Muscle-derived interleukin 6 increases exercise capacity by signaling in osteoblasts
Bibliographic record
Abstract
Given the numerous health benefits of exercise, understanding how exercise capacity is regulated is a question of paramount importance. Circulating interleukin 6 (IL-6) levels surge during exercise and IL-6 favors exercise capacity. However, neither the cellular origin of circulating IL-6 during exercise nor the means by which this cytokine enhances exercise capacity has been formally established yet. Here we show through genetic means that the majority of circulating IL-6 detectable during exercise originates from muscle and that to increase exercise capacity, IL-6 must signal in osteoblasts to favor osteoclast differentiation and the release of bioactive osteocalcin in the general circulation. This explains why mice lacking the IL-6 receptor only in osteoblasts exhibit a deficit in exercise capacity of similar severity to the one seen in mice lacking muscle-derived IL-6 (mIL-6), and why this deficit is correctable by osteocalcin but not by IL-6. Furthermore, in agreement with the notion that IL-6 acts through osteocalcin, we demonstrate that mIL-6 promotes nutrient uptake and catabolism into myofibers during exercise in an osteocalcin-dependent manner. Finally, we show that the crosstalk between osteocalcin and IL-6 is conserved between rodents and humans. This study provides evidence that a muscle-bone-muscle endocrine axis is necessary to increase muscle function during exercise in rodents and humans.
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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) | 0.001 | 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".