Chronic pain is a major public health problem affecting one fifth of the general population(1). Major depressive disorder (MDD) is also a major health concern and is now the leading cause of disability worldwide(2). Clinical studies indicate an overlap in the presentation of both conditions: 85% of patients with chronic pain experience MDD and over half of patients with MDD report symptoms of pain. Importantly, comorbid pain and depression has a worse long-term outcome than either condition alone.
Emerging evidence suggests a degree of overlap in the genetic architecture of these conditions, including genes involved in neurogenesis, synaptic plasticity and neuronal development(3). There are however few studies that have explored potential overlaps in neurobiology itself, and none at a population-wide level. Furthermore, the biological intermediates lying on causal pathways between depression and chronic pain have not been fully explored. This has important clinical implications as existing interventions currently fail to effectively target underlying mechanisms that maintain both conditions in these individuals. Similarly, an understanding of the challenging relationship between cancer pain and depression which occurs in over a third of cancer patients, has never been extensively explored, and has significant therapeutic potential.
This is an ambitious ‘big data’ project that seeks to leverage state-of-the-art datasets (UK Biobank, N~0.5m, STRADL/Generation Scotland, N=23k), with a wide variety of phenotyping, including details of chronic pain(3), and depression(4), and with subsamples of participants with imaging, genomic, epigenetic and biomarker panel data, that will provide a comprehensive, multi-level understanding of the neurobiology of chronic pain/MDD comorbidity. Specifically, to establish:
The levels 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)
The neurobiological, behavioural, lifestyle, and cognitive features specific to this co-morbid group (imaging measures. including structural brain connectivity (DTI), cortical thickness (sMRI), subcortical brain volumes (hippocampus and striatum) and functional connectivity (rsFMRI)).
The differential levels of peripheral biomarkers for e.g. of inflammation (CRP) and stress (cortisol), and epigenetic markers of both, in the co-morbid group, and their relation to symptoms and neuroimaging features.
Furthering existing work on causative directionality between these conditions(3), using multi-trait Mendelian randomization and mediation modelling, to determine whether clinical/symptomatic features are mediated through these biomarkers.
This proposal benefits greatly from the existing strong interdisciplinary collaboration between the University of Edinburgh and the University of Glasgow, spanning expertise in neuroimaging, psychiatry, palliative medicine, chronic pain and genomics. As part of this PhD, extensive training in a wide variety of quantitative skills will be undertaken, including in MRI image processing, the use of widely used neuroimaging software packages. A thorough understanding would also be gained of the complex modeling procedures offered by the powerful statistical package ‘R’, as well as familiarisation with data access procedures and use of large, locally and publicly available cohorts. In addition, there is also opportunity to maximize the use of these cohorts with available genomic/epigenomic data and linkage to e-health records.
The training outcomes would therefore culminate in a sound understanding of techniques at the forefront of neuroimaging, chronic pain and psychiatric genetics as applied to a large data sources in the context of transforming clinical research. It will equip the PhD student with a wide range of precision medicine skills and training opportunities.
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.
All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow. http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919
Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine