Abstract
Background
The majority of COPD patients are characterized by non-type 2 inflammation, yet there are no available non-type 2 biomarkers, as opposed to blood eosinophil count for type 2 inflammation. We aimed to test readily obtainable immune cell ratios as biomarkers for clinical phenotypes in COPD and to determine pathways represented by these ratios using multi-omics data.
Methods
Using complete blood counts with differential collected at the Phase 2 (5-year) visit in the COPDGene Study, we calculated three immune cell ratios previously described in COPD and other diseases: the neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), and the Systemic Immune-Inflammation Index (SII = NLR*platelets). We tested for associations with COPD outcomes, including lung function, chest CT scan phenotypes, and exacerbations. Blood RNA-sequencing and proteomics data were used to identify genes, proteins and pathways associated with the ratios.
Results
In univariate analyses, the three biomarkers were associated with COPD severity measures. In zero inflated Poisson regression models, all three were associated with increased odds of having an exacerbation but were not associated with exacerbation counts. Conversely, the three biomarkers were generally associated with prospective exacerbation counts, but not the zero-inflation term. In logistic regression models, the three biomarkers were significantly associated with having two or more exacerbations in the prior year; however, receiver operating characteristic analyses did not lead to clear cutoff values. Complement and PI3K signaling pathways were enriched across more than one ratio in both the RNA-sequencing and proteomics results. Other inflammatory pathways relevant in COPD appeared in different enrichment sets in either omics data type.
Conclusions
Higher levels of three easily obtained blood cell ratios were associated with COPD severity and exacerbations outcomes; however, there are not clear thresholds which would be required for clinical application. Blood RNA-sequencing and proteomics identified inflammatory pathways associated with the three biomarkers, including targets for COPD therapies currently in human trials.
Keywords: COPD exacerbation, Lymphocytes, Neutrophils, Platelets, Proteomics, RNA-sequencing
Background
Chronic obstructive pulmonary disease is a heterogeneous condition, yet there are limited biomarkers available to define the different inflammatory pathways.(1) Blood eosinophil count, a biomarker for type 2 inflammation, is associated with exacerbation risk(2) and response to inhaled corticosteroids,(3) and has been used as an entry criteria into clinical trials of biologic therapies targeting type 2 inflammation in COPD.(4, 5) Fractional exhaled nitric oxide and immunoglobulin E levels are other biomarkers of type 2 inflammation that are used in clinical care for asthma and have been applied in research studies of COPD.(5, 6) Despite the clinical relevance of type 2 inflammation in COPD, this mechanism is likely important only in a minority of COPD patients. Studies have shown that 20–40% of COPD patients have evidence of type 2 inflammation, indicated by elevated blood eosinophil counts.(2, 7) Non-type 2 inflammation is defined only by the absence of elevated blood eosinophils; there are no available biomarkers for this relevant COPD mechanism.
Complete blood count with white blood cell differential is a commonly obtained clinical laboratory measurement. Both blood neutrophil counts and platelet counts are elevated in inflammatory states and are associated with COPD severity.(8, 9) Several blood cell ratios have been developed to capture systemic inflammation, including the neutrophil to lymphocyte ratio (NLR), the platelet to lymphocyte ratio (PLR) and the systemic inflammatory-immune index (SII = NLR * platelets). These ratios have been predominantly applied in cancer studies, but are increasingly shown to be relevant for COPD outcomes including exacerbations and mortality.(10-13) These indices are purported to reflect systemic inflammation, but the specific inflammatory pathways represented are unknown.
Our hypothesis is that these blood cell ratios can serve as biomarkers of non-type 2 inflammation. We executed two aims to test this hypothesis. First, we sought to confirm the associations with respiratory outcomes, especially exacerbations, in a large population of current and former smokers with and without COPD in the Genetic Epidemiology of COPD Study (COPDGene). Second, we aimed to identify the biological pathways represented by the indices using blood RNA-sequencing and proteomics data in COPDGene.
Methods
The Genetic Epidemiology of COPD Study (COPDGene)
COPDGene is an observational study of over 10,000 current and former smokers (at least 10 pack years) with and without COPD, along with additional non-smoking controls.(14) One-third of COPDGene subjects are African Americans. Subjects were ages 45–80 at the initial visit. Subjects with cancer within the last 5 years and with other lung diseases except asthma were excluded. The baseline study visit included questionnaires, spirometry, a 6-minute walk test, and a high-resolution chest computed tomography (CT) scan with computerized analysis for emphysema and airway disease. Five- and ten-year follow-up visits (Phases 2 and 3) used similar phenotype assessments, with the addition of a complete blood count (CBC) with differential, which was assayed in each center’s clinical laboratory. COPD exacerbations in the past year are queried at each study visit, and prospective exacerbations are recorded using web or telephone-based longitudinal follow-up questionnaires every 6 months.(15) COPD exacerbations were defined by use of antibiotics and/or systemic steroids. Severe exacerbations required emergency department visits or hospitalization. Frequent exacerbators were defined as two or more exacerbations in the prior year. All subjects gave written informed consent. COPDGene was approved by the Institutional Review Boards at all participating centers.
RNA-sequencing and proteomics
At the Phase 2 visit, blood samples were collected for RNA-sequencing and proteomics; methods have been previously published.(16, 17) Briefly, whole blood was collected in PaxGene RNA tubes (BD Biosciences). RNA was extracted and used to generate stranded total RNA libraries with ribosomal reduction. Illumina sequencing used 75bp paired-end reads. Blood proteomics was generated using SomaScan v4.0 which uses aptamers to quantify approximately 5000 proteins. Whole genome sequencing is available through the NHLBI Trans-Omics in Precision Medicine (TOPMed) program.(18) Duffy null phenotype was determined by minor allele homozygosity for a single nucleotide polymorphism in the atypical chemokine receptor 1 gene (ACKR1; rs2814778).(19)
Statistical analysis
All statistical analyses were performed in R (v.4.3.2) in R Studio Pro Server (v2025.05.1 + 513). Unless otherwise stated, to test the univariate associations of NLR, PLR, and SII with clinical variables, we used the 2-tailed unpaired Student’s t test for categorical variables, and correlation for quantitative variables. To test the association between three immune cell ratios and exacerbations counts, zero-inflated Poisson regression analyses were performed using the pscl (v.1.5.9) package in R. Zero-inflated Poisson models were adjusted for age, sex, race, current smoking status, pack-years, FEV1 percent predicted and Duffy null phenotype, the major determinant of neutrophil count primarily in populations of African descent.(20) Scale function was applied to the predictors, which generally improves model stability and convergence, especially for predictors with large values or different scales. To test the association between the three immune cell ratios and binary exacerbation outcomes, logistic regression analyses were performed using stats (v.4.2.0) package in R. Logistic models adjusted for age, sex, race, smoking status, pack-years, FEV1 percent predicted and Duffy null phenotype. Models for prospective exacerbations were additionally adjusted for the number of exacerbations in the prior year.
We performed differential gene expression and proteomic analysis using Limma (v3.54.2) in R. Linear models were adjusted for age, sex, race, smoking status, pack-years, and FEV1 percent predicted. As per previous COPDGene analyses, proteomics were additionally adjusted for clinical center,(17) and RNA-seq was also adjusted for library batch.(16) We used Sigora (v3.1.1) to identify the underlying function of gene sets. Four annotated gene sets -- hallmark gene sets,(21) KEGG,(22) Gene Ontology(23) and Reactome(24) v2025.1 -- were chosen for the reference gene sets. An FDR adjusted p-value < 0.05 was set as the cutoff criteria.
Results
Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were derived from Phase 2 CBC data for 5,652 subjects, including 1,970 with COPD, defined as post-bronchodilator FEV1/FVC < 0.7 and FEV1 < 80% predicted, corresponding to GOLD stages 2–4 (Table 1). The three measures were highly correlated: NLR-PLR (Pearson) r = 0.66, NLR-SII r = 0.89, SII-PLR r = 0.76, in all subjects.
Table 1.
COPDGene Phase 2 subject characteristics. N(%) or mean (SD) are shown
| All Subjects(n = 5652) | COPD Only(n = 1970) | |
|---|---|---|
| Female sex | 2799 (49.5%) | 898 (45.6%) |
| Non-Hispanic white | 3930 (69.5%) | 1493 (75.%8) |
| African American | 1722 (30.5%) | 477 (24.2%) |
| Current smoker | 2194 (38.8%) | 677 (34.4%) |
| Age, years | 65.26 (8.66) | 67.76 (8.24) |
| Pack-years of smoking | 43.70 (24.09) | 51.66 (25.06) |
| FEV1% predicted, post-bronchodilator | 77.81 (24.54) | 52.27 (17.22) |
| SGRQ total score | 23.47 (21.31) | 36.32 (21.03) |
| 6 min walk distance, ft. | 1290.99 (436.86) | 1113.35 (434.12) |
| BODE score | 1.68 (2.15) | 3.32 (2.48) |
| % emphysema (LAA950) | 5.42 (9.02) | 11.86 (12.43) |
| Perc15 | 84.81 (30.36) | 66.18 (29.25) |
| Pi10 | 2.28 (0.58) | 2.69 (0.56) |
| Airway wall thickness of segmental airways | 1.04 (0.22) | 1.13 (0.23) |
| Wall area % of segmental airways | 50.29 (8.43) | 54.77 (7.98) |
| White blood cell count | 7.18 (2.34) | 7.56 (2.31) |
| Neutrophil count | 4.33 (1.80) | 4.76 (1.95) |
| Lymphocyte count | 2.06 (1.12) | 1.95 (0.87) |
| Monocyte count | 0.56 (0.21) | 0.6 (0.22) |
| Eosinophil count | 0.19 (0.15) | 0.2 (0.16) |
| Exacerbations in the prior year | 4652 (82.3%) | 1336 (67.8%) |
| 0 | 581 (10.3%) | 350 (17.8%) |
| 1 | 419 (7.4%) | 284 (14.4%) |
| 2 or more | ||
| Severe exacerbation in the prior year | 532 (9.4%) | 361 (18.3%) |
Abbreviations:
BODE: Body mass index, Obstruction, Dyspnea, Exercise Capacity
FEV1: forced expiratory volume in 1 second
LAA950: low attenuation area at −950 Hounsfield Units
Perc15: 15th percentile of lung density histogram
Pi10: square root wall area of hypothetical airway with 10mm internal perimeter
SGRQ: St. George’s Respiratory Questionnaire
In univariate analyses, all three biomarkers were significantly associated with demographic and clinical variables such as sex, race, smoking status, age, lung function (FEV1% predicted), disease-related quality of life (St. George’s Respiratory Questionnaire [SGRQ] total score), exercise capacity (6-minute walk distance), and chest CT scan emphysema measures in all subjects and in COPD only (Table 2); overall, the correlations were weak. NLR and SII, but not PLR, were associated with chest CT scan airway metrics, in all subjects and in COPD only. Weak negative correlations were found between blood eosinophil counts and NLR (r = −0.03) and PLR (r = −0.07); SII was not significantly correlated (r = −0.009). Moderate repeated measures correlations were observed between Phase 2 and Phase 3 values for NLR (r = 0.34, n = 2844), PLR (r = 0.53, n = 2826), and SII (r = 0.35, n = 2826).
Table 2.
Univariate associations of NLR, PLR, and SII with clinical variables Categorical variables (mean values are shown)
| All subjects | COPD | |||||
|---|---|---|---|---|---|---|
| Variable | NLR | PLR | SII | NLR | PLR | SII |
| Male | 2.63** | 132.1 | 594.1 | 3.12** | 141.9 | 715.9 |
| Female | 2.29 | 134.0 | 590.7 | 2.66 | 142.9 | 967.8 |
| White race | 2.70** | 136.9** | 648.6** | 3.12** | 145.2* | 763.0** |
| African American race | 1.90 | 124.0 | 461.7 | 2.24 | 133.3 | 533.1 |
| Current smoker | 2.19** | 121.9** | 539.4** | 2.49** | 126.8** | 614.3** |
| Former smoker | 2.63 | 140.1 | 625.8 | 3.12 | 150.4 | 756.1 |
p < 0.05
p < 0.001
A. Quantitative variables (correlation coefficients are shown)
Exacerbations were assessed both retrospectively and prospectively. Subjects were followed for an average of 7.05 (± 2.65) years. In zero inflated Poisson regression models for exacerbations in the past year, adjusted for clinical covariates and Duffy null genotype, higher values for all three biomarkers were associated with reduced odds for being a non-exacerbator (zero inflation term in Table 3A) – i.e. higher odds of having an exacerbation -- but were not associated with exacerbation counts. Conversely, the three biomarkers were generally associated with prospective exacerbation counts, but not the zero inflation term. Similar results were found for prospective severe exacerbation counts (data not shown). In logistic regression models adjusted for covariates, the three biomarkers were significantly associated with having frequent exacerbations, defined as two or more in the prior year (Table 3B). All three were associated with having a severe exacerbation in the past year; however only NLR and SII were associated with having a severe exacerbation in the prospective follow-up.
Table 3.
Associations with exacerbation outcomes Zero inflated Poisson regression models for exacerbations counts (beta (SE) are shown)
| All subjects | COPD | ||||||
|---|---|---|---|---|---|---|---|
| Outcome | Term | NLR | PLR | SII | NLR | PLR | SII |
| Exacerbations in past year | Zero inflation | −0.22 (0.05)** |
−0.22 (0.05)*** |
−0.25 (0.05)*** |
−0.18 (0.07)** |
−0.21 (0.07)** |
−0.24 (0.09)** |
| Count | 0.001 (0.018) |
−0.001 (0.018) |
0.002 (0.016) |
0.02 (0.03) |
−0.004 (0.03) |
0.003 (0.02) |
|
| Prospective exacerbations | Zero inflation | 0.14 (0.09) |
0.28 (0.08)*** |
0.13 (0.08) |
0.32 (0.21) |
0.28 (0.17) |
0.38 (0.21) |
| Count | 0.43 (0.01)*** |
0.37 (0.01)*** |
0.39 (0.01)*** |
0.65 (0.01)*** |
0.42 (0.02)*** |
0.54 (0.01)*** |
|
p < 0.05
p < 0.01
p < 0.001
A. Logistic regression models for frequent exacerbations (2 or more in the prior year) and severe exacerbations (beta (SE) shown)
To assess the discrimination of frequent exacerbation status (two or more in the prior year), we next constructed receiver operating characteristic (ROC) analyses. When combined with clinical covariates, all three biomarkers showed small but significantly increased area under the ROC curve (AUROC) values compared to covariates alone in all subjects (Fig. 1, Table 4). In COPD subjects, AUROCs were slightly lower than in all subjects (Supplemental Fig. 1, Table 4); NLR and SII showed significant improvement compared to covariates alone. We examined various cutoffs to dichotomize the three biomarkers, using the 1st, 2nd or 3rd quartile of each. ROC curve analyses did not show an optimal cutoff in all subjects or in COPD subjects only (Supplemental Table 1)
Figure 1.
Receiver operating characteristic curves for frequent exacerbations outcome (2 or more in the prior year)
Table 4.
Area under the receiver operating characteristic curve, frequent exacerbation outcome (2 or more in the prior year)
| Covariates only | NLR | NLR +covariates |
PLR | PLR +covariates |
SII | SII +covariates |
|
|---|---|---|---|---|---|---|---|
| All subjects | 78.7 | 60.6 | 79.6a,b | 58.2 | 79.5a,b | 62.9 | 79.7a,b |
| COPD only | 74.6 | 57.1 | 75.4a,b | 57.7 | 75.4b | 59.7 | 75.6a,b |
p<0.05 for comparison vs covariates only by Delong test
p<0.0001 for comparison with biomarker only (e.g. NLR) by Delong test
Abbreviations
NLR: neutrophil to lymphocyte ratio
PLR: platelet to lymphocyte ratio
SII: systemic immune-inflammation index
Blood RNA-sequencing and Proteomics
Using whole blood RNA-sequencing data we found that the majority of the expressed transcripts were significantly associated (false discovery rate < 0.05) with each of the three blood cell ratios: NLR 13,458 transcripts, PLR 13,380, SII 13,307 (Supplemental Tables 2–4). A large number of plasma proteins were also significantly associated with each ratio, although these represented a lower fraction of the total proteins assayed: NLR, 1429 proteins; PLR, 1602; SII, 1807 (Supplemental Tables 5–7). Pathway enrichment analysis of the top 300 genes in each analysis (ranked by adjusted p-value) was performed using several databases including Gene Ontology, KEGG, Reactome, and Hallmark gene sets (Table 5, Supplemental Tables 8–13). As positive controls, we employed pathways related to neutrophil degranulation (NLR and SII RNA-seq and proteomics) and platelet activation (PLR RNA-seq and proteomics). In the RNA-seq analysis, there was enrichment across more than one cell ratio in several pathways, including apoptosis, Rho GTPase, complement, and IL6/JAK/STAT3 signaling. Similarly, in the proteomics data, there was enrichment across more than one ratio in pathways related to collagen and extracellular matrix, epithelial-mesenchymal transition, interleukin signaling, complement, and PI3K signaling. Thus, the latter two pathways appeared in both RNA-seq and proteomics analyses. Other inflammatory pathways relevant in COPD appeared in different enrichment sets, including signaling pathways such as TNF-alpha, WNT, and IL-2/STAT5. Interestingly, IL-4/IL-13 signaling, relevant for T2 inflammation, was enriched in the SII RNA-seq and NLR proteomics results. A number of targets for COPD therapies currently in human trials appeared in the enriched proteomics pathways, such as neutrophil elastase, alpha-1 antitrypsin, and IL1RL1 (ST2 protein).(25, 26)
Table 5.
Selected results from pathway analysis of RNA-sequencing and proteomics data. Full results are available in Supplemental Tables 7–30.
| RNA-sequencing | Proteomics | |||||
|---|---|---|---|---|---|---|
| Pathway | NLR | PLR | SII | NLR | PLR | SII |
| Complement | ** | ** | ** | ** | ** | |
| PI3K signaling | ** | ** | ** | |||
| Apoptosis | ** | ** | ||||
| RhoGTPase | * | ** | * | |||
| Epithelial-mesenchymal transition | ** | ** | ** | |||
| Collagen/extracellular matrix | ** | ** | ** | |||
| IL6/JAK/STAT3 signaling | ** | ** | ** | |||
| IL2/STAT5 signaling | * | ** | ||||
| TNF-alpha signaling | ** | |||||
| WNT signaling | ** | |||||
| IL4/IL13 signaling | ** | ** | ||||
Bonferroni adjusted p < 0.05
Bonferroni adjusted p < 0.001
Discussion
We examined three blood cell ratios – neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and systemic immune-inflammatory index -- in a large study of current and former smokers with and without spirometry-defined COPD. We found that these ratios were orthogonal to blood eosinophil count, a currently used biomarker in COPD management. They were associated with almost all COPD severity outcomes, including retrospective and prospective exacerbations, though it is not clear whether they add substantially to clinical risk factors for exacerbations. Using RNA-sequencing and proteomics obtained at the same blood draw, we showed that these ratios were associated with multiple biologic pathways, including inflammatory pathways relevant for COPD.
The three ratios, especially the NLR, have been extensively studied in multiple chronic diseases in addition to COPD. Previous studies have demonstrated associations with COPD outcomes, including acute exacerbations. Studies of NLR and PLR have been reviewed elsewhere.(10, 27, 28) Despite the multiple studies, no clear consensus has emerged on an optimal cut-off which would be required to use a ratio as a clinical biomarker. Our study largely confirmed that higher levels of these biomarkers are associated with exacerbations and similarly did not find an optimal threshold value for any of the three ratios. We controlled for multiple clinical covariates including lung function, and found a statistically significant, but unlikely clinically relevant, improvement in discriminative ability in the ROC curve analyses.
The SPIROMICS study, another large observational study of COPD in the US, has recently reported associations between NLR and outcomes in current and former smokers with and without COPD, as well as non-smokers.(29) They found stability in NLR values at 6 weeks and 1 year (intraclass correlations 0.74 and 0.62, respectively). Subjects with NLR in the highest quartile had increased odds of an exacerbation within the following year and higher mortality. They found no difference in mortality for quartiles 1–3, but did not examine these thresholds for associations with exacerbations, as we did. Similar to our study, the SPIROMICS analyses were adjusted for Duffy null phenotype.
Our study addressed three easily obtained blood cell ratios in a large sample with and without COPD, finding consistent results regardless of disease status. The novelty of our study is the assessment of two different omics data types, blood RNA-sequencing and proteomics, allowing us to determine the biological pathways that may be indicated by these biomarkers. Reassuringly, we found strong enrichment for neutrophil and platelet-related pathways. Several inflammatory pathways were found in one of the two omics datasets. Complement and phosphatidylinositol 3-kinase (PI3K) signaling were shared by more than one biomarker in each of the two omics data types.
Proteins in the complement pathway are known to be altered in COPD.(30) Proteomics analysis of blood and bronchoalveolar lavage fluid found enrichment for proteins in the complement pathway in subjects with rapid lung function decline.(31, 32) Markers of complement activation are elevated in sputum during COPD exacerbations.(33) Hypocomplementemic urticarial vasculitis, a rare small vessel vasculitis, has been associated with COPD.(34)
PI3K signaling is involved in fundamental cellular processes such as growth and proliferation. Several PI3K inhibitors are FDA-approved for use in different types of cancer.(35) PI3K activation leads to secretion of inflammatory cytokines and generation of reactive oxygen species by several immune cell types in COPD, including neutrophils.(36) PI3K is a potential target for COPD therapies.(37)
Our study has several limitations. In COPDGene, CBC with differential were obtained starting at the Phase 2 (5-year) visit, not at the baseline visit. Despite this, we still have a large sample size with an average of 7 years follow-up time. Furthermore, the RNA-seq and proteomics data were collected concurrently at Phase 2. The blood cell ratios may not fully reflect lung inflammation; bronchoalveolar lavage or sputum samples would be required for comparison and were not included in COPDGene.
Conclusions
The Global Initiative for Chronic Obstructive Lung Disease (GOLD) includes measurement of blood eosinophil count as part of the initial assessment of a person with COPD.(1) The three ratios we analyzed can be derived from the same CBC with differential that is used to measure the blood eosinophil count. Therefore, there is the potential to gain additional information without additional testing. Currently, COPD patients are dichotomized into type 2 and non-type 2 inflammation subtypes using the blood eosinophil count. However, it is likely that both pathways, and other inflammatory pathways, are active to varying degrees in individual patients. Further studies will be needed to determine how these biomarkers can be used to determine response to existing therapies targeting type 2 inflammation – including inhaled corticosteroids and biologics – as well as novel therapies targeting other inflammatory mechanisms.
Supplementary Material
This is a list of supplementary files associated with this preprint. Click to download.
Supplement01.12.26.docx
SupplementalTables1113Proteomicspathways.xlsx
SupplementalTables27Differentialexpression.xlsx
SupplementalTables810RNAseqpathways.xlsx
Acknowledgements
COPDGene® Investigators – Core Units
Administrative Center: James D. Crapo, MD (PI); Edwin K. Silverman, MD, PhD (PI); Barry J. Make, MD; Elizabeth A. Regan, MD, PhD
Genetic Analysis Center: Terri H. Beaty, PhD; Peter J. Castaldi, MD, MSc; Michael H. Cho, MD, MPH; Dawn L. DeMeo, MD, MPH; Adel El Boueiz, MD, MMSc; Marilyn G. Foreman, MD, MS; Auyon Ghosh, MD; Lystra P. Hayden, MD, MMSc; Craig P. Hersh, MD, MPH; Jacqueline Hetmanski, MS; Brian D. Hobbs, MD, MMSc; John E. Hokanson, MPH, PhD; Wonji Kim, PhD; Nan Laird, PhD; Christoph Lange, PhD; Sharon M. Lutz, PhD; Merry-Lynn McDonald, PhD; Dmitry Prokopenko, PhD; Matthew Moll, MD, MPH; Jarrett Morrow, PhD; Dandi Qiao, PhD; Elizabeth A. Regan, MD, PhD; Aabida Saferali, PhD; Phuwanat Sakornsakolpat, MD; Edwin K. Silverman, MD, PhD; Emily S. Wan, MD; Jeong Yun, MD, MPH
Imaging Center: Juan Pablo Centeno; Jean-Paul Charbonnier, PhD; Harvey O. Coxson, PhD; Craig J. Galban, PhD; MeiLan K. Han, MD, MS; Eric A. Hoffman, Stephen Humphries, PhD; Francine L. Jacobson, MD, MPH; Philip F. Judy, PhD; Ella A. Kazerooni, MD; Alex Kluiber; David A. Lynch, MB; Pietro Nardelli, PhD; John D. Newell, Jr., MD; Aleena Notary; Andrea Oh, MD; Elizabeth A. Regan, MD, PhD; James C. Ross, PhD; Raul San Jose Estepar, PhD; Joyce Schroeder, MD; Jered Sieren; Berend C. Stoel, PhD; Juerg Tschirren, PhD; Edwin Van Beek, MD, PhD; Bram van Ginneken, PhD; Eva van Rikxoort, PhD; Gonzalo Vegas Sanchez-Ferrero, PhD; Lucas Veitel; George R. Washko, MD; Carla G. Wilson, MS;
PFT QA Center, Salt Lake City, UT: Robert Jensen, PhD
Data Coordinating Center and Biostatistics, National Jewish Health, Denver, CO: Douglas Everett, PhD; Jim Crooks, PhD; Katherine Pratte, PhD; Matt Strand, PhD; Carla G. Wilson, MS
Epidemiology Core, University of Colorado Anschutz Medical Campus, Aurora, CO: John E. Hokanson, MPH, PhD; Erin Austin, PhD; Gregory Kinney, MPH, PhD; Sharon M. Lutz, PhD; Kendra A. Young, PhD
Mortality Adjudication Core: Surya P. Bhatt, MD; Jessica Bon, MD; Alejandro A. Diaz, MD, MPH; MeiLan K. Han, MD, MS; Barry Make, MD; Susan Murray, ScD; Elizabeth Regan, MD; Xavier Soler, MD; Carla G. Wilson, MS
Biomarker Core: Russell P. Bowler, MD, PhD; Katerina Kechris, PhD; Farnoush Banaei-Kashani, PhD
COPDGene® Investigators – Clinical Centers
Ann Arbor VA: Jeffrey L. Curtis, MD; Perry G. Pernicano, MD
Baylor College of Medicine, Houston, TX: Nicola Hanania, MD, MS; Mustafa Atik, MD; Aladin Boriek, PhD; Kalpatha Guntupalli, MD; Elizabeth Guy, MD; Amit Parulekar, MD;
Brigham and Women’s Hospital, Boston, MA: Dawn L. DeMeo, MD, MPH; Craig Hersh, MD, MPH; Francine L. Jacobson, MD, MPH; George Washko, MD
Columbia University, New York, NY: R. Graham Barr, MD, DrPH; John Austin, MD; Belinda D’Souza, MD; Byron Thomashow, MD
Duke University Medical Center, Durham, NC: Neil MacIntyre, Jr., MD; H. Page McAdams, MD; Lacey Washington, MD
HealthPartners Research Institute, Minneapolis, MN: Charlene McEvoy, MD, MPH; Joseph Tashjian, MD
Johns Hopkins University, Baltimore, MD: Robert Wise, MD; Robert Brown, MD; Nadia N. Hansel, MD, MPH; Karen Horton, MD; Allison Lambert, MD, MHS; Nirupama Putcha, MD, MHS
Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA: Richard Casaburi, PhD, MD; Alessandra Adami, PhD; Matthew Budoff, MD; Hans Fischer, MD; Janos Porszasz, MD, PhD; Harry Rossiter, PhD; William Stringer, MD
Michael E. DeBakey VAMC, Houston, TX: Amir Sharafkhaneh, MD, PhD; Charlie Lan, DO
Minneapolis VA: Christine Wendt, MD; Brian Bell, MD; Ken M. Kunisaki, MD, MS
Morehouse School of Medicine, Atlanta, GA: Eric L. Flenaugh, MD; Hirut Gebrekristos, PhD; Mario Ponce, MD; Silanath Terpenning, MD; Gloria Westney, MD, MS
National Jewish Health, Denver, CO: Russell Bowler, MD, PhD; David A. Lynch, MB
Reliant Medical Group, Worcester, MA: Richard Rosiello, MD; David Pace, MD
Temple University, Philadelphia, PA: Gerard Criner, MD; David Ciccolella, MD; Francis Cordova, MD; Chandra Dass, MD; Gilbert D’Alonzo, DO; Parag Desai, MD; Michael Jacobs, PharmD; Steven Kelsen, MD, PhD; Victor Kim, MD; A. James Mamary, MD; Nathaniel Marchetti, DO; Aditi Satti, MD; Kartik Shenoy, MD; Robert M. Steiner, MD; Alex Swift, MD; Irene Swift, MD; Maria Elena Vega-Sanchez, MD
University of Alabama, Birmingham, AL: Mark Dransfield, MD; William Bailey, MD; Surya P. Bhatt, MD; Anand Iyer, MD; Hrudaya Nath, MD; J. Michael Wells, MD
University of California, San Diego, CA: Douglas Conrad, MD; Xavier Soler, MD, PhD; Andrew Yen, MD
University of Iowa, Iowa City, IA: Alejandro P. Comellas, MD; Karin F. Hoth, PhD; John Newell, Jr., MD; Brad Thompson, MD
University of Michigan, Ann Arbor, MI: MeiLan K. Han, MD MS; Ella Kazerooni, MD MS; Wassim Labaki, MD MS; Craig Galban, PhD; Dharshan Vummidi, MD
University of Minnesota, Minneapolis, MN: Joanne Billings, MD; Abbie Begnaud, MD; Tadashi Allen, MD
University of Pittsburgh, Pittsburgh, PA: Frank Sciurba, MD; Jessica Bon, MD; Divay Chandra, MD, MSc; Joel Weissfeld, MD, MPH
University of Texas Health, San Antonio, San Antonio, TX: Antonio Anzueto, MD; Sandra Adams, MD; Diego Maselli-Caceres, MD; Mario E. Ruiz, MD; Harjinder Singh
Funding:
NIH grants R01HL166231 and K24HL173667 (CPH).
COPDGene was supported by NHLBI grants U01HL089897 and U01HL089856 and by NIH contract 75N92023D00011. Proteomics data generation was supported by R01HL137995. The COPDGene study (NCT00608764) has also been supported by the COPD Foundation through contributions made to an Industry Advisory Committee that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.
Competing Interests:
JHY reports consulting fees from Bridge BioTherapeutics and serving on an Advisory Board for Genentech. PJC reports grants from Sanofi and Bayer, and consulting fees from Verona Pharma and Genentech. JLC reports grants from the COPD Foundation, consulting fees from AstraZeneca and participation on Data Safety Monitoring Board or Advisory Board for Novartis and Genetech. CPH reports grants from the Alpha-1 Foundation and Bayer, and consulting fees from Apogee Therapeutics, AstraZeneca, Chiesi, Genentech, Ono Pharma, Sanofi, Takeda, and Verona Pharma. None of the other authors report any conflicts of interest.
Abbreviations
- AUROC
area under the ROC curve
- BODE
Body mass index, Obstruction, Dyspnea, Exercise Capacity
- CBC
complete blood count
- CT
computed tomography
- FEV1
forced expiratory volume in 1 second
- FVC
forced vital capacity
- LAA950
low attenuation area at −950 Hounsfield Units
- NLR
neutrophil to lymphocyte ratio
- Perc15
15th percentile of lung density histogram
- Pi10
square root wall area of hypothetical airway with 10mm internal perimeter
- PLR
platelet to lymphocyte ratio
- ROC
receiver operating characteristic
- SGRQ
St. George’s Respiratory Questionnaire
- SII
systemic immune-inflammation index
Footnotes
Ethics approval: COPDGene was approved by the Institutional Review Board at Mass General Brigham and all participating centers. The research was conducted in accordance with the Belmont Report.
Availability of Data: COPDGene data are available on the NCBI’s Database of Genotypes and Phenotypes (dbGaP), accessions phs000179, phs000765, phs000951.
Artificial Intelligence declaration:
During the preparation of this work the authors used Microsoft Copilot to write an initial draft of the Results section. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
Contributor Information
Kaman So, Brigham and Women's Hospital.
Aabida Saferali, Brigham and Women's Hospital.
Jeong Yun, Brigham and Women's Hospital.
Min Hyung Ryu, University of British Columbia.
Enrico Schiavi, Catholic University of the Sacred Heart.
Peter Castaldi, Brigham and Women's Hospital.
Lisa Ruvuna, Cleveland Clinic.
Russell Bowler, Cleveland Clinic.
Jeffrey Curtis, University of Michigan–Ann Arbor.
Craig Hersh, Brigham and Women's Hospital.
References
- 1.Agusti A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Am J Respir Crit Care Med. 2023;207(7):819–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Yun JH, Lamb A, Chase R, Singh D, Parker MM, Saferali A, et al. Blood eosinophil count thresholds and exacerbations in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2018. June;141(6):2037–47. e10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bafadhel M, Peterson S, De Blas MA, Calverley PM, Rennard SI, Richter K, et al. Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials. Lancet Respir Med. 2018;6(2):117–26. [DOI] [PubMed] [Google Scholar]
- 4.Sciurba FC, Criner GJ, Christenson SA, Martinez FJ, Papi A, Roche N, et al. Mepolizumab to Prevent Exacerbations of COPD with an Eosinophilic Phenotype. N Engl J Med. 2025;392(17):1710–20. [DOI] [PubMed] [Google Scholar]
- 5.Bhatt SP, Rabe KF, Hanania NA, Vogelmeier CF, Cole J, Bafadhel M, et al. Dupilumab for COPD with Type 2 Inflammation Indicated by Eosinophil Counts. N Engl J Med. 2023. July;20(3):205–14. [Google Scholar]
- 6.Hersh CP, Zacharia S, Prakash Arivu Chelvan R, Hayden LP, Mirtar A, Zarei S, et al. Immunoglobulin E as a Biomarker for the Overlap of Atopic Asthma and Chronic Obstructive Pulmonary Disease. Chronic Obstr Pulm Dis. 2020;7(1):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Oishi K, Matsunaga K, Shirai T, Hirai K, Gon Y. Role of Type2 Inflammatory Biomarkers in Chronic Obstructive Pulmonary Disease. J Clin Med. 2020;9(8):2670. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Halper-Stromberg E, Yun JH, Parker MM, Singer RT, Gaggar A, Silverman EK, et al. Systemic Markers of Adaptive and Innate Immunity Are Associated with Chronic Obstructive Pulmonary Disease Severity and Spirometric Disease Progression. Am J Respir Cell Mol Biol. 2018;58(4):500–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Fawzy A, Putcha N, Paulin LM, Aaron CP, Labaki WW, Han MK, et al. Association of thrombocytosis with COPD morbidity: the SPIROMICS and COPDGene cohorts. Respir Res. 2018;19(1):20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zinellu A, Zinellu E, Mangoni AA, Pau MC, Carru C, Pirina P et al. Clinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future. Eur Respir Rev. 2022;31(166). [Google Scholar]
- 11.Fang L, Zhu J, Fu D. Predictive value of neutrophil-lymphocyte ratio for all-cause mortality in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. BMC Pulm Med. 2025;25(1):206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.El-Gazzar AG, Kamel MH, Elbahnasy OKM, El-Naggar MES. Prognostic value of platelet and neutrophil to lymphocyte ratio in COPD patients. Expert Rev Respir Med. 2020;14(1):111–6. [DOI] [PubMed] [Google Scholar]
- 13.Fu Y, Wang Y, Wang Y, Mou T, He X, Wang J, et al. Biomarkers (NLR, PLR, SII) for Frequent COPD Exacerbations: Diagnostic and Clinical Management Implications in a Retrospective Study. Int J Chron Obstruct Pulmon Dis. 2025;20:987–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, Beaty TH, et al. Genetic epidemiology of COPD (COPDGene) study design. COPD. 2010;7(1):32–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Stewart JI, Moyle S, Criner GJ, Wilson C, Tanner R, Bowler RP, et al. Automated telecommunication to obtain longitudinal follow-up in a multicenter cross-sectional COPD study. COPD. 2012;9(5):466–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ryu MH, Yun JH, Morrow JD, Saferali A, Castaldi P, Chase R, et al. Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations. Am J Respir Crit Care Med. 2023;208(3):247–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Serban KA, Pratte KA, Strange C, Sandhaus RA, Turner AM, Beiko T, et al. Unique and shared systemic biomarkers for emphysema in Alpha-1 Antitrypsin deficiency and chronic obstructive pulmonary disease. eBioMedicine. 2022;84:104262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature. 2021;590(7845):290–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Reich D, Nalls MA, Kao WHL, Akylbekova EL, Tandon A, Patterson N et al. Reduced Neutrophil Count in People of African Descent Is Due To a Regulatory Variant in the Duffy Antigen Receptor for Chemokines Gene. Visscher PM, editor. PLoS Genet. 2009;5(1):e1000360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Merz LE, Osei MA, Story CM, Freedman RY, Smeland-Wagman R, Kaufman RM, et al. Development of Duffy Null–Specific Absolute Neutrophil Count Reference Ranges. JAMA. 2023. June;20(23):2088–9. [Google Scholar]
- 21.Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011. June 15;27(12):1739–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39(Database issue):D691–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.De Soyza J, Pye A, Turner AM. Are clinical trials into emerging drugs for the treatment of alpha-1 antitrypsin deficiency providing promising results? Expert Opin Emerg Drugs. 2023;28(4):227–31. [DOI] [PubMed] [Google Scholar]
- 26.Yousuf AJ, Mohammed S, Carr L, Yavari Ramsheh M, Micieli C, Mistry V, et al. Astegolimab, an anti-ST2, in chronic obstructive pulmonary disease (COPD-ST2OP): a phase 2a, placebo-controlled trial. Lancet Respir Med. 2022;10(5):469–77. [DOI] [PubMed] [Google Scholar]
- 27.Paliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P et al. Neutrophil to lymphocyte ratio and clinical outcomes in COPD: recent evidence and future perspectives. Eur Respir Rev. 2018;27(147). [Google Scholar]
- 28.Zinellu A, Paliogiannis P, Sotgiu E, Mellino S, Fois AG, Carru C, et al. Platelet Count and Platelet Indices in Patients with Stable and Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis. COPD J Chronic Obstr Pulm Dis. 2021;18(2):231–45. [Google Scholar]
- 29.Hoesterey DT, Dang H, Markovic D, Buhr RG, Tashkin DP, Barr RG et al. Neutrophil-to-Lymphocyte Ratio (NLR) as a Biomarker in Clinically Stable Chronic Obstructive Pulmonary Disease: SPIROMICS cohort. Ann Am Thorac Soc. 2025. Sept 8;[online ahead of print]. [Google Scholar]
- 30.Miller RD, Kueppers F, Offord KP. Serum concentrations of C3 and C4 of the complement system in patients with chronic obstructive pulmonary disease. J Lab Clin Med. 1980;95(2):266–71. [PubMed] [Google Scholar]
- 31.DiLillo KM, Norman KC, Freeman CM, Christenson SA, Alexis NE, Anderson WH, et al. A blood and bronchoalveolar lavage protein signature of rapid FEV1 decline in smoking-associated COPD. Sci Rep. 2023;13(1):8228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.DiLillo KM, Ruvuna L, Pratte KA, Serban KA, Labaki WW, Han MK, et al. Validation of Systemic Complement Signatures in the Progression of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2024;210(10):1269–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Westwood JP, Mackay AJ, Donaldson G, Machin SJ, Wedzicha JA, Scully M. The role of complement activation in COPD exacerbation recovery. ERJ Open Res. 2016;2(4):00027–2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.David C, Jachiet M, Pineton De Chambrun M, Gamez AS, Mehdaoui A, Zenone T, et al. Chronic obstructive pulmonary disease associated with hypocomplementemic urticarial vasculitis. J Allergy Clin Immunol Pract. 2020;8(9):3222–e32241. [DOI] [PubMed] [Google Scholar]
- 35.Yu M, Chen J, Xu Z, Yang B, He Q, Luo P, et al. Development and safety of PI3K inhibitors in cancer. Arch Toxicol. 2023;97(3):635–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Liu Y, Kong H, Cai H, Chen G, Chen H, Ruan W. Progression of the PI3K/Akt signaling pathway in chronic obstructive pulmonary disease. Front Pharmacol. 2023. Sept 20;14. [Google Scholar]
- 37.Fagone E, Fruciano M, Gili E, Sambataro G, Vancheri C, Developing. PI3K Inhibitors for Respiratory Diseases. In: Dominguez-Villar M, editor. PI3K and AKT Isoforms in Immunity : Mechanisms and Therapeutic Opportunities. Cham: Springer International Publishing; 2022. pp. 437–66. [Google Scholar]