Biomarkers Predictive of Exacerbations in the SPIROMICS and COPDGene Cohorts

Rationale: Chronic obstructive pulmonary disease exacerbations are associated with disease progression, higher healthcare cost, and increased mortality. Published predictors of future exacerbations include previous exacerbation, airflow obstruction, poor overall health, home oxygen use, and gastroesophageal reflux. Objectives: To determine the value of adding blood biomarkers to clinical variables to predict exacerbations. Methods: Subjects from the SPIROMICS (Subpopulations and Intermediate Outcomes Measures in COPD Study) (n = 1,544) and COPDGene (Genetic Epidemiology of COPD) (n = 602) cohorts had 90 plasma or serum candidate proteins measured on study entry using Myriad‐RBM multiplex panels. We defined total exacerbations as subject‐reported worsening in respiratory health requiring therapy with corticosteroids and/or antibiotics, and severe exacerbations as those leading to hospitalizations or emergency room visits. We assessed retrospective exacerbations during the 12 months before enrollment and then documented prospective exacerbations in each cohort. Exacerbations were modeled for biomarker associations with negative binomial regression including clinical covariates (age, sex, percent predicted FEV1, self‐reported gastroesophageal reflux, St. George's Respiratory Questionnaire score, smoking status). We used the Stouffer‐Liptak test to combine P values for metaanalysis. Measurements and Main Results: Between the two cohorts, 3,471 total exacerbations (1,044 severe) were reported. We identified biomarkers within each cohort that were significantly associated with a history of exacerbation and with a future exacerbation, but there was minimal replication between the cohorts. Although established clinical features were predictive of exacerbations, of the blood biomarkers only decorin and &agr;2‐macroglobulin increased predictive value for future severe exacerbations. Conclusions: Blood biomarkers were significantly associated with the occurrence of exacerbations but were not robust between cohorts and added little to the predictive value of clinical covariates for exacerbations.

[1]  L. Held,et al.  The Inaccuracy of Patient Recall for COPD Exacerbation Rate Estimation and Its Implications: Results from Central Adjudication. , 2016, Chest.

[2]  E. Regan,et al.  Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD , 2016, PLoS genetics.

[3]  Robert Paine,et al.  Clinical Significance of Symptoms in Smokers with Preserved Pulmonary Function. , 2016, The New England journal of medicine.

[4]  B. Nordestgaard,et al.  Blood Eosinophils and Exacerbations in Chronic Obstructive Pulmonary Disease. The Copenhagen General Population Study. , 2016, American journal of respiratory and critical care medicine.

[5]  P. Agostoni,et al.  Prognostic implications of heart failure with preserved ejection fraction in patients with an exacerbation of chronic obstructive pulmonary disease , 2016, Internal and Emergency Medicine.

[6]  D. Mannino,et al.  Plasma Fibrinogen Qualification as a Drug Development Tool in Chronic Obstructive Pulmonary Disease. Perspective of the Chronic Obstructive Pulmonary Disease Biomarker Qualification Consortium. , 2016, American journal of respiratory and critical care medicine.

[7]  A. Dreher Modeling Survival Data Extending The Cox Model , 2016 .

[8]  Meilan K. Han,et al.  Clinical and Radiologic Disease in Smokers With Normal Spirometry. , 2015, JAMA internal medicine.

[9]  I. Pavord,et al.  Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials. , 2015, The Lancet. Respiratory medicine.

[10]  R. Tresserras,et al.  Seasonality, ambient temperatures and hospitalizations for acute exacerbation of COPD: a population-based study in a metropolitan area , 2015, International journal of chronic obstructive pulmonary disease.

[11]  D. Christiani,et al.  The Global Contribution of Outdoor Air Pollution to the Incidence, Prevalence, Mortality and Hospital Admission for Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis , 2014, International journal of environmental research and public health.

[12]  Meilan K. Han,et al.  The association of plasma biomarkers with computed tomography-assessed emphysema phenotypes , 2014, Respiratory Research.

[13]  Stephanie A. Santorico,et al.  Prediction of acute respiratory disease in current and former smokers with and without COPD. , 2014, Chest.

[14]  Edwin K Silverman,et al.  Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene , 2014, Respiratory Research.

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  Meilan K. Han,et al.  Comparison of serum, EDTA plasma and P100 plasma for luminex-based biomarker multiplex assays in patients with chronic obstructive pulmonary disease in the SPIROMICS study , 2014, Journal of Translational Medicine.

[17]  Lisa M LaVange,et al.  Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) , 2013, Thorax.

[18]  R. Bowler,et al.  The association of adiponectin with computed tomography phenotypes in chronic obstructive pulmonary disease. , 2013, American journal of respiratory and critical care medicine.

[19]  B. Nordestgaard,et al.  Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. , 2013, JAMA.

[20]  A. Yang,et al.  The Effect of Cold Temperature on Increased Exacerbation of Chronic Obstructive Pulmonary Disease: A Nationwide Study , 2013, PloS one.

[21]  K. Kostikas,et al.  Fetuin-A is Associated with Disease Severity and Exacerbation Frequency in Patients with COPD , 2013, COPD.

[22]  N. Bayraktar,et al.  Value of serum and induced sputum surfactant protein-D in chronic obstructive pulmonary disease , 2011, Multidisciplinary Respiratory Medicine.

[23]  A. Agustí,et al.  Systemic inflammation and comorbidities in chronic obstructive pulmonary disease. , 2012, Proceedings of the American Thoracic Society.

[24]  D. Mannino,et al.  Blood fibrinogen as a biomarker of chronic obstructive pulmonary disease , 2012, Thorax.

[25]  L. Edwards,et al.  COPD association and repeatability of blood biomarkers in the ECLIPSE cohort , 2011, Respiratory research.

[26]  J. Hokanson,et al.  The chronic bronchitic phenotype of COPD: an analysis of the COPDGene Study. , 2011, Chest.

[27]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[28]  J. Wedzicha,et al.  Susceptibility to exacerbation in chronic obstructive pulmonary disease. , 2010, The New England journal of medicine.

[29]  F. Karadağ,et al.  Adiponectin as a biomarker of systemic inflammatory response in smoker patients with stable and exacerbation phases of chronic obstructive pulmonary disease , 2009, Scandinavian journal of clinical and laboratory investigation.

[30]  T. Murphy,et al.  Infection in the pathogenesis and course of chronic obstructive pulmonary disease. , 2008, The New England journal of medicine.

[31]  F. Karadağ,et al.  Biomarkers of Systemic Inflammation in Stable and Exacerbation Phases of COPD , 2008, Lung.

[32]  A. Zeileis,et al.  Regression Models for Count Data in R , 2008 .

[33]  G. Donaldson,et al.  Inflammatory changes, recovery and recurrence at COPD exacerbation , 2007, European Respiratory Journal.

[34]  John R Hurst,et al.  Use of plasma biomarkers at exacerbation of chronic obstructive pulmonary disease. , 2006, American journal of respiratory and critical care medicine.

[35]  G. Donaldson,et al.  COPD exacerbations · 1: Epidemiology , 2006, Thorax.

[36]  W. MacNee,et al.  Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper , 2004, European Respiratory Journal.

[37]  Ciro Casanova,et al.  The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. , 2004, The New England journal of medicine.

[38]  S. Hurd,et al.  Global Strategy for the Diagnosis, Management and Prevention of COPD: 2003 update , 2003, European Respiratory Journal.

[39]  J. T. Wulu,et al.  Regression analysis of count data , 2002 .

[40]  N. Morton Genetic epidemiology , 1997, International Journal of Obesity.

[41]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[42]  P. Jones,et al.  A self-complete measure of health status for chronic airflow limitation. The St. George's Respiratory Questionnaire. , 1992, The American review of respiratory disease.

[43]  R. Prentice,et al.  Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study" , 1982 .

[44]  J. G. Cragg,et al.  The Demand for Automobiles , 1970 .