Paracrine Effects of Perivascular Adipose Tissue on Atherogenesis: Role of Extracellular Vesicles‐Mediated Intercellular Communications
Bibliographic record
Abstract
Background Development of atherosclerosis depends on the interaction between various factors. Type 2 diabetes accelerates these interactions and predisposes to rapid progression of atherosclerosis. Diminution of vasoprotective effects of perivascular adipose tissue (PVAT) in metabolic disorders suggests a molecular link between diabetes and atherosclerosis. Objectives To assess the paracrine role of the PVAT on the progression of diabetic atherosclerosis, via intercellular communications between PVAT and the underlying vasculature. Methods Periaortic adipose tissue from Type 2 diabetic (db/db) mice were transplanted around the right common carotid arteries of ApoE‐/‐ mice, followed by 16 weeks of atherogenic diet. Carotid arteries and adipose tissues were assessed for lesion formation and inflammatory markers, respectively. Adipose stem cells (ASCs) from PVAT were treated with lipopolysaccharide (1µg/ml), palmitate (200µM), and high‐glucose (42 mM) for 24 hrs (denoted as P‐ASCs) to mimic type 2 diabetes‐associated metabolic alterations. Small extracellular vesicles (sEV) were isolated from the conditioned media. Aortic vascular smooth muscle cells (SMCs) were incubated with ASC‐derived sEV (25µg/ml). The migratory potential of SMCs was evaluated by wound healing assay. Results Histological analysis displayed accelerated atherogenic plaque formation with transplantation of type 2 diabetic PVAT around the carotid arteries of ApoE‐/‐ mice, which is otherwise resistant to plaque formation. Pro‐inflammatory markers were significantly increased in periaortic adipose tissue from db/db compared to WT. sEV secreted from P‐ASCs greatly enhanced the SMC migration when compared with the control ASCs. Conclusions Our data shows type 2 diabetes‐accelerated progression of atherosclerosis is mediated by PVAT‐derived sEV.
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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 | 0.001 |
| 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".