Dietary HMB and β-alanine co-supplementation does not improve in situ muscle function in sedentary, aged male rats
Bibliographic record
Abstract
This study evaluated the effects of dietary β-hydroxy-β-methylbutyrate (HMB) combined with β-alanine (β-Ala) in sedentary, aged male rats. It has been suggested that dietary HMB or β-Ala supplementation may mitigate age-related declines in muscle strength and fatigue resistance. A total of 20 aged Sprague-Dawley rats were studied. At age 20 months, 10 rats were administered a control, purified diet and 10 rats were administered a purified diet supplemented with both HMB and β-Ala (HMB+β-Ala) for 8 weeks (approximately equivalent to 3 and 2.4 g per day human dose). We measured medial gastrocnemius (MG) size, force, fatigability, and myosin composition. We also evaluated an array of protein markers related to muscle mitochondria, protein synthesis and breakdown, and autophagy. HMB+β-Ala had no significant effects on body weight, MG mass, force or fatigability, myosin composition, or muscle quality. Compared with control rats, those fed HMB+β-Ala exhibited a reduced (41%, P = 0.039) expression of muscle RING-finger protein 1 (MURF1), a common marker of protein degradation. Muscle from rats fed HMB+β-Ala also exhibited a 45% reduction (P = 0.023) in p70s6K phosphorylation following fatiguing stimulation. These data suggest that HMB+β-Ala at the dose studied may reduce muscle protein breakdown by reducing MURF1 expression, but has minimal effects on muscle function in this model of uncomplicated aging. They do not, however, rule out potential benefits of HMB+β-Ala co-supplementation at other doses or durations of supplementation in combination with exercise or in situations where extreme muscle protein breakdown and loss of mass occur (e.g., bedrest, cachexia, failure-to-thrive).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".