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Chronic Obstructive Pulmonary Disease Biomarkers and Their Interpretation

The pathology and impact of chronic obstructive pulmonary disease (COPD) results from an abnormal inflammatory process resulting in tissue damage with ineffective repair in response to toxic inhalants (especially cigarette smoke). Identification of mechanisms provides the opportunity to develop new...

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Bibliographic Details
Published in:American journal of respiratory and critical care medicine 2019-05, Vol.199 (10), p.1195-1204
Main Authors: Stockley, Robert A, Halpin, David M G, Celli, Bartolome R, Singh, Dave
Format: Article
Language:English
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Summary:The pathology and impact of chronic obstructive pulmonary disease (COPD) results from an abnormal inflammatory process resulting in tissue damage with ineffective repair in response to toxic inhalants (especially cigarette smoke). Identification of mechanisms provides the opportunity to develop new therapies and a personalized approach to management. The collection of multiple genetic and detailed biochemical data from small and large patient cohorts has led to an explosion of studies investigating biomarkers to achieve these aims. Despite widespread enthusiasm and many statistically significant associations, the interpretation of COPD biomarker results requires thought and leaves many questions unanswered. The present review assesses the importance of these associations, whether they represent cause or effect, reflect disease severity or activity, the complexity of the pathway to the final pathogenic and hence interventional step, and problems with interpreting cross-sectional studies without knowing individual disease trajectories. The complexity of biomarker specificity without sufficient clinical phenotype and endotype information contributes to problems of interpretation. A strategic change is needed to develop useful COPD biomarkers; this includes focusing on endotype biomarkers within specific clinical phenotypes, biomarkers in early COPD, exacerbation subtype biomarkers, and biomarkers to predict or measure drug effects.
ISSN:1073-449X
1535-4970
DOI:10.1164/rccm.201810-1860SO