Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIA) are early measures of lung injury automatically detected on chest computed tomography (CT) scans. QIA is associated with impaired respiratory health and shares features with advanced lung diseases, but its biological underpinnings are not well understood. Objective: We analyzed high-throughput plasma proteomic panels within two multi-center cohorts to identify novel protein biomarkers of QIA. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (Genetic Epidemiology of COPD, COPDGene) and 2,925 participants in a younger population cohort (Coronary Artery Disease Risk in Young Adults, CARDIA) with the SomaLogic SomaScan assays. We measured QIA using a local density histogram method. We assessed the associations between proteomics levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg False Discovery Rate p-value ≤0.05). Measurements and Main Results: 852 proteins were significantly associated with QIA in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including Biological Processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; Cellular Components in extracellular regions; and Molecular Functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIA in an older, smoking population with higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.