Effect of Creatine and Weight Training on Muscle Creatine and Performance in Vegetarians
Bibliographic record
Abstract
PURPOSE: To compare the change in muscle creatine, fiber morphology, body composition, hydration status, and exercise performance between vegetarians and nonvegetarians with 8 wk of creatine supplementation and resistance training. METHODS: Eighteen VG and 24 NV subjects (19-55 yr) were randomly assigned (double blind) to four groups: VG + creatine (VGCr, N=10), VG + placebo (VGPl, N=8), NV + creatine (NVCr, N=12), and NV + placebo (NVPl, N=12). Before and at the end of the study, muscle biopsies were taken from the vastus lateralis m, body composition was assessed by DXA, and strength was assessed using 1-RM bench press and leg press. Subjects participated in the same 8-wk resistance-training program. Creatine dosage was based on lean tissue mass (0.25 g.kg(-1) LTM.d(-1) x 7 d; 0.0625 g.kg(-1) LTM.d(-1) x 49 d). RESULTS: Biopsy samples indicated that total creatine (TCr=free Cr + PCr) was significantly lower in VG compared with NV at baseline (VG=117 mmol.kg(-1); NV=130 mmol.kg(-1); P<0.05). For Cr subjects, there was a greater increase in PCr, TCr, bench-press strength, isokinetic work, Type II fiber area, and whole-body lean tissue compared with subjects on placebo (P<0.05). Vegetarians who took Cr had a greater increase in TCr, PCr, lean tissue, and total work performance than nonvegetarians who took Cr (P<0.05). The change in muscle TCr was significantly correlated with initial muscle TCr, and the change in lean tissue mass and exercise performance. These findings confirm an ergogenic effect of Cr during resistance training and suggest that subjects with initially low levels of intramuscular Cr (vegetarians) are more responsive to 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) | 0.000 | 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 | 0.000 | 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".