Serban, Karina A.

Abstract Background Sarcopenia is common in chronic obstructive pulmonary disease (COPD) and contributes to adverse outcomes. Alpha-1 antitrypsin deficiency (AATD) is the most common genetic cause of COPD, but its impact on skeletal muscle remains to be determined. Methods We performed a retrospective analysis of 487,207 participants in the UK Biobank with available SERPINA1 genotyping. Associations between AAT genotypes and measures of skeletal muscle mass were determined using multivariable linear and logistic regression models. Models adjusted for age, sex, smoking status, genetic principal components, lung function (FEV₁% predicted), and liver fibrosis risk (FIB-4). We determined that the Z-allele was associated with sarcopenia and mortality using regression models and mediation analysis. Results Compared with PI*MM individuals, carriers of the Z allele (PI*MZ, PI*SZ, PI*ZZ) demonstrated significantly lower fat free mass index (FFMI) and appendicular skeletal muscle index (ASMI), as well as higher odds of sarcopenia by binary definition. Associations persisted after adjustment for lung and liver function. A dose-dependent relationship was observed, with the greatest reductions in muscle mass and highest sarcopenia associated with PI*ZZ individuals (adjusted OR for FFMI-defined sarcopenia 2.02, 95% CI 1.16-3.32). Each additional Z allele was associated with increased mortality risk (adjusted OR 1.09, 95% CI 1.02-1.16), with sarcopenia mediating a modest proportion (∼2.6%) of this association. Conclusions. The SERPINA1 Z allele is associated with reduced muscle mass and increased risk for sarcopenia independent of lung and liver disease. Our findings expand the phenotypic spectrum of AATD-related diseases to include skeletal muscle as a modifiable target for intervention.

Abstract Rationale Alpha-1 antitrypsin deficiency (AATD) continues to be underdiagnosed. The estimated prevalence of the most common severe deficient genotype, PI*ZZ, is ∼1:3500 or 100,000 Americans but only ∼10,000 such individuals have been recognized. Diagnostic delays persist and these delays are associated with worse disease. High-resolution CT (HRCT) imaging may contain latent phenotypic AATD signatures that are not visibly apparent to clinicians. We hypothesized a deep learning model could identify imaging patterns predictive of AATD from inspiratory HRCT scans. Methods Confirmed genotypes and HRCT scans were pooled from COPDGene, SPIROMICS, and GRADS, yielding 350 MM, 102 MZ, and 69 ZZ participants. Only scans reconstructed with smooth kernels (GE STANDARD, Philips B, Siemens B31f/B35f) and slice thickness < =1.25mm were used. The cohort was selected to have similar distributions of quantitative emphysema, sex, and age by matching five MZ and two MM subjects to each ZZ subject (<10-year age difference, same sex, and <3% difference in %LAA). Tobacco smoke-exposure was more prevalent among the MM/MZ groups given these were predominantly from the tobacco smoke-enriched COPDGene and SPIROMICS cohorts. A DenseNet121 convolutional neural network (CNN) was trained using the MONAI platform to predict genotype by outputting probabilities for MM, MZ, and ZZ. Input to the CNN was a three-channel LAA-910, LAA-950, and LAA-970 map downsampled to 2.0mm3 resolution to remove bias due to differences in scanner and image reconstruction parameters. Five-fold cross-validation was performed. Area under the curve (AUC) was calculated for classification of ZZ vs other genotypes (MM/MZ) and MZ vs MM, with statistical significance assessed at p < 0.05. Results The three groups were similar across age (mean±SD years: MM 57.9±8.8, MZ: 57.5±8.9, ZZ: 55.0±10.7), sex (% female: MM: 48.2%, MZ: 46.7%, ZZ: 47.6), and emphysema (LAA-950%: MM: 13.0±12.2, MZ: 12.4±11.8, ZZ: 13.1±11.9). There was a higher prevalence of ever smokers among MM (100%) and MZ (91.7%), compared to ZZ (47.6%). The model achieved an AUC of 0.806 (p < 0.001) for classification of ZZ vs other genotypes, demonstrating robust discriminatory ability for homozygous AATD. Classification of MZ vs other genotypes did not achieve an AUC significantly greater than 0.5. Conclusions This proof-of-concept study demonstrates that deep learning applied to routine inspiratory HRCT can accurately identify homozygous AATD. These findings support the potential role of imaging-based machine learning as a non-invasive screening tool for undiagnosed ZZ individuals. Future work will focus on cohort enrichment, integration with clinical parameters, and explainability of model-derived imaging features. This abstract is funded by: NIH NHLBI K23HL173570

Introduction There is an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygotes for the alpha-1 antitrypsin (AAT) Z allele (PI*MZ), but there is significant variation in outcomes. Matrix metalloproteinases (MMPs) contribute to worse airway disease and emphysema. Given that the AAT protein is an antiprotease, we hypothesised that MMPs play a modifying role among AAT-deficient individuals. Methods We studied PI*MZ (n=39) and PI*ZZ (n=27) individuals from the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis Study cohort. MMP (MMP3, MMP9, MMP10, MMP12 and MMP15) gene expression was assessed in bronchoalveolar lavage cells and peripheral blood mononuclear cells. Plasma protein MMP (MMP3, MMP9, MMP10 and MMP12) expression was assessed using SomaScan and BAL MMP15 protein using ELISA. MMP values were log-transformed, and linear regression analyses were used to assess lung function, patient-reported outcomes and CT emphysema adjusted for age, body mass index, sex and duration of smoking. Results Alveolar MMP3 and MMP15 were uniquely associated with worse lung function, patient-reported outcomes and emphysema among PI*MZ individuals. MMP10 was associated with less disease severity among PI*MZ individuals. Discussion Our study shows that alveolar MMP3 and MMP15 may uniquely contribute to disease among PI*MZ individuals, raising the possibility that they could serve as biomarkers or therapeutic targets in this highly prevalent COPD phenotype. Furthermore, we show that MMP10 may serve a protective effect. In aggregate, these data support further studies of the MMP pathway among lung-affected PI*MZ individuals, as they may serve as biomarkers or pharmacological targets in future studies.