A high-whey-protein diet does not enhance mechanical and structural remodeling of cardiac muscle in response to aerobic exercise in rats
Bibliographic record
Abstract
PURPOSE: Aerobic exercise training results in distinct structural and mechanical myocardial adaptations. In skeletal muscle, whey protein supplementation is effective in enhancing muscle adaptation following resistance exercise. However, it is unclear whether cardiac adaptation to aerobic exercise can be enhanced by systematic protein supplementation. METHODS: Twelve-week-old rats were assigned to 12 weeks of either sedentary or aerobic exercise with either a standard (Sed+Standard, Ex+Standard) or high-protein (Sed+Pro, Ex+Pro) diet. Echocardiography was used to measure cardiac structural remodeling and performance. Skinned cardiac fiber bundles were used to determine the active and passive stress properties, maximum shortening velocity, and calcium sensitivity. RESULTS: Aerobic training was characterized structurally by increases in ventricle volume (Ex+Standard, 19%; Ex+Pro, 29%) and myocardial thickness (Ex+Standard, 26%; Ex+- Pro, 12%) compared to that of baseline. Skinned trabecula r fiber bundles also had a greater unloaded shortening velocity (Sed+Standard, 1.04±0.05; Sed+Pro, 1.07±0.03; Ex- +Standard, 1.16±0.04; Ex+Pro, 1.18±0.05 FL/s) and calcium sensitivity (pCa50: Sed+Standard, 6.04±0.17; Sed+Pro, 6.08±0.19; Ex+Standard, 6.30±0.09; Ex+Pro, 6.36±0.12) in trained hearts compared to that of hearts from sedentary animals. However, the addition of a high-protein diet did not provide additional benefits to either the structural or mechanical adaptations of the myocardium. CONCLUSION: Therefore, it seems that a high-whey-protein diet does not significantly enhance adaptations of the heart to aerobic exercise in comparison to that of a standard diet.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".