Estrogen receptor activation reduces lipid synthesis in pancreatic islets and prevents β cell failure in rodent models of type 2 diabetes
Bibliographic record
Abstract
The failure of pancreatic β cells to adapt to an increasing demand for insulin is the major mechanism by which patients progress from insulin resistance to type 2 diabetes (T2D) and is thought to be related to dysfunctional lipid homeostasis within those cells. In multiple animal models of diabetes, females demonstrate relative protection from β cell failure. We previously found that the hormone 17β-estradiol (E2) in part mediates this benefit. Here, we show that treating male Zucker diabetic fatty (ZDF) rats with E2 suppressed synthesis and accumulation of fatty acids and glycerolipids in islets and protected against β cell failure. The antilipogenic actions of E2 were recapitulated by pharmacological activation of estrogen receptor α (ERα) or ERβ in a rat β cell line and in cultured ZDF rat, mouse, and human islets. Pancreas-specific null deletion of ERα in mice (PERα-/-) prevented reduction of lipid synthesis by E2 via a direct action in islets, and PERα-/- mice were predisposed to islet lipid accumulation and β cell dysfunction in response to feeding with a high-fat diet. ER activation inhibited β cell lipid synthesis by suppressing the expression (and activity) of fatty acid synthase via a nonclassical pathway dependent on activated Stat3. Accordingly, pancreas-specific deletion of Stat3 in mice curtailed ER-mediated suppression of lipid synthesis. These data suggest that extranuclear ERs may be promising therapeutic targets to prevent β cell failure in T2D.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".