Gut-training: the impact of two weeks repetitive gut-challenge during exercise on gastrointestinal status, glucose availability, fuel kinetics, and running performance
Bibliographic record
Abstract
Due to gastrointestinal tract adaptability, the study aimed to determine the impact of gut-training protocol over 2 weeks on gastrointestinal status, blood glucose availability, fuel kinetics, and running performance. Endurance runners (n = 25) performed a gut-challenge trial (GC1), consisting of 2 h running exercise at 60% V̇O2max whilst consuming gel-discs containing 30 g carbohydrates (2:1 glucose/fructose, 10% w/v) every 20 min and a 1 h distance test. Participants were then randomly assigned to a carbohydrate gel-disc (CHO-S), carbohydrate food (CHO-F), or placebo (PLA) gut-training group for 2 weeks of repetitive gut-challenge intervention. Participants then repeated a second gut-challenge trial (GC2). Gastrointestinal symptoms reduced in GC2 on CHO-S (60%; p = 0.008) and CHO-F (63%; p = 0.046); reductions were greater than PLA (p < 0.05). H2 peak was lower in GC2 on CHO-S (mean (CI): 6 (4–8) ppm) compared with CHO-F (9 (6–12) ppm) and PLA (12 (2–21) ppm) (trial × time: p < 0.001). Blood glucose concentration was higher in GC2 on CHO-S (7.2 (6.3–8.1) mmol·L−1) compared with CHO-F (6.1 (5.7–6.5) mmol·L−1) and PLA (6.2 (4.9–7.5) mmol·L−1) (trial × time: p = 0.015). No difference in oxidation rates, plasma I-FABP, and cortisol concentrations were observed between groups and trials. Distance test improved on CHO-S (5.2%) and CHO-F (4.3%) in GC2, but not on PLA (–2.1%) (trial × time: p = 0.009). Two weeks of gut-training with CHO-S and CHO-F improved gastrointestinal symptoms and running performance compared with PLA. CHO-S also reduced malabsorption and increased blood glucose availability during endurance running compared with PLA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".