Effect of α-Lipoic Acid Combined with Creatine Monohydrate on Human Skeletal Muscle Creatine and Phosphagen Concentration
Bibliographic record
Abstract
Alpha-lipoic acid has been found to enhance glucose uptake into skeletal muscle in animal models. Studies have also found that the co-ingestion of carbohydrate along with creatine increases muscle creatine uptake by a process related to insulin-stimulated glucose disposal. The purpose of this study was to determine the effect of alpha-lipoic acid on human skeletal muscle creatine uptake by directly measuring intramuscular concentrations of creatine, phosphocreatine, and adenosine triphosphate when creatine monohydrate was co-ingested with alpha-lipoic acid. Muscle biopsies were acquired from the vastus lateralis m. of 16 male subjects (18-32 y) before and after the experimental intervention. After the initial biopsy, subjects ingested 20 g x d(-1) of creatine monohydrate, 20 g x d(-1) of creatine monohydrate + 100 g x d(-1) of sucrose, or 20 g x d(-1) of creatine monohydrate + 100 g x d(-1) of sucrose + 1000 mg x d(-1) of alpha-lipoic acid for 5 days. Subjects refrained from exercise and consumed the same balanced diet for 7 days. Body weight increased by 2.1% following the nutritional intervention, with no differences between the groups. There was a significant increase in total creatine concentration following creatine supplementation, with the group ingesting alpha-lipoic acid showing a significantly greater increase (p < .05) in phosphocreatine (87.6 --> 106.2 mmol x kg(-1) dry mass [dm]) and total creatine (137.8 --> 156.8 mmol x kg(-1) dm). These findings indicate that co-ingestion of alpha-lipoic acid with creatine and a small amount of sucrose can enhance muscle total creatine content as compared to the ingestion of creatine and sucrose or creatine 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".