Literature DB >> 27748465

Phenomenology of COPD: interpreting phenotypes with the ECLIPSE study.

Alberto Papi1, Maria Sandra Magnoni, Carmelo Caio Muzzio, Gianmarco Benso, Andrea Rizzi.   

Abstract

The Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study was a large 3-year observational multicentre international study aimed at defining COPD phenotypes and identifying biomarkers and/or genetic parameters that help to predict disease progression. The study has contributed to a better understanding of COPD heterogeneity, with the characterization of clinically important subtypes/phenotypes of patients, such as the frequent exacerbators or patient with persistent systemic inflammation, who may have different prognosis or treatment requirements. Because of the big amount of information that is starting to be produced from metabolomic, proteomic and genomic approaches, one of the biggest challenges is the integration of data in a biological prospective such as clinical prognosis and response to medicinal products. In this article we highlight some of the progress in phenotyping the heterogeneity of the disease that have been made thanks to the analyses of this longitudinal study.

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Year:  2016        PMID: 27748465     DOI: 10.4081/monaldi.2016.721

Source DB:  PubMed          Journal:  Monaldi Arch Chest Dis        ISSN: 1122-0643


  3 in total

1.  Inflammatory and Metabolic Responses to Different Resistance Training on Chronic Obstructive Pulmonary Disease: A Randomized Control Trial.

Authors:  Bruna S de Alencar Silva; Fábio S Lira; Fabrício E Rossi; Dionei Ramos; Juliana S Uzeloto; Ana P C F Freire; Fabiano F de Lima; Luís A Gobbo; Ercy M C Ramos
Journal:  Front Physiol       Date:  2018-03-23       Impact factor: 4.566

2.  Gender modifies the effect of body mass index on lung function decline in mild-to-moderate COPD patients: a pooled analysis.

Authors:  Wenjia Chen; Mohsen Sadatsafavi; J Mark FitzGerald; Larry D Lynd; Don D Sin
Journal:  Respir Res       Date:  2021-02-18

3.  Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.

Authors:  Vasilis Nikolaou; Sebastiano Massaro; Wolfgang Garn; Masoud Fakhimi; Lampros Stergioulas; David B Price
Journal:  BMJ Open Respir Res       Date:  2021-10
  3 in total

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