Acute high-intensity interval running increases markers of gastrointestinal damage and permeability but not gastrointestinal symptoms
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of high-intensity interval running on markers of gastrointestinal (GI) damage and permeability alongside subjective symptoms of GI discomfort. Eleven male runners completed an acute bout of high-intensity interval training (HIIT) (eighteen 400-m runs at 120% maximal oxygen uptake) where markers of GI permeability, intestinal damage, and GI discomfort symptoms were assessed and compared with resting conditions. Compared with rest, HIIT significantly increased serum lactulose/rhamnose ratio (0.051 ± 0.016 vs. 0.031 ± 0.021, p = 0.0047; 95% confidence interval (CI) = 0.006 to 0.036) and sucrose concentrations (0.388 ± 0.217 vs. 0.137 ± 0.148 mg·L −1 ; p < 0.001; 95% CI = 0.152 to 0.350). In contrast, urinary lactulose/rhamnose (0.032 ± 0.005 vs. 0.030 ± 0.005; p = 0.3; 95% CI = −0.012 to 0.009) or sucrose concentrations (0.169% ± 0.168% vs. 0.123% ± 0.120%; p = 0.54; 95% CI = −0.199 to 0.108) did not differ between HIIT and resting conditions. Plasma intestinal-fatty acid binding protein (I-FABP) was significantly increased (p < 0.001) during and in the recovery period from HIIT whereas no changes were observed during rest. Mild symptoms of GI discomfort were reported immediately and at 24 h post-HIIT, although these symptoms did not correlate to GI permeability or I-FABP. In conclusion, acute HIIT increased GI permeability and intestinal I-FABP release, although these do not correlate with symptoms of GI discomfort. Furthermore, by using serum sampling, we provide data showing that it is possible to detect changes in intestinal permeability that is not observed using urinary sampling over a shorter time-period.
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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) | 0.000 | 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) | 0.002 | 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".