The Proteomic Profile of Interstitial Lung Abnormalities

Knowledge on biomarkers of interstitial lung disease is incomplete. Interstitial lung abnormalities (ILAs) are radiologic changes that may present in its early stages. To uncover blood proteins associated with ILAs using large-scale proteomics methods. Data from two prospective cohort studies, the A...

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Vydané v:American journal of respiratory and critical care medicine Ročník 206; číslo 3; s. 337
Hlavní autori: Axelsson, Gisli Thor, Gudmundsson, Gunnar, Pratte, Katherine A, Aspelund, Thor, Putman, Rachel K, Sanders, Jason L, Gudmundsson, Elias F, Hatabu, Hiroto, Gudmundsdottir, Valborg, Gudjonsson, Alexander, Hino, Takuya, Hida, Tomoyuki, Hobbs, Brian D, Cho, Michael H, Silverman, Edwin K, Bowler, Russell P, Launer, Lenore J, Jennings, Lori L, Hunninghake, Gary M, Emilsson, Valur, Gudnason, Vilmundur
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.08.2022
Predmet:
Genetic Predisposition to Disease
Humans
Lung
Lung Diseases, Interstitial - epidemiology
Lung Diseases, Interstitial - genetics
Prospective Studies
Proteomics
Respiratory System Abnormalities
Tomography, X-Ray Computed
idiopathic pulmonary fibrosis
interstitial lung abnormalities
proteomics
interstitial lung disease
biomarkers
ISSN:1535-4970, 1535-4970
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Shrnutí:Knowledge on biomarkers of interstitial lung disease is incomplete. Interstitial lung abnormalities (ILAs) are radiologic changes that may present in its early stages. To uncover blood proteins associated with ILAs using large-scale proteomics methods. Data from two prospective cohort studies, the AGES-Reykjavik (Age, Gene/Environment Susceptibility-Reykjavik) study (  = 5,259) for biomarker discovery and the COPDGene (Genetic Epidemiology of COPD) study (  = 4,899) for replication, were used. Blood proteins were measured using DNA aptamers, targeting more than 4,700 protein analytes. The association of proteins with ILAs and ILA progression was assessed with regression modeling, as were associations with genetic risk factors. Adaptive Least Absolute Shrinkage and Selection Operator models were applied to bootstrap data samples to discover sets of proteins predictive of ILAs and their progression. Of 287 associations, SFTPB (surfactant protein B) (odds ratio [OR], 3.71 [95% confidence interval (CI), 3.20-4.30];  = 4.28 × 10 ), SCGB3A1 (Secretoglobin family 3A member 1) (OR, 2.43 [95% CI, 2.13-2.77];  = 8.01 × 10 ), and WFDC2 (WAP four-disulfide core domain protein 2) (OR, 2.42 [95% CI, 2.11-2.78];  = 4.01 × 10 ) were most significantly associated with ILA in AGES-Reykjavik and were replicated in COPDGene. In AGES-Reykjavik, concentrations of SFTPB were associated with the rs35705950 (mucin 5B) promoter polymorphism, and SFTPB and WFDC2 had the strongest associations with ILA progression. Multivariate models of ILAs in AGES-Reykjavik, ILAs 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. 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 favorable predictive potential. Several proteins are revealed as potential markers of early fibrotic lung disease.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1535-4970
1535-4970
DOI:10.1164/rccm.202110-2296OC