Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease

Abstract

<b>Rationale:</b> The ability of peripheral blood biomarkers to assess chronic obstructive pulmonary disease (COPD) risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict disease activity. <b>Objectives:</b> Develop a transcriptional risk score (TRS) for COPD and assess the contribution of the TRS and a polygenic risk score (PRS) for disease susceptibility and progression. <b>Methods:</b> We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-blood RNA sequencing into training (<i>n</i> = 1,945) and testing (<i>n</i> = 624) samples and used 468 ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) COPD cases with microarray data for replication. We developed a TRS using penalized regression (least absolute shrinkage and selection operator) to model FEV₁/FVC and studied the predictive value of TRS for COPD (Global Initiative for Chronic Obstructive Lung Disease 2-4), prospective FEV₁ change (ml/yr), and additional COPD-related traits. We adjusted for potential confounders, including age and smoking. We evaluated the predictive performance of the TRS in the context of a previously derived PRS and clinical factors. <b>Measurements and Main Results:</b> The TRS included 147 transcripts and was associated with COPD (odds ratio, 3.3; 95% confidence interval [CI], 2.4-4.5; <i>P</i> < 0.001), FEV₁ change (β, -17 ml/yr; 95% CI, -28 to -6.6; <i>P</i> = 0.002), and other COPD-related traits. In ECLIPSE cases, we replicated the association with FEV₁ change (β, -8.2; 95% CI, -15 to -1; <i>P</i> = 0.025) and the majority of other COPD-related traits. Models including PRS, TRS, and clinical factors were more predictive of COPD (area under the receiver operator characteristic curve, 0.84) and annualized FEV₁ change compared with models with one risk score or clinical factors alone. <b>Conclusions:</b> Blood transcriptomics can improve prediction of COPD and lung function decline when added to a PRS and clinical risk factors.