Voluntary physical activity counteracts the proliferative tumor growth microenvironment created by adipose tissue via high-fat diet feeding in female rats
Bibliographic record
Abstract
The adipokine secretion profile created from adipose tissue may represent the molecular mechanism behind the obesity-breast cancer association. Two adipokines, adiponectin (ADIPO), and leptin (LEP), are altered with obesity and exert antagonistic effects on breast cancer proliferation. We set out to determine whether the adipose-dependent tumor promoting growth environment created by a high-fat diet (HFD) in female Sprague-Dawley rats is altered compared to established responses in male rats and whether voluntary physical activity (PA) ameliorates any HFD-dependent effects. We found that conditioned media (CM) created from the adipose tissue of female HFD-fed rats increased the proliferation of MCF7 cells compared to those cells grown in CM prepared from lean adipose tissue. HFD-CM inhibited AMPK and activated Akt signaling, decreased p27 phosphorylation at T198, reduced total p27 and AdiporR1 protein levels and promoted cell-cycle entry. PA reversed the proliferative effects of HFD-CM on MCF7 cells by preventing the effects of HFD on AMPK, Akt, p27 and AdipoR1, ultimately resulting in cell-cycle withdrawal. Overexpressing AdipoR1 abolished the proliferative effects of the HFD-CM on MCF7 cells and enhanced the anti-proliferative effects PA on the HFD-CM Thus, PA represents a means to prevent deleterious obesity-related alterations in tumor growth environment which are brought about by changes in adipokine secretion profile from adipose tissue in the presence of estrogen. Furthermore, although adipose produces hundreds of adipokines, the ADIPO:LEP ratio may serve to indicate the contribution of adipose in creating a tumor growth microenvironment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".