Pancreatic Islet Adaptation to Fasting Is Dependent on Peroxisome Proliferator-Activated Receptor α Transcriptional Up-Regulation of Fatty Acid Oxidation
Bibliographic record
Abstract
The cellular response to fasting and starvation in tissues such as heart, skeletal muscle, and liver requires peroxisome proliferator-activated receptor-alpha (PPARalpha)-dependent up-regulation of energy metabolism toward fatty acid oxidation (FAO). PPARalpha null (PPARalphaKO) mice develop hyperinsulinemic hypoglycemia in the fasting state, and we previously showed that PPARalpha expression is increased in islets at low glucose. On this basis, we hypothesized that enhanced PPARalpha expression and FAO, via depletion of lipid-signaling molecule(s) for insulin exocytosis, are also involved in the normal adaptive response of the islet to fasting. Fasted PPARalphaKO mice compared with wild-type mice had supranormal ip glucose tolerance due to increased plasma insulin levels. Isolated islets from the PPARalpha null mice had a 44% reduction in FAO, normal glucose use and oxidation, and enhanced glucose-induced insulin secretion. In normal rats, fasting for 24 h increased islet PPARalpha, carnitine palmitoyltransferase 1, and uncoupling protein-2 mRNA expression by 60%, 62%, and 82%, respectively. The data are consistent with the view that PPARalpha, via transcriptionally up-regulating islet FAO, can reduce insulin secretion, and that this mechanism is involved in the normal physiological response of the pancreatic islet to fasting such that hypoglycemia is avoided.
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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 | 0.001 | 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".