Objectives. To construct multi-trait polygenic scores (PRS) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validate their performance in diverse cohorts, and identify PRS-related proteins for potential therapeutic targeting.

Design Prospective cohort studies.

Setting. Genetic Epidemiology of COPD (COPDGene; 2007-present), Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE; 2005-2008), Mass General Brigham Biobank (MGBB; 2010-present), All of Us (2016-present), and UK Biobank (UKB; 2006-present).

Participants. 6,647 non-Hispanic White (NHW) and 2,466 African American (AA) participants from COPDGene; 1,858 participants from ECLIPSE; 118,566 from All of Us; 15,142 from MGBB with genetic data. 5,173 COPDGene and 5,012 UKB participants with proteomic data.

Main outcome measures. COPD status (GOLD 2-4 vs. GOLD 0) and COPD exacerbation frequency.

Results. PRSmix+, a multi-trait PRS framework, selected 7 traits for a composite PRS (PRSmulti). In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects (RE) OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE beta 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRS that were also validated with measured protein levels in COPDGene and UK biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include AGER (RAGE), IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.

Conclusions. Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.

MM discloses consulting fees from 2ndMD, TheaHealth, Axon Advisors, Dialectica, Sanofi, Verona Pharma; grant support from Genentech; service as a medical expert; and honoraria from ATS 2024 and NYSTS 2024.

Matthew R. Moll is supported by NIH grant K08HL159318. Edwin K. Silverman is supported by NIH grants R01 HL152728, P01 HL114501, U01 HL089856, R01 HL147148, and R01 HL133135. Michael H. Cho is supported by NIH grants R01 HL153248, R01 HL135142, R01 HL149861, R01 HL137927, and R01 HL137148. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

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

Ethics committee/IRB of Mass General Brigham gave ethical approval for this work.

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Yes

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This article has an online data supplement.

Funding: MM is supported by NIH grant K08HL159318. EKS is supported by NIH grants R01 HL152728, P01 HL114501, U01 HL089856, R01 HL147148, and R01 HL133135. MHC is supported by NIH grants R01 HL153248, R01 HL135142, R01 HL149861, R01 HL137927, and R01 HL137148. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.

Disclosures: MM discloses consulting fees from 2ndMD, TheaHealth, Axon Advisors, Dialectica, Sanofi, Verona Pharma; grant support from Genentech; service as a medical expert; and honoraria from ATS 2024 and NYSTS 2024.

Some of the data produced in the present work are contained in the manuscript. Further data are available upon reasonable request to the authors.