Tamoxifen Targets Wisp2 to Impair Subcutaneous Adipocyte Progenitor Self-Renewal and Adipogenic Differentiation
Bibliographic record
Abstract
ABSTRACT Breast cancer endocrine therapy, which systemically disrupts estrogen receptor signaling, increases type 2 diabetes (T2D) risk in some women. Sustained treatment with low-dose tamoxifen depletes subcutaneous adipocyte progenitors and promotes glucose intolerance and hepatic lipid deposition in obese female mice. Hyperplastic adipose tissue expansion, especially in subcutaneous depots, preserves metabolic health during a chronic positive energy balance by facilitating nutrient storage and attenuating inflammation. Adipocyte progenitors are renewed in part through Wnt signaling pathway activation, which is altered in women with obesity or T2D. Estrogen receptors are expressed in several adipose cell types, but the distinct actions of tamoxifen in adipocyte progenitors and the mechanisms that explain their depletion during endocrine therapy are not defined. The direct impact of tamoxifen was evaluated in subcutaneous adipose stromal cells from humans and adult mice. Self-renewal, proliferation, and differentiation were measured, and analyses of gene expression and progenitor or preadipocyte populations were performed. Mechanistic insight was gained from primary adipose stromal cells of obese female mice, in which the Wnt1 inducible signaling pathway protein 2 (Wisp2) was lost following endocrine therapy. Wisp2 gain and loss of function studies were carried out in adipose stromal cells to define the link between estrogen signaling and adipocyte progenitor maintenance. We found that tamoxifen treatment disrupts the protection of adipocyte progenitors by estrogen, mediated through suppression of Wisp2. These studies reveal potential metabolic effects of tamoxifen therapy that precede and could drive T2D development in breast cancer survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".