Aging alters PPARγ in rodent and human adipose tissue by modulating the balance in steroid receptor coactivator‐1
Bibliographic record
Abstract
Age is an important risk factor for the development of metabolic diseases (e.g. obesity, diabetes and atherosclerosis). Yet, little is known about the molecular mechanisms occurring upon aging that affect energy metabolism. Although visceral white adipose tissue (vWAT) is known for its key impact on metabolism, recent studies have indicated it could also be a key regulator of lifespan, suggesting that it can serve as a node for age-associated fat accretion. Here we show that aging triggers changes in the transcriptional milieu of the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARgamma) in vWAT, which leads to a modified potential for transactivation of target genes upon ligand treatment. We found that in vWAT of mice, rats and men, aging induced a specific decrease in the expression of steroid receptor coactivator-1 (SRC-1), whose recruitment to PPARgamma is associated with improved insulin sensitivity and low adipogenic activity. In contrast, aging and oxidative stress did not impact on PPARgamma expression and PPARgamma ligand production. Age-induced loss of PPARgamma/SRC-1 interactions increased the binding of PPARgamma to the promoter of the adipogenic gene aP2. These findings suggest that strategies aimed at increasing SRC-1 expression and recruitment to PPARgamma upon aging might help improve age-associated metabolic disorders.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".