Publication Date

1-1-2023

Journal

Chest

DOI

10.1016/j.chest.2022.06.030

PMID

35780812

PMCID

PMC9859724

PubMedCentral® Posted Date

6-30-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Female, Molecular Epidemiology, Tomography, X-Ray Computed, Proportional Hazards Models, Lung, Pulmonary Disease, Chronic Obstructive, 6-min walk test, interstitial lung disease, pulmonary fibrosis, pulmonary function test, radiology

Abstract

BACKGROUND: The risk factors and clinical outcomes of quantitative interstitial abnormality progression over time have not been characterized.

RESEARCH QUESTIONS: What are the associations of quantitative interstitial abnormality progression with lung function, exercise capacity, and mortality? What are the demographic and genetic risk factors for quantitative interstitial abnormality progression?

STUDY DESIGN AND METHODS: Quantitative interstitial abnormality progression between visits 1 and 2 was assessed from 4,635 participants in the Genetic Epidemiology of COPD (COPDGene) cohort and 1,307 participants in the Pittsburgh Lung Screening Study (PLuSS) cohort. We used multivariable linear regression to determine the risk factors for progression and the longitudinal associations between progression and FVC and 6-min walk distance, and Cox regression models for the association with mortality.

RESULTS: Age at enrollment, female sex, current smoking status, and the MUC5B minor allele were associated with quantitative interstitial abnormality progression. Each percent annual increase in quantitative interstitial abnormalities was associated with annual declines in FVC (COPDGene: 8.5 mL/y; 95% CI, 4.7-12.4 mL/y; P < .001; PLuSS: 9.5 mL/y; 95% CI, 3.7-15.4 mL/y; P = .001) and 6-min walk distance, and increased mortality (COPDGene: hazard ratio, 1.69; 95% CI, 1.34-2.12; P < .001; PLuSS: hazard ratio, 1.28; 95% CI, 1.10-1.49; P = .001).

INTERPRETATION: The objective, longitudinal measurement of quantitative interstitial abnormalities may help identify people at greatest risk for adverse events and most likely to benefit from early intervention.

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