Volatile organic compounds (VOCs) contain hundreds of chemicals and human exposure to VOCs is pervasive. However, most studies have considered only a single chemical or a class of similar chemicals. We aimed to investigate the association between urinary volatile organic compound metabolites (mVOCs) and the risk of cardiovascular disease (CVD) in the general population. The data in this study were collected from the National Health and Nutrition Examination Survey in 2011-2018. Eligible patients were aged ≥20 years for whom complete data for 20 types of urinary mVOCs and CVD outcomes were available. Multivariate logistic regression models were used to elucidate the association between mVOCs and CVD. Generalized additive models were used to examine the nonlinear relationships between mVOCs and CVD. 6814 indiviuals were included in the final analysis, of whom 508 had CVD. Higher urinary concentrations of N-acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA) and N-Acetyl-S-(2-cyanoethyl)-l-cysteine (CYMA) and a lower urinary concentration of 2-aminothiazoline-4-carboxylic acid (ATCA) were associated with CVD outcomes after the adjustment for potential confounding factors. A nonlinear relationship and a threshold effect were only observed between N-acetyl-S-(N-methylcarbamoyl)-l-cysteine (AMCC) and CVD among 20 types of mVOCs. There was a significantly positive correlation between AMCC and CVD when AMCC concentration was >2.32 g/mL. The findings of this study suggested a significant correlation between urinary VOC metabolites and CVD. Urinary mVOCs may indicate hazardous exposure or distinct metabolic traits in patients with CVD.

BACKGROUND: Genetic variants in desmosomal cadherins, desmoglein 2 ( DSG2 ) and desmocollin 2 ( DSC2 ), cause a distinct form of arrhythmogenic right ventricular cardiomyopathy (ARVC), which remains poorly reported. In this study, we aimed to provide a comprehensive description of the phenotypic expression, natural history, and clinical outcomes of patients with this ARVC subset. METHODS: Genetic and clinical data of DSG2 and DSC2 variant carriers were collected from 5 countries in Europe and Asia. We assessed the phenotypic profile of these patients and their clinical outcomes, focusing on heart failure and ventricular arrhythmia events. RESULTS: Overall, 271 subjects, 254 with DSG2 variants, were included in this study (median age, 38 years [interquartile range, 25–52]; 62.7% male). Of these, 165 were probands, and 200 were diagnosed with definite ARVC. A total of 181 (66.8%) individuals carried missense variants, mainly distributed in the extracellular domains. Notably, we included 78 (28.8%) individuals with multiple variants. Of the 200 cases with diagnosed ARVC, 41 (20.5%) experienced premature cardiac death before the age of 65. Among the 81 individuals for whom both left ventricular ejection fraction and right ventricular fractional area change data were available at presentation, 29 (35.8%) had isolated right ventricular dysfunction, and 16 (19.8%) had biventricular dysfunction. Single-variant carriers who engaged in intense physical exercise were younger at disease onset compared with those who did not ( P =0.001). Compared with single-variant carriers, those with multiple variants were more likely to be diagnosed with ARVC (96.2% versus 64.8%; P <0.001) and exhibited more severe left ventricular dysfunction (44.4% versus 22.1%; P =0.001) and right ventricular dilation (88.9% versus 55.8%, P <0.001). Multiple-variant carriers were significantly younger at ARVC diagnosis compared with single-variant carriers (33 [18–49] years versus 42 [27–54] years; P <0.001]. During follow-up, end-stage heart failure ( P <0.001) and malignant ventricular arrhythmias ( P =0.004) were significantly more frequent in multiple-variant compared with single-variant carriers. Compared with PKP2 patients, DSG2/DSC2 patients exhibited a significantly higher risk of end-stage heart failure ( P <0.001). CONCLUSIONS: ARVC attributable to variants in desmosomal cadherins mostly present with right ventricular or biventricular disease. Multiple variants are common in these patients and are associated with more frequent clinical penetrance, earlier onset of disease, and adverse clinical outcomes.

Objective: Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. Methods: We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai East Hospital. The dataset is partitioned into training (80%), validation (10%), and test (10%) cohorts for comprehensive evaluation. Each cohort contains ECGs from distinct patients, considering some patients took repeated ECG measurements. The proposed algorithm is evaluated in two levels, self-performance measurement and comparison with the residual neural network classification model. Two scores are used for self-performance measurement, including Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). To compare the performance of the proposed model with the residual neural network model, we assessed the F1 score and area under the receiver operating characteristic curve (AUC). Results: We have observed promising performance metrics across multiple evaluation criteria through an extensive evaluation of a large 12-lead ECG database comprising 1,128,553 ECG readings from 754,920 patients. The CardioGPT model exhibited high BLEU and ROUGE scores with 0.68 (95% CI: 0.66, 0.71) and 0.81 (95% CI: 0.79, 0.84). Furthermore, in the classification performance measurement setting, the CardioGPT achieved an average F1-score of 0.91(95% CI: 0.89, 0.93) and AUC of 0.82(95% CI: 0.79, 0.84) and has higher scores than that of the convolutional neural network model, indicating its proficiency in accurately classifying ECG recordings. Conclusion: By leveraging the power of transformer structure model and natural language processing, the GPT model addresses the challenge of imbalanced learning commonly encountered in ECG classification tasks. The results indicate that the GPT model can accurately interpret ECG using natural language, providing valuable insights into the underlying patterns and abnormalities present in the data. Significance: The pioneering application of the GPT model for interpreting ECGs with natural language demonstrates its potential to address ECG classification challenges and offer valuable insights into cardiac health.

Pulsed field ablation (PFA) is an emerging non-thermal ablative modality demonstrating considerable promise for catheter ablation of atrial fibrillation (AF). However, these PFA trials have almost universally included only Caucasian populations, with little data on its effect on other races/ethnicities. The PLEASE-AF trial sought to study the 12-month efficacy and the safety of a multi-electrode hexaspline PFA catheter in treating a predominantly Asian/Chinese population of patients with drug-refractory paroxysmal AF. Patients underwent pulmonary vein (PV) isolation (PVI) by delivering different pulse intensities at the PV ostium (1800 V) and atrium (2000 V). Acute success was defined as no PV potentials and entrance/exit conduction block of all PVs after a 20-min waiting period. Follow-up at 3, 6, and 12 months included 12-lead electrocardiogram and 24-h Holter examinations. The primary efficacy endpoint was 12-month freedom from any atrial arrhythmias lasting at least 30 s. The cohort included 143 patients from 12 hospitals treated by 28 operators: age 60.2 ± 10.0 years, 65.7% male, Asian/Chinese 100%, and left atrial diameter 36.6 ± 4.9 mm. All PVs (565/565, 100%) were successfully isolated. The total procedure, catheter dwell, total PFA application, and total fluoroscopy times were 123.5 ± 38.8 min, 63.0 ± 30.7 min, 169.7 ± 34.6 s, and 27.3 ± 10.1 min, respectively. The primary endpoint was observed in 124 of 143 patients (86.7%). One patient (0.7%) developed a small pericardial effusion 1-month post-procedure, not requiring intervention. The novel hexaspline PFA catheter demonstrated universal acute PVI with an excellent safety profile and promising 12-month freedom from recurrent atrial arrhythmias in an Asian/Chinese population with paroxysmal AF. ClinicalTrials.gov Identifier: NCT05114954

Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterized with progressive cardiac fibrosis and heart failure. However, the exact mechanism driving the progression of cardiac fibrosis and heart failure in ACM remains elusive. This study aims to investigate the underlying mechanisms of progressive cardiac fibrosis in ACM caused by newly identified Desmoglein-2 (DSG2) variation. We identified homozygous DSG2F531C variant in a family with 8 ACM patients using whole-exome sequencing and generated Dsg2F536C knock-in mice. Neonatal and adult mouse ventricular myocytes isolated from Dsg2F536C knock-in mice were used. We performed functional, transcriptomic and mass spectrometry analyses to evaluate the mechanisms of ACM caused by DSG2F531C variant. All eight patients with ACM were homozygous for DSG2F531C variant. Dsg2F536C/F536C mice displayed cardiac enlargement, dysfunction, and progressive cardiac fibrosis in both ventricles. Mechanistic investigations revealed that the variant DSG2-F536C protein underwent misfolding, leading to its recognition by BiP within the endoplasmic reticulum, which triggered endoplasmic reticulum stress, activated the PERK-ATF4 signaling pathway and increased ATF4 levels in cardiomyocytes. Increased ATF4 facilitated the expression of TGF-β1 in cardiomyocytes, thereby activating cardiac fibroblasts through paracrine signaling and ultimately promoting cardiac fibrosis in Dsg2F536C/F536C mice. Notably, inhibition of the PERK-ATF4 signaling attenuated progressive cardiac fibrosis and cardiac systolic dysfunction in Dsg2F536C/F536C mice. Hyperactivation of the ATF4/TGF-β1 signaling in cardiomyocytes emerges as a novel mechanism underlying progressive cardiac fibrosis in ACM. Targeting the ATF4/TGF-β1 signaling may be a novel therapeutic target for managing ACM.

Mutant desmoglein 2 (DSG2) is the second most common pathogenic gene in arrhythmogenic cardiomyopathy (ACM), accounting for approximately 10% of ACM cases. In addition to common clinical and pathological features, ACM caused by mutant DSG2 has specific characteristics, manifesting as left ventricle involvement and a high risk of heart failure. Pathological studies have shown extensive cardiomyocyte necrosis, infiltration of immune cells, and fibrofatty replacement in both ventricles, as well as abnormal desmosome structures in the hearts of humans and mice with mutant DSG2-related ACM. Although desmosome dysfunction is a common pathway in the pathogenesis of mutant DSG2-related ACM, the mechanisms underlying this dysfunction vary among mutations. Desmosome dysfunction induces cardiomyocyte injury, plakoglobin dislocation, and gap junction dysfunction, all of which contribute to the initiation and progression of ACM. Additionally, dysregulated inflammation, overactivation of transforming growth factor-beta-1 signaling and endoplasmic reticulum stress, and cardiac metabolic dysfunction contribute to the pathogenesis of ACM caused by mutant DSG2. These features demonstrate that patients with mutant DSG2-related ACM should be managed individually and precisely based on the genotype and phenotype. Further studies are needed to investigate the underlying mechanisms and to identify novel therapies to reverse or attenuate the progression of ACM caused by mutant DSG2.