Influence of exercise training with resveratrol supplementation on skeletal muscle mitochondrial capacity
Bibliographic record
Abstract
Physical inactivity reduces, and exercise training increases, mitochondrial capacity. In rodents, exercise training effects can be augmented by large doses of resveratrol supplementation but whether this can occur in humans with a smaller dose is unclear. This study sought to determine the effects of resveratrol supplementation in combination with exercise training on skeletal muscle mitochondrial capacity. Sixteen healthy young adults were randomly assigned in a double-blind fashion to consume either placebo or 500 mg of resveratrol plus 10 mg of piperine, a bioenhancer to increase bioavailibilty and bioefficacy of resveratrol. Participants ingested the pills daily for 4 weeks and completed 3 sessions per week of submaximal endurance training of the wrist flexor muscles of the nondominant arm. The contralateral arm served as an untrained control. Skeletal muscle mitochondrial capacity was measured using near-infrared spectroscopy. Changes in mitochondrial capacity from baseline to post-testing indicated significant differences between the resveratrol+piperine-trained arm and the placebo-trained arm (p = 0.02), with the resveratrol+piperine group increasing about 40% from baseline (Δk = 0.58), while the placebo group increased about 10% from baseline (Δk = 0.13). Neither the placebo group nor the resveratrol+piperine group exhibited changes in mitochondrial capacity in the untrained arm. In conclusion, low-intensity exercise training can increase forearm skeletal muscle mitochondrial capacity when combined with resveratrol and piperine 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 | 0.000 |
| 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 | 0.000 | 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".