Adipocyte Glucocorticoid Receptor Activation With High Glucocorticoid Doses Impairs Healthy Adipose Tissue Expansion by Repressing Angiogenesis
Bibliographic record
Abstract
In humans, glucocorticoids (GCs) are commonly prescribed because of their anti-inflammatory and immunosuppressive properties. However, high doses of GCs often lead to side effects, including diabetes and lipodystrophy. We recently reported that adipocyte glucocorticoid receptor (GR)-deficient (AdipoGR-KO) mice under corticosterone (CORT) treatment exhibited a massive adipose tissue (AT) expansion associated with a paradoxical improvement of metabolic health compared with control mice. However, whether GR may control adipose development remains unclear. Here, we show a specific induction of hypoxia-inducible factor 1α (HIF-1α) and proangiogenic vascular endothelial growth factor A (VEGFA) expression in GR-deficient adipocytes of AdipoGR-KO mice compared with control mice, together with an increased adipose vascular network, as assessed by three-dimensional imaging. GR activation reduced HIF-1α recruitment to the Vegfa promoter resulting from Hif-1α downregulation at the transcriptional and posttranslational levels. Importantly, in CORT-treated AdipoGR-KO mice, the blockade of VEGFA by a soluble decoy receptor prevented AT expansion and the healthy metabolic phenotype. Finally, in subcutaneous AT from patients with Cushing syndrome, higher VEGFA expression was associated with a better metabolic profile. Collectively, these results highlight that adipocyte GR negatively controls AT expansion and metabolic health through the downregulation of the major angiogenic effector VEGFA and inhibition of vascular network development.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".