β-Cell compensation concomitant with adaptive endoplasmic reticulum stress and β-cell neogenesis in a diet-induced type 2 diabetes model
Bibliographic record
Abstract
Insulin-secreting pancreatic β-cells adapt to obesity-related insulin resistance via increases in insulin secretion and β-cell mass. Failed β-cell compensation predicts the onset of type 2 diabetes (T2D). However, the mechanisms of β-cell compensation are not fully understood. Our previous study reported changes in β-cell mass during the progression of T2D in the Nile rat (NR; Arvicanthis niloticus) fed standard chow. In the present study, we measured other β-cell adaptive responses, including glucose metabolism and β-cell insulin secretion in NRs at different ages, thus characterizing NR at 2 months as a model of β-cell compensation followed by decompensation at 6 months. We observed increased proinsulin secretion in the transition from compensation to decompensation, which is indicative of impaired insulin processing. Subsequently, we compared adaptive unfolded protein response in β-cells and demonstrated a positive role of endoplasmic reticulum (ER) chaperones in insulin secretion. In addition, the incidence of insulin-positive neogenic but not proliferative cells increased during the compensation phase, suggesting nonproliferative β-cell growth as a mechanism of β-cell mass adaptation. In contrast, decreased neogenesis and β-cell dedifferentiation were observed in β-cell dysfunction. Furthermore, the progression of T2D and pathophysiological changes of β-cells were prevented by increasing fibre content of the diet. Novelty Our study characterized a novel model for β-cell compensation with adaptive responses in cell function and mass. The temporal association of adaptive ER chaperones with blood insulin and glucose suggests upregulated chaperone capacity as an adaptive mechanism. β-Cell neogenesis but not proliferation contributes to β-cell mass adaptation.
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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 | 0.001 |
| 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) | 0.001 | 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".