Ling Xue

Background: Asymptomatic cerebral infarction (ACI) is a frequent yet under-recognized complication following radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF). Early identification of patients at risk remains challenging, particularly in the absence of clinically overt neurological symptoms. This study aimed to develop a multi-parameter predictive model integrating clinical, procedural, and biomarker-based factors, with a particular focus on vascular endothelial growth factor (VEGF). Methods: This retrospective cohort study included 300 consecutive AF patients undergoing first-time RFCA. Brain magnetic resonance imaging was performed within 24–72 h post-procedure to detect ACI. Clinical, procedural, and laboratory data were systematically collected. Serum VEGF levels were measured using an enzyme-linked immunosorbent assay. Univariable and multivariable logistic regression analyses were conducted to identify independent predictors of ACI. Model performance was evaluated using receiver operating characteristic (ROC) analysis. Results: ACI was detected in 48 patients (16.0%). Patients with ACI were significantly older and had higher body mass index compared to those without ACI ( p < 0.001). Serum VEGF levels were markedly elevated in the ACI group (350 ± 45 vs. 200 ± 30 pg/mL, p < 0.001). Multivariable analysis identified age (OR: 1.08, p = 0.01), BMI (OR: 1.15, p = 0.003), and VEGF (OR: 1.02, p < 0.001) as independent predictors. The predictive model demonstrated good discriminative ability with an AUC of 0.82. Additionally, RFCA was associated with significant improvements in cardiac function and autonomic regulation ( p < 0.001). Conclusion: ACI remains a clinically relevant complication following RFCA. A predictive model incorporating VEGF alongside clinical factors provides improved risk stratification. These findings highlight the importance of endothelial dysfunction in ACI pathogenesis and support the integration of biomarkers into clinical decision-making.

Cardiovascular complications have emerged as the predominant cause of mortality in diabetes mellitus (DM), with diabetic cardiomyopathy (DCM) representing a critical clinical challenge due to its distinct pathogenesis independent of coronary artery disease or hypertension. DCM is characterized by left ventricular diastolic dysfunction, hypertrophy, and fibrosis, with impaired myocardial angiogenesis contributing to adverse cardiovascular outcomes. Despite this, targeted therapies for angiogenesis restoration in DCM remain elusive. Through bioinformatic analysis of public databases, we identified egl-9 family hypoxia inducible factor 1 (EGLN1) as a differentially expressed gene in both preclinical DCM models and clinical specimens. To validate its role, we established mice model of diabetes and corresponding endothelial cells cultures under hyperglycemic conditions. Results demonstrated significant upregulation of EGLN1 in diabetic mice, correlating with angiogenesis impairment. Intriguingly, EGLN1 inhibition attenuated high glucose-induced endothelial dysfunction by activating autophagy pathways, as evidenced by increased LC3-II/LC3-I ratios and decreased P62 levels. These findings unveil EGLN1 as a novel regulator of myocardial angiogenesis in DCM, proposing autophagy activation as a potential therapeutic strategy to ameliorate diabetic cardiovascular complications. This study provides the first evidence linking EGLN1 dysregulation to angiogenesis defects in DCM, offering insights for targeted intervention development.

Ventricular arrhythmia (VA) is one of the common complications of many heart diseases in clinical practice, even some of its clinical symptoms are mild and non-specific, but the other may lead to sudden cardiac death (SCD) and most life-threatening VA is associated with ischemic cardiomyopathy (ICM). Nowadays, the developments in imaging techniques have provided clues to identify these highly variable VAs, which make clinicians identify patients with VA early and effectively who may have fatal consequences. Thereafter, it is beneficial to manage the risk stratification of patients, optimize their follow-up treatment, and improve clinical outcomes. This article reviews current ultrasound and magnetic resonance imaging techniques that can aid in diagnosis, treatment and prognosis, and provides clinicians with practical imaging and analytical recommendations to further identify patients with ICM who may develop VA. Clinical trial number Not applicable.

Backgrounds: Diabetic cardiomyopathy has a very high incidence and serious clinical consequences, making it an urgent clinical problem to be solved. Angiogenesis is a significant phenotype in the occurrence and development of diabetic cardiomyopathy, especially the damage to angiogenesis of cardiac microvessels, which is inextricably linked to the cardiac risk of diabetic patients. In the current basic and clinical research, there is still a lack of treatment methods that directly target the angiogenesis of diabetic cardiomyopathy. This study hopes to discover the key molecules related to diabetic cardiomyopathy and angiogenesis damage, to provide ideas for possible interventions.Methods: Sequencing data of animals and cells were obtained from the GEO database, and differentially expressed genes were analyzed. Subsequently, the angiogenesis-related genes were clustered for functional and pathway analysis. Then, the microangiogenesis of the diabetic mice and the angiogenesis changes of high glucose-stimulated HUVECs were verified, and the top three genes related to diabetic cardiomyopathy and angiogenesis were verified using western blot.Results: 24 differentially expressed genes associated with angiogenesis were found in GSE241565(human) and GSE215979(mice). Among them, 11 genes showed the same trend in the two databases. Then CD31 staining of diabetic mice hearts showed that microvascular angiogenesis was impaired, high glucose-stimulated HUVECs decreased tube formation, and wound healing migration was weakened. Finally, the top 3 genes most associated with diabetic cardiomyopathy were verified, and there was no significant difference between the changes of Edn1 and Lepr. At the same time, Efnb2 was significantly increased under high glucose stimulation.Conclusion: Combined with the sequencing data of animal and cell models of diabetic cardiomyopathy, the differential genes associated with angiogenesis were screened. These findings not only elucidate a novel molecular axis linking angiogenesis damage to diabetic cardiomyopathy but also highlight Efnb2 as a potential therapeutic target.

Background: Observational studies have found that vascular endothelial growth factor (VEGF) levels are associated with the risk of cardiovascular disease. However, it remains unclear whether VEGF levels have a causal effect on the risk of atrial fibrillation.Methods: A two-sample Mendelian randomization (MR) study was conducted to explore the causal relationship between VEGF levels and the risk of atrial fibrillation. Genetic variants associated with VEGF [VEGF-A, VEGF-C, VEGF-D, VEGF receptor−2 (VEGFR-2), VEGFR-3] and atrial fibrillation (atrial fibrillation, atrial fibrillation and flutter) were used as instrumental variables. Data on genetic variants were obtained from published genome-wide association studies (GWAS) or the IEU Open GWAS project. Inverse-variance weighted (IVW) analysis was used as the primary basis for the results, and sensitivity analyses were used to reduce bias. Causal relationships were expressed as odds ratio (OR) with 95% confidence interval (CI), and a P-value of P_FDR < 0.1) was considered to have a significant causal relationship.Results: Genetically predicted high levels of VEGF-A [OR = 1.025 (95%CI: 1.004–1.047), P_FDR = 0.060] and VEGF-D [OR = 1.080 (95%CI: 1.039–1.123), P_FDR = 0.001]] were associated with an increased risk of atrial fibrillation, while no causal relationship was observed between VEGF-C (P_FDR = 0.419), VEGFR-2 (P_FDR = 0.784), and VEGFR-3 (P_FDR = 0.899) and atrial fibrillation risk. Moreover, only genetically predicted high levels of VEGF-D [OR = 1.071 (95%CI: 1.014–1.132), P_FDR = 0.087] increased the risk of atrial fibrillation and flutter. Sensitivity analysis demonstrated that the relationship between VEGF-D levels and the risk of atrial fibrillation was robust.Conclusion: This study supports a causal association between high VEGF-D levels and increased risk of atrial fibrillation.