Background: Irisin is a new muscle factor discovered in recent years that shows a strong association with metabolic diseases. However, its role in coronary artery disease (CAD) is still controversial. We performed this study to determine the relationship of serum irisin with the characteristics and prognosis of CAD.
Materials And Methods: Patients with acute coronary syndrome (ACS) (n = 355), stable coronary artery disease (SCAD) (n = 162), nonobstructive coronary artery disease (NO-CAD) (n = 126) and normal coronary arteries (n = 109) were enrolled. An enzyme-linked immunosorbent assay kit was used to measure serum irisin concentrations. Major adverse cardiovascular events (MACEs) of patients with SCAD (n = 132) and ACS (n = 331) after percutaneous coronary intervention (PCI) were recorded during a 12-month follow-up. Receiver-operator characteristic (ROC) curve analysis was used to explore predictors of CAD. Kaplan-Meier survival analysis and the Cox proportional hazards regression model were used to explore the association between serum irisin levels and MACEs.
Results: Serum irisin levels in patients with ACS, SCAD, NO-CAD and normal coronary arteries were 196.62±72.05 ng/ml, 216.81±79.69 ng/ml, 245.26±77.92 ng/ml and 300.17±76.74 ng/ml, respectively (p<0.001). ROC curve analysis indicated that serum irisin concentrations were a valuable biomarker of coronary lesions (AUC=0.799), CAD (AUC=0.734) and ACS (AUC=0.681). Survival analysis demonstrated that patients with high irisin levels exhibited a higher event-free survival rate in both the SCAD and ACS groups after successful PCI.
Conclusions: Serum irisin levels were significantly decreased in patients with CAD. Patients with ACS exhibited the lowest serum irisin levels. Furthermore, serum irisin levels were interrelated with prognosis in patients with CAD after PCI.
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http://dx.doi.org/10.1016/j.amjms.2021.02.020 | DOI Listing |
Eur J Radiol
January 2025
Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Electronic address:
Objectives: Coronary CT angiography (CCTA) is an excellent tool in ruling out coronary artery disease (CAD) but tends to overestimate especially highly calcified plaques. To reduce diagnostic invasive catheter angiographies (ICA), current guidelines recommend CT-FFR to determine the hemodynamic significance of coronary artery stenosis. Photon-Counting Detector CT (PCCT) revolutionized CCTA and may improve CT-FFR analysis in guiding patients.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
National Heart Center Singapore, Singapore, Singapore.
Aims: To identify differences in CT-derived perivascular (PVAT) and epicardial adipose tissue (EAT) characteristics that may indicate inflammatory status differences between post-treatment acute myocardial infarction (AMI) and stable coronary artery disease (CAD) patients.
Methods And Results: A cohort of 205 post-AMI patients (age 59.8±9.
PLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFCoron Artery Dis
January 2025
Department of Cardiology and Electrotherapy, Silesian Center for Heart Diseases.
Eur J Prev Cardiol
January 2025
Department of Clinical Sciences and Community Health, University of Milan, Via Commenda 19, Milan 20122, Italy.
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