Clinical Effect of Alpha-1 Antitrypsin Deficiency in Antineutrophil Cytoplasmic Antibody–associated Vasculitis: Results from a French Retrospective Monocentric Cohort
Bibliographic record
Abstract
OBJECTIVE: Deficiency in alpha-1 antitrypsin (AAT) is a possible pathogenic cofactor in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). However, the clinical effect of AAT deficiency remains poorly established in this setting. This study aimed to describe the clinical phenotypes and outcomes of AAV according to AAT phenotypes. METHODS: This study was conducted retrospectively at Caen University Hospital and included all consecutive granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) patients with positive proteinase 3-ANCA or myeloperoxidase-ANCA, from January 2000 or September 2011, respectively, to June 2016. AAT dosage (nephelometry) and phenotyping (isoelectric focusing in agarose gel) were performed. RESULTS: Among the 142 patients with AAV, including 88 GPA and 54 MPA, 102 (72%) had the MM phenotype, 5 (4%) had a nonpolymerogenic M-variant phenotype, 18 (13%) had the deficient allele MZ, 12 (8%) had MS, 2 (1%) had ZZ, 2 (1%) had SZ, and 1 (1%) had SS. M, Z, and S allele frequencies were 84%, 8%, and 6%, respectively. No association was observed between AAT deficiency and ANCA subtype or AAV phenotype, except for intraalveolar hemorrhage (IAH), which was more frequent in patients harboring at least 1 of the deficient Z or S alleles than in those without any deficient alleles (p < 0.01). Global, renal, or relapse-free survival rates were similar for all subgroups. CONCLUSION: This study shows that AAT deficiency confers, independently of ANCA subtype, a higher risk of IAH. Prospective studies are required to refine these data and to assess the need for replacement therapy in AAT-deficient patients with AAV.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".