A machine learning platform applied to multi-modal data to uncover risk factors for chronic pain conditions to direct mechanistic studies.
Approved Research ID: 93141
Approval date: April 4th 2023
Our project uses modern machine learning techniques to predict whether people will develop certain diseases, specifically focussing on chronic pain, such as diabetic polyneuropathy, Chronic Regional Pain Syndrome (CRPS) and chronic lower back pain. We aim to use this platform to help understand the reasons why people develop those conditions and to inform further research into candidate mechanisms through which chronic pain occurs. By using the large sample size available through the UK Biobank (UKB, n > 500,000), we will determine for example whether certain blood biomarkers are co-incident with chronic pain onset, which genes are most highly associated with chronic pain, and what is the intersection between genetics, lifestyle, biomarkers and brain structure in predicting chronic pain conditions.
This research supports a movement towards more personalised medicine by helping researchers develop a deeper and more bespoke understanding of which individuals are at higher risk of chronic pain based on clustering populations according to both genetics and phenotype. Through this research we hope to cast light upon how different aspects of pain affect different people, based on their genetics, biometrics, brain scans and many other features.
The UKB is unique in the sense that it contains multi-modal data for a range of variables across a large number of individuals. This means that for the first time it is feasible to build models that look across thousands of variables without pre-supposing any particular association, to uncover novel risk factors for a disease of interest. As a result of this it will be possible to firstly validate the existing factors responsible for a disease, but in addition to both identify new factors and uncover more complex connections between different factors, for example the importance of certain inflammatory biomarkers in predicting chronic pain.