Publication Date
3-14-2024
Journal
BMJ Open Respiratory Research
DOI
10.1136/bmjresp-2023-002219
PMID
38485250
PMCID
PMC10941119
PubMedCentral® Posted Date
3-14-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Biomarkers, Idiopathic Pulmonary Fibrosis, Proteomics, Retrospective Studies, Tomography, X-Ray Computed, Interstitial Fibrosis, Imaging/CT MRI etc
Abstract
INTRODUCTION/RATIONALE: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT.
METHODS: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p
RESULTS: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features.
CONCLUSIONS: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.
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Critical Care Commons, Internal Medicine Commons, Medical Biochemistry Commons, Medical Cell Biology Commons, Medical Genetics Commons, Pulmonology Commons, Sleep Medicine Commons
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