Increased Tumor Growth in Mice with Diet-Induced Obesity: Impact of Ovarian Hormones
Bibliographic record
Abstract
Obesity increases the risk of many cancers in both males and females. This study describes a link between obesity, obesity-associated metabolic alterations, and the risk of developing cancer in male and female mice. The goal of this study was to evaluate the relationship between gender and obesity and to determine the role of estrogen status in obese females and its effect on tumor growth. We examined the susceptibility of C57BL/6 mice to diet-induced obesity, insulin resistance/glucose intolerance, and tumors. Mice were injected sc with one of two tumorigenic cell lines, Lewis lung carcinoma, or mouse colon 38-adenocarcinoma. Results show that tumor growth rate was increased in obese mice vs. control mice irrespective of the tumor cell type. To investigate the effect of estrogen status on tumor development in obese females, we compared metabolic parameters and tumor growth in ovariectomized (ovx) and intact obese female mice. Obese ovx female mice developed insulin resistance and glucose intolerance similar to that observed in obese males. Our results demonstrate that body adiposity increased in ovx females irrespective of the diet administered and that tumor growth correlated positively with body adiposity. Overall, these data point to more rapid tumor growth in obese mice and suggest that endogenous sex steroids, together with diet, affect adiposity, insulin sensitivity, and tumor growth in female mice.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".