Potential role of epicardial adipose tissue in coronary artery endothelial cell dysfunction in type 2 diabetes
Bibliographic record
Abstract
Cardiovascular disease is the most prevalent cause of morbidity and mortality in diabetes. Epicardial adipose tissue (EAT) lies in direct contact with the myocardium and coronary arteries and can influence cardiac (patho) physiology through paracrine signaling pathways. This study hypothesized that the proteins released from EAT represent a critical molecular link between the diabetic state and coronary artery endothelial cell dysfunction. To simulate type 2 diabetes-associated metabolic and inflammatory status in an ex vivo tissue culture model, human EAT samples were treated with a cocktail composed of high glucose, high palmitate, and lipopolysaccharide (gplEAT) and were compared with control EAT (conEAT). Compared to conEAT, gplEAT showed a markedly increased gene expression profile of proinflammatory cytokines, corroborating EAT inflammation, a hallmark feature observed in patients with type 2 diabetes. Luminex assay of EAT-secretome identified increased release of various proinflammatory cytokines, including tumor necrosis factor-alpha (TNF-alpha), interferon-alpha 2 (IFNA2), interleukin 1 beta (IL1B), interleukin 5 (IL5), interleukin 13 (IL13), and CCL5, among others, in response to high glucose, high palmitate, and lipopolysaccharide. Conditioned culture media was used to collect the concentrated proteins (CPs). In response to gplEAT-CPs, human coronary artery endothelial cells (HCAECs) exhibited an inflammatory endothelial cell phenotype, featuring a significantly increased gene expression of proinflammatory cytokines and cell surface expression of VCAM-1. Moreover, gplEAT-CPs severely decreased Akt-eNOS signaling, nitric oxide production, and angiogenic potential of HCAECs, when compared with conEAT-CPs. These findings indicate that EAT inflammation may play a key role in coronary artery endothelial cell dysfunction in type 2 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".