Variability in sensitivity to inflammation in muscle and lung of patients with COPD may underlie susceptibility to lung function decline
Bibliographic record
Abstract
BACKGROUND: Muscle wasting and weakness (sarcopenia) are commonly associated with COPD causing frailty and reduced quality of life. The contribution of inflammation to muscle loss and the susceptibility to rapid lung function decline is debated. We hypothesised that comparing the muscle transcriptome to circulating inflammatory cytokine profiles in patients would identify any contribution of systemic inflammation to muscle atrophy. METHODS: Quadriceps differential gene expression was determined between mild-COPD (n=28) and severe-COPD (n=51) using GSE100281. These microarray data were compared by biweight mid-correlation with lung function and plasma cytokine levels from the same patients. RESULTS: Patients with severe COPD had reduced fat-free mass index (a measurement of muscle mass) compared with patients with mild COPD despite similar physical activity and inflammatory cytokine levels. Gene sets associated with inflammation and epithelial mesenchymal transition (EMT) were elevated in severe COPD, suggesting that inflammation may contribute to the loss of muscle mass. In patients with severe COPD, EMT and inflammation gene sets were strongly associated with circulating proinflammatory and anti-inflammatory cytokines. However, in patients with mild COPD, anti-inflammatory cytokines showed negative associations with these gene sets and associations with proinflammatory cytokines were weak. In data from lung and blood samples, patients with severe COPD had elevated inflammatory and EMT gene expression compared with patients with mild COPD suggesting that this phenomenon is not muscle-specific. CONCLUSIONS: In patients at the severe end of the COPD spectrum, the proinflammatory response in muscle predominates, whereas in patients at the mild end of the spectrum, the anti-inflammatory response predominates. This suggestion needs confirming in a longitudinal cohort.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".