Creatine supplementation protocols with or without training interventions on body composition: a GRADE-assessed systematic review and dose-response meta-analysis
Bibliographic record
Abstract
Background Despite the robust evidence demonstrating positive effects from creatine supplementation (primarily when associated with resistance training) on measures of body composition, there is a lack of a comprehensive evaluation regarding the influence of creatine protocol parameters (including dose and form) on body mass and estimates of fat-free and fat mass.Methods Randomized controlled trials (RCTs) evaluating the effect of creatine supplementation on body composition were included. Electronic databases, including PubMed, Web of Science, and Scopus were searched up to July 2023. Heterogeneity tests were performed. Random effect models were assessed based on the heterogeneity tests, and pooled data were examined to determine the weighted mean difference (WMD) with a 95% confidence interval (CI).Results From 4831 initial records, a total of 143 studies met the inclusion criteria. Creatine supplementation increased body mass (WMD: 0.86 kg; 95% CI: 0.76 to 0.96, I2 = 0%) and fat-free mass (WMD: 0.82 kg; 95% CI: 0.57 to 1.06, I2 = 0%) while reducing body fat percentage (WMD: −0.28 %; 95% CI: −0.47 to −0.09; I2 = 0%). Studies that incorporated a maintenance dose of creatine or performed resistance training in conjunction with supplementation had greater effects on body composition.Conclusion Creatine supplementation has a small effect on body mass and estimates of fat-free mass and body fat percentage. These findings were more robust when combined with resistance training.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".