Background Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, 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. This study evaluates the predictive value of ORS for COPD-related traits in both smoking-enriched and general population cohorts.

Methods ORS were developed and tested in 3,339 participants of Genetic Epidemiology of COPD (COPDGene) with blood RNA-sequencing, proteomic, and metabolomic. Single- and multi-omic risk scores were trained 24 cross-sectional and five longitudinal traits using 80% of the data, focusing on disease severity, exacerbations, and traits from spirometry and computed tomography scans. Multivariable models were used to test ORS associations with outcomes in remaining COPDGene participants and externally validated in SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) (n = 2,177) and Multi-Ethnic Study of Atherosclerosis (MESA) (n = 1,000).

Results In the COPDGene testing set, 69 of 72 single-omic ORS showed significant associations with 24 cross-sectional traits (adjusted p-value < 0·05). One of 15 longitudinal ORS was associated with changes in trait values between COPDGene visits. Significant associations were observed for all 38 cross-sectional ORS tested in SPIROMICS and for 16 of 24 in MESA. Proteomic and metabolomic risk scores generally displayed stronger associations than transcriptomic scores.

Discussion Blood-based ORS can predict cross-sectional and future COPD-related traits in both smoking-enriched and general population cohorts.

DLD has received grant support from Bayer. BDH received an honorarium from AstraZeneca for an educational lecture and has received grant support from Bayer. VEO is a member of the Independent Data and Monitoring Committee for Regeneron and Sanofi. EKS has received grant support from Bayer and Northpond Laboratories. MHC has received grant support from Bayer.

IRK is supported by the TOPMed Fellowship. DLD is supported by P01HL114501, HG011393, and K24HL171900. BDH was supported by R01HL162813, R01HL155749, R01HL160008, U01HL089856, and a Research Grant from the Alpha-1 Foundation. KJK was supported by NIH/NHLBI R01HL152735. MHC is supported by R01HL162813 and R01HL153248. MM is supported by NIH K08HL159318.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Colorado Multiple Institutional Review Board for the University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, USA, waived ethical approval for this work (COMIRB #19-2835).

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COPDGene proteomic and metabolomic data generated through the TOPMed program can be accessed through dbGaP accession phs000951·v5·p5 with an approved TOPMed proposal. COPDGene transcriptomic data can be accessed through an ancillary study request. MESA proteomic, transcriptomic, and metabolomic data is available through dbGaP accession phs001416·v3·p1. SPIROMICS RNA-Sequencing data generated through the TOPMed program are available through dbGaP accession phs001927·v1·p1 with an approved TOPMed proposal. SPIROMICS proteomic and metabolomic data can be accessed with an approved ancillary study. Feature weights for final omic risk scores as well as R scripts used to process data, analyze ORS, perform statistical analysis, and generate figures in this manuscript can be found at https://github.com/konigsbergi/OmicRiskScoresCOPD. Example scripts for generating ORS models are available as well.