TY - JOUR T1 - A network-based approach to defining phenotypes in COPD JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.congress-2015.PA3355 VL - 46 IS - suppl 59 SP - PA3355 AU - Lucretia Udrescu AU - Mihai Udrescu AU - Alexandru Topîrceanu AU - Stefan Mihaicuta Y1 - 2015/09/01 UR - http://erj.ersjournals.com/content/46/suppl_59/PA3355.abstract N2 - Objectives: To identify phenotypes in a population of COPD patients by defining a compatibility relationship.Methods: An 88 patient database from Timisoara Victor Babes Hospital was built (Oct 30, 2014–Jan 21, 2015): spirometry, anthropometric parameters and questionnaire results. This data is used with the Network Medicine (Udrescu, M. et al. Chest 2014; 145(3S):609A) approach. Each patient is a node in a network where the link between two nodes represents a compatibility relationship. The compatibility exists if the two nodes have at least 5 out of 6 identical parameters: gender, age group, obesity, smoking score (SS), CAT and MRC. The graphical representation is generated in Gephi (Jacomy, M. et al. PlosONE 2014; 9:e98679) in order to reveal the phenotype clusters.Results: Fig. 1a presents 6 relevant compatibility clusters, corresponding to specific COPD patient phenotypes. Fig 1b-f depict distribution of phenotype identifiers. The phenotypes are: P1 – emphysematous cachectic males, P2 – heavy smokers and normal weight females, with moderate COPD, P3 – heavy smokers, overweight and obese males, with moderate to severe COPD, P4 – overweight and obese males and females, with moderate to severe COPD, P5 – heavy smokers men, with severe COPD, P6 – overweight and obese ex-smokers men. Conclusions: Network analysis in a population of COPD patients reveals specific phenotypes. This insight paves the way for COPD severity prediction and patient scorecarding. ER -