journal.pone.0157550.s009

(DOCX)

Author Contributions

Conceived and designed the experiments: GP DC. Performed the experiments: KR. Analyzed the data: KR. Contributed reagents/materials/analysis tools: KR GP DC. Wrote the paper: KR GP DC. Provided significant input and improvement of the whole paper: SA MB TH AJZ BAK KP PR DC GP.

References

1. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991;22: 983–8. pmid:1866765
- View Article
- PubMed/NCBI
- Google Scholar
2. Conen D, Chae CU, Glynn RJ, Tedrow UB, Everett BM, Buring JE, et al. Risk of death and cardiovascular events in initially healthy women with new-onset atrial fibrillation. JAMA. 2011;305: 2080–2087. pmid:21610240
- View Article
- PubMed/NCBI
- Google Scholar
3. Stewart S, Hart CL, Hole DJ, McMurray JJ V. A population-based study of the long-term risks associated with atrial fibrillation: 20-Year follow-up of the Renfrew/Paisley study. Am J Med. 2002;113: 359–364. pmid:12401529
- View Article
- PubMed/NCBI
- Google Scholar
4. Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of Atrial Fibrillation on the Risk of Death: The Framingham Heart Study. Circulation. 1998;98: 946–952. pmid:9737513
- View Article
- PubMed/NCBI
- Google Scholar
5. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby J V, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285: 2370–2375. pmid:11343485
- View Article
- PubMed/NCBI
- Google Scholar
6. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study. Circulation. 2014;129: 837–847. pmid:24345399
- View Article
- PubMed/NCBI
- Google Scholar
7. Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114: 119–125. pmid:16818816
- View Article
- PubMed/NCBI
- Google Scholar
8. Van Gelder IC, Hagens VE, Bosker HA, Kingma JH, Kamp O, Kingma T, et al. A comparison of rate control and rhythm control in patients with recurrent persistent atrial fibrillation. N Engl J Med. 2002;347: 1834–1840. pmid:12466507
- View Article
- PubMed/NCBI
- Google Scholar
9. Cappato R, Calkins H, Chen S-A, Davies W, Iesaka Y, Kalman J, et al. Prevalence and causes of fatal outcome in catheter ablation of atrial fibrillation. J Am Coll Cardiol. American College of Cardiology Foundation; 2009;53: 1798–1803. pmid:19422987
- View Article
- PubMed/NCBI
- Google Scholar
10. Haïssaguerre M, Jaïs P, Shah DC, Takahashi a, Hocini M, Quiniou G, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med. 1998;339: 659–666. pmid:9725923
- View Article
- PubMed/NCBI
- Google Scholar
11. Deshmukh A, Barnard J, Sun H, Newton D, Castel L, Pettersson G, et al. Left atrial transcriptional changes associated with atrial fibrillation susceptibility and persistence. Circ Arrhythm Electrophysiol. 2015;8: 32–41. pmid:25523945
- View Article
- PubMed/NCBI
- Google Scholar
12. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ Res. 2005;96: 1022–9. pmid:15817885
- View Article
- PubMed/NCBI
- Google Scholar
13. Ohki R, Yamamoto K, Ueno S, Mano H, Misawa Y, Fuse K, et al. Gene expression profiling of human atrial myocardium with atrial fibrillation by DNA microarray analysis. 2005;102: 233–238. pmid:15982490
- View Article
- PubMed/NCBI
- Google Scholar
14. Lin H, Yin X, Lunetta KL, Dupuis J, McManus DD, Lubitz S a., et al. Whole blood gene expression and atrial fibrillation: the framingham heart study. PLoS One. 2014;9: e96794. pmid:24805109
- View Article
- PubMed/NCBI
- Google Scholar
15. Ritchie ME, Dunning MJ, Smith ML, Shi W, Lynch AG. BeadArray expression analysis using bioconductor. PLoS Comput Biol. 2011;7: e1002276. pmid:22144879
- View Article
- PubMed/NCBI
- Google Scholar
16. Shi W, Oshlack A, Smyth GK. Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. Nucleic Acids Res. 2010;38: e204. pmid:20929874
- View Article
- PubMed/NCBI
- Google Scholar
17. Schmid R, Baum P, Ittrich C, Fundel-Clemens K, Huber W, Brors B, et al. Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3. BMC Genomics. BioMed Central; 2010;11: 349. pmid:20525181
- View Article
- PubMed/NCBI
- Google Scholar
18. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. BioMed Central Ltd; 2011;12: 77. pmid:21414208
- View Article
- PubMed/NCBI
- Google Scholar
19. Pencina MJ, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27: 157–172. pmid:17569110
- View Article
- PubMed/NCBI
- Google Scholar
20. Harrel FE. Hmisc: A package of miscellaneous R functions [Internet]. Vanderbilt; 2015 [cited 1 Feb 2015]. Available: https://cran.r-project.org/web/packages/Hmisc/index.html
21. Dobson MG, Galvin P, Barton DE. Emerging technologies for point-of-care genetic testing. Expert Rev Mol Diagn. 2007;7: 359–370. pmid:17620045
- View Article
- PubMed/NCBI
- Google Scholar
22. Ramsay RR, Tubbs PK. The mechanism of fatty acid uptake by heart mitochondria: an acylcarnitine-carnitine exchange. FEBS Lett. 1975;54: 21–25. pmid:1132491
- View Article
- PubMed/NCBI
- Google Scholar
23. Pande S V. A mitochondrial carnitine acylcarnitine translocase system. Proc Natl Acad Sci U S A. 1975;72: 883–887. pmid:1055387
- View Article
- PubMed/NCBI
- Google Scholar
24. Patel MS, Korotchkina LG. Regulation of the pyruvate dehydrogenase complex. Biochem Soc Trans. 2006;34: 217–222. pmid:16545080
- View Article
- PubMed/NCBI
- Google Scholar
25. Jeoung NH, Harris R a. Role of pyruvate dehydrogenase kinase 4 in regulation of blood glucose levels. Korean Diabetes J. 2010;34: 274–283. pmid:21076574
- View Article
- PubMed/NCBI
- Google Scholar
26. Pathak RK, Middeldorp ME, Meredith M, Mehta AB, Mahajan R, Wong CX, et al. Long-Term Effect of Goal-Directed Weight Management in an Atrial Fibrillation Cohort. J Am Coll Cardiol. 2015;65: 2159–2169. pmid:25792361
- View Article
- PubMed/NCBI
- Google Scholar
27. Abed HS, Wittert G a, Leong DP, Shirazi MG, Bahrami B, Middeldorp ME, et al. Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. Jama. 2013;310: 2050–60. pmid:24240932
- View Article
- PubMed/NCBI
- Google Scholar
28. Dole VP. A relation between non-esterified fatty acids in plasma and the metabolism of glucose. J Clin Invest. 1956;35: 150–154. pmid:13286333
- View Article
- PubMed/NCBI
- Google Scholar
29. Bouwens M, Afman L a, Müller M. Fasting induces changes in peripheral blood mononuclear cell gene expression profiles related to increases in fatty acid beta-oxidation: functional role of peroxisome proliferator activated receptor alpha in human peripheral blood mononuclear cells. Am J Clin Nutr. 2007;86: 1515–23. pmid:17991667
- View Article
- PubMed/NCBI
- Google Scholar
30. Raman K, O’Donnell MJ, Czlonkowska A, Duarte YC, Lopez-Jaramillo P, Peñaherrera E, et al. Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis. Stroke. 2016;47: 652–8. pmid:26846866
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991;22: 983–8. pmid:1866765
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Conen D, Chae CU, Glynn RJ, Tedrow UB, Everett BM, Buring JE, et al. Risk of death and cardiovascular events in initially healthy women with new-onset atrial fibrillation. JAMA. 2011;305: 2080–2087. pmid:21610240
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Stewart S, Hart CL, Hole DJ, McMurray JJ V. A population-based study of the long-term risks associated with atrial fibrillation: 20-Year follow-up of the Renfrew/Paisley study. Am J Med. 2002;113: 359–364. pmid:12401529
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of Atrial Fibrillation on the Risk of Death: The Framingham Heart Study. Circulation. 1998;98: 946–952. pmid:9737513
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby J V, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285: 2370–2375. pmid:11343485
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study. Circulation. 2014;129: 837–847. pmid:24345399
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114: 119–125. pmid:16818816
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Van Gelder IC, Hagens VE, Bosker HA, Kingma JH, Kamp O, Kingma T, et al. A comparison of rate control and rhythm control in patients with recurrent persistent atrial fibrillation. N Engl J Med. 2002;347: 1834–1840. pmid:12466507
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Cappato R, Calkins H, Chen S-A, Davies W, Iesaka Y, Kalman J, et al. Prevalence and causes of fatal outcome in catheter ablation of atrial fibrillation. J Am Coll Cardiol. American College of Cardiology Foundation; 2009;53: 1798–1803. pmid:19422987
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Haïssaguerre M, Jaïs P, Shah DC, Takahashi a, Hocini M, Quiniou G, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med. 1998;339: 659–666. pmid:9725923
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Deshmukh A, Barnard J, Sun H, Newton D, Castel L, Pettersson G, et al. Left atrial transcriptional changes associated with atrial fibrillation susceptibility and persistence. Circ Arrhythm Electrophysiol. 2015;8: 32–41. pmid:25523945
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ Res. 2005;96: 1022–9. pmid:15817885
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Ohki R, Yamamoto K, Ueno S, Mano H, Misawa Y, Fuse K, et al. Gene expression profiling of human atrial myocardium with atrial fibrillation by DNA microarray analysis. 2005;102: 233–238. pmid:15982490
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Lin H, Yin X, Lunetta KL, Dupuis J, McManus DD, Lubitz S a., et al. Whole blood gene expression and atrial fibrillation: the framingham heart study. PLoS One. 2014;9: e96794. pmid:24805109
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Ritchie ME, Dunning MJ, Smith ML, Shi W, Lynch AG. BeadArray expression analysis using bioconductor. PLoS Comput Biol. 2011;7: e1002276. pmid:22144879
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Shi W, Oshlack A, Smyth GK. Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. Nucleic Acids Res. 2010;38: e204. pmid:20929874
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Schmid R, Baum P, Ittrich C, Fundel-Clemens K, Huber W, Brors B, et al. Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3. BMC Genomics. BioMed Central; 2010;11: 349. pmid:20525181
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. BioMed Central Ltd; 2011;12: 77. pmid:21414208
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Pencina MJ, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27: 157–172. pmid:17569110
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Harrel FE. Hmisc: A package of miscellaneous R functions [Internet]. Vanderbilt; 2015 [cited 1 Feb 2015]. Available: https://cran.r-project.org/web/packages/Hmisc/index.html

[ref21] 21. Dobson MG, Galvin P, Barton DE. Emerging technologies for point-of-care genetic testing. Expert Rev Mol Diagn. 2007;7: 359–370. pmid:17620045
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. Ramsay RR, Tubbs PK. The mechanism of fatty acid uptake by heart mitochondria: an acylcarnitine-carnitine exchange. FEBS Lett. 1975;54: 21–25. pmid:1132491
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Pande S V. A mitochondrial carnitine acylcarnitine translocase system. Proc Natl Acad Sci U S A. 1975;72: 883–887. pmid:1055387
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref24] 24. Patel MS, Korotchkina LG. Regulation of the pyruvate dehydrogenase complex. Biochem Soc Trans. 2006;34: 217–222. pmid:16545080
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref25] 25. Jeoung NH, Harris R a. Role of pyruvate dehydrogenase kinase 4 in regulation of blood glucose levels. Korean Diabetes J. 2010;34: 274–283. pmid:21076574
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref26] 26. Pathak RK, Middeldorp ME, Meredith M, Mehta AB, Mahajan R, Wong CX, et al. Long-Term Effect of Goal-Directed Weight Management in an Atrial Fibrillation Cohort. J Am Coll Cardiol. 2015;65: 2159–2169. pmid:25792361
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref27] 27. Abed HS, Wittert G a, Leong DP, Shirazi MG, Bahrami B, Middeldorp ME, et al. Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. Jama. 2013;310: 2050–60. pmid:24240932
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref28] 28. Dole VP. A relation between non-esterified fatty acids in plasma and the metabolism of glucose. J Clin Invest. 1956;35: 150–154. pmid:13286333
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref29] 29. Bouwens M, Afman L a, Müller M. Fasting induces changes in peripheral blood mononuclear cell gene expression profiles related to increases in fatty acid beta-oxidation: functional role of peroxisome proliferator activated receptor alpha in human peripheral blood mononuclear cells. Am J Clin Nutr. 2007;86: 1515–23. pmid:17991667
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref30] 30. Raman K, O’Donnell MJ, Czlonkowska A, Duarte YC, Lopez-Jaramillo P, Peñaherrera E, et al. Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis. Stroke. 2016;47: 652–8. pmid:26846866
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

Figures

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Methods

Study population

Study procedures

Blood sampling and biomarker measurements

RNA extraction

Microarray hybridization

Microarray pre-processing and quality control

Quantitative Real-time Polymerase Chain Reaction

Statistical analysis

Results

Patient characteristics

Association between gene expression and AF

SLC25A20 and PDK4 expression are not associated with clinical variables

Association between plasma biomarkers and AF

Discriminative capacity of NT-proBNP, SLC25A20 and PDK4 for AF

Replication of SLC25A20 and PDK4 in the validation cohort

Discussion

Supporting Information

S1 Fig. Boxplots of SLC25A20 expression pre- and post- cardioversion.

S2 Fig. Boxplots of PDK4 expression pre- and post-cardioversion.

S3 Fig. Boxplots of ITGB5 expression pre- and post-cardioversion.

S4 Fig. Receiver-operating characteristic curves for the discrimination of pre-cardioversion AF from post-cardioversion sinus rhythm.

S5 Fig. qPCR gene expression boxplots in the independent validation cohort.

S6 Fig. Boxplots of microarray gene expression in control[30] population grouped based on hours from symptom onset.

S1 Table. Results of logistic regression model for AF status including biomarkers.

S2 Table. Results of logistic regression model for AF status including biomarkers and LA maximum volume.

S3 Table. Demographics for the independent validation in participants with successful cardioversion.

Author Contributions

References