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Tejwani, V

Mayo Clinic

Cleveland Clinic

Citations 3

C101-06 Interactions of Leukotriene-B4 and Interleukin-8 Gene Variants and Protein Levels With PI*MZ Heterozygosity Among the COPDGene and SPIROMICS Cohorts
Tejwani, V, Bazeley, P, Barjaktarevic, I, Barnard, J, Bleecker, E R, Cho, M H, Curtis, J L, Moll, M, Meyers, D A, Ruvuna, L, Shrivastav, A, Siddharthan, T, Silverman, E K, Stoller, J K, Zhang, J, Bowler, R P, Zein, J G, Hersh, C P, Ortega, V E, Bazeley, Peter , Cho, Michael H. , Curtis, Jeffrey L. , Meyers, Deborah A. , Ruvuna, L. , Stoller, James K. , Zein, Joe , Hersh, Craig P.

American Journal of Respiratory and Critical Care Medicine | May 18, 2026 | Volume: 212 | Issue: Supplement_1 | Pages: No page available

10.1093/ajrccm/aamag162.2025 Get It

Abstract Rationale Deficiency of alpha-1 antitrypsin (AAT), the protein encoded by SERPINA1, remains the strongest genetic risk factor for COPD. Individuals heterozygote for the type Z variant, termed the PI*MZ genotype (estimated global prevalence n = 35,000,000), experience varying respiratory morbidity beyond expected from tobacco smoking alone. AAT is a pleiotropic protein which inhibits not only neutrophil elastase but also neutrophil chemotaxis mediated by leukotriene B4 (LTB4) [by antagonizing the LTB4 receptor (LTB4R)] and interleukin-8 (IL-8). Although LTB-4 and IL-8 gene variants and protein levels have independently been implicated in COPD, their potential role in PI*MZ is unknown. Methods COPDGene and SPIROMICS are observational tobacco-smoke and COPD-enriched cohort studies. Participants were phenotyped by spirometry and CT imaging, with whole genome sequencing data available via TOPMed. We analyzed participants with PI*MM (COPDGene n = 9,732; SPIROMICS n = 2,105) and PI*MZ (COPDGene n = 300; SPIROMICS n = 83). LTB-4 (ALOX5AP, LTA4H, LTB4R) and IL-8 (CXCL8, CXCR1, CXCR2) associated gene variants (exonic and intronic) with a minor allele frequency of ≥ 0.05 were extracted. We assessed statistical interactions between MZ status and chest CT outcomes or FEV1% predicted using the SPAGxECCT package with adjustment for relatedness (genetic related matrix) and ancestry (top ten PCs). Baseline protein LTB4R (SPIROMICS only) and IL-8 (both cohorts) protein levels were measured via SomaScan, and statistical interactions between MZ status and AAT concentration were explored. Models were adjusted for age, sex, BMI, smoking status, and pack-years. Given the candidate gene discovery approach, significance was defined as nominal p-value<0.05 Results We identified the following number of variants in genes of the LTB-4 or IL-8 pathways: 87 shared between cohorts; 133 unique to COPDGene; 1 unique to SPIROMICS. LTB-4-related polymorphisms were associated (p < 0.05) with any radiographic outcomes in 22 variants (COPDGene) vs. 23 variants (SPIROMICS), with a single overlap (rs17074984 in ALOX5AP). Two LTB-4-pathway variants were associated with FEV1% predicted in SPIROMICS (both in ALOX5AP), but none in COPDGene. AAT plasma concentrations displayed a statistically significant interaction with FEV1% predicted (p = 0.046) and PRMemphysema (p = 0.013) for LTBR4, but not for IL-8. There was not an interaction by MM/MZ status and protein levels. Conclusions Predominantly LTB-4, and not IL-8, pathway-related genetic variants and protein levels may contribute to disease heterogeneity among PI*MZ individuals. The association of circulating LTB4R protein with reduced lung function, particularly among those with low AAT levels, further suggest interactions between AAT and the leukotriene pathway in PI*MZ individuals. Thus, PI*MZ individuals may uniquely derive benefit from leukotriene-targeting pharmacotherapy. This abstract is funded by: NIH NHLBI K23HL173570
B102-02 Deep Learning-based Detection of Homozygous Alpha-1 Antitrypsin Deficiency From HRCT: A Proof-of-concept Study Across COPDGene, SPIROMICS, and GRADS Cohorts
Tejwani, V, Hatt, C R, Barjaktarevic, I, Bhatt, S P, Bowler, R P, Drummond, M B, Han, M K, Hersh, C P, Ortega, V E, Serban, K A, Stoller, J K, Washko, G R, Wells, J M, Yadav, R, Strange, C, Siddharthan, T, Wells, J. Michael , Wells, J Michael , Hatt, Charles R. , Bhatt, Surya P. , Han, MeiLan King , Hersh, Craig P. , Serban, Karina A. , Stoller, James K. , Strange, C, Siddharthan, T

American Journal of Respiratory and Critical Care Medicine | May 18, 2026 | Volume: 212 | Issue: Supplement_1 | Pages: No page available

10.1093/ajrccm/aamag162.1900 Get It

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
A101-20 Polygenic Risk Score and Cigarette Smoking Modify Risk of Airflow Obstruction in Individuals With Alpha-1 Antitrypsin MZ Genotype
Zhang, J, Moll, M, Choi, S, Tejwani, V, Ortega, V E, Wilson, A A, Dupuis, J, Cho, M H, O’Connor, G T, Cho, Michael H.

American Journal of Respiratory and Critical Care Medicine | May 18, 2026 | Volume: 212 | Issue: Supplement_1 | Pages: No page available

10.1093/ajrccm/aamag162.1737 Get It

Abstract Rationale Alpha-1 antitrypsin (AAT) deficiency, primarily caused by homozygous SERPINA1 Z mutation, increases the risk of COPD. Individuals carrying one Z and one normal M allele (MZ) have higher COPD risk than MM individuals, although many never develop disease. COPD is a polygenic condition influenced by numerous genetic factors across the genome and by environmental exposures such as smoking. We aimed to examine how polygenic factors and smoking modify risk of airflow obstruction in MZ compared with MM. Methods We included participants with the MZ and MM genotypes from the Framingham Heart Study. We first tested the association between AAT genotype (MZ coded as 1 and MM as 0) and FEV1/FVC, stratified by ever-smoking status, and further evaluated interactions between AAT genotype and smoking pack-years. Next, for each participant we calculated a polygenic risk score (PRS) for FEV1/FVC, which was derived from external genome-wide association studies in the UK Biobank and SpiroMeta Consortium. To reflect risk for airflow limitation, higher PRSs correspond to lower FEV1/FVC. We then tested the interaction between the PRS and AAT genotype, stratified by ever-smoking. Models were adjusted for age, sex, height, current smoking, pack-years (among ever-smokers), familial relatedness, and in PRS models, principal components of genetic ancestry. Results Among 4,220 participants (mean age 53.7 years, 54.2% female, 50.2% ever-smokers, mean FEV1/FVC 74.6%), 133 (3.2%) had MZ genotype. In never-smokers, MZ individuals had 1.72% higher FEV1/FVC compared with MM (P = 0.032), whereas no difference was observed among ever-smokers (β= -0.39%; P = 0.65). Cumulative smoking modified the effect of MZ genotype on FEV1/FVC (interaction β= -0.13% per pack-year; P = 0.003); this interaction persisted among ever-smokers (β= -0.11% per pack-year; P = 0.044). A one-standard-deviation increase in the FEV1/FVC PRS was associated with lower FEV1/FVC in both never- (β= -2.37%; P < 0.0001) and ever-smokers (β= -2.46%; P < 0.0001). A higher PRS was disproportionately associated with reduced FEV1/FVC in MZ compared to MM (interaction β= -1.87% per standard-deviation PRS increase, P = 0.027) among ever-smokers, but not in never-smokers (P = 0.15). Conclusions In this community-based cohort, the AAT MZ genotype was associated with higher FEV1/FVC among non-smokers compared to MM. The interaction between MZ genotype and pack-years suggested a dose-dependent modifying effect of smoking on the MZ-related COPD risk. Among ever-smokers, higher polygenic risk further amplified susceptibility to airflow obstruction in MZ compared with MM. These findings highlight the roles of both polygenic and environmental risk factors in COPD susceptibility in MZ. Replication of the results is needed. This abstract is funded by: This work is supported by an Alpha-1 Foundation Grant and a Boston University Department of Medicine Career Investment Award