Unlocking a novel determinant of athletic performance: The role of the gut microbiota, short-chain fatty acids, and “biotics” in exercise
Bibliographic record
Abstract
The gut microbiota refers to the collection of trillions of intestinal microorganisms that modulate central aspects of health and disease through influential effects on host physiology. Recently, a connection has been made between the gut microbiota and exercise. Initial investigations demonstrated the beneficial effects of exercise on the gut microbiota, with cross-sectional studies revealing positive correlations between exercise-associated states, and healthy gut microbiota and exercise interventions showed post-intervention increases in the abundance of beneficial bacterial taxa. More recent investigations have focused on exploring the reverse relationship: the influence of the gut microbiota on exercise performance. Murine investigations have revealed that certain bacterial taxa may enhance endurance exercise performance by augmenting various aspects of lactate metabolism. Further, short-chain fatty acids-which modulate metabolism at various organ sites, including within skeletal muscle-have been shown to enhance endurance exercise capacity in mice. This review highlights what is currently known about the connection between the gut microbiota and exercise, with a particular focus on the ergogenic potential of the gut microbiota and how it may be leveraged to enhance endurance exercise performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".