Effects of Creatine and β-Alanine Co-Supplementation on Exercise Performance and Body Composition: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Creatine and β-alanine are two widely used dietary supplements known to enhance exercise performance and improve body composition; however, less is known regarding the synergistic effects of combining the two supplements. Methods: A systematic search was conducted across PubMed/MEDLINE, Scopus, and Web of Science databases for randomized controlled trials (RCTs) published up to March 2025. Eligible studies included adult participants receiving creatine and β-alanine together compared to creatine or β-alanine alone for at least four weeks and assessed measures of exercise performance and/or body composition. Study quality was assessed using the Cochrane Risk of Bias tool. Results: A total of 7 randomized controlled trials (n = 263 participants; 231 males and 32 females) met the inclusion criteria. Collectively, the combination of creatine and β-alanine supplementation enhanced high-intensity exercise performance, particularly anaerobic power and repeated-bout performance, compared to creatine or β-alanine alone. Co-ingestion of creatine and β-alanine supplementation did not increase measures of maximal strength compared to creatine alone. The effects of creatine and β-alanine supplementation on body composition were equivocal, with one study reporting greater lean mass gains and fat mass reductions compared to creatine and β-alanine supplementation individually, while another found no significant improvements. Additionally, no significant improvements in aerobic endurance capacity (VO2max, lactate threshold, or time to exhaustion) were observed from creatine and β-alanine supplementation co-ingestion. Conclusions: The combination of creatine and β-alanine supplementation may be effective for enhancing high-intensity exercise performance but has no greater effect on maximal strength, body composition, or measures of aerobic capacity compared to creatine or β-alanine alone.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".