Statins Reduce Epicardial Adipose Tissue Attenuation Independent of Lipid Lowering: A Potential Pleiotropic Effect
Bibliographic record
Abstract
Background High epicardial adipose tissue (EAT) attenuation (Hounsfield units [ HUs] ) on computed tomography is considered a marker of inflammation and is associated with an increased risk of cardiovascular events. Statins reduce the volume of EAT , but it is unknown whether they affect EAT HUs . Methods and Results We reviewed the chest computed tomographic scans of 420 postmenopausal women randomized to either 80 mg of atorvastatin or 40 mg of pravastatin daily and rescanned after 1 year to measure change in coronary artery calcium score. EAT HUs were measured near the proximal right coronary artery and remote from any area of coronary artery calcium. Computed tomographic images were also queried for subcutaneous adipose tissue (SubQ) attenuation ( HUs ) change over time. The mean patients' age was 65±6 years. The baseline EAT HU value was higher than the SubQ HU value (-89.4±24.0 HU versus -123.3±30.4 HU ; P<0.001). The EAT HU value decreased significantly in the entire cohort (-5.4±29.7 HU [-6% change]; P<0.001), but equally in the patients given atorvastatin and pravastatin (-6.35+31 HU and -4.55+28 HU ; P=0.55). EAT HU change was not associated with change in total cholesterol, low-density lipoprotein cholesterol, coronary artery calcium, and EAT volume (all P=not significant). Change in high-density lipoprotein cholesterol was marginally associated with EAT HU change ( P=0.07). Statin treatment did not induce a change in SubQ HUs . Conclusions Statins induced a decrease in EAT HUs over time, independent of intensity of low-density lipoprotein cholesterol lowering. The positive effect on EAT and the neutral effect on SubQ suggest that statins induced a decrease in metabolic activity in EAT by reduction in cellularity, vascularity, or inflammation. The clinical significance of the observed change in EAT HUs remains to be demonstrated.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".