Epicardial Adipose Tissue Adiponectin Expression is Related to Intracoronary Adiponectin Levels
Bibliographic record
Abstract
The role of adiponectin and epicardial adipose tissue in coronary artery disease (CAD) is a subject of debate. Whether plasma adiponectin concentration in the coronary circulation is locally modulated by the epicardial fat is still unexplored. We evaluated the hypothesis whether intracoronary plasma adiponectin levels are related to adiponectin expression in epicardial adipose tissue in vivo in patients with CAD and without CAD (non-CAD). We examined 12 patients with CAD who required CABG and 10 patients with non-CAD who underwent cardiac surgery for valve replacement. Plasma levels of adiponectin were measured in peripheral vein circulation and in left coronary artery (LCA) during coronary angiography. Epicardial adipose tissue biopsy for adiponectin protein extraction was performed during cardiac surgery in both CAD and non-CAD subjects. Adiponectin protein expression in epicardial adipose tissue was lower in patients with CAD than in those with non-CAD (0.45+/-0.4 vs. 1.1+/-1.0, p<0.05). LCA plasma adiponectin levels significantly correlated with epicardial adipose tissue adiponectin protein expression (r=0.68, p=0.02) in all subjects. Peripheral adiponectin levels and epicardial fat adiponectin protein expression were the best correlates of LCA adiponectin, r (2)=0.49, p<0.01, p<0.05, respectively). Our study showed that intracoronary adiponectin levels reflect systemic adiponectin levels. Epicardial adipose tissue could partially contribute to adiponectin levels in the coronary circulation.
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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".