Longitudinal stability of asthma characteristics and biomarkers from the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study
Bibliographic record
Abstract
BACKGROUND: Asthma is a biologically heterogeneous disease and development of novel therapeutics requires understanding of pathophysiologic phenotypes. There is uncertainty regarding the stability of clinical characteristics and biomarkers in asthma over time. This report presents the longitudinal stability over 12 months of clinical characteristics and clinically accessible biomarkers from ADEPT. METHODS: Mild, moderate, and severe asthma subjects were assessed at 5 visits over 12 months. Assessments included patient questionnaires, spirometry, bronchodilator reversibility, fractional exhaled nitric oxide (FENO), and biomarkers measured in induced sputum. RESULTS: Mild (n = 52), moderate (n = 55), and severe (n = 51) asthma cohorts were enrolled from North America and Western Europe. For all clinical characteristics and biomarkers, group mean data showed no significant change from visit to visit. However, individual data showed considerable variability. FEV1/FVC ratio showed excellent reproducibility while pre-bronchodilator FEV1 and FVC were only moderately reproducible. Of note bronchodilator FEV1 reversibility showed low reproducibility, with the nonreversible phenotype much more reproducible than the reversible phenotype. The 7-item asthma control questionnaire (ACQ7) demonstrated moderate reproducibility for the combined asthma cohorts, but the uncontrolled asthma phenotype (ACQ7 > 1.5) was inconstant in mild and moderate asthma but stable in severe asthma. FENO demonstrated good reproducibility, with the FENO-low phenotype (FENO < 35 ppb) more stable than the FENO-high phenotype (FENO ≥ 35 ppb). Induced sputum inflammatory phenotypes showed marked variability across the 3 sputum samples taken over 6 months. CONCLUSIONS: The ADEPT cohort showed group stability, individual stability in some parameters e.g. low FEV1/FVC ratio, and low FENO, but marked individual variability in other clinical characteristics and biomarkers e.g. type-2 biomarkers over 12 months. This variability is possibly related to seasonal variations in climate and allergen exposure, medication changes and acute exacerbations. The implications for patient selection strategies based on clinical biomarkers may be considerable.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".