Targeting perivascular and epicardial adipose tissue inflammation: therapeutic opportunities for cardiovascular disease
Bibliographic record
Abstract
Major shifts in human lifestyle and dietary habits toward sedentary behavior and refined food intake triggered steep increase in the incidence of metabolic disorders including obesity and Type 2 diabetes. Patients with metabolic disease are at a high risk of cardiovascular complications ranging from microvascular dysfunction to cardiometabolic syndromes including heart failure. Despite significant advances in the standards of care for obese and diabetic patients, current therapeutic approaches are not always successful in averting the accompanying cardiovascular deterioration. There is a strong relationship between adipose inflammation seen in metabolic disorders and detrimental changes in cardiovascular structure and function. The particular importance of epicardial and perivascular adipose pools emerged as main modulators of the physiology or pathology of heart and blood vessels. Here, we review the peculiarities of these two fat depots in terms of their origin, function, and pathological changes during metabolic deterioration. We highlight the rationale for pharmacological targeting of the perivascular and epicardial adipose tissue or associated signaling pathways as potential disease modifying approaches in cardiometabolic syndromes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".