Equivalent Hypertrophy and Strength Gains in β-Hydroxy-β-Methylbutyrate- or Leucine-supplemented Men
Bibliographic record
Abstract
Ingestion of proteins with high leucine content during resistance training (RT) can augment hypertrophy. Some data suggest that a leucine metabolite, β-hydroxy, β-methylbutyrate (HMB), is substantially more anabolically efficacious than leucine. PURPOSE: We aimed to test whether supplementation with HMB versus leucine, added to whey protein, would result in differential muscle hypertrophy and strength gains in young men performing RT. METHODS: Twenty-six resistance-trained men (23 ± 2 yr) performed 12 wk of RT with three phases. Phase 1: 8 wk of periodized RT (three training sessions per week). Phase 2: 2 wk overreaching period (five sessions per week). Phase 3: 2 wk taper (three sessions per week). Participants were randomly assigned to twice daily ingestion of: whey protein (25 g) plus HMB (1.5 g) (whey+HMB; n = 13) or whey protein (25 g) plus leucine (1.5 g) (whey+leu; n = 13). Skeletal muscle biopsies were performed before and after RT. Measures of fat- and bone-free mass, vastus lateralis (VL) muscle thickness and muscle cross-sectional area (CSA) (both by ultrasound), muscle fiber CSA, and 1-repetition maximum (1-RM) strength tests were determined. RESULTS: We observed increases in fat- and bone-free mass, VL muscle thickness, muscle CSA and fiber type CSA and 1-RM strength with no differences between groups at any phase. We observed no differences between groups or time-group interactions in hormone concentrations at any phase of the RT program. CONCLUSIONS: β-Hydroxy-β-methylbutyrate added to whey did not result in greater increases in any measure of muscle mass, strength, or hormonal concentration compared to leucine added to whey. Our results show that HMB is no more effective in stimulating RT-induced hypertrophy and strength gains than leucine.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".