Cytokine and Sclerostin Response to High-Intensity Interval Running versus Cycling
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it