Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression

Peter Marx, Peter Antal, Bence Bolgár, Gyorgy Bagdy, Bill Deakin, Gabriella Juhász

Abstract: 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.