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Profiling Serum Biomarkers in Patients with COPD: Associations with Clinical Parameters
  1. Victor Pinto-Plata (vpinto{at}copdnet.org)
  1. Caritas St Elizabeth's Medical Center, United States
    1. John Toso (john.f.toso{at}gsk.com)
    1. GlaxoSmithKline, United States
      1. Kwan Lee (kwan.r.lee{at}gsk.com)
      1. GlaxoSmithKline, United States
        1. Daniel Parks (daniel.k.parks{at}gsk.com)
        1. GlaxoSmithKline, United States
          1. John Bilello (john{at}humanbiomarkers.com)
          1. GlaxoSmithKline, United States
            1. Hana Mullerova (hana.x.mullerova{at}gsk.com)
            1. GlaxoSmithKline, United Kingdom
              1. Mary DeSouza (mary.m.desouza{at}gsk.com)
              1. GlaxoSmithKline, United States
                1. Rupert S Vessey (rupert.s.vessey{at}gsk.com)
                1. GlaxoSmithKline, United States
                  1. Bartolome Celli (bcelli{at}copdnet.org)
                  1. Pulmonary, Critical Care and Sleep Division. Caritas St Elizabeth’s Medical Center. Tufts University, United States

                    Abstract

                    Rationale: Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease associated with significant systemic consequences. Recognition of the systemic manifestations has stimulated interest in identifying circulating biomarkers in these patients.

                    Objectives: We report on a systematic analysis of multiple protein analytes in the serum of well characterized patients with COPD and matched controls using a novel protein microarray platform technology (PMP). Methods: We studied 48 patients (65% men) with COPD (FEV1 <55%) and 48 matched controls. We measured anthropometrics, pulmonary function tests, 6-minute walk distance (6MWD), the BODE index and number of exacerbations. We explored the association of these outcomes with the baseline levels of 143 serum biomarkers, measured by PMP.

                    Results: Thirty biomarkers clusters were identified and ranked by computing the predictive value of each cluster for COPD (partial least square discriminant analysis or PLS-DA). From the 19 best predictive clusters we selected 2-3 biomarkers based on their pathophysiological profile (chemoattractants, inflammation, tissue destruction and repair) and tested the statistical significance of their relationship with clinically important endpoints. The selected panel of 24 biomarkers correlated (p=<0.01) with FEV1, DLCO, 6MWD, BODE index and exacerbation frequency.

                    Conclusion: Protein microarray platform technology can be useful in identifying potential biomarkers in patients with COPD. Panels of selected serum markers are associated with important clinical predictors of outcome in these patients.

                    • COPD
                    • Proteomics
                    • Serum Biomarkers

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