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COPACETIC

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COPD Pathology: Addressing Critical gaps, Early Treatment and Innovative Concepts

Coordinator: Pieter ZANEN
Project Number: 201379
EC contribution: 2,981,143.00
Project website: http://www.copacetic-study.eu/

COPD is the only chronic disease with an increasing mortality and it will constitute the 4th leading cause of death by 2035 world wide. To alleviate the burden of COPD an accurate and easy diagnosis in the early stages of COPD needs to be developed. Many studies strongly suggest that there is an individual genetic susceptibility to COPD: only 10-15% of all smokers develop COPD. In another study, 53% of subjects had mild and 13.3% severe emphysema. When that genetic susceptibility becomes known, new diagnostics and a better insight into the pathogenesis of COPD will become available. Treatment or prevention come into reach. Several studies have elucidated a low number of significant genetic differences obtained via the candidate gene approach: a better approach is a genome wide scan. The results of such a genome wide scan will be compared between smokers with and without COPD. This approach has proven itself in e.g. diabetes mellitus. Data show that diagnosing COPD by only using pulmonary function tests, will only pick up a minority of the patients with emphysema. So, a diagnostic bias is often present, but avoided in the current study: it is the first time that data on CT scans and pulmonary function are combined for an accurate diagnosis. We will carry out a 300.000 SNP genome wide scan in a 4000 male heavy smokers with and without COPD. The diagnosis of COPD is based on using pulmonary function tests and CT-scanning. A replication study with the ~3,000 most significant SNPs will be performed in 5 European replication cohorts to remove false positives findings, study sex influences, population stratification and disease severity. We will also investigate gene expression in peripheral blood of smokers with and without COPD. The gene expression data will assist in SNP selection. We will build COPD prediction models based on the significant SNPs from the replication studies and validate the rule in all avalable cohorts.

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