RATIONALE
Emphysema is a COPD phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies.
OBJECTIVES
Discover blood omics biomarkers for chest CT-quantified emphysema and develop predictive biomarker panels.
METHODS
Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing (RNA-seq), plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n=1,016) and external validation was done in the ECLIPSE study (n=526). Since low BMI and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-seq, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity.
MEASUREMENTS AND MAIN RESULTS
3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema and yielded 11 biological pathways. 74% of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (AUROC in COPDGene testing: 0.90, 95% CI: 0.85-0.90).
CONCLUSIONS
Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema.