Interleukin-6 and associated cytokine responses to an acute bout of high-intensity interval exercise: the effect of exercise intensity and volume
Bibliographic record
Abstract
Acute increases in interleukin (IL)-6 following prolonged exercise are associated with the induction of a transient anti-inflammatory state (e.g., increases in IL-10) that is partly responsible for the health benefits of regular exercise. The purposes of this study were to investigate the IL-6-related inflammatory response to high-intensity interval exercise (HIIE) and to determine the impact of exercise intensity and volume on this response. Ten participants (5 males and 5 females) completed 3 exercise bouts of contrasting intensity and volume (LOW, MOD, and HIGH). The HIGH protocol was based upon standard HIIE protocols, while the MOD and LOW protocols were designed to enable a comparison of exercise intensity and volume with a fixed duration. Inflammatory cytokine concentrations were measured in plasma (IL-6, IL-10) and also determined the level of gene expression (IL-6, IL-10, and IL-4R) in peripheral blood. The plasma IL-6 response to exercise (reported as fold changes) was significantly greater in HIGH (2.70 ± 1.51) than LOW (1.40 ± 0.32) (P = 0.04) and was also positively correlated to the mean exercise oxygen uptake (r = 0.54, P < 0.01). However, there was no change in anti-inflammatory IL-10 or IL-4R responses in plasma or at the level of gene expression. HIIE caused a significant increase in IL-6 and was greater than that seen in low-intensity exercise of the same duration. The increases in IL-6 were relatively small in magnitude, and appear to have been insufficient to induce the acute systemic anti-inflammatory effects, which are evident following longer duration exercise.
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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 | 0.000 |
| 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".