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      Inhaled Steroids and Active Smoking Drive Chronic Obstructive Pulmonary Disease Symptoms and Biomarkers to a Greater Degree Than Airflow Limitation

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          Abstract

          Rationale:

          Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, and development of novel therapeutics requires an understanding of pathophysiologic phenotypes.

          Objectives:

          The purpose of the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study was to correlate clinical features and biomarkers with molecular characteristics in a well-profiled COPD cohort.

          Methods:

          A total of 67 COPD subjects (forced expiratory volume in the first second of expiration [FEV 1]: 45%-80% predicted) and 63 healthy smoking and nonsmoking controls underwent multiple assessments including patient questionnaires, lung function, and clinical biomarkers including fractional exhaled nitric oxide (FENO), induced sputum, and blood.

          Measurements and main results:

          The impact of inhaled corticosteroids (ICSs), and to a lesser extent current smoking, was more associated with symptom control, exacerbation rates, and clinical biomarkers, than severity by FEV 1. The ICS-treated smoking subjects were most symptomatic, with significantly elevated scores on patient-reported outcomes and more annual exacerbations ( P < .05). Inhaled corticosteroid users had greater airflow obstruction and air trapping compared with non-ICS users, regardless of smoking status. Smoking, regardless of ICS use, was associated with significantly lower FENO ( P < .05). Smoking, in non-ICS users, was associated with an elevated proportion of sputum neutrophils and reduced sputum macrophages. Increased serum C-reactive protein was observed in smokers but not in ICS and nonsmoking ICS users ( P < .05). In contrast, only air trapping and neutrophilic inflammation increased with severity, defined by postbronchodilator FEV 1.

          Conclusions:

          Compared with COPD severity by FEV 1, ICS use and current smoking were better determinants of clinical characteristics and biomarkers. Use of the ADEPT COPD data promises to prove useful in defining biological phenotypes to facilitate personalized therapeutic approaches.

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          Most cited references18

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          The Asthma-COPD Overlap Syndrome.

          Although in textbooks asthma and chronic obstructive pulmonary disease (COPD) are viewed as distinct disorders, there is increasing awareness that many patients have features of both. This article reviews the asthma-COPD overlap syndrome.
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            Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS).

            Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) is a multicentre observational study of chronic obstructive pulmonary disease (COPD) designed to guide future development of therapies for COPD by providing robust criteria for subclassifying COPD participants into groups most likely to benefit from a given therapy during a clinical trial, and identifying biomarkers/phenotypes that can be used as intermediate outcomes to reliably predict clinical benefit during therapeutic trials. The goal is to enrol 3200 participants in four strata. Participants undergo a baseline visit and three annual follow-up examinations, with quarterly telephone calls. Adjudication of exacerbations and mortality will be undertaken.
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              GOLD 2011 disease severity classification in COPDGene: a prospective cohort study.

              The 2011 GOLD (Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease [COPD]) consensus report uses symptoms, exacerbation history, and forced expiratory volume (FEV1)% to categorise patients according to disease severity and guide treatment. We aimed to assess both the influence of symptom instrument choice on patient category assignment and prospective exacerbation risk by category. Patients were recruited from 21 centres in the USA, as part of the COPDGene study. Eligible patients were aged 45-80 years, had smoked for 10 pack-years or more, and had an FEV1/forced vital capacity (FVC) <0·7. Categories were defined with the modified Medical Research Council (mMRC) dyspnoea scale (score 0-1 vs ≥2) and the St George's Respiratory Questionnaire (SGRQ; ≥25 vs <25 as a surrogate for the COPD Assessment Test [CAT] ≥10 vs <10) in addition to COPD exacerbations in the previous year (<2 vs ≥ 2), and lung function (FEV1% predicted ≥50 vs <50). Statistical comparisons were done with k-sample permutation tests. This study cohort is registered with ClinicalTrials.gov, number NCT00608764. 4484 patients with COPD were included in this analysis. Category assignment using the mMRC scale versus SGRQ were similar but not identical. On the basis of the mMRC scale, 1507 (33·6%) patients were assigned to category A, 919 (20·5%) to category B, 355 (7·9%) to category C, and 1703 (38·0%) to category D; on the basis of the SGRQ, 1317 (29·4%) patients were assigned to category A, 1109 (24·7%) to category B, 221 (4·9%) to category C, and 1837 (41·0%) to category D (κ coefficient for agreement, 0·77). Significant heterogeneity in prospective exacerbation rates (exacerbations/person-years) were seen, especially in the D subcategories, depending on the risk factor that determined category assignment (lung function only [0·89, 95% CI 0·78-1·00]), previous exacerbation history only [1·34, 1·0-1·6], or both [1·86, 1·6-2·1; p<0·0001]). The GOLD classification emphasises the importance of symptoms and exacerbation risk when assessing COPD severity. The choice of symptom measure influences category assignment. The relative number of patients with low symptoms and high risk for exacerbations (category C) is low. Differences in exacerbation rates for patients in the highest risk category D were seen depending on whether risk was based on lung function, exacerbation history, or both. National Heart, Lung, and Blood Institute, and the COPD Foundation through contributions from AstraZeneca, Boehringer Ingelheim, Novartis, and Sepracor. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Biomark Insights
                Biomark Insights
                BMI
                spbmi
                Biomarker Insights
                SAGE Publications (Sage UK: London, England )
                1177-2719
                07 September 2017
                2017
                : 12
                : 1177271917730306
                Affiliations
                [1 ]Immunology, Janssen Research & Development, LLC, Spring House, PA, USA
                [2 ]Medicines Evaluation Unit, University Hospital of South Manchester Foundation Trust, University of Manchester, Manchester, UK
                [3 ]Centre for Heart and Lung Health, The Lung Centre, Vancouver General Hospital, Vancouver, BC, Canada
                [4 ]IKF Pneumologie Frankfurt, Institut für klinische Forschung Pneumologie, Clinical Research Centre Respiratory Diseases, Frankfurt, Germany
                [5 ]KLB Gesundheitsforschung Lübeck GmbH, Lübeck, Germany
                [6 ]Pulmonary Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
                [7 ]Respiratory Research Unit, Department of Respiratory Medicine, Bispebjerg University Hospital, Copenhagen, Denmark
                [8 ]Centre de Recherche Cardio-Thoracique de Bordeaux, Université de Bordeaux, Bordeaux, France
                [9 ]Division of Pulmonary, Allergy & Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
                [10 ]Pulmonary, Allergy, and Critical Care Medicine, Birmingham VA Medical Center, Birmingham, AL, USA
                Author notes
                [*]Philip E Silkoff, Janssen Research & Development, LLC, 1400 McKean RD., Spring House, PA 19477, USA. Email: philsilkoff@ 123456gmail.com
                Article
                10.1177_1177271917730306 BMI-0042723
                10.1177/1177271917730306
                5593220
                28959121
                ff6317c9-a65b-427e-86fe-dcbcd6a73fb5
                © The Author(s) 2017

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 4 June 2017
                : 10 August 2017
                Categories
                Original Research
                Custom metadata
                January-December 2017

                Clinical chemistry
                copd,severity,phenotypes,profiling,personalized
                Clinical chemistry
                copd, severity, phenotypes, profiling, personalized

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