Leucine Metabolites Do Not Enhance Training-induced Performance or Muscle Thickness
Bibliographic record
Abstract
Leucine metabolites, α-hydroxyisocaproic acid (α-HICA) and β-hydroxy-β-methylbutyrate (calcium, HMB-Ca and free acid, HMB-FA), have been proposed to augment resistance training-induced changes in body composition and performance. PURPOSE: We aimed to conduct a double-blind randomized controlled pragmatic trial to evaluate the effects of off-the-shelf leucine metabolite supplements of α-HICA, HMB-FA, and HMB-Ca on resistance training-induced changes in muscle thickness and performance. METHODS: Forty men were randomly assigned to receive α-HICA (n = 10, fat-free mass [FFM] = 62.0 ± 7.1 kg), HMB-FA (n = 11, FFM = 62.7 ± 10.5 kg), HMB-Ca (n = 9, FFM = 65.6 ± 10.1 kg), or placebo (PLA; n = 10, FFM = 64.2 ± 5.7 kg). The training program consisted of whole body thrice weekly resistance training for 8 wk (seven exercises per session, three to four sets per session, at 70%-80% one repetition maximum). Skeletal muscle thickness by ultrasound, performance measures, and blood measures (creatine kinase, insulin-like growth factor 1, growth hormone, cortisol, and total testosterone) were evaluated at baseline and at the end of weeks 4 and 8. RESULTS: Time-dependent changes were observed for muscle thickness (P < 0.001), one repetition maximum bench press and squat (P < 0.001), Wingate peak power (P = 0.02), countermovement jump height (P = 0.03), power (P = 0.006), creatine kinase, insulin-like growth factor-1, growth hormone, and cortisol (all P < 0.001). No significant between-group or time-group interactions were observed. CONCLUSIONS: No leucine metabolite resulted in any ergogenic effects on any outcome variable. Supplementation with leucine metabolites-α-HICA, HMB-FA, or HMB-Ca-is not a supplementation strategy that improves muscle growth and strength development in young adult men.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".