Epicardial adipose tissue as target of the incretin-based therapies in cardio-metabolic pathologies: a narrative review
Bibliographic record
Abstract
The epicardial adipose tissue (EAT) serves in physiological conditions as a mechanical and thermal myocardial protective layer, as well as a readily available lipid-storage unit. In pathological conditions, EAT expansion becomes deleterious and is currently recognized as an independent risk factor for the progression of cardiovascular diseases. The EAT phenotypic shift from protective to pro-inflammatory/pro-oxidant is facilitated by the presence of metabolic diseases (obesity, metabolic syndrome, and diabetes), which further increase its expansion and dysregulation, favor the occurrence of complications (mainly atrial fibrillation), and promote progression towards heart failure. Glucagon-like peptide-1 (GLP-1) receptor agonists are novel antidiabetic medications belonging to the incretin class that have demonstrated efficacy beyond glycemic control, in terms of weight reduction and cardiorenal protection in patients with type 2 diabetes mellitus. The GLP-1 receptors and glucose-dependent insulinotropic polypeptide (GIP) receptors are expressed in the human EAT and are targeted by an increasing number of pharmacological agonists, with pleiotropic protective effects on EAT structure and function. Herein we review the literature characterizing the benefits of GLP-1 and GIP receptors activation by single and dual agonists with particular emphasis on their effects on EAT and highlight the role of incretin-based therapy for the management of cardiometabolic pathologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".