No effect of HMB or α‐HICA supplementation on training‐induced changes in body composition
Bibliographic record
Abstract
Abstract β‐hydroxy‐β‐methylbutyrate (calcium: HMB‐Ca and free acid: HMB‐FA) and α‐hydroxyisocaproic acid (α‐HICA) are leucine metabolites that have been proposed to improve body composition and strength when combined with resistance exercise training (RET). In this double‐blind randomized controlled pragmatic trial, we evaluated the effects of off‐the‐shelf supplements: α‐HICA, HMB‐FA and HMB‐Ca, on RET‐induced changes in body composition and performance. Forty men were blocked randomized 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 protocol consisted of a whole‐body resistance training routine, thrice weekly for 8 weeks. Body composition was assessed by dual‐energy x‐ray absorptiometry (DXA) and total body water (TBW) by whole‐body bioimpedance spectroscopy (BIS), both at baseline and at the end of weeks 4 and 8. Time‐dependent changes were observed for increase in trunk FFM (p < 0.05). No statistically significant between‐group or group‐by‐time interactions were observed. Supplementation with HMB (FA and Ca) or α‐HICA failed to enhance body composition to a greater extent than placebo. We do not recommend these leucine metabolites for improving body composition changes with RET in young adult resistance trained 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) | 0.005 | 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".