High levels of dietary soy decrease mammary tumor latency and increase incidence in MTB-IGFIR transgenic mice
Bibliographic record
Abstract
BACKGROUND: Epidemiologic data indicates that Asian diets, which are high in soy protein, reduce a women's risk of developing breast cancer. However, it has been difficult to dissociate the benefits of soy from other variables including environmental and lifestyle factors. Since prospective studies in humans would take decades to complete, rodent models provide a valuable research alternative. METHODS: In this study, MTB-IGFIR transgenic mice, which develop mammary tumors resulting from overexpression of the type I insulin-like growth factor receptor (IGF-IR), were utilized. MTB-IGFIR mice were fed a soy-based or casein-based diet throughout all stages of development to reflect soy exposure in Asian cultures. Mammary tumors were initiated at 2 different developmental stages by commencing IGF-IR transgene expression either during puberty or in adult mice. RESULTS: MTB-IGFIR mice fed a soy-based diet displayed increased tumor incidence and accelerated tumor onset compared to MTB-IGFIR mice fed a casein diet. Two markers of estrogen receptor signaling, Pgr and Areg, were elevated in mammary tissue from mice fed the soy diet compared to mice fed the casein diet suggesting that high levels of soy may promote mammary tumor development through acting as an estrogen receptor agonist. Mammary tumors from mice fed a soy diet more frequently expressed metaplastic markers such as cytokeratins 5 and 14 as well as p63 and displayed reduced lung metastases compared to mammary tumors from mice fed a casein diet. CONCLUSIONS: Diets consisting of very high levels of soy protein promote mammary tumor development and decrease tumor latency possibly through activating estrogen receptor signaling. Additional studies are required to determine whether a more moderate amount of dietary soy can inhibit oncogene-induced mammary tumorigenesis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".