Do multi-ingredient protein supplements augment resistance training-induced gains in skeletal muscle mass and strength? A systematic review and meta-analysis of 35 trials
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of multi-ingredient protein (MIP) supplements on resistance exercise training (RT)-induced gains in muscle mass and strength compared with protein-only (PRO) or placebo supplementation. DATA SOURCES: Systematic search of MEDLINE, Embase, CINAHL and SPORTDiscus. ELIGIBILITY CRITERIA: Randomised controlled trials with interventions including RT ≥6 weeks in duration and a MIP supplement. DESIGN: Random effects meta-analyses were conducted to determine the effect of supplementation on fat-free mass (FFM), fat mass, one-repetition maximum (1RM) upper body and 1RM lower body muscular strength. Subgroup analyses compared the efficacy of MIP supplementation relative to training status and chronological age. RESULTS: The most common MIP supplements included protein with creatine (n=17) or vitamin D (n=10). Data from 35 trials with 1387 participants showed significant (p<0.05) increases in FFM (0.80 kg (95% CI 0.44 to 1.15)), 1RM lower body (4.22 kg (95% CI 0.79 to 7.64)) and 1RM upper body (2.56 kg (95% CI 0.79 to 4.33)) where a supplement was compared with all non-MIP supplemented conditions (means (95% CI)). Subgroup analyses indicated a greater effect of MIP supplements compared with all non-MIP supplements on FFM in untrained (0.95 kg (95% CI 0.51 to 1.39), p<0.0001) and older participants (0.77 kg (95% CI 0.11 to 1.43), p=0.02); taking MIP supplements was also associated with gains in 1RM upper body (1.56 kg (95% CI 0.80 to 2.33), p=0.01) in older adults. SUMMARY/CONCLUSIONS: When MIP supplements were combined with resistance exercise training, there were greater gains in FFM and strength in healthy adults than in counterparts who were supplemented with non-MIP. MIP supplements were not superior when directly compared with PRO supplements. The magnitude of effect of MIP supplements was greater (in absolute values) in untrained and elderly individuals undertaking RT than it was in trained individuals and in younger people. TRIAL REGISTRATION NUMBER: CRD42017081970.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".