Network Analysis in the Investigation of Chronic Respiratory Diseases. From Basics to Application

Díez, Diego; Agustí, Àlvar; Wheelock, Craig E.

doi:10.1164/rccm.201403-0421pp

Cited by 45 publications

(58 citation statements)

References 48 publications

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“…If we assume that the COPD Comorbidity Network is similar in behaviour to common networks seen in daily life, like airports composed of important hubs with many incoming and outgoing connecting flights with less influential airports in minor destinations, then disruption of air traffic will be mostly felt when delays affect large hubs [44]. Likewise we could hypothesise that perturbation of the comorbidities network could be achieved by selecting highly connected and treatable comorbidities and measure the impact of specific treatments [27,45,46]. This postulation is provocative and untested, however prior evidence indirectly supports this idea.…”

Section: Discussionmentioning

confidence: 99%

COPD comorbidities network

et al. 2015

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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 interlinkage 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. @ERSpublications COPD patients are affected by interlinked comorbidities forming structured networks

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Section: Discussionmentioning

confidence: 99%

COPD comorbidities network

et al. 2015

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“…A useful way to conceptualize and present graphically a complex "system" is a network, where the different elements of the system are represented as nodes and their structural and/or functional relationship as edges (or links) 15 . The "nature" (i.e., type) of the "nodes" and "edges" of a given system depends on the research question(s) we want to address.…”

Section: Network Analysismentioning

confidence: 99%

Relevance of Systems Biology to Respiratory Medicine

Agustí¹,

López-Giraldo²,

Cruz³

et al. 2016

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“…In addition, static measurements ("omics") are certainly not sufficient to understand the underlying dynamics of interactions that occur with varying time and spatial scales: at some point modelling tools are required since relations are mostly nonlinear, making straight inferences hazardous. Addressing this complexity is what systems or networks biology and medicine try to do, using bioinformatic tools and knowledge engineering to transform data into understanding and ultimately gain the ability to modulate individual target mechanisms to treat each patient more effectively, which is one fundamental goal of personalised medicine [12][13][14].…”

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confidence: 99%