Obesity and insulin resistance in breast cancer – Chemoprevention strategies with a focus on metformin
Bibliographic record
Abstract
Obesity and insulin resistance have been associated with breast cancer risk, and breast cancer outcomes. Recent research has focused on insulin as a potential biologic mediator of these effects given frequent expression of insulin/IGF-1 receptors on breast cancer cells which, when activated, can stimulate signaling through PI3K and Ras-Raf signaling pathways to enhance proliferation. Metformin, a commonly used diabetes drug, lowers insulin in non-breast diabetic cancer patients, likely by reducing hepatic gluconeogenesis; it also appears to have potential insulin independent direct effects on tumor cells which are mediated by activation of AMPK with downstream inhibition of mTOR. There is growing epidemiologic, clinical and preclinical (in vitro and in vivo) evidence in keeping with anticancer effects of metformin in breast and other cancers. This has led to the hypothesis that metformin may be effective in breast cancer prevention and treatment. Clinical studies in the neoadjuvant and adjuvant settings are ongoing; additional Phase 2 trials in the metastatic setting and proof of principle studies in the prevention setting are planned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".