Kim, J

Abstract Rationale Impaired lung health may serve as a risk factor for, and a post-acute sequela of, SARS-CoV-2 infection. Yet most prior studies of lung health sequelae of COVID-19 lacked information on pre-infection lung health, a major antecedent confounder. To identify associations of COVID-19 with changes in lung health, we leveraged paired pre- and post-pandemic data on respiratory symptoms, lung function, and lung structure in a general population-based cohort. Methods We studied participants in the Multi-Ethnic Study of Atherosclerosis Lung Study with paired pre-pandemic (2016-2018) and post-pandemic (2022-2024) assessments of respiratory symptoms, spirometry, and lung CT. SARS-CoV-2 infection (2020-2023) was ascertained using a composite of questionnaires, serology, and medical record adjudication, and classified as severe (requiring hospitalization) or non-severe (not requiring hospitalization). Lung health was assessed by questionnaire (mMRC grade ≥2 dyspnea, chronic cough, chronic phlegm, chronic bronchitis), pre-bronchodilator spirometry (FEV1, FVC, FEV1:FVC), and lung density on non-contrast lung CT (% low attenuation areas, % high attenuation areas). Each post-pandemic lung health measure was regressed on infection history and the corresponding pre-pandemic lung health measure, adjusting for age at post-pandemic exam, sex, race/ethnicity, smoking status, and vaccination status at infection. Sensitivity analyses were included- (a) restriction to participants without pre-pandemic respiratory symptoms and (b) with additional adjustment for time since infection and variant wave. Results Among 2,015 participants (mean age 77.2±7.5 years; 55% female; 40% non-Hispanic White; 25% Black; 11% Asian; 23% Hispanic/Latino), 59 (3%) experienced severe and 506 (25%) non-severe SARS-CoV-2 infection. The prevalence of respiratory symptoms was similar or slightly higher at the post- vs. pre-pandemic exam (grade ≥2 dyspnea: 14% vs. 10%; chronic cough: 10% vs. 8%; chronic phlegm: 9% vs. 8%; chronic bronchitis: 4% vs. 4%; p-values <0.0001). Compared with participants without infection, those with severe infection had higher odds of chronic cough (OR:2.9, 95%CI:1.3-6.3) and chronic bronchitis (OR:3.3, 95%CI:1.2-9.0) at the post-pandemic exam (Table). Associations were stronger among individuals infected >90 days prior to the post-pandemic assessment and during pre-Omicron waves. Results were similar when restricted to participants without pre-pandemic symptoms. No significant associations were observed for non-severe infection (vs. no infection) or for any other lung health outcomes. Conclusions Controlling for pre-pandemic lung health, severe COVID-19 was associated with increased odds of chronic cough and chronic bronchitis but not with changes in lung function or structure in a general population-based sample. Non-severe COVID-19 was not associated with lung health outcomes. This abstract is funded by: OT2HL156812, R21HL165405, R01HL077612, 75N92025D00022, 75N92025D00026, 75N92020D00002, 75N92025D00024, 75N92025D00027, 75N92025D00025, 75N92025D00028

Abstract Rationale Interstitial lung abnormalities (ILA) are abnormal findings on chest CT scans that may represent early-stages of interstitial lung disease. The presence of fibrosis (traction bronchiectasis and/or honeycombing) identifies those with ILA at an increased risk for mortality, however, determining who will progress remains a challenge, and the role of blood-based biomarkers in ILA risk-stratification is unknown. Methods Data from four prospective cohort studies were used to identify associations between plasma proteins measured with DNA aptamers (SomaScan) and ILA, fibrosis, and mortality. The Genetic Epidemiology of COPD Study (COPDGene) was used to identify protein associations, and findings were replicated by meta-analysis of the Age, Gene/Environment Susceptibility-Reykjavik study (AGES-Reykjavik), SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS), and Framingham Heart Study (FHS). Protein associations with ILA and with fibrotic ILA compared to non-fibrotic ILA were performed using multivariable logistic regression; among those with ILA, Cox proportional hazards models were used to identify protein associations with mortality. Models were adjusted for age, gender, race, BMI, smoking status, pack-year smoking history, and for mortality, COPD. Benjamini-Hochberg false discovery rate p-values <0.05 were considered significant. Results 524 (10.7%) of 4,899 COPDGene participants had ILA, of whom 57 (10.9%) had fibrosis; of 8,146 AGES-Reykjavik, SPIROMICS and FHS participants included in the meta-analysis, 666 (8.2%) had ILA, of whom 198 (29.7%) had fibrosis. In COPDGene, 230 proteins were associated with ILA; the most significant of which were SFTPD (adjusted odds ratio per standard deviation [SD] of protein [OR]=2.21, p = 3.7E-41) and SFTPB (OR = 1.82, p = 7.9E-37). In the meta-analysis, 159 of the ILA-associated proteins were replicated. Of the 230 ILA-associated proteins in COPDGene, 9 were associated with fibrosis, and 5 of these 9 proteins were associated with mortality among those with ILA. The 5 proteins associated with all three outcomes were WFDC2 (ORILA=1.90, p = 2.6E-34; ORFibrosis=2.00, p = 0.0001; adjusted hazards of mortality per SD of protein [HR]=1.59, p = 3.4E-05), GDF15 (ORILA=1.67, p = 3.6E-23; ORFibrosis=1.78, p = 0.0006; HR = 1.63, p = 1.4E-06), SCGB3A1 (ORILA=1.52, p = 4.5e-14; ORFibrosis=1.80, p = 0.0002; HR = 1.25, p = 0.02), TREM1 (ORILA=1.44, p = 1.4e-14; ORFibrosis=1.62, p = 0.001; HR = 1.25, p = 0.02) ICAM5 (ORILA=1.42, p = 1.2e-11; ORFibrosis=1.79, p = 0.0001; HR = 1.31, p = 0.002). In the meta-analysis, 6 of the 9 fibrosis-associated proteins and all 5 ILA mortality-associated proteins replicated. Conclusion This represents the first plasma proteomic screen demonstrating reproducible biomarker associations not only with ILA presence, but also with measures of severity and adverse outcomes. These results provide compelling targets for further study, including their prospective evaluation as clinical biomarkers for early disease identification and prognostication. This abstract is funded by: Multiple Sources