98%
921
2 minutes
20
Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of hospitalization for heart failure and cardiovascular death with type 2 diabetes; however, their effect on arrhythmias is unclear. The purpose of this study was to investigate the effects of empagliflozin on ventricular arrhythmias in patients with type 2 diabetes.
Methods: A total of 150 patients with type 2 diabetes who were treated with an implantable cardioverter-defibrillator or cardiac resynchronization therapy defibrillator (ICD/CRT-D) were randomized to once-daily empagliflozin or placebo for 24 weeks. The primary endpoint was the change in the number of ventricular arrhythmias from the 24 weeks before to the 24 weeks during treatment. Secondary endpoints included the change in the number of appropriate device discharges and other values.
Results: In the empagliflozin group, the number of ventricular arrhythmias recorded by ICD/CRT-D decreased by 1.69 during treatment compared to before treatment, while in the placebo group, the number increased by 1.79. The coefficient for the between-group difference was - 1.07 (95% confidence interval [CI] - 1.29 to - 0.86; P < 0.001). The change in the number of appropriate device discharges during and before treatment was 0.06 in the empagliflozin group and 0.27 in the placebo group, with no significant difference between the groups (P = 0.204). Empagliflozin was associated with an increase in blood ketones and hematocrit and a decrease in blood brain natriuretic peptide and body weight.
Conclusions: In patients with type 2 diabetes treated with ICD/CRT-D, empagliflozin reduces the number of ventricular arrhythmias compared with placebo. Trial registration jRCTs031180120.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11214255 | PMC |
http://dx.doi.org/10.1186/s12933-024-02309-9 | DOI Listing |
Case Rep Cardiol
August 2025
Department of Clinical Medical Sciences, University of the West Indies, St. Augustine, North, Trinidad and Tobago.
Overconsumption of energy drinks containing high levels of caffeine has been increasingly linked to cardiovascular morbidity and mortality. This case report describes a 24-year-old Caribbean-Black male with no prior comorbidities who experienced an aborted sudden cardiac death (SCD) after a recent energy drink binge a few hours prior to his ventricular fibrillation (VF) cardiac arrest. Primary percutaneous coronary intervention (PPCI) was successfully performed for a dreaded widowmaker lesion, thought to have arisen as a sequela of his excessive energy drink intake.
View Article and Find Full Text PDFJ Geriatr Cardiol
August 2025
Division of Cardiology, Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China.
Eur Heart J Case Rep
September 2025
Duke University Medical Center, Division of Cardiology, Box 3182, Durham, NC 27710, USA.
Background: Genetic aetiologies of early-onset arrhythmias and cardiomyopathy (CM) are common, but timely diagnosis requires a high index of suspicion.
Case Summary: An asymptomatic 47-year-old man presented to cardiology clinic for smartwatch low-rate alarms. His brother had exertional syncope and died in his 20s from heart failure.
Front Cardiovasc Med
August 2025
Department of Ultrasonic Medicine, Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Gap junctions (GJs) are critical structures for cardiac electrical signal conduction and synchronized contraction. Their fundamental components are transmembrane proteins from the connexin (Cx) family, which assemble into hexameric channels to form intercellular ion-permeable pathways, ensuring efficient electrical transmission and coordinated contraction between cardiac cells. Connexin 43 (Cx43), the most abundant connexin in the heart, serves as the primary constituent of ventricular gap junctions.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Universidad Internacional Iberoamericana, Arecibo, PR, United States.
Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures.
View Article and Find Full Text PDF