Circulating lipids are lowered but pancreatic islet lipid metabolism and insulin secretion are unaltered in exercise-trained female rats
Bibliographic record
Abstract
Deteriorating islet beta-cell function is key in the progression of an impaired glucose tolerance state to overt type 2 diabetes (T2D), a transition that can be delayed by exercise. We have previously shown that trained rats are protected from heart ischemia-reperfusion injury in correlation with an increase in cardiac tissue fatty-acid oxidation. This trained metabolic phenotype, if induced in the islet, could also prevent beta-cell failure in the pathogenesis of T2D. To assess the effect of training on islet lipid metabolism and insulin secretion, female Sprague-Dawley rats were exercised on a treadmill for 90 min/d, 4 d/week, for 10 weeks. Islet fatty-acid oxidation, the expression of key lipid metabolism genes, and glucose-stimulated insulin secretion were determined in freshly isolated islets from trained and sedentary control rats after a 48 h rest period from the last exercise. Although this moderate training reduced plasma glycerol, free fatty acids, and triglyceride levels by about 40%, consistent with reduced lipolysis from adipose tissue, it did not alter islet fatty-acid oxidation, nor the islet expression of key transcription factors and enzymes of lipid metabolism. The training also had no effect on glucose-stimulated insulin secretion or its amplification by free fatty acids. In summary, chronic exercise training did not cause an intrinsic change in islet lipid metabolism. Training did, however, substantially reduce the exposure of islets to exogenous lipid, thereby providing a potential mechanism by which exercise can prevent islet beta-cell failure leading to 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".