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.
Association between diet and depression
Depressive illness is common and costly to the individual and society. Genetic makeup accounts for about 1/3rd of the risk of depression and environmental factors for about 2/3rds. Psychosocial adversity and stress are important aspects of the environment that contribute to depression. Other potentially important environmental factors have been little studied. It is known that what we eat and drink, our diet (carbohydrates, fats etc.) and nutrients (e.g. vitamins) is an important influence on the risk of medical disorders such as obesity, diabetes and cardiovascular disease. These disorders are associated with an increased risk of subsequent depression and are more common in those with previous depression. This suggests that obesity-related disorders and depression may have some similar pathways of risk. The proposed cross-sectional study aims to identify shared and specific interactions between diet and psychosocial and genetic factors for self-reported depression and related disorders. We will use the unique combination of psychosocial, dietary and mental health data available in a subset of 122,000 of the UK Biobank cohort to decisively determine whether or not there are dietary patterns and constituents that lower the risk of self-reported lifetime depression in the face of life stresses. We will then factor-in genetic information (genotyping data will be requested, i.e. no DNA samples) and, using sophisticated statistical techniques, find new dietary and genetic factors that are highlighted because they converge on shared biochemical pathways. Understanding the role of diet in depression meets the UK Biobank's stated purpose of improved disease prevention; in contrast to genetic and psychosocial factors, dietary behaviour is potentially modifiable. For example, preventative, public health strategies could reduce the prevalence of depression by promoting resilience to psychosocial adversity and by offsetting the biochemical consequences of genetic risk.
|Lead investigator:||Dr Gabriella Juhasz|
|Lead institution:||Semmelweis University|