Inhaled Steroids and Active Smoking Drive Chronic Obstructive Pulmonary Disease Symptoms and Biomarkers to a Greater Degree Than Airflow Limitation
Bibliographic record
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, and development of novel therapeutics requires an understanding of pathophysiologic phenotypes. Objectives: The purpose of the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study was to correlate clinical features and biomarkers with molecular characteristics in a well-profiled COPD cohort. Methods: A total of 67 COPD subjects (forced expiratory volume in the first second of expiration [FEV 1 ]: 45%-80% predicted) and 63 healthy smoking and nonsmoking controls underwent multiple assessments including patient questionnaires, lung function, and clinical biomarkers including fractional exhaled nitric oxide (FENO), induced sputum, and blood. Measurements and main results: The impact of inhaled corticosteroids (ICSs), and to a lesser extent current smoking, was more associated with symptom control, exacerbation rates, and clinical biomarkers, than severity by FEV 1 . The ICS-treated smoking subjects were most symptomatic, with significantly elevated scores on patient-reported outcomes and more annual exacerbations ( P < .05). Inhaled corticosteroid users had greater airflow obstruction and air trapping compared with non-ICS users, regardless of smoking status. Smoking, regardless of ICS use, was associated with significantly lower FENO ( P < .05). Smoking, in non-ICS users, was associated with an elevated proportion of sputum neutrophils and reduced sputum macrophages. Increased serum C-reactive protein was observed in smokers but not in ICS and nonsmoking ICS users ( P < .05). In contrast, only air trapping and neutrophilic inflammation increased with severity, defined by postbronchodilator FEV 1 . Conclusions: Compared with COPD severity by FEV 1 , ICS use and current smoking were better determinants of clinical characteristics and biomarkers. Use of the ADEPT COPD data promises to prove useful in defining biological phenotypes to facilitate personalized therapeutic approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".