Combining Creatine and Protein Supplementation to Optimize Resistance Training Adaptations: A Narrative Review
Bibliographic record
Abstract
Abstract Protein (PRO) and creatine (Cr) supplementation are both purported dietary strategies to enhance resistance training (RT) gains in muscular strength and lean tissue mass (LTM). PROs that contain all essential amino acids, have a high concentration of leucine, and are rapidly absorbed provide the greatest increase in muscle PRO synthesis. Cr acts as a pleiotropic molecule within the muscle and plays a critical role as an energy buffer. Coingestion of PRO with Cr may act synergistically and further enhance RT adaptations. The purpose of this narrative review is to summarize recent evidence exploring PRO and Cr supplementation combined with RT on muscular strength and LTM. PRO and Cr supplemented individually appear to have a small but positive effect on RT gains in LTM (PRO = 0.3 kg; Cr = 0.99 kg) and strength (PRO = 2.49 kg; Cr = upper body 4.43 kg, lower body 11.35 kg) compared with placebo (PLA). Cosupplementation of PRO and Cr has been shown to further enhance RT gains in LTM (Cr + PRO = 4.0 kg; PRO = 2.3 kg) and whole-body strength (Cr + PRO = 20.5 kg; PRO = 14.4 kg) compared with PRO alone; however, individual studies did not find any further benefit of coingestion compared with Cr alone. In summary, for individuals engaged in RT, achieving a daily PRO intake via supplementation (>1.6-2.0 g/kg/d) and Cr (∼0.1 g/kg/d) individually is effective to augment gains in LTM and strength. Coingestion of Cr and PRO further enhances RT gains compared with PRO alone; however, if ingesting sufficient daily PRO, only Cr supplementation appears to be effective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".