COPD comorbidities network
Divo MJ (1), Casanova C (2), Marin JM (3), Pinto-Plata VM (4), de-Torres JP [SP] (5), Zulueta JJ (5), Cabrera C (6), Zagaceta J (5), Sanchez-Salcedo P (5), Berto J [SP] (5), Davila RB (2), Alcaide AB [SP] (5), Cote C (7), Celli BR (4); BODE Collaborative Group.
(1) Pulmonary and Critical Care Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
(2) Pulmonary Department, Hospital Universitario La Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.
(3) Respiratory Service, Hospital Universitario Miguel Servet, Zaragoza, Spain.
(4) Pulmonary and Critical Care Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
(5) Department of Pulmonology, University Clinic of Navarra, Pamplona, Spain.
(6) Pulmonary Department, Hospital Universitario de Gran Canaria Dr Negrin, Las Palmas de Gran Canarias, Spain.
(7) Division of Pulmonary and Critical Care Medicine, The Bay Pines Veterans Affairs Healthcare System, University of South Florida, Tampa, FL, USA.
Magazine: The European Respiratory Journal
Date: Sep 1, 2015Pneumology
Multimorbidity frequently affects the ageing population and their co-existence may not occur at random. Understanding their interactions and that with clinical variables could be important for disease screening and management.In a cohort of 1969 chronic obstructive pulmonary disease (COPD) patients and 316 non-COPD controls, we applied a network-based analysis to explore the associations between multiple comorbidities.
Clinical characteristics (age, degree of obstruction, walking, dyspnoea, body mass index) and 79 comorbidities were identified and their interrelationships quantified. Using network visualisation software, we represented each clinical variable and comorbidity as a node with linkages representing statistically significant associations.
The resulting COPD comorbidity network had 428, 357 or 265 linkages depending on the statistical threshold used (p≤0.01, p≤0.001 or p≤0.0001). There were more nodes and links in COPD compared with controls after adjusting for age, sex and number of subjects.
In COPD, a subset of nodes had a larger number of linkages representing hubs. Four sub-networks or modules were identified using an inter-linkage affinity algorithm and their display provided meaningful interactions not discernible by univariate analysis.
COPD patients are affected by larger number of multiple interlinked morbidities which clustering pattern may suggest common pathobiological processes or be utilised for screening and/or therapeutic interventions.
CITATION Eur Respir J. 2015 Sep;46(3):640-50. doi: 10.1183/09031936.00171614. Epub 2015 Jul 9.
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