Identification of Sputum Biomarkers Predictive of Pulmonary Exacerbations in COPD

识别可预测慢性阻塞性肺疾病肺部急性加重的痰液生物标志物

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Abstract

BACKGROUND: Improved understanding of the pathways associated with airway pathophysiologic features in COPD will identify new predictive biomarkers and novel therapeutic targets. RESEARCH QUESTION: Which physiologic pathways are altered in the airways of patients with COPD and will predict exacerbations? STUDY DESIGN AND METHODS: We applied a mass spectrometric panel of metabolomic biomarkers related to mucus hydration and inflammation to sputa from the multicenter Subpopulations and Intermediate Outcome Measures in COPD Study. Biomarkers elevated in sputa from patients with COPD were evaluated for relationships to measures of COPD disease severity and their ability to predict future exacerbations. RESULTS: Sputum supernatants from 980 patients were analyzed: 77 healthy nonsmokers, 341 smokers with preserved spirometry, and 562 patients with COPD (178 with Global Initiative on Chronic Obstructive Lung Disease [GOLD] stage 1 disease, 303 with GOLD stage 2 disease, and 81 with GOLD stage 3 disease) were analyzed. Biomarkers from multiple pathways were elevated in COPD and correlated with sputum neutrophil counts. Among the most significant analytes (false discovery rate, 0.1) were sialic acid, hypoxanthine, xanthine, methylthioadenosine, adenine, and glutathione. Sialic acid and hypoxanthine were associated strongly with measures of disease severity, and elevation of these biomarkers was associated with shorter time to exacerbation and improved prediction models of future exacerbations. INTERPRETATION: Biomarker evaluation implicated pathways involved in mucus hydration, adenosine metabolism, methionine salvage, and oxidative stress in COPD airway pathophysiologic characteristics. Therapies that target these pathways may be of benefit in COPD, and a simple model adding sputum-soluble phase biomarkers improves prediction of pulmonary exacerbations. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01969344; URL: www. CLINICALTRIALS: gov.

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