Abstract 13607: Seasonal Variation in the Attenuation of Epicardial Adipose Tissue on Cardiac Computed Tomography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".