Epicardial fat gene expression after aerobic exercise training in pigs with coronary atherosclerosis: relationship to visceral and subcutaneous fat
Bibliographic record
Abstract
Epicardial adipose tissue (EAT) is contiguous with coronary arteries and myocardium and potentially may play a role in coronary atherosclerosis (CAD). Exercise is known to improve cardiovascular disease risk factors. The purpose of this study was to investigate the effect of aerobic exercise training on the expression of 18 genes, measured by RT-PCR and selected for their role in chronic inflammation, oxidative stress, and adipocyte metabolism, in peri-coronary epicardial (cEAT), peri-myocardial epicardial (mEAT), visceral abdominal (VAT), and subcutaneous (SAT) adipose tissues from a castrate male pig model of familial hypercholesterolemia with CAD. We tested the hypothesis that aerobic exercise training for 16 wk would reduce the inflammatory profile of mRNAs in both components of EAT and VAT but would have little effect on SAT. Exercise increased mEAT and total heart weights. EAT and heart weights were directly correlated. Compared with sedentary pigs matched for body weight to exercised animals, aerobic exercise training reduced the inflammatory response in mEAT but not cEAT, had no effect on inflammatory genes but preferentially decreased expression of adiponectin and other adipocyte-specific genes in VAT, and had no effect in SAT except that IL-6 mRNA went down and VEGFa mRNA went up. We conclude that 1) EAT is not homogeneous in its inflammatory response to aerobic exercise training, 2) cEAT around CAD remains proinflammatory after chronic exercise, 3) cEAT and VAT share similar inflammatory expression profiles but different metabolic mRNA responses to exercise, and 4) gene expression in SAT cannot be extrapolated to VAT and heart adipose tissues in exercise intervention studies.
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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 | 0.000 |
| 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) | 0.001 | 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".