Sclerostin and bone turnover markers response to cycling and running at the same moderate-to-vigorous exercise intensity in healthy men
Bibliographic record
Abstract
Abstract Recreational cycling is a popular activity which stimulates and improves cardiovascular fitness. The corresponding benefits for bone are unclear. This study examined the effect of running (high-impact) vs. cycling (low-impact), at the same moderate-to-vigorous exercise intensity, on markers of bone formation (N-terminal propeptide of type I collagen, PINP) and bone resorption (C-telopeptide of type I collagen, CTX-1), a non-collagenous bone remodeling marker (osteocalcin), as well as bone-modulating factors, including parathyroid hormone (PTH), irisin (myokine) and sclerostin (osteokine). Thirteen healthy men (23.7±1.0 y) performed two progressive exercise tests to exhaustion (peak VO2) on a cycle ergometer (CE) and on a treadmill (TM). On subsequent separate days, in randomized order, participants performed 30-min continuous running or cycling at 70% heart rate reserve (HRR). Blood was drawn before, immediately post- and 1h into recovery. PTH transiently increased (CE, 51.7%; TM, 50.6%) immediately after exercise in both exercise modes. Sclerostin levels increased following running only (27.7%). Irisin increased following both running and cycling. In both exercise-modes, CTX-1 decreased immediately after exercise, with no significant change in PINP and osteocalcin. At the same moderate-to-vigorous exercise intensity, running appears to result in a greater transient sclerostin response compared with cycling, while the responses of bone markers, PTH and irisin are similar. The longer-term implications of this differential bone response need to be further examined.
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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 | 0.001 |
| 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".