Breast cancer endocrine therapy exhausts adipocyte progenitors promoting weight gain and glucose intolerance
Bibliographic record
Abstract
Abstract Breast cancer survivors treated with anti-estrogen therapies report weight gain and have an elevated risk of type 2 diabetes. Here, we show that current tamoxifen use associated with larger breast adipocyte diameter only in women with a BMI >30 kg/m 2 . To understand the mechanisms behind these clinical findings, we investigated the impact of estrogen deprivation and tamoxifen in a relevant pre-clinical model of obesity. Specifically, 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 single-cell RNA sequencing of mesenchymal stem cells from adipose tissue, endocrine therapies induced adipose accumulation and preadipocyte expansion, but resulted in adipocyte progenitor depletion only in the context of HFHS. Consequently, 7-week endocrine therapy supported adipocyte hypertrophy and was associated with hepatic steatosis, hyperinsulinemia, insulin resistance, and glucose intolerance, particularly in HFHS fed females. Metformin or pioglitazone, glucose lowering drugs used to treat diabetes, prevented the effects of tamoxifen but not estrogen deprivation on adipocyte size and insulin resistance in HFHS-fed mice. This translational study suggests that endocrine therapies act via ERα to directly disrupt adipocyte progenitors and support adipocyte hypertrophy, leading to ectopic lipid deposition that may promote hyperinsulinemia, insulin resistance and type 2 diabetes. 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 | 0.000 |
| 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) | 0.001 | 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".