Abstract
Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks.
Highlights
It has long been recognised that medical disorders frequently co-occur in the same individual [1] but the significance of comorbidity in revealing shared mechanisms of pathogenesis and outcome is a more recent realisation [2,3,4]
We found that the great majority of disease associations were indirect consequences of a sparse network of ‘direct’ comorbidities (‘sparse diseaseome’) constructed using probabilistic graphical models (PGMs) within the Bayesian statistical framework
In this paper we demonstrate that probabilistic graphical models (PGMs) in the Bayesian statistical framework provide a principled, unified solution for filtering such disease-mediated indirect relations, for correcting for potential external confounders and for coping with limitations and uncertainty of the data
Summary
It has long been recognised that medical disorders frequently co-occur in the same individual [1] but the significance of comorbidity in revealing shared mechanisms of pathogenesis and outcome is a more recent realisation [2,3,4]. Earlier diseasome-wide works focused on the exploration of shared genetic background (1) behind comorbidities [2, 3, 12, 13] and the underlying molecular networks [3, 14,15,16,17,18]. These works relied on pairwise comorbid relations partly controlled for potential confounding factors such as age
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