Literature DB >> 28817459

Relation Between Epicardial Fat and Subclinical Atherosclerosis in Asymptomatic Individuals.

Rami M Abazid1, Osama A Smettei, Mohammad Obadah Kattea, Sawsan Sayed, Hanaa Saqqah, Adel M Widyan, Maksymilian P Opolski.   

Abstract

PURPOSE: Excessive quantity of epicardial adipose tissue (EAT) is considered a risk factor for coronary artery disease (CAD). There are limited data, however, on the relationship between the quality of EAT and CAD. We investigated the association between the density and volume of EAT and subclinical CAD defined by positive coronary artery calcification (CAC), using computed tomography (CT).
MATERIALS AND METHODS: We reviewed 609 consecutive CT scans of patients with a mild to moderate risk for CAD to assess the EAT volume, which was measured manually by tracing the parietal pericardial sac on axial images. Fat density was recorded in mean Hounsfield units (HU), and CAC was measured using the Agatston method.
RESULTS: The mean patients' age was 50±11 years, and 393 (64.5%) were men. Overall, CAC was present in 135 (22%) patients, of whom 97 (72%) were men and 38 (28%) were women. The mean EAT volume and density were 65±27 cm and -87.0±3.4 HU, respectively. Patients with CAC had significantly higher EAT volume (74±27 vs. 62±26 cm, P<0.001) and lower EAT density (-88±3 vs. -87±3.4 HU, P<0.001) compared with patients without CAC. Multivariate regression analysis showed that both EAT density (hazard ratio, 0.879; 95% confidence interval, 0.817-0.946; P=0.001) and EAT volume >100 cm (hazard ratio, 1.693; 95% confidence interval, 1.256-2.999; P=0.029) predicted the presence of CAC.
CONCLUSIONS: Both the quality and quantity of EAT derived from noncontrast CT scan predict subclinical CAD, with lower density and higher volume of epicardial fat associated with higher CACs.

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Year:  2017        PMID: 28817459     DOI: 10.1097/RTI.0000000000000296

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  11 in total

Review 1.  Perivascular Adipose Tissue and Coronary Atherosclerosis: from Biology to Imaging Phenotyping.

Authors:  Andrew Lin; Damini Dey; Dennis T L Wong; Nitesh Nerlekar
Journal:  Curr Atheroscler Rep       Date:  2019-11-19       Impact factor: 5.113

2.  Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects.

Authors:  Markus Goeller; Stephan Achenbach; Mohamed Marwan; Mhairi K Doris; Sebastien Cadet; Frederic Commandeur; Xi Chen; Piotr J Slomka; Heidi Gransar; J Jane Cao; Nathan D Wong; Moritz H Albrecht; Alan Rozanski; Balaji K Tamarappoo; Daniel S Berman; Damini Dey
Journal:  J Cardiovasc Comput Tomogr       Date:  2017-11-24

3.  Coronary computed tomography angiography-based assessment of vascular inflammation in patients with obstructive sleep apnoea and coronary artery disease.

Authors:  Jeremy Yuvaraj; William Cameron; Jordan Andrews; Andrew Lin; Nitesh Nerlekar; Stephen J Nicholls; Garun S Hamilton; Dennis T L Wong
Journal:  Cardiovasc Diagn Ther       Date:  2022-02

4.  Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.

Authors:  Frederic Commandeur; Markus Goeller; Julian Betancur; Sebastien Cadet; Mhairi Doris; Xi Chen; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey
Journal:  IEEE Trans Med Imaging       Date:  2018-02-09       Impact factor: 10.048

5.  Greasing the Skids: Deep Learning for Fully Automated Quantification of Epicardial Fat.

Authors:  U Joseph Schoepf; Andres F Abadia
Journal:  Radiol Artif Intell       Date:  2019-11-27

6.  Fat volume measurements as a predictor of image noise in coronary computed tomography angiography.

Authors:  Rami M Abazid; Osama A Smettei; Ahmad Almeman; Sawsan Sayed; Hanaa Alsaqqa; Salma M Abdelmageed; Fahad J Alharbi; Abdullah M Alhabib; Mouaz H Al-Mallah
Journal:  J Saudi Heart Assoc       Date:  2018-11-17

7.  Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.

Authors:  Frederic Commandeur; Markus Goeller; Aryabod Razipour; Sebastien Cadet; Michaela M Hell; Jacek Kwiecinski; Xi Chen; Hyuk-Jae Chang; Mohamed Marwan; Stephan Achenbach; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey
Journal:  Radiol Artif Intell       Date:  2019-11-27

Review 8.  Diabetes, Heart Failure and Beyond: Elucidating the Cardioprotective Mechanisms of Sodium Glucose Cotransporter 2 (SGLT2) Inhibitors.

Authors:  Utkarsh Ojha; Lenisse Reyes; Florence Eyenga; Diane Oumbe; Justyna Watkowska; Henock Saint-Jacques
Journal:  Am J Cardiovasc Drugs       Date:  2021-06-30       Impact factor: 3.571

Review 9.  The Emerging Role of CT-Based Imaging in Adipose Tissue and Coronary Inflammation.

Authors:  Jeremy Yuvaraj; Kevin Cheng; Andrew Lin; Peter J Psaltis; Stephen J Nicholls; Dennis T L Wong
Journal:  Cells       Date:  2021-05-13       Impact factor: 6.600

10.  Effects of Hormone Therapy on Heart Fat and Coronary Artery Calcification Progression: Secondary Analysis From the KEEPS Trial.

Authors:  Samar R El Khoudary; Qian Zhao; Vidya Venugopal; JoAnn E Manson; Maria M Brooks; Nanette Santoro; Dennis M Black; S Mitchell Harman; Marcelle I Cedars; Paul N Hopkins; Ann E Kearns; Virginia M Miller; Hugh S Taylor; Matthew J Budoff
Journal:  J Am Heart Assoc       Date:  2019-08-01       Impact factor: 5.501

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