Dr Lauren Walker BSc (Hons) MBChB (Hons) PHD MRCP (UK) FHEA

The population in the UK is ageing and as a result we are seeing more patients with more than two long-term health conditions, termed “multimorbidity”. Multimorbidity is associated with taking lots of medicines (“polypharmacy”). Some medicines are used to treat symptoms (such as pain or depression) whilst others try to prevent diseases happening further down the line, called “preventative” medicines. Most studies for new medicines don’t include women, very old or very young people or those that have many different conditions. Therefore it is hard to know whether these medicines are working as well in these groups meaning some patients are at risk of experiencing medication-harm (side effects) and may not be getting the benefit that we believe them to be. We therefore need to develop better ways to manage patients with both multimorbidity and polypharmacy. Although we know that multimorbidity is on the rise, it is not clear at the moment which diseases commonly occur together and importantly, how long it takes from developing two conditions until the third, fourth, fifth occurs. We want to develop a computer approach (called “machine learning”) that can look at patient records and cluster the commonly occurring diseases together to tell us which clusters are the most common, whether this differs for different age groups, and what factors lead to the development of a disease cluster. Understanding the make-up of multimorbidity will allow us to re-design clinic services to better meet the needs of the population and to try to reduce medication-related harm.

Epilepsy affects 3-5% of people at some point in their lives, making it one of the most common serious neurological disorders. 30% of patients continue to have seizures despite antiepileptic drug treatment, significantly impeding their quality of life. The mechanisms underlying the development of epilepsy, particularly after an injury to the brain, are poorly understood. Recent evidence from animal models and brain tissue from patients with epilepsy suggest that inflammatory pathways may be important to the development of epilepsy and its severity. We want to investigate this with a view to evaluating whether a protein found in peripheral blood called HMGB1 can act as a marker of the inflammation occurring in the brain. This is important as it will provide novel ways of treating patients, particularly the 30% who do not become seizure free. The first part of this study will use models of epilepsy to characterise the role of inflammatory mediators in the blood and brain following seizures. The second part will map the course of inflammatory up-regulation in the blood in patients with newly-diagnosed epilepsy pre-treatment and those established on long-term therapy. We will also look at how genetic factors predispose to inflammation following seizures.