Actions of PPARγ agonism on adipose tissue remodeling, insulin sensitivity, and lipemia in absence of glucocorticoids
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma (PPARgamma) agonists improve insulin sensitivity and lipemia partly through enhancing adipose tissue proliferation and capacity for lipid retention. The agonists also reduce local adipose glucocorticoid production, which may in turn contribute to their metabolic actions. This study assessed the effects of a PPARgamma agonist in the absence of glucocorticoids (adrenalectomy, ADX). Intact, ADX, and intact pair-fed (PF) rats were treated with the PPARgamma agonist rosiglitazone (RSG) for 2 wk. RSG increased inguinal (subcutaneous) white (50%) and brown adipose tissue (6-fold) weight but not that of retroperitoneal (visceral) white adipose tissue. ADX but not PF reduced fat accretion in both inguinal and retroperitoneal adipose depots but did not affect brown adipose mass. RSG no longer increased inguinal weight in ADX and PF rats but increased brown adipose mass, albeit less so than in intact rats. RSG increased cell proliferation in white (3-fold) and brown adipose tissue (6-fold), as assessed microscopically and by total DNA, an effect that was attenuated but not abrogated by ADX. RSG reduced the expression of the glucocorticoid-activating enzyme 11beta-hydroxysteroid dehydrogenase 1 (11beta-HSD1) in all adipose depots. RSG improved insulin sensitivity (reduction in fasting insulin and homeostasis model assessment of insulin resistance, both -50%) and triacylglycerolemia (-75%) regardless of the glucocorticoid status, these effects being fully additive to those of ADX and PF. In conclusion, RSG partially retained its ability to induce white and brown adipose cell proliferation and brown adipose fat accretion and further improved insulin sensitivity and lipemia in ADX rats, such effects being therefore independent from the PPARgamma-mediated modulation of glucocorticoids.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".