Epicardial Adipose Tissue and Intracoronary Adrenomedullin Levels in Coronary Artery Disease
Bibliographic record
Abstract
The aim of the study was to test 1) whether chronic and stable coronary artery disease (CAD) could downregulate epicardial fat adrenomedullin synthesis and secretion, and decrease intracoronary plasma adrenomedullin levels, and 2) whether intracoronary plasma adrenomedullin levels could be related to epicardial adipose tissue adrenomedullin gene and protein expression in subjects with CAD. We examined 12 patients with CAD who required coronary artery bypass graft (CABG) and 10 patients with non-CAD who underwent cardiac surgery for valve replacement. Plasma levels of adrenomedullin were measured in peripheral vein circulation, in left coronary artery (LCA) and coronary sinus (CS) during coronary angiography. Epicardial adipose tissue biopsy for Reverse Transcription and Real-Time PCR (RT-PCR) adrenomedullin mRNA analysis and Western Blotting (WB) protein expression was performed during cardiac surgery in all subjects. Peripheral, LCA, and CS plasma adrenomedullin levels were significantly lower in CAD patients than in those with non-CAD (3.0+/-0.9 vs. 4.4+/-0.9 pg/ml p<0.01; 2.9+/-1 vs. 4.05+/-0.8 pg/ml, p<0.01, 3.1+/-0.9 vs. 3.98+/-0.9 pg/ml p=0.04, respectively). However, CS adrenomedullin levels were not statistically different than those in LCA suggesting that adrenomedullin was not secreted from epicardial fat into the coronary artery lumen. Epicardial fat adrenomedullin mRNA levels and protein expression were lower in patients with CAD than in those with non-CAD (p<0.01 for both). We conclude that 1) epicardial fat adrenomedullin gene and protein expression can be downregulated in CAD subjects, and 2) intracoronary adrenomedullin levels are lower in CAD. No evidence that epicardial adipose tissue really contributes intracoronary adrenomedullin can be provided at this time.
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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".