Effects of insulin and analogues on carcinogen-induced mammary tumours in high-fat-fed rats
Bibliographic record
Abstract
It is not fully clarified whether insulin glargine, an analogue with a high affinity for insulin-like growth factor-1 receptor (IGF-1R), increases the risk for cancers that abundantly express IGF-1R such as breast cancer or some types of breast cancer. To gain insight into this issue, female Sprague–Dawley rats fed a high-fat diet were given the carcinogen N-methyl-N-nitrosourea and randomly assigned to vehicle (control), NPH (unmodified human insulin), glargine or detemir ( n = 30 per treatment). Insulins were given subcutaneously (15 U/kg/day) 5 days a week. Mammary tumours were counted twice weekly, and after 6 weeks of treatment, extracted for analysis. None of the insulin-treated groups had increased mammary tumour incidence at any time compared with control. At 6 weeks, tumour multiplicity was increased with NPH or glargine ( P < 0.05) and tended to be increased with detemir ( P = 0.2); however, there was no difference among insulins (number of tumours per rat: control = 0.8 ± 0.1, NPH = 1.8 ± 0.3, glargine = 1.5 ± 0.4, detemir = 1.4 ± 0.4; number of tumours per tumour-bearing rat: control = 1.3 ± 0.1, NPH = 2.2 ± 0.4, glargine = 2.7 ± 0.5, detemir = 2.3 ± 0.5). IGF-1R expression in tumours was lower than that in Michigan Cancer Foundation-7 (MCF-7) cells, a cell line that shows greater proliferation with glargine than unmodified insulin. In rats, glargine was rapidly metabolised to M1 that does not have greater affinity for IGF-1R. In conclusion, in this model of oestrogen-dependent breast cancer in insulin-resistant rats, insulin and insulin analogues increased tumour multiplicity with no difference between insulin types.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".