Central nervous system inflammation induces muscle atrophy via activation of the hypothalamic–pituitary–adrenal axis
Bibliographic record
Abstract
Skeletal muscle catabolism is a co-morbidity of many chronic diseases and is the result of systemic inflammation. Although direct inflammatory cytokine action on muscle promotes atrophy, nonmuscle sites of action for inflammatory mediators are less well described. We demonstrate that central nervous system (CNS)-delimited interleukin 1β (IL-1β) signaling alone can evoke a catabolic program in muscle, rapidly inducing atrophy. This effect is dependent on hypothalamic-pituitary-adrenal (HPA) axis activation, as CNS IL-1β-induced atrophy is abrogated by adrenalectomy. Furthermore, we identified a glucocorticoid-responsive gene expression pattern conserved in models of acute and chronic inflammatory muscle atrophy. In contrast with studies suggesting that the direct action of inflammatory cytokines on muscle is sufficient to induce catabolism, adrenalectomy also blocks the atrophy program in response to systemic inflammation, demonstrating that glucocorticoids are requisite for this process. Additionally, circulating levels of glucocorticoids equivalent to those produced under inflammatory conditions are sufficient to cause profound muscle wasting. Together, these data suggest that a significant component of inflammation-induced muscle catabolism occurs indirectly via a relay in the CNS.
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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) | 0.002 | 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".