Mechanisms of the Depot Specificity of Peroxisome Proliferator–Activated Receptor γ Action on Adipose Tissue Metabolism
Bibliographic record
Abstract
In this study, we aimed to establish the mechanisms whereby peroxisome proliferator-activated receptor gamma (PPARgamma) agonism brings about redistribution of fat toward subcutaneous depots and away from visceral fat. In rats treated with the full PPARgamma agonist COOH (30 mg x kg(-1) x day(-1)) for 3 weeks, subcutaneous fat mass was doubled and that of visceral fat was reduced by 30% relative to untreated rats. Uptake of triglyceride-derived nonesterified fatty acids was greatly increased in subcutaneous fat (14-fold) and less so in visceral fat (4-fold), with a concomitant increase, restricted to subcutaneous fat only, in mRNA levels of the uptake-, retention-, and esterification-promoting enzymes lipoprotein lipase, aP2, and diacylglycerol acyltransferase 1. Basal lipolysis and fatty acid recycling were stimulated by COOH in both subcutaneous fat and visceral fat, with no frank quantitative depot specificity. The agonist increased mRNA levels of enzymes of fatty acid oxidation and thermogenesis much more strongly in visceral fat than in subcutaneous fat, concomitantly with a stronger elevation in O2 consumption in the former than in the latter. Mitochondrial biogenesis was stimulated equally in both depots. These findings demonstrate that PPARgamma agonism redistributes fat by stimulating the lipid uptake and esterification potential in subcutaneous fat, which more than compensates for increased O2 consumption; conversely, lipid uptake is minimally altered and energy expenditure is greatly increased in visceral fat, with consequent reduction in fat accumulation.
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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 | 0.001 |
| 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".