Dose-response Of Creatine Supplementation On Cognitive Function In Healthy Young Adults
Bibliographic record
Abstract
PURPOSE: To determine if creatine (CR) supplementation could influence cognitive performance and whether any changes were related to changes in prefrontal cortex (PFC) activation during such cognitive tasks. METHODS: Thirty (M = 11, F = 19) participants were randomized to supplement with CR (CR10: n = 10, 22 ± 4 yrs, 10 g/day; or CR20: n = 10, 20 ± 1 yrs, 20 g/day) or placebo (PLA: n = 10, 21 ± 1 yrs, 10 g/day) for 6 weeks. Participants completed a cognitive test battery (processing speed (PS), episodic memory (EM), and attention (AT)) on two separate occasions prior to and following supplementation. Functional near-infrared spectroscopy (fNIRS) was used to measure PFC oxyhemoglobin (O2Hb) during the cognitive evaluation. A two-way repeated measures ANOVA was used to determine differences between groups and timepoints for cognitive performance scores and PFC O2Hb. In addition, a one-way ANOVA of % change ((post-pre/pre) *100) was used to determine pre- and post- differences between groups for cognitive performance scores and PFC O2Hb. RESULTS: Creatine (independent of dosage) had no significant effect on measures of cognitive performance (including relative % change) (PS: CR10: 4%, CR20: 19%, PLA: 19%; EM: CR10: 12%, CR20: 9%, PLA: 10%; AT: CR10: 8%, CR20: 12%, PLA: 5%). There was a trend for decreased relative PFC O2Hb in the CR10 and CR20 groups versus PLA group in the PS test (CR10: -165% vs. PLA: 72% (p = 0.06), CR20: -100% vs. PLA: 72% (p = 0.21)). CONCLUSIONS: Six weeks CR supplementation at a moderate (10 g/day) or high (20 g/day) dose does not improve cognitive performance or change PFC activation in young adults. Future research is warranted to further understand the optimal dosing for cognitive improvements as well as relationships between changes in brain CR, cognitive processing, and brain hemodynamics following CR supplementation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".