Exploring clinical heterogeneity in alpha-1 antitrypsin deficiency: a clustering analysis of patients with Pi*ZZ and Pi*SZ genotypes from the Spanish data in EARCO registry

Abstract Objective To identify distinct clinical groups within the Pi*ZZ and Pi*SZ genotypes using unsupervised Machine Learning (ML) methods applied to a large multicenter national cohort, with the aim of describing disease heterogeneity and exploring potential patterns for future stratification. Methods A cross-sectional multicentre study was conducted using data from the Spanish population enrolled in the EARCO international registry. Patients with severe deficiency (serum AAT < 11 µM or genotypes Pi*ZZ or Pi*SZ) included between February 2020 and December 2024 were analysed. Clinical, functional, and demographic variables were examined using the k-prototypes clustering algorithm, which integrates continuous and categorical data. Differences between clusters were evaluated with Mann–Whitney U and Chi-square tests ( p < 0.05). Results A total of 697 patients were analyzed (253 Pi*ZZ, 444 Pi*SZ). Four distinct groups were identified within each genotype. In Pi*ZZ, two groups showed predominant parenchymal lung disease with emphysema and variable obstruction severity, one consisted mainly of middle-aged non-smoking women with bronchiectasis, and another included young, asymptomatic individuals with normal lung function. Among Pi*SZ, the phenotypic spectrum ranged from asymptomatic subjects identified by family screening to patients with advanced COPD, emphysema, and high comorbidity burden. Cardiovascular and metabolic comorbidities contributed to the characterization of disease expression, particularly in Pi*SZ. Conclusions Unsupervised ML allowed the identification of clinically coherent descriptive profiles within severe alpha-1 antitrypsin deficiency, highlighting the wide heterogeneity of its respiratory manifestations. This stratification may contribute to a more individualized understanding of disease expression and provide a framework for future longitudinal validation.