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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".