The Effect of a Multi-ingredient Supplement on Resistance Training–induced Adaptations
Bibliographic record
Abstract
INTRODUCTION: Resistance exercise training (RET) induces muscle hypertrophy that, when combined with co-temporal protein ingestion, is enhanced. However, fewer studies have been conducted when RET is combined with multi-ingredient supplements. PURPOSE: We aimed to determine the effect of a high-quality multi-ingredient nutritional supplement (SUPP) versus an isonitrogenous (lower protein quality), isoenergetic placebo (PL) on RET-induced gains in lean body mass (LBM), muscle thickness, and muscle cross-sectional area (CSA). We hypothesized that RET-induced gains in LBM and muscle CSA would be greater in SUPP versus PL. METHODS: In a double-blind randomized controlled trial, 26 (13 male, 13 female) healthy young adults (mean ± SD, 22 ± 2 yr) were randomized to either the SUPP group (n = 13; 20 g whey protein, 2 g leucine, 2.5 g creatine monohydrate, 300 mg calcium citrate, 1000 IU vitamin D) or the PL group (n = 13; 20 g collagen peptides, 1.4 g alanine, 0.6 g glycine) groups, ingesting their respective supplements twice daily. Measurements were obtained before and after a 10-wk linear progressive RET program. RESULTS: Greater increases in LBM were observed for SUPP versus PL (SUPP: +4.1 ± 1.3 kg, PL: +2.8 ± 1.7 kg, P < 0.05). No additive effect of the supplement could be detected on vastus lateralis muscle CSA, but SUPP did result in increased biceps brachii muscle CSA and thickness (P < 0.05). CONCLUSIONS: We conclude that when combined with RET, the consumption of SUPP increased LBM and upper-body CSA and thickness to a greater extent than to that observed in the PL group of healthy young adults.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".