Dynamics of insulin sensitivity, β-cell function, and β-cell mass during the development of diabetes in <i>fa</i>/<i>fa</i> rats
Bibliographic record
Abstract
Both male Zucker Fatty (mZF) and lower-fat-fed female Zucker diabetic fatty (LF-fZDF) rats are obese but remain normoglycemic. Male ZDF (mZDF) and high-fat-fed female ZDF rats (HF-fZDF) are also obese but develop diabetes between 7 and 10 wk of age. Although these models have been well studied, the mechanisms governing the adaptations to obesity in the normoglycemic animals, and the failure of adaptation in the animals that develop diabetes, remain unclear. Here we use quantitative morphometry and our recently developed coupled beta-cell mass (beta(m)), insulin, and glucose model to elucidate the dynamics of insulin sensitivity (S(I)), beta-cell secretory capacity (beta(sc)), and beta(m) in these four animal models. Both groups that remained normoglycemic with increasing obesity (mZF, LF-fZDF) exhibited increased beta(m) and constant beta(sc) in response to a falling S(I). In rats that developed hyperglycemia (mZDF, HF-fZDF), there was a greater reduction in S(I) and slower expansion of beta(m), with constant beta(sc). beta(sc) decreased after glucose levels rose above 20 mM. Taken together, these data suggest that excessive insulin resistance and insufficient beta(m) adaptation play a primary role in the pathogenesis of diabetes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".