Why is this research needed?
Transient ischaemic attack (TIA) is a type of stroke whose symptoms resolve by themselves within a few hours. While the symptoms of a TIA stop when the blood clot moves away from the brain, the danger is not over: you are now four times more likely to have a stroke in the next year. It is therefore vital to get medical help right away.
Prompt treatment with blood thinners can reduce the risk of another stroke after a TIA, but it can be hard to know who to give them to since not all TIA patients will go on to have a stroke. Blood thinners can have serious side effects so asking everyone who has a TIA to take these drugs might create more problems than it solves.
The PREDICT-EV team think they may have found a way to predict which TIA patients are likely to go on to have a stroke so that they can be given targeted treatment to prevent this from happening.
What are the aims of this research?
PREDICT-EV is based on particles called extracellular vesicles (EVs), which are released by almost all cells in our bodies. EVs are vital to the functioning of our bodies, acting as a cell-to-cell package delivery system for proteins, DNA and other important material, as well as a waste disposal system for damaged parts of cells.
However, previous research has shown that blood samples from people who’ve recently had a stroke have particularly high levels of EVs. These EVs are especially likely to have come from the innermost layer of the blood vessels.
From their work in the lab, the PREDICT-EV team think that these extra EVs may cause blood to form clots at random, increasing the risk of a stroke. They now want to understand whether that’s actually the case. They also want to see if they can predict stroke risk from EV levels in the blood of someone who’s recently had a TIA.
In the first stage of the project, they will take blood samples from TIA patients, people who had a suspected TIA which turned out to be something else, and people who haven’t had a stroke or TIA. They will analyse the blood to understand the levels of EVs and which cells they’ve come from and perform a blood clotting test called a PT test. They will then track which participants have a stroke in the next year and see whether they can find a link between stroke risk, EV levels and PT test results.
Then, they will use the SAIL databank to get a broader understanding of the links between TIA, stroke risk and PT test results. SAIL is a Welsh population databank containing info on GP and hospital admissions over the last 20 years. The team are focusing on PT tests rather than EVs here because PT tests are a standard test used in hospitals and EV analyses are not. Comparing a larger number of people over a longer period of time will allow the team to find out the longer-term stroke risk associated with PT test results and analyse whether other factors like gender and ethnicity might affect this link.
What is the benefit of this research?
If the PREDICT-EV team are correct that EVs are linked to stroke risk, EV analysis and PT tests could be used to personalise treatments to help reduce the risk of stroke after TIA. This could even be done retrospectively for people who had a PT test at the time of a TIA.
We already know what actions to take to reduce stroke risk once we’ve identified that someone is at risk, so the benefits of this research could be realised in the next few years. Ultimately, this should mean that fewer people who’ve had a TIA go on to have another stroke.
What PSP priorities does this research link to?
From 2019 to 2021, we worked with the James Lind Alliance on the Stroke Priority Setting Partnership (PSP). During the PSP process, we collaborated with people with lived experience of stroke and stroke professionals to find out what they thought were the top priorities in stroke research. From this, we identified the top ten priorities in two areas: prevention, diagnosis and short-term care, and rehabilitation and long-term care.
Now, when researchers apply to us for funding, we require that their work addresses at least one of these priorities, or a priority from the Childhood Neurological Disabilities PSP Top 10 as it relates to childhood stroke.
PREDICT-EV addresses the following priorities from the Stroke PSP:
- Prevention 1: Stop stroke from happening for the first time (primary prevention).
- Prevention 2: Recognising and responding to stroke and TIA.
- Prevention 5: Recurrent stroke risk (secondary prevention).
- Prevention 9: Risks and benefits of using blood-thinning treatments
Meet the team
PREDICT-EV is led by Professor Philip James, who is Professor of Cardiovascular Metabolism at Cardiff Metropolitan University. Philip is an expert in the factors that influence how blood vessels behave.
The other members of the team are:
- Dr Jessica Williams, a Research Fellow at Cardiff Metropolitan University. Jess is leading the part of the blood sample analysis that will unravel how EVs affect stroke risk, as well as ensuring that people affected by stroke are actively involved in the work of PREDICT-EV.
- Dr Cass Whelan, a Research Associate at Cardiff Metropolitan University. Cass is assisting Jess with analysis and writing up reports.
- Dr James White, a Consultant Physician in Stroke Medicine at Cwm Taf Morgannwg University Health Board. James is the clinical lead for PREDICT-EV and oversees participant recruitment and safety, as well as providing advice on analysing data from the SAIL databank.
- Professor John Geen, Assistant Director of Pathology at Prince Charles Hospital and oversees taking blood samples and tracking participants.
- Caroline Hamilton, a Clinical Research Nurse at Cwm Taf Morgannwg University Health Board. Caroline is responsible for recruiting participants, taking blood samples, and tracking whether they go on to have a stroke.
- Dr Mark Crabtree, Senior Lecturer in the School of Biosciences at the University of Surrey. Mark is analysing the blood samples for proteins that show which cells the EVs have come from.
- Dr Renin Toms, Lecturer in Population Health at Cardiff Metropolitan University and is analysing the data from the SAIL databank.
- Professor Keith Morris, Chair of Biomedical Sciences at Cardiff Metropolitan University. Keith is providing statistical advice and leading on data analysis.