Cardiovascular Therapeutics最新文献

Background: The vein of Marshall (VOM) ethanol infusion is an adjunctive strategy for persistent atrial fibrillation (AF) ablation, traditionally guided by VOM venography. However, venography does not allow real-time evaluation of ethanol distribution and carries procedural risks. Intracardiac echocardiography (ICE) provides high-resolution visualization for precise guidance but remains understudied clinically. This study was aimed at evaluating the feasibility, efficacy, and safety of a novel ICE-guided VOM ethanol infusion.

Methods: A retrospective, matched cohort study was conducted including 126 patients (42 ICE-guided, 84 venography-guided) undergoing de novo radiofrequency (RF) catheter ablation for persistent AF. Propensity score matching (1:2) balanced the groups' baseline characteristics. Procedural endpoints included ethanol-induced low-voltage area (LVA), procedural efficiency (procedure duration and radiation exposure), complications, and 12-month AF/atrial tachycardia (AT) recurrence. ICE-guided procedures utilized real-time monitoring of ethanol-induced tissue changes, whereas venography relied on contrast extravasation.

Results: ICE guidance produced significantly larger ethanol-induced LVAs, shorter total procedure times, reduced fluoroscopy times, shorter VOM ethanol infusion times, shorter mitral isthmus (MI) ablation times, and lower radiation exposure (all p < 0.05). Major complications were rare and similar between groups (p = 0.719). At 12 months, freedom from AF/AT recurrence was comparable between groups (log-rank test, p = 0.66).

Conclusions: ICE-guided VOM ethanol infusion enhances procedural precision, reduces radiation exposure, and achieves more extensive substrate modification compared with venography. Although clinical recurrence rates were similar, ICE improves workflow efficiency and improves safety by avoiding distal VOM cannulation. These findings support ICE as a transformative tool for optimizing VOM ethanol ablation. Limitations include the nonrandomized design and small sample size, which warrant further validation in randomized trials.

Background: CKM encompasses a complex group of disease states involving cardiovascular, kidney, and metabolic dysfunction. Frailty is a significant health issue among older adults and is closely associated with adverse outcomes. However, there is currently a lack of predictive tools specifically designed for assessing frailty risk in middle-aged and older patients with CKM Stages 0-3.

Objective: This study is aimed at developing a machine learning model to predict frailty risk in this population and to identify key predictive factors.

Methods: Using data from NHANES 2007-2018, a total of 6749 participants aged 40 years and older were included. Feature selection was conducted using LASSO regression, and the cohort was randomly divided into training and testing sets in a ratio of 7:3. Five machine learning models LR, LightGBM, XGBoost, RF, and DT were compared. Model performance was evaluated via 10-fold cross-validation, and the optimal model was selected based on accuracy, AUC, precision, recall, and F1 score. Feature importance was further explored using SHAP values.

Results: Among the 6749 participants, 1427 (21.14%) were identified as frail. LASSO regression identified 21 important variables, which were used to construct the five machine learning models. The RF model achieved the best performance in predicting frailty risk, as demonstrated by its AUC (0.90), accuracy (0.81), precision (0.54), recall (0.84), and F1 score (0.66). Decision curve analysis and calibration plots further confirmed the clinical utility of this approach. SHAP analysis revealed that the most significant predictors were PIR, antihypertensive medication use, hemoglobin, red blood cell count, albumin, and diabetes.

Conclusion: The machine learning model developed in this study can effectively predict the risk of frailty in middle-aged and older adults with CKM Stages 0-3, providing a valuable tool for early identification of high-risk individuals in clinical practice. This model may help optimize healthcare resource allocation, guide-targeted interventions, and improve health management for patients with CKM.

Although oxidative stress (OS) links to the pathogenesis of coronary artery disease (CAD), its underlying genetic mechanisms remain unclear. Through summary data-based Mendelian randomization (SMR) and colocalization, this research seeks to assess the potential causal links between OS-related genes and CAD. Summary-level data on the methylation, expression, and protein abundance levels of OS-related genes were obtained from the corresponding quantitative trait loci (QTL) studies. We obtained genome-wide association study summary statistics for CAD from a previous study (discovery), the FinnGen and UK Biobank (replication). Two-sample MR analysis was conducted to verify the associations between key genes expressions and meta-analysis cohort of CAD risk. Mediation analysis was conducted to evaluate the mediating role of gene expression variation in the causal pathway linking methylation levels of key loci to the risk or progression of the disease. We identified 35 methylation loci, 7 genes, and 12 proteins in the discovery cohort. By integrating multiomic data, we identified SMARCA4, NAGLU, SREBF1, RPTOR, and HLA-B as potential causal targets associated with CAD. The two-sample MR analysis once again confirmed that the expressions of the SMARCA4, SREBF1, and HLA-B genes in the meta-analysis cohort showed a significant association with the risk of CAD, and this association was consistent with the direction of the SMR. Furthermore, both the expression and methylation of SMARCA4 were positively associated with CAD, and the direct and indirect effects of SMARCA4 methylation were confirmed. In summary, our results identified potential causal associations between SMARCA4, NAGLU, SREBF1, RPTOR, HLA-B, and CAD. The findings highlight the necessity for further exploration into the underlying etiology of CAD.

Background: Myocardial infarction (MI) remains a predominant contributor to global cardiovascular disease death, with apoptosis playing a pivotal yet incompletely characterized role in adverse cardiac remodeling. Despite advances in bulk RNA sequence analyses, the single-cell level analyses of apoptotic signaling of MI progression remain unclear.

Methods: Multiomics data integration was synthesized through single-nucleus RNA-seq (snRNA-seq) (GSE270788) and bulk RNA-seq (GSE21610). Three hundred ninety-seven ARGs (apoptosis-related genes) were downloaded from GeneCards (relevance score > 7), followed by identification of candidate genes through AUCell scoring and correlation analysis. Random Forest, Boruta, and LASSO algorithms were employed to refine hub genes. Hub genes were validated by animal and cell experiments.

Results: Single-nucleus sequence analysis reveals dramatic variation in ARGs activity across cardiac cells after MI, with fibroblasts demonstrating significantly elevated apoptotic activity compared with other cell types. Integration of single-nucleus and bulk sequence confirms TNFRSF12A as the major molecule regulating ARGs activation in MI. Experiment validation confirmed that upregulation of TNFRSF12A in the MI model and TGF-β induced NIH3T3.

Conclusion: This study revealed that fibroblast dominates apoptotic activity post-MI. We identified TNFRSF12A as a key regulator in MI through multiomics analysis, with potential as a diagnostic biomarker and therapeutic target.

Macrophage-driven inflammation, induced by dysfunctional lipid metabolism, is a pivotal process in the pathogenesis of atherosclerosis (AS). While long noncoding RNAs (LncRNAs) are established regulators, the specific mechanisms governing their roles remain largely uncharacterized. Here, we demonstrate that the LncRNA colon cancer–associated transcript 1 (CCAT1) is significantly upregulated in THP1-derived macrophages upon stimulation with oxidized low-density lipoprotein (ox-LDL). Silencing LncRNA CCAT1 markedly attenuated the ox-LDL-induced inflammatory response, establishing its proinflammatory function. Mechanistically, we identified miR-296-3p as a direct downstream target of LncRNA CCAT1, which acts as a molecular sponge. This interaction was validated by dual-luciferase and RNA pull-down assays. Furthermore, we show that miR-296-3p suppresses inflammation by directly targeting the mRNA of FOS-like antigen 1 (FOSL1). Crucially, overexpression of LncRNA CCAT1 increased FOSL1 levels, confirming this regulatory axis. Collectively, our findings delineate a novel pro-inflammatory pathway where LncRNA CCAT1 promotes macrophage inflammation by sponging miR-296-3p, thereby derepressing its target FOSL1. Therefore, targeting LncRNA CCAT1 represents a promising therapeutic strategy for treating atherosclerotic cardiovascular disease.