Objectives: The study objective was to determine whether the coronary artery disease (CAD)-associated genotype at chromosome 9p21 modulates basal or induced expression of type I interferons (IFN-I).
Background: The mechanism responsible for the association between common variants in chromosome 9p21.3 and CAD remains unclear. It has been reported that the CAD risk locus is rich in enhancer-like elements and that chromosome looping can lead to its physical proximity with the IFN-I gene cluster, raising the possibility that the locus influences CAD risk by modulating expression of IFN-Is.
Methods: We examined whether genotype at the lead CAD-associated single nucleotide polymorphism (rs1333049) in 9p21 was associated with: 1) basal levels of IFN-I in plasma from 148 healthy male subjects; 2) induction of IFN-I by Toll-like receptor stimulants in peripheral blood mononuclear cells of 60 healthy volunteers assessed by enzyme-linked immunosorbent assay, quantitative polymerase chain reaction, Western blot, and IFN-I bioassay; and 3) enhancer activity of predicted IFN regulatory factor 3/7 binding sites within the 9p21 CAD risk region in reporter assays.
Results: No significant effects of 9p21 genotype were observed for plasma levels of IFN-α, IFN-α21, or CXCL10, or leukocyte induction of IFN-α, IFN-α21, IFN-β, CXCL10, or total IFN-I measured at the mRNA, protein, and biological activity levels. There was also no enhancement of reporter activity by predicted IFN regulatory factor 3/7 binding sites in the CAD risk locus of either genotype.
Conclusions: The mechanism underlying the association between common 9p21 variants and CAD does not involve differential regulation of IFN-I responses.
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http://dx.doi.org/10.1016/j.jacc.2013.07.031 | DOI Listing |
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFCardiovasc Diabetol
January 2025
Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Background: Insulin resistance proxy indicators are significantly associated with cardiovascular disease (CVD) and diabetes. However, the correlations between the estimated glucose disposal rate (eGDR) index and CVD and its subtypes have yet to be thoroughly researched.
Methods: 10,690 respondents with diabetes and prediabetes from the NHANES 1999-2016 were enrolled in the study.
Cardiovasc Drugs Ther
January 2025
Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangzhou, Guangdong Province, China.
Purpose: Coronary endarterectomy combined with coronary artery bypass grafting (CE-CABG) effectively achieves coronary revascularization in patients with diffuse atherosclerotic coronary artery disease (CAD). However, the loss of the subendothelial tissue at the CE-CABG coronary artery accelerates local thrombosis, leading to CE-CABG graft failure. Dual antiplatelet therapy (DAT) and warfarin plus aspirin (WPA) are the two most common anticoagulation strategies post CE-CABG.
View Article and Find Full Text PDFEur J Clin Invest
January 2025
Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University, and Liverpool Heart & Chest Hospital, Liverpool, UK.
Background: Coronary artery disease (CAD) and atrial fibrillation (AF) often coexist, but the impact of clinical phenotypes of CAD on outcomes in AF patients in the non-vitamin K antagonist oral anticoagulant drugs (NOACs) era is less well understood.
Methods: This was a post-hoc of the GLORIA-AF registry, a global, multicenter, prospective AF registry study. Patients were divided into three groups: prior history of myocardial infarction (MI)/unstable angina group (Group 1); stable angina group (Group 2); and a control group without stable angina or history of MI/unstable angina.
Int J Cardiovasc Imaging
January 2025
Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans.
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