Peroxisome Proliferator-Activated Receptor γ and Adipose Tissue—Understanding Obesity-Related Changes in Regulation of Lipid and Glucose Metabolism
Bibliographic record
Abstract
CONTEXT: Adipose tissue is a metabolically dynamic organ, serving as a buffer to control fatty acid flux and a regulator of endocrine function. In obese subjects, and those with type 2 diabetes or the metabolic syndrome, adipose tissue function is altered (i.e. adipocytes display morphological differences alongside aberrant endocrine and metabolic function and low-grade inflammation). EVIDENCE ACQUISITION: Articles on the role of peroxisome proliferator-activated receptor gamma (PPARgamma) in adipose tissue of healthy individuals and those with obesity, metabolic syndrome, or type 2 diabetes were sourced using MEDLINE (1990-2006). EVIDENCE SYNTHESIS: Articles were assessed to provide a comprehensive overview of how PPARgamma-activating ligands improve adipose tissue function, and how this links to improvements in insulin resistance and the progression to type 2 diabetes and atherosclerosis. CONCLUSIONS: PPARgamma is highly expressed in adipose tissue, where its activation with thiazolidinediones alters fat topography and adipocyte phenotype and up-regulates genes involved in fatty acid metabolism and triglyceride storage. Furthermore, PPARgamma activation is associated with potentially beneficial effects on the expression and secretion of a range of factors, including adiponectin, resistin, IL-6, TNFalpha, plasminogen activator inhibitor-1, monocyte chemoattractant protein-1, and angiotensinogen, as well as a reduction in plasma nonesterified fatty acid supply. The effects of PPARgamma also extend to macrophages, where they suppress production of inflammatory mediators. As such, PPARgamma activation appears to have a beneficial effect on the relationship between the macrophage and adipocyte that is distorted in obesity. Thus, PPARgamma-activating ligands improve adipose tissue function and may have a role in preventing progression of insulin resistance to diabetes and endothelial dysfunction to atherosclerosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".