Interaction of Diet and Training on Endurance Performance in Rats
Bibliographic record
Abstract
We determined the interaction of diet and training on metabolic adaptations in skeletal muscle and liver, and the consequences of these adaptations for endurance. Eighty rats performed a baseline treadmill run to exhaustion at 16 m min(-1) (RUN1) and were then divided into two groups and given one of two diets: high carbohydrate (CHO) or high fat (FAT). Each dietary group was then divided into one of four subgroups: sedentary control that performed no training (NT); low-intensity running (8 m min(-1); LOW) and two groups who trained at their maximal voluntary running speed without electrical stimulation (28 m min(-1); VMAX). Training volume was identical for LOW and VMAX (1000 m session(-1)) and animals ran 4 days week(-1) for 8 weeks. To assess the interaction of the higher intensity exercise with diet, a second endurance test (RUN2) was undertaken after 6 weeks at either 16 m min(-1) or 28 m min(-1). The NT group ran for a longer duration (increase of 77 %) after FAT than CHO (239 +/- 28 vs. 135 +/- 30 min, P < 0.05) at 16 m min(-1). There were no differences in RUN2 for the LOW group when rats ran at 16 m min(-1) (454 +/- 86 vs. 427 +/- 75 min for CHO and FAT groups, respectively), but rats in the VMAX group fed FAT ran longer than rats fed CHO at 28 m min(-1) (100 +/- 28 vs. 58 +/- 11 min, respectively, P < 0.05). FAT increased the activities of the enzymes citrate synthase, beta-hydroxyacyl-CoA dehydrogenase and carnitine palmitoyl-transferase compared to CHO (P < 0.01), but there was no systematic effect of training. We conclude: (1) there was no additive effect of a high-fat diet on endurance performance when rats performed low-intensity training; (2) running performance at 28 m min(-1) was only enhanced by a high-fat diet after more intense training; (3) diet-induced and training-induced adaptations that increase exercise capacity may be under independent control. Experimental Physiology (2001) 86.4, 499-508.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".