Breast Cancer Endocrine Therapy Promotes Weight Gain With Distinct Adipose Tissue Effects in Lean and Obese Female Mice
Bibliographic record
Abstract
Breast cancer survivors treated with tamoxifen and aromatase inhibitors report weight gain and have an elevated risk of type 2 diabetes, especially if they have obesity. These patient experiences are inconsistent with, preclinical studies using high doses of tamoxifen which reported acute weight loss. We investigated the impact of breast cancer endocrine therapies in a preclinical model of obesity and in a small group of breast adipose tissue samples from women taking tamoxifen to understand the clinical findings. Mature female mice were housed at thermoneutrality and fed either a low-fat/low-sucrose (LFLS) or a high-fat/high-sucrose (HFHS) diet. Consistent with the high expression of Esr1 observed in mesenchymal stem cells from adipose tissue, endocrine therapy was associated with adipose accumulation and more preadipocytes compared with estrogen-treated control mice but resulted in fewer adipocyte progenitors only in the context of HFHS. Analysis of subcutaneous adipose stromal cells revealed diet- and treatment-dependent effects of endocrine therapies on various cell types and genes, illustrating the complexity of adipose tissue estrogen receptor signaling. Breast cancer therapies supported adipocyte hypertrophy and associated with hepatic steatosis, hyperinsulinemia, and glucose intolerance, particularly in obese females. Current tamoxifen use associated with larger breast adipocyte diameter only in women with obesity. Our translational studies suggest that endocrine therapies may disrupt adipocyte progenitors and support adipocyte hypertrophy, potentially leading to ectopic lipid deposition that may be linked to a greater type 2 diabetes risk. Monitoring glucose tolerance and potential interventions that target insulin action should be considered for some women receiving life-saving endocrine therapies for breast cancer.
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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) | 0.000 | 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 | 0.002 |
| 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".