Epicardial Adipose Tissue As New Cardio-Metabolic Risk Marker and Potential Therapeutic Target in the Metabolic Syndrome
Bibliographic record
Abstract
Increased visceral adiposity, is an emerging cardiovascular risk factor. There is now a compelling need to quantify visceral adipose tissue not only for diagnostic purposes, but also for therapeutic interventions with weight reduction drugs or pharmaceuticals targeted to adipose tissue, as well as anti-obesity medications, thiazolidinediones, fibrates, angiotensin receptor blockers, highly active antiretroviral therapy and hormone replacement therapy. Among visceral adipose tissues, growing evidences suggest that cardiac adiposity may play an important role in the development of an unfavorable cardiovascular risk profile. Recent papers suggest that epicardial fat, index of cardiac and visceral adiposity, could locally modulate the morphology and function of the heart. The close anatomical relationship between epicardial adipose tissue and the adjacent myocardium should readily allow local paracrine interactions between these tissues. Echocardiography has been recently proposed for the direct assessment of epicardial adipose tissue. Echocardiographic assessment of epicardial fat may be a helpful tool not only for diagnostic purposes, as marker of visceral adiposity and inflammation, but also for therapeutic interventions with drugs that can modulate the adipose tissue. In this article, epicardial adipose tissue's structure, function, method of assessment and reliability as a diagnostic tool and potential therapeutic target is reviewed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".