scholarly journals Season and clinical factors influence epicardial adipose tissue attenuation measurement on computed tomography and may hamper its utilization as a risk marker

2021 ◽  
Vol 321 ◽  
pp. 8-13
Author(s):  
John M. Archer ◽  
Paolo Raggi ◽  
Sagar B. Amin ◽  
Chao Zhang ◽  
Varuna Gadiyaram ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Epicardial Adipose Tissue ◽  
Risk Marker ◽  
Clinical Factors ◽  
Attenuation Measurement

Abstract 13607: Seasonal Variation in the Attenuation of Epicardial Adipose Tissue on Cardiac Computed Tomography

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
John M Archer ◽  
Paolo Raggi ◽  
Amin B Sagar ◽  
Chao Zhang ◽  
Varuna Gadiyaram ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Seasonal Variation ◽  
Coronary Artery ◽  
Epicardial Adipose Tissue ◽  
Subcutaneous Tissue ◽  
Seasonal Temperature ◽  
Clinical Factors ◽  
Brown Adipose ◽  
Coronary Artery Atherosclerosis

Introduction: The role of epicardial adipose tissue (EAT) in the development and vulnerability of coronary artery atherosclerosis has been the focus of extensive research for the past several years. EAT is visceral fat that surrounds the coronary arteries and it consists of beige adipose tissue that is functionally similar to brown adipose tissue and has a higher computed tomography (CT) attenuation than subcutaneous white adipose tissue. Given the brown-like composition of EAT, its attenuation may be affected by several factors including seasonal temperature variations and clinical factors. Hypothesis: We investigated the effect of season on EAT attenuation and additional clinical factors that may influence attenuation measurements. Methods: Single center, retrospective study of 597 cardiac CT exams performed for coronary artery calcium (CAC) scoring obtained on a single CT scanner during winter and summer months. Summer was defined as June, July, and August. Winter was defined as December, January, and February. EAT attenuation in Hounsfield units (HU) was measured in a region of interest near the right coronary artery ostium. Subcutaneous adipose tissue (SCAD) attenuation was measured in the thoracic subcutaneous tissue. Patients’ demographic and clinical characteristics were obtained by questionnaire and chart review. Results: The clinical and demographic characteristics of patients scanned during the summer (N=253) and the winter (N=344) months were similar. One third of patients were women, one quarter used statins and anti-hypertensive drugs each and 30% had a BMI>30. There was a significantly lower EAT attenuation measured during the summer than the winter months (-98.17±6.94 HUs vs -95.64±7.99 HUs; P<0.001). Additionally, gender, obesity, treatment with statins and anti-hypertensive agents significantly modulated the seasonal variation in EAT attenuation. SCAD attenuation was not affected by season or any other factor. Conclusions: Our study shows that the measurement of EAT attenuation is complex and is likely affected by season, demographics and clinical factors. Attempts to use EAT attenuation as a biomarker for risk of cardiovascular events should take these potential confounders into consideration.

P6429Epicardial adipose tissue is a robust measure of increased risk of myocardial infarction, a meta-analysis on over 6.600 patients

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Hendricks ◽  
I Dykun ◽  
B Balcer ◽  
T Rassaf ◽  
A A Mahabadi
Keyword(s):  
Myocardial Infarction ◽  
Computed Tomography ◽  
Adipose Tissue ◽  
Patient Management ◽  
Epicardial Adipose Tissue ◽  
Meta Analysis ◽  
Free Text ◽  
Emergency Setting ◽  
3 Dimensional ◽  
Coronary Events

Abstract Background Epicardial adipose tissue surrounds the heart and the coronary arteries. Endocrine and paracrine activity is accredited to EAT. Studies descripted the association between increased EAT and traditional cardiovascular risk factors as well as coronary events. While computed tomography is the gold standard for the assessment of 3-dimensional EAT-volume, echocardiography based EAT thickness is an easy accessible alternative in particular in an emergency setting. So far, little is known, how quantification of EAT in patients presenting with chest pain could alter patient management. Purpose To perform a meta-analysis on existing studies, comparing EAT in patients with and without myocardial infarction, stratifying by imaging technique. Methods We performed a systematic search using the Pubmed, Cochrane, SCOPUS, and Web of Science databases for studies, describing EAT in patients with and without myocardial infarction. Manuscripts, published until 1st of October 2018, were included. We made our search specific and sensitive using Medical Subject Headings terms and free text and considered studies published in English language. Search terms used were “epicardial adipose tissue” or “pericardial adipose tissue” and “myocardial infarction”, “coronary events”, or “acute coronary syndrome”. For comparability, EAT measures were normalized to mean values for patients without myocardial infarction for each study separately. Random effect models were calculated. All analyses were performed using Review Manager 5.3. Results Overall, 6.641 patients (mean aged 58.9 years, 53% male) from 7 studies were included. Patients with myocardial infarction had 37% higher measures of EAT compared to patients without myocardial infarction (95% CI: 21–54%, Figure A). Comparing studies using echocardiography for assessment of EAT thickness with studies using computed tomography based EAT volume, similar relative differences in EAT with wide overlap of confidence intervals were observed (Echo measures: 40 [4–76]%, CT measures: 36 [16–57]%, Figure B and C). No relevant heterogeneity and inconsistency between groups was present in all analyses (detailed data not shown). Figure 1 Conclusion EAT is increased in patients with myocardial infarction. Our data suggests that quantification of EAT thickness using echocardiography distinguishes equally between patients with and without myocardial infarction as compared to 3-dimensional EAT volume from computed tomography. Therefore, it may be an easy accessible alternative in clinical settings. However, further studies are warranted to determine, whether quantification of EAT may lead to improved patient management.

Relationship between coronary arterial 18F-sodium fluoride uptake and epicardial adipose tissue analyzed using computed tomography

2020 ◽  
Vol 47 (7) ◽  
pp. 1746-1756 ◽  
Author(s):  
Toshiro Kitagawa ◽  
Yumiko Nakamoto ◽  
Yuto Fujii ◽  
Ko Sasaki ◽  
Fuminari Tatsugami ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Sodium Fluoride ◽  
Epicardial Adipose Tissue ◽  
Fluoride Uptake

scholarly journals Plasma microRNA Profiling Reveals Novel Biomarkers of Epicardial Adipose Tissue: A Multidetector Computed Tomography Study

2019 ◽  
Vol 8 (6) ◽  
pp. 780 ◽  
Author(s):  
David de Gonzalo-Calvo ◽  
David Vilades ◽  
Pablo Martínez-Camblor ◽  
Àngela Vea ◽  
Andreu Ferrero-Gregori ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Multidetector Computed Tomography ◽  
Epicardial Adipose Tissue ◽  
Significant Proportion ◽  
Epicardial Fat ◽  
P Value ◽  
Net Reclassification Improvement ◽  
Novel Biomarkers ◽  
Plasma Microrna

Epicardial adipose tissue (EAT) constitutes a novel parameter for cardiometabolic risk assessment and a target for therapy. Here, we evaluated for the first time the plasma microRNA (miRNA) profile as a source of biomarkers for epicardial fat volume (EFV). miRNAs were profiled in plasma samples from 180 patients whose EFV was quantified using multidetector computed tomography. In the screening study, 54 deregulated miRNAs were identified in patients with high EFV levels (highest tertile) compared with matched patients with low EFV levels (lowest tertile). After filtering, 12 miRNAs were selected for subsequent validation. In the validation study, miR-15b-3p, miR-22-3p, miR-148a-3p miR-148b-3p and miR-590-5p were directly associated with EFV, even after adjustment for confounding factors (p value < 0.05 for all models). The addition of miRNA combinations to a model based on clinical variables improved the discrimination (area under the receiver-operating-characteristic curve (AUC) from 0.721 to 0.787). miRNAs correctly reclassified a significant proportion of patients with an integrated discrimination improvement (IDI) index of 0.101 and a net reclassification improvement (NRI) index of 0.650. Decision tree models used miRNA combinations to improve their classification accuracy. These results were reproduced using two proposed clinical cutoffs for epicardial fat burden. Internal validation corroborated the robustness of the models. In conclusion, plasma miRNAs constitute novel biomarkers of epicardial fat burden.

scholarly journals Comparison of epicardial adipose tissue volume quantification between cardiac and chest computed tomography scans

2020 ◽  
Author(s):  
Lingyu Xu ◽  
Yuancheng Xu ◽  
Stanislau Hrybouski ◽  
D Ian Paterson ◽  
Richard B. Thompson ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Cardiac Ct ◽  
Epicardial Adipose Tissue ◽  
Hounsfield Unit ◽  
Chest Ct ◽  
Strongly Correlated ◽  
Imaging Protocol ◽  
Chest Computed Tomography ◽  
Contrast Enhanced Ct

ABSTRACTBackgroundThis study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in chest computed tomography (CT) scans.Methods and resultsEAT volume was quantified semi-automatically using a standard Hounsfield unit threshold (-190U, -30) in three independent cohorts: (1) Cohort 1 (N = 30) consisted of paired 120 KV cardiac non-contrast CT (NCCT) and 120 KV chest NCCT; (2) Cohort 2 (N = 20) consisted of paired 120 KV cardiac NCCT and 100 KV chest NCCT; (3) Cohort 3 (N = 20) consisted of paired chest NCCT and chest contrast-enhanced CT (CECT) datasets. Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets. In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, whilst chest NCCT-5 mm underestimated the EAT volume by 7.0%. In Cohort 2, 100 KV chest NCCT-1.25mm and -5 mm EAT volumes were 9.7% and 6.4% larger than corresponding 120 KV cardiac NCCT EAT volumes. In Cohort 3, the chest CECT dataset underestimated EAT volumes by ∼25%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were strongly correlated with their cardiac CT counterparts.ConclusionsThe chest NCCT-1.25 mm EAT volume with the 120 KV tube energy produced EAT volumes that are comparable to cardiac NCCT. All chest CT EAT volumes were strongly correlated with EAT volumes obtained from cardiac CT, if imaging protocol is consistently applied to all participants.

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