Molecular links between obesity and breast cancer
Bibliographic record
Abstract
For over 40 years there has been a statistical linkage between obesity and cancer, yet little is known about the molecular mechanisms underlying this long standing deleterious association. Recent work has shown that adipocyte-derived peptides can affect cell cycle entry in mammary epithelial cells. Thus we examined the paracrine role of the adipokines leptin and adiponectin on mammary epithelial cell cycle regulation. G0 synchronized MCF7 cells were treated with leptin and adiponectin, separately and in concert, in time and dose dependent experiments. Immunoblotting demonstrated that p27 protein levels decreased with leptin treatment which suggested cell cycle entry. In contrast adiponectin increased p27 levels suggesting cell cycle arrest. To complement these experiments MCF7 cells were co-cultured with subcutaneous adipocytes isolated from lean rats to mimic in vivo paracrine conditions. Moderate increases in adipocyte concentrations increased p27 levels up to a peak, mimicking the effects of adiponectin. At high adipocyte concentrations p27 levels decreased mirroring the effects of leptin. The relevance of these findings is that obese individuals exhibit high leptin levels and low adiponectin levels suggesting a stoichiometric imbalance may underlie cancer development and progression. This may also provide the basis for the development of novel cancer therapeutic and prevention strategies.
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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 | 0.001 | 0.001 |
| 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".