The Proteomic Profile of Interstitial Lung Abnormalities.

Rationale Knowledge on biomarkers of interstitial lung disease is incomplete. Interstitial lung abnormalities (ILA) are radiologic changes that may present in its early stages. Objectives To uncover blood proteins associated with ILA using large-scale proteomics methods. Methods Data from two prospective cohort studies, the AGES-Reykjavik study (n=5,259) for biomarker discovery and the Genetic Epidemiology of COPD (COPDGene) study (n=4,899) for replication, were used. Blood proteins were measured using DNA aptamers, targeting over 4,700 protein analytes. The association of proteins with ILA and ILA progression was assessed with regression modelling, as were associations with genetic risk factors. Adaptive LASSO models were applied to bootstrap data samples to discover sets of proteins predictive of ILA and their progression. Measurements and Main Results Of 287 associations, SFTPB (OR 3.71 [95% CI 3.20-4.30], P 4.28×10-67), SCG3AB1 (OR 2.43 [2.13-2.77], P 8.01×10-40) and WFDC2 (OR 2.42 [2.11-2.78], P 4.01×10-36) were most significantly associated with ILA in AGES-Reykjavik and were replicated in COPDGene. In AGES-Reykjavik, levels of SFTPB were associated with the rs35705950 MUC5B promoter polymorphism and SFTPB and WFDC2 had the strongest associations with ILA progression. Multivariate models of ILA in AGES-Reykjavik, ILA in COPDGene and ILA progression in AGES-Reykjavik, had validated areas under the receiver operating characteristic curve of 0.880, 0.826 and 0.824, respectively. Conclusions Novel, replicated associations of ILA, its progression and genetic risk factors with numerous blood proteins are demonstrated as well as machine-learning based models with favourable predictive potential. Several proteins are revealed as potential markers of early fibrotic lung disease.