BMC Cardiovascular Disorders最新文献

Objective: To explore the association between apolipoprotein E (APOE) gene polymorphisms and the risk of premature (age of onset: men ≤ 55 years old, women ≤ 65 years old) myocardial infarction (PMI).

Methods: This study retrospectively collected the medical records (age, gender, hypertension, diabetes mellitus, smoking, drinking, and serum lipid) of 379 PMI patients and 628 age-matched non-AMI individuals (controls), from December 2018 to March 2024. The relationship between APOE polymorphisms and PMI was analyzed.

Results: 15(1.5%) individuals carried ɛ2/ɛ2, 147(14.6%) had ɛ2/ɛ3, 16(1.6%) presented with ɛ2/ɛ4, 670(66.5%) were ɛ3/ɛ3 carriers, 149(14.8%) had ɛ3/ɛ4, and 10 (1.0%) carried ɛ4/ɛ4. The proportion of ɛ2/ɛ3 genotype was significantly lower in the PMI group than in controls (7.7% vs. 18.8%, p < 0.001), whereas the prevalence of ɛ3/ɛ4 genotype was substantially higher in the PMI group (20.6% vs. 11.3%, p < 0.001). Logistic regression analysis identified some associated factors: smoking (odds ratio [OR]: 3.057, 95% confidence interval [CI]: 2.098-4.455, p < 0.001), hypertension (OR: 4.474, 95% CI: 3.273-6.117, p < 0.001), and dyslipidemia (OR: 1.805, 95% CI: 1.333-2.443, p < 0.001). Additionally, genetic factors were associated with PMI: the APOE ɛ3/ɛ4 genotype (vs. ɛ3/ɛ3, OR: 1.548, 95% CI: 1.038-2.309, p = 0.032) and the presence of ɛ4 allele (vs. ɛ3, OR: 1.521, 95% CI: 1.033-2.241, p = 0.034) were confirmed as independent associated factors.

Conclusions: APOE ε3/ε4 genotype was significantly associated with PMI, suggesting that this genotype could serve as a potential genetic marker for PMI risk assessment.

Background: Coronary artery aneurysm(CAA) represents a rare subset of coronary artery disease and is associated with heightened risks of cardiovascular mortality. Because of its low prevalence, large-scale data are scarce, therefore specific therapeutic guidelines remain underdeveloped. We report the case of a 64-year-old female who presented to our hospital due to unstable angina. The coronary angiogram showed an aneurysm in the mid-segment of the right coronary artery, the arterial lumen preceded the aneurysm was severe stenosis with calcification. Considering the elevated risks of rupture and embolic events, the heart team opted for a percutaneous treatment strategy. Pre-procedural three-dimensional computed tomography(CT) reconstruction and intravascular ultrasound (IVUS) guidance facilitated the successful exclusion of the aneurysm using two drug-eluting stents.At 12-month follow-up, the patient reported no further episodes of angina.

Conclusion: For patients with acute coronary syndrome caused by a coronary artery aneurysm culprit, percutaneous coronary intervention(PCI) is viable therapeutic option when anatomical conditions are favorable.

Background: Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment. Given the limited accuracy of conventional risk scores in identifying patients at high risk of recurrence after catheter ablation, this study sought to develop and validate a machine learning (ML) model for predicting AF recurrence using a wide array of clinical and laboratory variables.

Methods: Of the 438 patients with AF included in this study who underwent catheter ablation between 2016 and 2023. Comprehensive demographic, clinical, echocardiographic, laboratory, medication, and risk score data were collected. The primary endpoint was AF recurrence, defined as documented AF, atrial flutter, or atrial tachycardia ≥ 30 s occurring ≥ 3 months post-procedure. The dataset was randomly divided into training set and validation set in a 6:4 ratio. Univariate and multivariate logistic regression were used to identify independent risk factors for the risk of recurrence after catheter ablation of AF. Eleven ML algorithms were established on the training set-including random forest (RF), gradient boosting machine(GBM), logistic regression (LR), support vector machine(SVM) and XGBoost. Model performance was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, and calculating the area under the curve (AUC). A calibration curve assessed the model's accuracy, while decision curve analysis (DCA) evaluated its clinical applicability. In addition, to avoid overfitting, we conducted an internal validation of best model using Bootstrap. Finally, Shapley additive explanations (SHAP) were employed to interpret the importance of predictor variables.

Results: Of the 438 patients with AF included in this study who underwent catheter ablation, 147 experienced recurrence during follow-up. The median age of the total population was 63 years, with 64 years in the non-recurrence group and 63 years in the recurrence group (P = 0.303). The proportion of females was 36.1% in the recurrence group vs. 52.6% in the non-recurrence group (P = 0.018). The RF model demonstrated superior performance, achieving an AUC of 0.878 in the training set and 0.925 in the validation set. It also showed excellent calibration (Brier score: 0.186) and clinical utility across a wide risk threshold range. Key predictors included alcohol consumption [OR = 2.12 (1.15-3.91), P = 0.017)], fibrin degradation products [FDP, OR = 1.22 (1.02-1.46), P = 0.027], and hypertension [OR = 0.47 (0.26-0.85), P = 0.012].

Conclusion: An interpretable ML model based on RF accurately predicts AF recurrence post-ablation and outperforms conventional risk scores. This tool may enhance individualized patient counseling, follow-up strategy design, and resource allocation in clinical practice.

Background: Myocardial ischemia-reperfusion injury (MIRI) markedly impairs cardiac functional recovery and represents a major determinant of adverse outcomes in patients with ischemic heart disease. Ginsenosides, the principal bioactive constituents of ginseng, exert significant cardioprotection against MIRI. This review systematically summarizes and analyzes in vivo (animal) studies to clarify the efficacy and underlying mechanisms of ginsenosides in MIRI.

Methods: The PubMed, EMbase, Web of Science, Cochrane Library, CNKI, WanFang, and Cqvip databases were systematically searched from inception to 31 July 2024. In vivo studies evaluating ginsenosides pretreatment or post-treatment in models of MIRI were identified. Outcome measures comprised myocardial infarct size and indices of hemodynamic performance, myocardial injury, apoptosis, inflammation, and oxidative stress. A meta-analysis was conducted with RevMan 5.4 and Stata/MP 14.0.

Results: Thirty-four eligible articles encompassing 505 experimental animals were included. Funnel plots, Egger's tests, and sensitivity analyses confirmed the robustness of the findings. Compared with controls, ginsenosides treatment significantly reduced myocardial infarct size and improved hemodynamic indices (P < 0.0001). Ginsenosides also attenuated MIRI-induced elevations of lactate dehydrogenase, creatine kinase-MB, creatine kinase, malondialdehyde, tumor necrosis factor-α, interleukin-6, interleukin-1β, and cardiomyocyte apoptosis (P < 0.0001). Subgroup analysis further revealed that pre-ischemic ginsenosides administration conferred greater protection than post-reperfusion treatment.

Conclusion: Ginsenosides play a significant role in the prevention and treatment of MIRI. Ginsenosides can reduce the area of myocardial infarction and improve myocardial damage through anti-inflammatory, antioxidative stress, anti-apoptosis, regulation of autophagy, and energy metabolism.

Objectives: To investigate the association of Procollagen I N-Terminal Propeptide (PINP) with myocardial fibrosis (MF) and its predictive effect on major adverse cardiovascular events (MACEs) in patients with primary diagnosis of heart failure (HF).

Methods: A total of 76 newly diagnosed HF patients with reduced ejection fraction were followed up for one year. Serum PINP levels and extracellular volumes (ECV) quantified by cardiac magnetic resonance(CMR) imaging at baseline were collected. MF was defined when a ECV value was greater than 28.5%. The first MACEs defined as rehospitalization and/or cardiovascular death, were recorded during the visit.

Results: The levels of PINP in patients with MF were higher than those in patients without MF (39.90 VS 49.10 ng/ml, P = 0.030). Multivariate logistic regression analysis showed that PINP was an independent risk factor for MF (OR 1.058, 95% CI 1.018 ~ 1.098, P = 0.004). The linear regression model showed a linear correlation between the PINP levels and ECVs (R = 0.280, P = 0.014). Patients with higher PINP levels had a significant increased risk of incident MACE than those with lower PINP levels (HR 1.95, 95%CI 1.03 ~ 3.69, P = 0.033).

Conclusion: In patients with newly diagnosed HF, serum PINP levels were associated with MF and an increased risk of MACEs during a one-year follow-up.