Approved Research

The interplay of psychiatric symptoms with genetic and environmental risk factors: a network analysis in UK Biobank

Lay summary

Traditionally, clinicians have viewed psychiatric symptoms as originating from psychiatric disorders. For example, depressed mood is caused by major depressive disorder. However, this concept has led to insufficient success in understanding the roots and the mechanisms of psychiatric disorders. Instead of viewing psychiatric symptoms as the consequences of a disease, a novel approach based on network theory allows researchers to view symptoms as nodes that are linked to each other in a network, in which with their interactions give rise to psychopathology.

This project aims at examining the psychiatric symptom network and identifying core symptoms of this network in the British population. Moreover, we want to understand the interactions between the network and one's genes and living environment, with a view of mental health as a dynamic network under the influence of one's genetic heritage and surroundings. To obtain a constant measure of the living environment, we will use satellite data from participants' addresses to construct different indexes to characterize built-up land, greenness and nightlight exposure. We will also use these indexes to construct a measure that captures socio-economic deprivation.

The present project will contribute to a new perspective of psychiatric disorders, and it will allow us to understand better the interaction between different psychiatric symptoms. By knowing which symptoms are more important, clinicians can potentially target these key symptoms to stop the activation of the whole 'symptom network'. By examining the role of genetic and environmental factors in this network, we will also be able to identify risk factors that can be prioritized in prevention efforts. Finally, the use of satellite data to illustrate the physical environment will allow the results to be comparable across administrative boundaries, and potentially allow decision-makers to allocate resources more efficiently.