Exercise training restores abnormal myocardial glucose utilization and cardiac function in diabetes
Bibliographic record
Abstract
BACKGROUND: Clinical and experimental studies have shown that a reduction in myocardial glucose utilization is a factor contributing to diabetic cardiomyopathy. This study determined whether exercise training could prevent the depression in glucose utilization observed in the diabetic rat heart. METHODS: Diabetes was induced in Sprague-Dawley rats by an intravenous injection of streptozotocin (60 mg/kg). After 10 weeks of treadmill running, exogenous myocardial glucose utilization and cardiac function were determined in isolated working hearts perfused under aerobic conditions and then subjected to a 60-min period of low-flow ischemia followed by reperfusion. RESULTS: Compared to aerobically perfused sedentary control hearts, rates of myocardial glucose oxidation and glycolysis were lower in diabetic hearts. Diabetes was also characterized by a pronounced decrease in cardiac function. Following exercise training, rates of myocardial glucose oxidation and glycolysis were restored and cardiac performance was improved compared to sedentary diabetic hearts. During low-flow ischemia, the decrease in glycolysis observed in hearts of sedentary diabetic rats was attenuated following exercise training. Following ischemia, glucose oxidation and glycolysis returned to preischemic levels in all groups. However, hearts from trained diabetic animals had higher rates of glucose oxidation compared to their respective sedentary group. This was accompanied by an enhanced recovery of heart function following ischemia. CONCLUSIONS: Our results indicate that exercise training is effective in preventing the depression in myocardial glucose metabolism observed in the diabetic rat. This may explain the benefits of exercise in preventing cardiac dysfunction in 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 | 0.000 | 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 | 0.000 |
| 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".