Clinical characteristics of AATD-related COPD patients vary with age at diagnosis: data from the EARCO international registry
Bibliographic record
Abstract
This study aims to evaluate the clinical characteristics of COPD patients with AATD according to their age at diagnosis. Data was obtained from the European Alpha-1 Research Collaboration (EARCO) registry, an international prospective cohort study. AATD patients with COPD registered between February 2020 and October 2024 were analysed. Clinical charateristics were compared between groups, stratified by age of AATD diagnosis as follows; <45, 45–65 and ≥65 years. A multivariable logistic regression model explored factors associated with age at diagnosis. A total of 1,565 AATD-COPD patients were included, with 18.2% receiving an AATD diagnosis at age ≥ 65. In univariate comparisons according to diagnosis age revealed that the prevalence of patients with Pi*ZZ mutation was lower in the ≥ 65 age group (47.1%) compared to the 45–65 (65.5%) and < 45 (78.5%) age groups. In contrast, the prevalence of Pi*SZ and Pi*SS were higher in the ≥ 65 group compared to the younger age groups. The proportion of never-smokers was highest in the ≥ 65 group (39.5%), whereas only 15.3% of patients under 45 were never-smokers. Multivariate analysis showed that; compared to never-smokers, former smoking (OR: 0.08; 95% CI: 0.03–0.23) and current smoking (OR: 0.43; 95% CI: 0.27–0.70) were negatively associated with a diagnosis at age ≥ 65 in all sample. Compared to the Pi*ZZ genotype, among all sample, Pi*SS was associated with more than a 3-fold increased likelihood of diagnosis at age ≥ 65 and when considering only index cases Pi*SZ was associated with diagnosis age of ≥ 65 (OR: 2.01, 95%CI: 1.01–4.04). Among patients with the Pi*ZZ, current smoking was negatively associated (OR:0.24,95%CI: 0.13–0.47) with a diagnosis at age ≥ 65, whereas higher FEV 1 % and serum AAT levels were positively associated with later diagnosis. Patients diagnosed at an older age had lower tobacco exposure and less severe disease. This suggests that subclinical symptoms may contribute to delays in diagnosis, as COPD features can be subtle or under-recognised until later in life. Our findings highlight the importance of considering AATD in all COPD patients, regardless of age, to avoid missed or delayed diagnoses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".