Dexamethasone Inhibits White Adipose Tissue Browning
Bibliographic record
Abstract
White adipose tissue (WAT) regulates energy balance through energy storage, adipokines secretion and the thermogenesis process. Beige adipocytes are responsible for WAT thermogenesis. They are generated by adipogenesis or transdifferentiation during cold or β3-adrenergic agonist stimulus through a process called browning. Browning has gained significant interest for to its preventive effect on obesity. Glucocorticoids (GCs) have several functions in WAT biology; however, their role in beige adipocyte generation and WAT browning is not fully understood. The aim of our study was to determine the effect of dexamethasone (DXM) on WAT thermogenesis. For this purpose, rats were treated with DXM at room temperature (RT) or cold conditions to determine different thermogenic markers. Furthermore, the effects of DXM on the adipogenic potential of beige precursors and on mature beige adipocytes were evaluated in vitro. Our results showed that DXM decreased UCP-1 mRNA and protein levels, mainly after cold exposure. In vitro studies showed that DXM decreased the expression of a beige precursor marker (Ebf2), affecting their ability to differentiate into beige adipocytes, and inhibited the thermogenic response of mature beige adipocytes (Ucp-1, Dio2 and Pgc1α gene expressions and mitochondrial respiration). Overall, our data strongly suggest that DXM can inhibit the thermogenic program of both retroperitoneal and inguinal WAT depots, an effect that could be exerted, at least partially, by inhibiting de novo cell generation and the thermogenic response in beige adipocytes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".