Characteristics of bronchiectasis in patients with different genotypes of severe α <sub>1</sub> -antitrypsin deficiency from the EARCO registry
Bibliographic record
Abstract
Background α-1 antitrypsin deficiency (AATD) is a rare genetic disorder caused by mutations in the SERPINA1 gene and associated with reduced levels of α-1 antitrypsin (AAT). It predisposes individuals to pulmonary diseases, including bronchiectasis, through protease–antiprotease imbalance and immune dysregulation. While the Pi*ZZ genotype has been extensively studied, the prevalence and characteristics of bronchiectasis in other genotypes remain unclear. Methods This cross-sectional study analysed data from the European α-1 Research Collaboration (EARCO) registry, focusing on individuals with bronchiectasis on computed tomography (CT). Participants were stratified by AATD genotypes (Pi*ZZ , Pi*SZ, Pi*SS and rare variants) and data were compared. Disease severity was evaluated using FACED (forced expiratory volume in 1 s (FEV 1 ), age, chronic colonisation, extension and dyspnoea) score and bronchiectasis severity index (BSI) scores. Results 349 patients had bronchiectasis on a CT scan, of whom 70.5% had Pi*ZZ, 18.6% had Pi*SZ, 4.3% had Pi*SS and 6.6% had rare variants. Lower lobe involvement was predominant across genotypes, whereas Pi*SS exhibited distinct upper lobe patterns and Pi*SZ showed more frequent middle lobe involvement. People with rare genotypes and Pi*ZZ had worse lung function (FEV 1 % of 65.3% and 71.4%, respectively) and higher disease severity scores. Emphysema co-occurrence was most frequent in Pi*ZZ (60.6%). No significant differences were observed in sputum microbiology or systemic inflammatory markers, except for lower platelet counts in Pi*ZZ subjects. Conclusion Bronchiectasis in AATD is not limited to the Pi*ZZ genotype, with significant phenotypic variability across genotypes. Lower lobe involvement and mild disease predominate; however, severe forms are more frequent in rare genotypes and Pi*ZZ. These findings underscore the importance of systematic screening and genotype-specific management to improve patient outcomes.
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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 | 0.001 | 0.001 |
| 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".