The Effect of Creatine Supplementation on Lean Body Mass with and Without Resistance Training
Bibliographic record
Abstract
Background/Objectives: Creatine monohydrate (CrM) is considered to be one of the most effective supplements for enhancing lean body mass during resistance training. However, CrM may influence body water content, potentially confounding lean body mass measurements. Therefore, this randomised controlled trial assessed the effect of CrM alone on lean body mass following a supplement wash-in, and when combined with a resistance training program. Methods: Sixty-three (34 females, 29 males, 31 ± 8 years) participants were randomised to supplement with CrM (5 g/day for 13 weeks: wash-in + 12-week resistance training) or serve as a control (received no creatine or placebo). Lean body mass was measured using dual X-ray absorptiometry at baseline, post 7-day wash-in, and post 12 weeks of resistance training. Both groups began the same training program post CrM wash-in. Results: After the 7-day wash-in, the supplement group gained 0.51 ± 1.79 kg more lean body mass than the control group (p = 0.03). Following the wash-in, both groups gained 2 kg after resistance training (p < 0.0001), with no between-group difference in lean body mass growth (p = 0.71). Sex-disaggregated analysis showed that the supplement group, only in females, gained 0.59 ± 1.61 kg more lean body mass than the controls (p = 0.04). There were no group differences in lean body mass growth following resistance training in females (p = 0.10) or males (p = 0.35). Conclusions: A 7-day CrM wash-in increased lean body mass, particularly in females. Thereafter, CrM did not enhance lean body mass growth when combined with resistance training, likely due to its short-term effects on lean body mass measurements. A maintenance dose of higher than 5 g/day may be necessary to augment lean body mass growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".