 Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depressionPeter Marx, Peter Antal, Bence Bolgar, Gyorgy Bagdy, Bill Deakin and Gabriella Juhasz PLOS Computational Biology, 2017, vol. 13, issue 6, 1-23 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.Author summary: Depression is one of the most common of psychiatric disorders and its causation is correspondingly multifactorial, complex and heterogeneous. It occurs in combination with a number of physical illnesses far more commonly than expected by chance. Such comorbidities may be important clues pointing to shared environmental and genetic risk factors and could identify different causal types of depression. However, a method is still needed to weed out statistically significant pairings that nevertheless arise through indirect routes involving comorbidities between other diseases. We examined the pairwise associations among 247 diseases of 117,392 participants recorded in the UK Biobank database. 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 a depression-related subset of illnesses, we found that several pairwise associations of depression were indirect and due to their comorbidities with obesity which had a strong direct connection with depression. Furthermore, the direct comorbidities in a depression-related subset of disorders, but not the pairwise associations, strongly mapped onto an underlying molecular network (‘interactome’) suggesting that this approach significantly improved correspondence with molecular reality. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005487 DOI: 10.1371/journal.pcbi.1005487 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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