Background: Individuals with chronic obstructive pulmonary disease (COPD) demonstrate marked heterogeneity with respect to lung function decline, emphysema, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. In this study, we aimed to assess the predictive value of ORS for COPD-related traits in both smoking-enriched and general population cohorts.
Methods: We developed and tested ORS in n=3,339 participants of the Genetic Epidemiology of COPD (COPDGene) study with blood RNA-sequencing, proteomic, and metabolomic data collected at the second study visit. On 80% of the data, we trained single- and multi-omic risk scores on a variety of traits using elastic net penalized regression with 10-fold cross-validation. We included 24 cross-sectional and 5 longitudinal traits (where the trait was measured approximately 5 years apart), enriched for measures of disease severity, exacerbations, and traits derived from spirometry and computed tomography scans. We used multivariable models to test association of ORS with outcomes in a held-out COPDGene testing set and externally validated findings in participants of SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) (n= 2,177) and Multi-Ethnic Study of Atherosclerosis (MESA) (n=1,000), adjusting for potential confounders and multiple testing.
Results: Among the 24 cross-sectional traits in the COPDGene testing set, there were significantly associations with 70 of 72 single-omic ORS (false discovery rate adjusted p-value < 0.05). Significant associations were found in 5 of 15 longitudinal ORS with changes in trait values between COPDGene visits, including with forced expiratory volume at one second (FEV1) decline over 5 years and annually. We observed significant association with the relevant traits for all 38 cross-sectional ORS tested in SPIROMICS and for 15 of 24 in MESA. Generally, proteomic and metabolomic risk scores displayed stronger trait associations than transcriptomic risk scores, and multi-omic risk scores had higher predictive capacity than single-omic risk scores.
Conclusions: ORS constructed from blood-based omics can be leveraged to predict cross-sectional and future COPD-related traits in both smoking-enriched and general population cohorts. ORS for clinical use would require phenotype-focused risk score construction and replication.