Unrecognized Ciliary Motility Disorders in Neutrophilic Severe Asthma Exacerbations
Bibliographic record
Abstract
Abstract Background Airway bacterial infections are frequent in severe asthma and are often under-appreciated as contributors to symptoms and exacerbations. We report our experience using integrative diagnostic methods to identify ciliary motility disorders as contributors to neutrophilic exacerbations in patients with severe asthma. Methods Targeted exome sequencing for primary ciliary dyskinesia (PCD) was performed on 52 patients with severe asthma who met predefined criteria (≥ 3 respiratory infections or intense sputum neutrophilia, within a 2-year period, along with evidence of type 2 inflammation, including peripheral or sputum eosinophilia, elevated fractional exhaled nitric oxide (FeNO), or elevated serum immunoglobulin E), and without other obvious immunodeficiencies. A subset of patients with PCD-related gene variant(s) underwent ciliary beat frequency (CBF) analysis from nasal epithelial brushings ( N = 20 with analyzable data), nasal nitric oxide (nNO) measurement ( N = 15), and T1/2/17 sputum cytokine assays ( N = 18). Results Among 52 patients (mean age 54.2 ± 15.1 years; 59.6% female), 32(61.5%) had PCD-related gene variants. CBF was reduced in 19/20(95%) patients who underwent motility studies (mean CBF of 8.8 ± 2.7 Hz; normal 14.2 ± 1.0 Hz), and correlated significantly with FEV 1 (r s =0.65, P = 0.0017) and inversely with peak sputum neutrophils (r s =-0.62, P = 0.0097). Subnormal nNO levels (< 250 nL/min) were observed in 12/15(80%) patients; 3/15(20%) were < 77 nL/min (PCD threshold). 5 patients showed FeNO discordance (> 25 ppb). Cytokines associated with inflammasome activation were increased in sputum in majority of patients with PCD-related gene variants. Sputum-guided strategy and 7% hypertonic saline treatment enable significant inhaled corticosteroid doses reduction across the entire population and airway-eosinophilia subgroup ( P = 0.0026 and P = 0.0273). Oral corticosteroid doses were reduced in 6/8(75%), and biologics were not initiated in 26/32(81.3%). FEV 1 improved by 130 ± 291mL, P = 0.0273. Conclusions Ciliary dyskinesias are prevalent in patients with severe asthma. Identifying ciliary motility disorders can guide effective treatment and reduce unnecessary biologics use in severe asthma when symptoms are infection-driven. Even variants of uncertain significance in PCD-related genes may be associated with airway infections due to ciliary dysfunction, proven by ciliary beat frequency analysis. nNO could be a useful screening tool, but its optimal cutoff in this population needs further study.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".