Depot-specific effects of the PPARγ agonist rosiglitazone on adipose tissue glucose uptake and metabolism
Bibliographic record
Abstract
We investigated mechanisms whereby peroxisome proliferator-activated receptor gamma (PPARgamma) agonism redistributes lipid from visceral (VF) toward subcutaneous fat (SF) by studying the impact of PPARgamma activation on VF and SF glucose uptake and metabolism, lipogenesis, and enzymes involved in triacylglycerol (TAG) synthesis. VF (retroperitoneal) and SF (inguinal) of rats treated or not for 7 days with rosiglitazone (15 mg/kg/day) were evaluated in vivo for glucose uptake and lipogenesis and in vitro for glucose metabolism, gene expression, and activities of glycerolphosphate acyltransferase (GPAT), phosphatidate phosphatase-1 (or lipin-1), and diacylglycerol acyltransferase. Rosiglitazone increased SF glucose uptake, GLUT4 mRNA, and insulin-stimulated glucose oxidation, conversion to lactate, glycogen, and the glycerol and fatty acid components of TAG. In VF, only glucose incorporation into TAG-glycerol was stimulated by rosiglitazone and less so than in SF (1.5- vs. 3-fold). mRNA levels of proteins involved in glycolysis, Krebs cycle, glycogen synthesis, and lipogenesis were markedly upregulated by rosiglitazone in SF and again less so in VF. Rosiglitazone activated TAG-glycerol synthesis in vivo (2.8- vs. 1.9-fold) and lipin activity (4.6- vs. 1.5-fold) more strongly in SF than VF, whereas GPAT activity was increased similarly in both depots. The preferential increase in glucose uptake and intracellular metabolism in SF contributes to the PPARgamma-mediated redistribution of TAG from VF to SF, which in turn favors global insulin sensitization.
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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 | 0.001 |
| 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) | 0.001 | 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".