114 research outputs found

    Atherosclerosis regression and TP receptor inhibition: effect of S18886 on plaque size and composition—a magnetic resonance imaging study

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    Aims Endothelial dysfunction, platelet hyperactivity, and inflammation play a crucial role in atherogenesis. A growing body of evidence suggests that inhibition of the thromboxane A2 (TxA2 or TP) receptor may improve endothelial function and reduce the inflammatory component of atherosclerosis in addition to its demonstrated antiplatelet activity. Consequently, we sought to assess the effect of a novel TP receptor antagonist S18886, on atherosclerotic lesion progression and composition by serial non-invasive magnetic resonance imaging (MRI). Methods and results S18886 was compared with control in an experimental model of established aortic atherosclerosis in New Zealand White rabbits (n=10). The animals underwent MRI of the abdominal aorta at the time of randomization and at the end of treatment. Subsequently, animals were euthanized and specimens were stained for histopathology and immunohistochemistry with anti-α-actin antibodies for vascular smooth muscle cells (VSMC), anti-RAM-11 for macrophages, anti-caspase-3 for apoptotic cells, anti-MMP-1 for metalloproteinases, and anti-endothelin-1 (ET-1) as a marker of endothelial dysfunction. MRI analysis revealed a significant reduction in total vessel area (TVA) and vessel wall area (VWA) in the S18886 group (P<0.05). Immunostaining analysis showed a significant decrease in RAM-11, caspase-3, MMP-1, ET-1 and an increase in α-actin in the treated group (P<0.05 vs. control). Conclusion Inhibition of the TP receptor by S18886 causes a regression of advanced atherosclerotic plaques. In addition, the reduction in the markers for macrophages, apoptotic cells, metalloproteinases, and endothelin-1 and the increase in VSMC, suggests that S18886 may not only halt the progression of atherosclerosis, but also transform lesions towards a more stable phenotype. The possibility of combining antithrombotic and antiatherosclerotic activity by means of the administration of TP inhibitors deserves further investigation in a clinical settin

    Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

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    BACKGROUND: Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. METHODS: The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. RESULTS: The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. CONCLUSION: The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients

    Weighted gene coexpression network analysis strategies applied to mouse weight

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    Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm

    Differential clinical characteristics and prognosis of intraventricular conduction defects in patients with chronic heart failure

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    Intraventricular conduction defects (IVCDs) can impair prognosis of heart failure (HF), but their specific impact is not well established. This study aimed to analyse the clinical profile and outcomes of HF patients with LBBB, right bundle branch block (RBBB), left anterior fascicular block (LAFB), and no IVCDs. Clinical variables and outcomes after a median follow-up of 21 months were analysed in 1762 patients with chronic HF and LBBB (n = 532), RBBB (n = 134), LAFB (n = 154), and no IVCDs (n = 942). LBBB was associated with more marked LV dilation, depressed LVEF, and mitral valve regurgitation. Patients with RBBB presented overt signs of congestive HF and depressed right ventricular motion. The LAFB group presented intermediate clinical characteristics, and patients with no IVCDs were more often women with less enlarged left ventricles and less depressed LVEF. Death occurred in 332 patients (interannual mortality = 10.8%): cardiovascular in 257, extravascular in 61, and of unknown origin in 14 patients. Cardiac death occurred in 230 (pump failure in 171 and sudden death in 59). An adjusted Cox model showed higher risk of cardiac death and pump failure death in the LBBB and RBBB than in the LAFB and the no IVCD groups. LBBB and RBBB are associated with different clinical profiles and both are independent predictors of increased risk of cardiac death in patients with HF. A more favourable prognosis was observed in patients with LAFB and in those free of IVCDs. Further research in HF patients with RBBB is warranted

    Role of Cardiac Imaging in Evaluating Risk for Sudden Cardiac Death

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    Sudden cardiac death (SCD) is a major cause of death from cardiovascular disease. Our ability to predict patients at the highest risk of developing lethal ventricular arrhythmias remains limited. Despite recent studies evaluating risk stratification tools, there is no optimal strategy. Cardiac imaging provides the opportunity to assess left ventricular ejection fraction, strain, fibrosis, and sympathetic innervation, all of which are pathophysiologically related to SCD risk. These modalities may play a role in the identification of vulnerable anatomic substrates that provide the pathophysiologic basis for SCD. Further studies are required to identify optimal imaging platform for risk assessment
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