β-Alanine Supplementation Does Not Augment the Skeletal Muscle Adaptive Response to 6 Weeks of Sprint Interval Training
Bibliographic record
Abstract
Sprint interval training (SIT), repeated bouts of high-intensity exercise, improves skeletal muscle oxidative capacity and exercise performance. β-alanine (β-ALA) supplementation has been shown to enhance exercise performance, which led us to hypothesize that chronic β-ALA supplementation would augment work capacity during SIT and augment training-induced adaptations in skeletal muscle and performance. Twenty-four active but untrained men (23 ± 2 yr; VO2peak = 50 ± 6 mL · kg(-1) · min(-1)) ingested 3.2 g/day of β-ALA or a placebo (PLA) for a total of 10 weeks (n = 12 per group). Following 4 weeks of baseline supplementation, participants completed a 6-week SIT intervention. Each of 3 weekly sessions consisted of 4-6 Wingate tests, i.e., 30-s bouts of maximal cycling, interspersed with 4 min of recovery. Before and after the 6-week SIT program, participants completed a 250-kJ time trial and a repeated sprint test. Biopsies (v. lateralis) revealed that skeletal muscle carnosine content increased by 33% and 52%, respectively, after 4 and 10 weeks of β-ALA supplementation, but was unchanged in PLA. Total work performed during each training session was similar across treatments. SIT increased markers of mitochondrial content, including cytochome c oxidase (40%) and β-hydroxyacyl-CoA dehydrogenase maximal activities (19%), as well as VO2peak (9%), repeated-sprint capacity (5%), and 250-kJ time trial performance (13%), but there were no differences between treatments for any measure (p < .01, main effects for time; p > .05, interaction effects). The training stimulus may have overwhelmed any potential influence of β-ALA, or the supplementation protocol was insufficient to alter the variables to a detectable extent.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".