Background: It is difficult to distinguish between arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM) because of their similar clinical manifestations. This study aimed to develop a novel diagnostic algorithm for distinguishing ACM from DCM.Methods: Two public datasets containing human ACM and DCM myocardial samples were used. Consensus clustering, non-negative matrix factorization and principal component analysis were applied. Weighted gene co-expression network analysis and machine learning methods, including random forest and the least absolute shrinkage and selection operator, were used to identify candidate genes. Receiver operating characteristic curves and nomograms were performed to estimate diagnostic efficacy, and Spearman's correlation analysis was used to assess the correlation between candidate genes and cardiac function indices.Results: Both ACM and DCM showed highly similar gene expression patterns in the clustering analyses. Hub gene modules associated with cardiomyopathy were obtained using weighted gene co-expression network analysis. Thirteen candidate genes were selected using machine learning algorithms, and their combination showed a high diagnostic value (area under the ROC curve = 0.86) for distinguishing ACM from DCM. In addition, TATA-box binding protein associated factor 15 showed a negative correlation with cardiac index (R = À0.54, p = 0.0054) and left ventricular ejection fraction (R = À0.48, p = 0.0015).Conclusions: Our study revealed an effective diagnostic model with key gene signatures, which indicates a potential tool to differentiate between ACM and DCM in clinical practice. In addition, we identified several genes that are highly related to cardiac function, which may contribute to our understanding of ACM and DCM.Youming Zhang, Jiaxi Xie and Yizhang Wu contributed equally and share the first authorship.

BackgroundCardiac sympathetic nerve system (SNS) might play an important role in arrhythmogenesis of arrhythmogenic cardiomyopathy (ACM). This study aims to assess the activity of cardiac SNS in ACM patients by heart rate variability (HRV), and to investigate its predictive value for sustained ventricular tachycardia (sVT).MethodsA total of 88 ACM patients and 65 sex- and age- matched healthy participants were enrolled. The time domain measures were used to evaluate the activity of cardiac SNS. An independent cohort with 48 ACM patients was as the validation cohort.ResultsACM patients had lower levels of standard deviation of all NN intervals (SDNN) [118.0 (90.3, 136.8) vs. 152.0 (132.5, 174.5) ms, p < 0.001] compared with healthy participants. Further analysis showed ACM patients with sVT had lower levels of SDNN than those without sVT (105.0 ± 28.1 vs. 131.8 ± 33.1 ms, p < 0.001). Multivariate logistic regression analysis showed SDNN was independently associated with sVT in ACM patients [odds ratio (OR) 0.59, 95% confidence interval (CI) (0.45–0.78), p < 0.001]. Receiver operating characteristics curve demonstrated SDNN had clinical values in predicting sVT in ACM patients [area under the curve (AUC) = 0.73, 95% CI (0.63–0.84), p < 0.001], which was verified in the validation cohort.ConclusionThe present study suggests that HRV is impaired in patients with ACM, and the SDNN level has a moderate value in risk stratification for sVT in ACM patients. In addition, the finding might provide new target for the further management of ACM with integrated traditional Chinese and western medicine.