Peroxisome proliferator‐activated receptor <i>γ</i> activation favours selective subcutaneous lipid deposition by coordinately regulating lipoprotein lipase modulators, fatty acid transporters and lipogenic enzymes
Bibliographic record
Abstract
AIM: Peroxisome proliferator-activated receptor (PPAR) γ activation is associated with preferential lipoprotein lipase (LPL)-mediated fatty acid storage in peripheral subcutaneous fat depots. How PPARγ agonism acts upon the multi-level modulation of depot-specific lipid storage remains incompletely understood. METHODS: We evaluated herein triglyceride-derived lipid incorporation into adipose tissue depots, LPL mass and activity, mRNA levels and content of proteins involved in the modulation of LPL activity and fatty acid transport, and the expression/activity of enzymes defining adipose tissue lipogenic potential in rats treated with the PPARγ ligand rosiglitazone (30 mg kg(-1) day(-1) , 23 days) after either a 10-h fasting period or a 17-h fast followed by 6 h of ad libitum refeeding. RESULTS: Rosiglitazone stimulated lipid accretion in subcutaneous fat (SF) ~twofold and significantly reduced that of visceral fat (VF) to nearly half. PPARγ activation selectively increased LPL mass, activity and the expression of its chaperone LMF1 in SF. In VF, rosiglitazone had no effect on LPL activity and downregulated the mRNA levels of the transendothelial transporter GPIHBP1. Overexpression of lipid uptake and fatty acid transport proteins (FAT/CD36, FATP1 and FABP4) and stimulation of lipogenic enzyme activities (GPAT, AGPAT and DGAT) upon rosiglitazone treatment were of higher magnitude in SF. CONCLUSIONS: Together these findings demonstrate that the depot-specific transcriptional control of LPL induced by PPARγ activation extends to its key interacting proteins and post-translational modulators to favour subcutaneous lipid storage.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".