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The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.
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http://dx.doi.org/10.1038/s41588-022-01233-6 | DOI Listing |
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
Eur J Cardiothorac Surg
September 2025
Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York, NY, USA.
Objectives: The no-touch (NT) technique for saphenous vein (SV) harvesting in coronary artery bypass surgery preserves perivascular tissue and has been proposed to improve vein graft patency compared to conventional (CON) harvesting. However, recent large randomized clinical trials (RCTs) have reported conflicting results. We performed a meta-analysis of all available RCTs comparing graft patency and clinical outcomes between NT-SV and CON-SV harvesting techniques.
View Article and Find Full Text PDFJ Invasive Cardiol
September 2025
Department of Cardiology, Centre Hospitalier La Rochelle Ré Aunis, La Rochelle, France.
Objectives: The management of patients with calcified de novo lesions remains a major clinical challenge even in the era of drug-eluting stents (DES). Drug-coated balloon (DCB) therapy has emerged as an alternative to DES to treat de novo lesions. Nevertheless, the management of calcified lesions using intravascular lithotripsy (IVL) combined with DCB to treat de novo lesions has not been investigated.
View Article and Find Full Text PDFJ Invasive Cardiol
September 2025
Newark Beth Israel Medical Center, Newark, New Jersey.
Objectives: The authors hypothesized that the origin of the right coronary artery (RCA) is a direct continuation of the major aortic arch branches (MAAB) takeoff plane, which may have implications for brachiocephalic interventions and next generation transcatheter aortic valve intervention (TAVI) embolic protection devices (EPDs).
Methods: In this single-center, retrospective, cross-sectional study, the authors analyzed computed tomographic angiography (CTA) images from 92 patients undergoing TAVI evaluation to determine the spatial relationship between the origin of the RCA and the MAAB takeoff plane. Patients with prior cardiothoracic or aortic interventions and those with anomalous RCA origin were excluded.
PLoS One
September 2025
Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.
Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.