Understanding the neurobiological basis of co-morbid chronic pain and depression by integrating genomics, peripheral biomarkers and neuroimaging data.

Background:

Chronic pain represents a significant global health burden that is estimated to affect one fifth of the general population (Goldberg and McGee 2011). Another significant global health issue is major depressive disorder (MDD), which is the leading cause of disability worldwide (World Health Organisation 2017). Previous research has identified an overlap in the presentation of both conditions with 85% of chronic pain sufferers presenting with comorbid MDD and over half of MDD patients reporting symptoms of pain. It is important to note this co-morbidity results in a worse long-term outcome than either condition alone. Recently, evidence has emerged suggesting genetic correlation between both conditions, specifically, genes involved in neurogenesis, synaptic plasticity and neuronal development (Johnston et al. 2019). Despite this overlap in genetic architecture, few studies have evaluated the potential neurobiological overlap between chronic pain and MDD, and none at a population-wide level. In addition to this knowledge gap, the biological intermediates lying on causal pathways between depression and chronic pain have not been fully explored.

Addressing these gaps in our understanding of chronic pain and MDD has important clinical implications as current treatments do not effectively modify underlying pathophysiological mechanisms. Additionally, adding to our currently limited understanding of the relationship between cancer pain and depression, which occurs in more than a third of cancer patients, holds significant therapeutic potential.

Aims:

This project will use state-of-the-art population datasets (UK Biobank, N~0.5m, STRADL/Generation Scotland, N=23k), containing extensive phenotyping data, including variables relevant to chronic pain and depression. These datasets also contain subsamples of participants with available imaging, genomic, epigenetic and biomarker panel data. These datasets will provide the necessary data to obtain a comprehensive, multi-level understanding of the neurobiology of chronic pain/MDD comorbidity. Specifically, this project will aim to establish:

1) The prevalence of chronic pain in depression at a population level, and vice versa (including e.g. types of pain most commonly reported in depression, eg cancer-related pain, & types of depressive traits reported in chronic pain).

2) The neurobiological, behavioural, lifestyle, and cognitive features specific to individuals with both chronic pain and depression. Imaging measures, such as structural brain connectivity (DTI), cortical thickness (sMRI), subcortical brain volumes (hippocampus and striatum) and functional connectivity (rsFMRI) will be included in this analysis.

3) The differential expression of blood and urine biomarkers for conditions such as inflammation (e.g. CRP) and stress (cortisol), and their epigenetic markers, in the co-morbid group, and their relation to symptoms and neuroimaging features.

4) A more robust understanding of causative directionality between these conditions, by furthering previous research in this area (Johnston et al. 2019). This will be achieved through the use of multi-trait Mendelian randomization and mediation modelling, to determine whether clinical/symptomatic features are mediated through these biomarkers.



Bibliography

Goldberg, D. S. and McGee, S. J. 2011. Pain as a global public health priority. BMC Public Health 11(1), p. 770. doi: 10.1186/1471-2458-11-770

Johnston, K. J. A. et al. 2019. Genome-wide association study of multisite chronic pain in UK Biobank. PLOS Genetics 15(6), p. e1008164. doi: 10.1371/journal.pgen.1008164

World Health Organisation. 2017. Depression: Let's Talk. Available at: http://www.who.int/mediacentre/news/releases/2017/worldhealthday/en/