Rationale: Emphysema is a key component of COPD with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and possible development of targeted therapies. Objectives: Discover blood transcriptomic and proteomic biomarkers for chest computed tomography-quantified emphysema in smokers 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 set of 2,370 COPDGene participants with available whole blood RNA sequencing, plasma SomaScan proteomics, and clinical data. Validation was conducted in a testing set of 1,016 COPDGene subjects. Since body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Predictive models were also developed using elastic net to predict quantitative emphysema from cell blood count, RNA sequencing, and proteomic biomarkers. Model accuracy was assessed by area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: 4,913 genes, 1,478 isoforms, 386 exons, and 881 proteins were significantly associated with emphysema (FDR 10%). 75% and 77% of genes and proteins, respectively, were mediated by BMI. The significantly enriched biological pathways were involved in inflammation and cell differentiation, differing between the most and least BMI-mediated genes. The cell blood count plus protein model achieved the highest performance with an AUROC of 0.89. Conclusions: Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema.

Introduction: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking), will facilitate studies of the health effects of vaping. We conducted a blood biomarker discovery study for vaping and dual use in a longitudinal cohort of current and former adult smokers with high-throughput transcriptomic and proteomic data, and we tested biomarkers for association to multiple health outcomes. Methods: We studied 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data according to their self-reported current vaping and smoking behavior. Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified 3 transcriptomic and 3 proteomic associations to vaping, and 90 transcriptomic and 100 proteomic associations to dual use (FDR 10%). Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (3 transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (2 transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality and respiratory exacerbations. Conclusion: Vaping and dual use are associated with multiple blood-based biomarkers that are also associated with adverse health outcomes.

Background: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking) will facilitate studies of the health effects of vaping. To identify putative biomarkers of vaping and dual use, we performed association analysis in an observational cohort of 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data and self-reported current vaping and smoking behavior. Methods: Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified three transcriptomic and three proteomic associations with vaping, and 90 transcriptomic and 100 proteomic associations to dual use. Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (three transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (two transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality, and respiratory exacerbations. Conclusions: In summary, vaping and dual use are associated with RNA and protein blood-based biomarkers that are also associated with adverse health outcomes.