Differential effects of metformin on breast cancer proliferation according to markers of insulin resistance and tumor subtype in a randomized presurgical trial
Bibliographic record
Abstract
Treatment of diabetics with metformin is associated with decreased breast cancer risk in observational studies, but it remains unclear if this drug has clinical antineoplastic activity. In a recent presurgical trial, we found a heterogeneous effect of metformin on breast cancer proliferation (ki-67) depending upon insulin resistance (HOMA index). Here, we determined the associations of additional serum biomarkers of insulin resistance, tumor subtype, and drug concentration with ki-67 response to metformin. Two-hundred non-diabetic women were randomly allocated to metformin (850 mg/bid) or placebo for 4 weeks prior to breast cancer surgery. The ki-67 response to metformin was assessed comparing data obtained from baseline biopsy (ki-67 and tumor subtype) and serum markers (HOMA index, C-peptide, IGF-I, IGFBP-1, IGFBP-3, free IGF-I, hs-CRP, adiponectin) with the same measurements at definitive surgery. For patients with a blood sample taken within 24 h from last drug intake, metformin level was measured. Compared with placebo, metformin significantly decreased ki-67 in women with HOMA > 2.8, those in the lowest IGFBP-1 quintile, those in the highest IGFBP-3 quartile, those with low free IGF-I, those in the top hs-CRP tertile, and those with HER2-positive tumors. In women with HOMA index > 2.8, drug levels were positively correlated with the ki-67 decrease, whereas no trend was noted in women with HOMA < 2.8 (p-interaction = 0.07). At conventional antidiabetic doses, the effect of metformin on tumor ki-67 of non-diabetic breast cancer patients varies with host and tumor characteristics. These findings are relevant to design breast cancer prevention and treatment trials with metformin.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".