Dynamic and prognostic proteomic associations with FEV1 decline in chronic obstructive pulmonary disease

Abstract

Rationale: Identification and validation of circulating biomarkers for lung function decline in COPD remains an unmet need.

Objective: Identify prognostic and dynamic plasma protein biomarkers of COPD progression.

Methods: We measured plasma proteins using SomaScan from two COPD-enriched cohorts, the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) and Genetic Epidemiology of COPD (COPDGene), and one population-based cohort, Multi-Ethnic Study of Atherosclerosis (MESA) Lung. Using SPIROMICS as a discovery cohort, linear mixed models identified baseline proteins that predicted future change in FEV₁ (prognostic model) and proteins whose expression changed with change in lung function (dynamic model). Findings were replicated in COPDGene and MESA-Lung. Using the COPD-enriched cohorts, Gene Set Enrichment Analysis (GSEA) identified proteins shared between COPDGene and SPIROMICS. Metascape identified significant associated pathways.

Measurements and main results: The prognostic model found 7 significant proteins in common (p < 0.05) among all 3 cohorts. After applying false discovery rate (adjusted p < 0.2), leptin remained significant in all three cohorts and growth hormone receptor remained significant in the two COPD cohorts. Elevated baseline levels of leptin and growth hormone receptor were associated with slower rate of decline in FEV₁. Twelve proteins were nominally but not FDR significant in the dynamic model and all were distinct from the prognostic model. Metascape identified several immune related pathways unique to prognostic and dynamic proteins.

Conclusion: We identified leptin as the most reproducible COPD progression biomarker. The difference between prognostic and dynamic proteins suggests disease activity signatures may be different from prognosis signatures.

Keywords: Disease Progression; Proteomics; Spirometry.

Figure 1:. Consort Diagram.

Flow diagram depicting the number of participants included after application of our study specific exclusion criteria. The SPIROMICS enrollment criteria was applied to the COPDGene cohort to reduce the variability between participants in each COPD-enriched cohort.

Figure 2:. Baseline proteins associated with FEV₁ change over time in SPIROMICS, COPDGene, and MESA-Lung

(A) The x-axes of each panel display the signed −log₁₀(p-value) (sign of beta estimate * −log₁₀(p-value)) from our discovery cohort, SPIROMICS. The y-axes show the signed −log₁₀(p-value) from our validation cohorts, COPDGene and MESA-Lung. Proteins highlighted in orange overlapped between our discovery cohort and one of our validation cohorts (nominal p-value < 0.05 and beta estimate in same direction). Proteins highlighted in blue were FDR significant (adjusted p-value < 0.2) in discovery and nominally significant (p-value < 0.05) in validation, with beta estimate in the same direction. Points are labeled with Entrez Gene symbol, see Supp. Table 1 for corresponding protein target name and additional information. (B) Subjects in each cohort were stratified into 4 quartiles based on their baseline leptin expression, with group 1 having the lowest baseline leptin and group 4 having the highest. The average change in FEV₁ (post-bronchodilator for SPIROMICS and COPDGene, pre-bronchodilator for MESA-Lung) from baseline was calculated for each quartile group at each visit. Baseline leptin is predictive of rate of FEV₁ change.

Figure 3:. Predicted FEV₁ change per year by composite prognostic model versus observed FEV₁ change per year for COPD enriched cohorts (SPIROMICS and COPDGene).

The plot is limited to the last follow-up visit at least 3 years after baseline for each subject.

Figure 4:. Proteins dynamically associated with FEV₁ over time in SPIROMICS and COPDGene.

The axes display the signed −log₁₀(p-value) (sign of beta estimate * −log₁₀(p-value)) from our discovery cohort, SPIROMICS, and our validation cohort, COPDGene. 72 proteins were identified that overlapped between two cohorts changing in the same direction (nominal p-value < 0.05) highlighted in orange. 12 of those proteins highlighted in blue were FDR significant (adjusted p-value < 0.2) in SPIROMICS. Points are labeled with Entrez Gene symbol, see Supp. Table 4 for corresponding protein target name and additional information.

Figure 5:. Enrichment between COPD datasets for prognostic and dynamic biomarkers in response to change in FEV₁ over time.

(A) Figure shows the strategy flow diagram that was used to identify proteins that are most enriched between the SPIROMICS and COPDGene data sets for the Prognostic and Dynamic model separately. The leading-edge proteins were then passed to functional annotation tool, Metascape, to identify the biological similarities between the two models. B) The relationship between the effect of protein expression on FEV₁ overtime in the COPD