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RationaleChronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and patterns of systemic inflammation have not been fully described.ObjectivesTo identify novel relationships between lung structural changes measured by chest computed tomography (CT) and systemic inflammation measured by blood RNA sequencing.MethodsCT scan images and blood RNA-seq gene expression from 1,223 subjects in the COPDGene study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we refer to as Image-Expression Axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment.Measurements and Main ResultsWe identified two distinct IEAs: IEAemphcaptures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to FEV1and Body Mass Index (BMI); IEAairwaycaptures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemphand IEAairway, respectively (adjusted pConclusionsIntegration of CT scans and gene expression data identified two IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.At a Glance CommentaryScientific Knowledge on the SubjectChronic obstructive pulmonary disease (COPD) is characterized by lung structural changes and has a prominent systemic inflammatory component, but the links between lung structural changes and patterns of systemic inflammation in COPD have not been fully described.What This Study Adds to the FieldWe identified novel relationships between lung structural changes and systemic inflammation by simultaneously analyzing CT scans and blood RNA-sequencing gene expression using deep learning models. We identified two distinct Image-Expression Axes (IEAs) that characterize different inflammatory processes associated with emphysema and airway predominant COPD.This article has an online data supplement, which is accessible from this issue’s table of content online atwww.atsjournals.org.

The Mendelian Randomization (MR) Steiger approach is used to determine the direction of a possible causal effect between two phenotypes [1]. For two phenotypes, denoted phenotype 1 and 2, the MR Steiger approach is composed of two parts: (1) MR is performed for a set of single nucleotide polymorphisms (SNPs) that serve as instrumental variables for phenotype 1 and (2) the difference of two correlations, the correlation between the SNPs and phenotype 1 and the correlation between the SNPs and phenotype 2, is calculated. These two parts are then used to determine the direction of a possible causal effect between the two phenotypes. The original MR Steiger paper [1] shows that unmeasured confounding of the two phenotypes affects the validity of the MR Steiger approach, but does not elucidate as to how this occurs. In particular, it was argued that if the magnitude of the observational variance explained between the two phenotypes is above 0.2, the MR Steiger method may return the incorrect causal direction due to unmeasured confounding. This may initially seem surprising since unmeasured confounding does not induce spurious associations between the SNP and phenotype 2, as we demonstrate using directed acyclic graphs. In this note, we show that this is because unmeasured confounding may rescale the magnitude of a non-zero association, and thereby distort the comparison of the correlation between the SNP and phenotype 2 and the correlation between the SNP and phenotype 1. We will end with a number of cautionary remarks on the MR Steiger method, which are partly motivated by this and mentioned in the original MR Steiger paper [1].

Reduced lung function predicts mortality and is key to the diagnosis of COPD. In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, one-half of which are new. In combination these variants strongly predict COPD in deeply-phenotyped patient populations. Furthermore, the combined effect of these variants showed generalisability across smokers and never-smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.