Identify shared and specific interactions between diet and psychosocial and genetic factors for self-reported depression and related disorders
Approved Research ID: 1602
Approval date: June 17th 2020
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 cross-sectional investigations identified the complex environmental- and comorbidity-network of depression, and demonstrated that comorbidity-relationships are influenced by the temporal order of diseases. Thus we would like to extend the scope of this project and carry out a longitudinal analysis in the full UK Biobank cohort. Our hypothesis is that comorbidities may inform us about the different biological pathways leading to and active in subgroups of depressed patients and thus can guide personalized medicine. In this project we aim to apply advanced machine learning methods using first occurrences of disorders and medication history to construct a temporal disease map and identify clusters of patient trajectories. Next, we will characterise these trajectories by investigating the genetic, metabolic, socio-economic, environmental, lifestyle, laboratory and cognitive profile, and calculate multimorbidity-adjusted burden of diseases. Additionally, we will perform in silico screening of multi-target drug candidates using the polypharmacy, genetic, and multimorbidity profiles of the trajectory classes. The derived results will further improve public health strategies by identifying the most frequent longitudinal multimorbidity profiles of depressed patients, and inform healthcare providers about patients' characteristics and needs, and contribute to better health risk assessment, prevention and treatment strategies.