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Hansel, N.N.

Mayo Clinic

Johns Hopkins University

Citations 5

Differences in Airway Epithelial Gene Expression in Resilient vs Non-resilient Smokers
Oh, Anna , Niederhuber, Matthew J. , Jones, Corbin D. , Brennan, Tara A., Currin, Kevin W., Kennedy, Sarah , Christenson, S., Bon, Jessica , Comellas, Alejandro P. , Cooper, C.B., Hansel, N.N., Kaner, R.J. , Marchetti, Nathaniel , Onofrei, C.D., Ortega, Victor E. , Paine, Robert , Woodruff, P., Investigators, SPIROMICS, Christenson, Stephanie A. , Comellas, Alejandro P , Christenson, Stephanie A , Oh, A., Liu, Heng , Kennedy, S., Couper, David , Barjaktarević, Igor , Barr, R. Graham , Bowler, Russell P. , Cooper, C.B. , Han, Mingyuan , Krishnan, Jerry A , Martínez, Fernando J. , Wells, J. Michael

American Journal of Respiratory and Critical Care Medicine | May 11, 2025 | Volume: 211 | Issue: Supplement_1 | Pages: A7375-A7375

10.1164/ajrccm.2025.211.abstracts.a7375 Get It

Abstract Introduction Resilient smokers, who maintain respiratory health despite smoking, remain poorly characterized. Airway epithelial cell gene expression in resilient smokers can provide insight into mechanisms and pathways that may protect smokers from developing smoking-related lung disease. The objective of this study is to identify genes and potential pathways associated with lung-related resilience. Methods Resilient smokers were defined based on a previously published multi-dimensional definition which includes the absence of: 1) spirometric airflow limitation, 2) symptoms (cough/sputum, dyspnea), 3) radiographic (HRCT) abnormalities, 4) exacerbations, and 5) an accelerated FEV1 decline. “Non-resilient” smokers had preserved spirometry but showed abnormalities in at least one domain of resilience. We analyzed airway epithelial brush samples from 21 resilient and 34 non-resilient smokers from SPIROMICS. Differential gene expression was performed using DESeq2 package in R, which estimates differential expression using generalized linear models. We adjusted for sequencing batch, age (continuous), race, gender, and current smoking status. While controlling for these covariates we evaluated differential gene expression between resilient and non-resilient smokers, the interaction between resilience and smoking status, and in each sub-category of resilient (absence of symptoms, radiographic abnormalities, exacerbations, and accelerated decline in FEV1). Results We identified 98 genes that were differentially expressed (FDR ≤0.1) when comparing resilient to non-resilient smokers, 20 of those genes were up regulated in resilient smokers (Figure 1). Notable genes included cadherin-12 (CDH12), aspartate beta-hydroxylase (ASPH), and protein kinase AMP-activated alpha 2 (PRKAA2). CDH12 is involved in cell adhesion and epithelial integrity. ASPH influences cell adhesion and signaling. PRKAA2 plays role in cellular homeostasis and stress response, which may be altered in non-resilient smokers, affecting cellular metabolism and inflammation. BPI folding containing family A member (BPIFA1) was significantly associated with smoking status in the interaction analysis, suggesting its role in modulating the impact of smoking on resilience. BPIFA1 has anti-microbial properties, regulates immune responses, and contributes mucosal surface protection. Sub-category analysis identified 17 genes associated with resilience to symptoms and 2 genes related to physiologic resilience (FEV1 decline), though no significant genes were associated with radiographic or exacerbation resilience. Conclusion Differential gene expression of airway epithelial cells identified distinct expression patterns between resilient and non-resilient smokers. Specifically, genes associated with epithelial cell adhesion, signaling, and cellular homeostasis were up regulated in resilient smokers. These findings suggest that resilient smokers may have transcriptomic profiles and potential mechanisms that mitigate the effects of cigarette smoke.
Food Desert is Associated With All-cause and Cardiovascular Mortality in SPIROMICS
Offor, Obiageli , Hansel, N.N., Barjaktarevic, I., Bowler, R.P., Bosma, C. , Curtis, J.L., Dolezal, Brett A. , LaFon, David C. , Guo, C. , Alexis, Neil E. , Ortega, V.E., Woodruff, P., Putcha, Nirupama , Buhr, Russell G. , Barjaktarevic, I., Barr, R. Graham , Bosma, Christopher B , Curtis, J.L., Ohar, Jill , O’Neal, Wanda K. , Pistenmaa, Carrie L. , Fawzy, Ashraf

American Journal of Respiratory and Critical Care Medicine | May 11, 2025 | Volume: 211 | Issue: Supplement_1 | Pages: A6182-A6182

10.1164/ajrccm.2025.211.abstracts.a6182 Get It

Abstract Rationale: Adverse social determinants of health (SDOH) are common in COPD and associated with increased mortality. However, cause-specific mortality is understudied. We evaluated associations of neighborhood-level SDOH with all-cause and cause-specific mortality among individuals with or at-risk for COPD. Methods: SPIROMICS is an ongoing, multi-center, longitudinal observational study of individuals across the US with current or former personal smoking histories (≥20 pack-years), with or without spirometrically-defined COPD. We established geocoded neighborhood-level SDOH at baseline using 2010 census tract data, including specifically, area deprivation index (ADI) (range 0-100, 100 = most disadvantaged), poverty level (percentage of households below the poverty line), poor food access (>1/3 of residents with no access to fresh food stores within 1 mile in urban areas and 10 miles in rural areas), and food desert (low income plus poor food access). We ascertained all-cause and cause-specific mortality through 12/31/2021 using the National Death Index. Using Cox proportional hazards models, we evaluated the risk of all-cause mortality for each SDOH. We utilized competing-risk regression models to evaluate the risk of cardiovascular, respiratory, COPD (a subset of respiratory), and neoplastic causes of death individually with each SDOH. Models were adjusted for demographics, smoking status, pack-years, BMI categories, coronary heart disease, FEV1% predicted and study site. Effect modification by lung function was assessed using multiplicative interactions between SDOH and FEV1% predicted. Results: Among 2977 participants, mean ADI was 40.1±27.8. Within a participant's neighborhood, on average 10.4±10.4% of households were below the poverty line, 933 (31.3%) had poor food access, and 220 (7.4%) resided in a food desert. During a median follow-up of 7.8 years (IQR 5.6-9.2), 744 (25.0%) participants died, most commonly from respiratory causes including COPD. Neither ADI nor neighborhood poverty level were associated with all-cause mortality or any specific cause of death (Table). Despite lack of associations between food access and mortality (Table), individuals residing in a food desert had elevated risks of all-cause (HR: 1.36 [95%CI: 1.03-1.79]) and cardiovascular (HR: 2.62 [95%CI: 1.5-4.35]) deaths. There was no significant effect modification by lung function. Conclusions: In a large cohort of individuals with personal smoking histories, with or without COPD, living in a food desert was associated with increased all-cause and cardiovascular mortality. However, neighborhood poverty level and food access, the two components of food desert, were not independently associated with mortality, suggesting the association of multimodal SDOH variables with mortality in this population.
Deep Residual Convolutional Network Predicts Future Severe Exacerbations of COPD in SPIROMICS
Awan, Hira A. , Chaudhary, Muhammad F. A. , Bhatt, S.P. , Cooper, C.B., Criner, G.J., Hansel, N.N., Krishnan, J.A., Ortega, Victor E. , Paine, Robert , Schroeder, Joyce , Gerard, Sarah E. , Comellas, Alejandro P. , Hoffman, Eric A. , Barjaktarević, Igor , Bhatt, Surya P. , Cooper, C.B. , Criner, G.J., Reinhardt, Joseph M.

B41. RISK FACTORS AND OUTCOMES FOR COPD EXACERBATIONS | May 1, 2023 | Volume: No volume available | Issue: No issue available | Pages: A3318-A3318

10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a3318 Get It