Cytokine and Sclerostin Response to High-Intensity Interval Running versus Cycling
Bibliographic record
Abstract
PURPOSE: This study examined whether the exercise-induced changes in inflammatory cytokines differ between impact and no-impact high-intensity interval exercise, and whether they are associated with postexercise changes in sclerostin. METHODS: Thirty-eight females (n = 19, 22.6 ± 2.7 yr) and males (n = 19, 22.3 ± 2.4 yr) performed two high-intensity interval exercise trials in random order (crossover design): running on a treadmill and cycling on a cycle ergometer. Trials consisted of eight repetitions of 1 min running or cycling at ≥90% maximal heart rate, separated by 1 min passive recovery intervals. Blood was collected preexercise and 5 min, 1 h, 24 h, and 48 h postexercise, and it was analyzed for serum levels of interleukins (IL-1β, IL-6, and IL-10), tumor necrosis factor alpha (TNF-α), and sclerostin. RESULTS: Inflammatory cytokines significantly increased over time in both sexes with some differences between trials. Specifically, IL-1β significantly increased from pre- to 5 min after both trials (23%, P < 0.05), IL-6 increased 1 h after both trials (39%, P < 0.05), IL-10 was elevated 5 min after running (20%, P < 0.05) and 1 h after both running and cycling (41% and 64%, respectively, P < 0.05), and TNF-α increased 5 min after running (10%, P < 0.05). Sclerostin increased 5 min after both trials, with a greater increase in males than that in females (62 vs 32 pg·mL in running, P = 0.018; 63 vs 30 pg·mL in cycling, P = 0.004). In addition, sclerostin was significantly correlated with the corresponding changes in inflammatory cytokines, and 34% of the variance in its postexercise gain score (Δ) was explained by sex and the corresponding gain scores in TNF-α, which was the strongest predictor. CONCLUSION: A single bout of either impact or no-impact high-intensity exercise induces changes in inflammatory cytokines, which are associated with the postexercise increase in sclerostin.
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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) | 0.002 | 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".