Peripheral blood eosinophils: a surrogate marker for airway eosinophilia in stable COPD.

INTRODUCTION: Sputum eosinophilia occurs in approximately one-third of stable chronic obstructive pulmonary disease (COPD) patients and can predict exacerbation risk and response to corticosteroid treatments. Sputum induction, however, requires expertise, may not always be successful, and does not provide point-of-care results. Easily applicable diagnostic markers that can predict sputum eosinophilia in stable COPD patients have the potential to progress COPD management. This study investigated the correlation and predictive relationship between peripheral blood and sputum eosinophils. It also examined the repeatability of blood eosinophil counts. METHODS: Stable COPD patients (n=141) were classified as eosinophilic or noneosinophilic based on their sputum cell counts (≥3%), and a cross-sectional analysis was conducted comparing their demographics, clinical characteristics, and blood cell counts. Receiver operating characteristic curve analysis was used to assess the predictive ability of blood eosinophils for sputum eosinophilia. Intraclass correlation coefficient was used to examine the repeatability of blood eosinophil counts. RESULTS: Blood eosinophil counts were significantly higher in patients with sputum eosinophilia (n=45) compared to those without (0.3×10(9)/L vs 0.15×10(9)/L; P<0.0001). Blood eosinophils correlated with both the percentage (ρ=0.535; P<0.0001) and number of sputum eosinophils (ρ=0.473; P<0.0001). Absolute blood eosinophil count was predictive of sputum eosinophilia (area under the curve =0.76, 95% confidence interval [CI] =0.67-0.84; P<0.0001). At a threshold of ≥0.3×10(9)/L (specificity =76%, sensitivity =60%, and positive likelihood ratio =2.5), peripheral blood eosinophil counts enabled identification of the presence or absence of sputum eosinophilia in 71% of the cases. A threshold of ≥0.4×10(9)/L had similar classifying ability but better specificity (91.7%) and higher positive likelihood ratio (3.7). In contrast, ≥0.2×10(9)/L offered a better sensitivity (91.1%) for ruling out sputum eosinophilia. There was a good agreement between two measurements of blood eosinophil count over a median of 28 days (intraclass correlation coefficient =0.8; 95% CI =0.66-0.88; P<0.0001). CONCLUSION: Peripheral blood eosinophil counts can help identify the presence or absence of sputum eosinophilia in stable COPD patients with a reasonable degree of accuracy

Severity of Lung Function Impairment Drives Transcriptional Phenotypes of COPD and Relates to Immune and Metabolic Processes

Netsanet A Negewo,1 Peter G Gibson,2– 4 Jodie L Simpson,1 Vanessa M McDonald,2– 5 Katherine J Baines1 1Immune Health Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; 2Centre of Excellence in Treatable Traits, University of Newcastle, New Lambton Heights, NSW, Australia; 3Department of Respiratory and Sleep Medicine, John Hunter Hospital, Newcastle, NSW, Australia; 4Asthma and Breathing Research Centre, Hunter Medical Research Centre, New Lambton Heights, NSW, Australia; 5School of Nursing and Midwifery, The University of Newcastle, Callaghan, NSW, AustraliaCorrespondence: Katherine J Baines, Hunter Medical Research Institute, Level 2 East Wing, Locked Bag 1000, New Lambton Heights, NSW, 2305, Australia, Tel +61 2 40420090, Fax +61 2 40420046, Email [email protected]: This study sought to characterize transcriptional phenotypes of COPD through unsupervised clustering of sputum gene expression profiles, and further investigate mechanisms underlying the characteristics of these clusters.Patients and methods: Induced sputum samples were collected from patients with stable COPD (n = 72) and healthy controls (n = 15). Induced sputum was collected for inflammatory cell counts, and RNA extracted. Transcriptional profiles were generated (Illumina Humanref-8 V2) and analyzed by GeneSpring GX14.9.1. Unsupervised hierarchical clustering and differential gene expression analysis were performed, and gene alterations validated in the ECLIPSE dataset (GSE22148).Results: We identified 2 main clusters (Cluster 1 [n = 35] and Cluster 2 [n = 37]), which further divided into 4 sub-clusters (Sub-clusters 1.1 [n = 14], 1.2 [n = 21], 2.1 [n = 20] and 2.2 [n = 17]). Compared with Cluster 1, Cluster 2 was associated with significantly lower lung function (p = 0.014), more severe disease (p = 0.009) and breathlessness (p = 0.035), and increased sputum neutrophils (p = 0.031). Sub-cluster 1.1 had significantly higher proportion of people with comorbid cardiovascular disease compared to the other 3 sub-clusters (92.5% vs 57.1%, 50% and 52.9%, p < 0.013). Through supervised analysis we determined that degree of airflow limitation (GOLD stage) was the predominant factor driving gene expression differences in our transcriptional clusters. There were 452 genes (adjusted p < 0.05 and ≥ 2 fold) altered in GOLD stage 3 and 4 versus 1 and 2, of which 281 (62%) were also found to be significantly expressed between these GOLD stages in the ECLIPSE data set (GSE22148). Differentially expressed genes were largely downregulated in GOLD stages 3 and 4 and connected in 5 networks relating to lipoprotein and cholesterol metabolism; metabolic processes in oxidation/reduction and mitochondrial function; antigen processing and presentation; regulation of complement activation and innate immune responses; and immune and metabolic processes.Conclusion: Severity of lung function drives 2 distinct transcriptional phenotypes of COPD and relates to immune and metabolic processes.Keywords: COPD, gene expression, inflammation, sputu

Treatment burden, clinical outcomes, and comorbidities in COPD: an examination of the utility of medication regimen complexity index in COPD

Netsanet A Negewo,1,2 Peter G Gibson,1&ndash;3 Peter AB Wark,1&ndash;3 Jodie L Simpson,1,2 Vanessa M McDonald1&ndash;4 1Priority Research Centre for Healthy Lungs, 2Hunter Medical Research Institute, Faculty of Health and Medicine, The University of Newcastle, Callaghan, 3Department of Respiratory and Sleep Medicine, John Hunter Hospital, Newcastle, 4School of Nursing and Midwifery, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW, Australia Background: COPD patients are often prescribed multiple medications for their respiratory disease and comorbidities. This can lead to complex medication regimens resulting in poor adherence, medication errors, and drug&ndash;drug interactions. The relationship between clinical outcomes and medication burden beyond medication count in COPD is largely unknown.Objectives: The aim of this study was to explore the relationships of medication burden in COPD with clinical outcomes, comorbidities, and multidimensional indices.Methods: In a cross-sectional study, COPD patients (n=222) were assessed for demographic information, comorbidities, medication use, and clinical outcomes. Complexity of medication regimens was quantified using the validated medication regimen complexity index (MRCI).Results: Participants (58.6% males) had a mean (SD) age of 69.1 (8.3)&nbsp;years with a postbronchodilator forced expiratory volume in 1&nbsp;second % predicted of 56.5 (20.4) and a median of five comorbidities. The median (q1, q3) total MRCI score was 24 (18.5, 31). COPD-specific medication regimens were more complex than those of non-COPD medications (median MRCI: 14.5 versus 9, respectively; P&lt;0.0001). Complex dosage formulations contributed the most to higher MRCI scores of COPD-specific medications while dosing frequency primarily drove the complexity associated with non-COPD medications. Participants in Global Initiative for Chronic Obstructive Lung Disease quadrant D had the highest median MRCI score for COPD medications (15.5) compared to those in quadrants A (13.5; P=0.0001) and B (12.5; P&lt;0.0001). Increased complexity of COPD-specific treatments showed significant but weak correlations with lower lung function and 6-minute walk distance, higher St George&rsquo;s Respiratory Questionnaire and COPD assessment test scores, and higher number of prior year COPD exacerbations and hospitalizations. Comorbid cardiovascular, gastrointestinal, or metabolic diseases individually contributed to higher total MRCI scores and/or medication counts for all medications. Charlson Comorbidity Index and COPD-specific comorbidity test showed the highest degree of correlation with total MRCI score (&rho;=0.289 and &rho;=0.326; P&lt;0.0001, respectively).Conclusion: In COPD patients, complex medication regimens are associated with disease severity and specific class of comorbidities. Keywords: medication burden, medication counts, complex pharmacotherapy, clinical scores&nbsp