Major depression is one of the largest burdens in developed societies including the UK. The disease is associated with low mood, lack of interest and several other symptoms including sleep and eating disturbances, cognitive symptoms and libido changes. Especially puzzling are those symptoms of depression like under- and oversleeping, under- and overeating and restlessness and inactivity, which are contradictory in a sense that both of these can be present in different patients, yet they all receive the same diagnosis of depression. It is reasonable to assume that by better understanding, how these symptoms develop and what are the underlying mechanisms in their development we can hope to achieve better therapies in the future. During the projects’ 36 months, our aim is to identify the biology of these contradictory symptoms in healthy and depressed individuals by using genetic data of the UK Biobank, combining it with previous knowledge from other sources and utilizing machine learning algorithms on combined data. By finding relevant genetic factors we hope to understand what biological correlates participate in the development of these symptoms in depression. These factors then can be selectively targeted by novel therapies in patients who show these symptoms and our results may also help the better characterization of depressive patients. However, these are not the only gains of the project. Many current antidepressants could be able to treat specific symptoms and by identifying the biology of these symptoms we hope that they can be used in a more targeted and, thus, efficient way, mitigating the problems of the current trial-and-error type approach. Furthermore, the methods developed during the project can be deployed to decipher the background of other symptoms beyond the above mentioned contradictory pairs, although we believe that, we can achieve the highest gain in their case. Therefore, the public health impact of the project is based on at least three pillars 1) we develop a method to handle depression on a symptom level and understand the biology of these symptoms, 2) this provides the possibility to use current antidepressants more efficiently and 3) also secures the possibility to discover novel intervention pathways and drug targets for the different symptoms of depression. All in all, the project might deliver substantial benefits in the therapy of a disorder where currently only one third of patients show response to the first antidepressant trial and many of them is resistant to any antidepressant pharmacotherapy.
