Effects of exercise‐induced beta‐hydroxybutyrate on muscle function and cognitive function
Bibliographic record
Abstract
Recent studies have shown that exercise improves skeletal muscle and cognitive function by stimulating the secretion of numerous molecules. In particular, previous studies have suggested that exercise-induced beta-hydroxybutyrate (BHB) release might improve skeletal muscle and cognitive function, but to date these studies have been limited to cell and animal models. Therefore, we aimed to determine how an exercise-induced increase in BHB affects skeletal muscle and cognitive function at a cellular level, in an animal model, and in humans. The effects of BHB on skeletal muscle and cognitive function were determined by treating C2C12 and C6 cell lines with BHB, and by measuring the skeletal muscle and serum BHB concentrations in aged mice after endurance or resistance exercise. In addition, serum BHB concentration was measured before and after high-speed band exercise in elderly people, and its relationships with muscle and cognitive function were analyzed. We found that BHB increased cell viability and brain-derived neurotrophic factor expression level in C6 cells, and endurance exercise, but not resistance exercise, increased the muscle BHB concentration in aged mice. Furthermore, the BHB concentration was positively related to skeletal muscle and cognitive function. Exercise did not increase the serum BHB concentration in the elderly people and BHB did not correlate with cognitive function, but after excluding the five people with the highest preexisting serum concentrations of BHB, the BHB concentrations of the remaining participants were increased by exercise, and the concentration showed a tendency toward a positive correlation with cognitive function. Thus, the BHB released by skeletal muscle following endurance exercise may improve muscle and cognitive function in animals and humans.
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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) | 0.000 | 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 | 0.001 |
| 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".