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There are currently no accepted and validated blood tests available for diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD). In this study, we sought to determine the discriminatory power of blood C-reactive protein (CRP) and N-terminal prohormone brain natriuretic peptide (NT-proBNP) in the diagnosis of AECOPD requiring hospitalizations. The study cohort consisted of 468 patients recruited in the COPD Rapid Transition Program who were hospitalized with a primary diagnosis of AECOPD, and 110 stable COPD patients who served as controls. Logistic regression was used to build a classification model to separate AECOPD from convalescent or stable COPD patients. Performance was assessed using an independent validation set of patients who were not included in the discovery set. Serum CRP and whole blood NT-proBNP concentrations were highest at the time of hospitalization and progressively decreased over time. Of the 3 classification models, the one with both CRP and NT-proBNP had the highest AUC in discriminating AECOPD (cross-validated AUC of 0.80). These data were replicated in a validation cohort with an AUC of 0.88. A combination of CRP and NT-proBNP can reasonably discriminate AECOPD requiring hospitalization versus clinical stability and can be used to rapidly diagnose patients requiring hospitalization for AECOPD.

Background Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD. Methods We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods. Results Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p

Background COPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown. Objective Identify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD. Methods Whole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results 25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington’s disease, Parkinson’s disease and acute myeloid leukemia gene signatures. Conclusion The blood gene signature of statins’ use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology.

Rationale C-reactive protein (CRP) is a systemic marker of inflammation that correlates with disease status in cystic fibrosis (CF). The clinical utility of CRP measurement to guide pulmonary exacerbation (PEx) treatment decisions remains uncertain. Objectives To determine whether monitoring CRP during PEx treatment can be used to predict treatment response. We hypothesized that early changes in CRP can be used to predict treatment response. Methods We reviewed all PEx events requiring hospitalization for intravenous (IV) antibiotics over 2 years at our institution. 83 PEx events met our eligibility criteria. CRP levels from admission to day 5 were evaluated to predict treatment non-response, using a modified version of a prior published composite definition. CRP was also evaluated to predict time until next exacerbation (TUNE). Measurements and main results 53% of 83 PEx events were classified as treatment non-response. Paradoxically, 24% of PEx events were characterized by a ≥ 50% increase in CRP levels within the first five days of treatment. Absolute change in CRP from admission to day 5 was not associated with treatment non-response (p = 0.58). Adjusted for FEV1% predicted, admission log10 CRP was associated with treatment non-response (OR: 2.39; 95% CI: 1.14 to 5.91; p = 0.03) and shorter TUNE (HR: 1.60; 95% CI: 1.13 to 2.27; p = 0.008). The area under the receiver operating characteristics (ROC) curve of admission CRP to predict treatment non-response was 0.72 (95% CI 0.61–0.83; p 75 mg/L with a specificity of 90% for treatment non-response. Conclusions Admission CRP predicts treatment non-response and time until next exacerbation. A very elevated admission CRP (>75mg/L) is highly specific for treatment non-response and might be used to target high-risk patients for future interventional studies aimed at improving exacerbation outcomes.