LONDON - A new blood test could predict whether a person could have Parkinson’s disease up to seven years before symptoms begin to show.
The test has been developed by scientists from University College London (UCL) and University Medical Centre Goettingen in Germany using artificial intelligence (AI) – and they hope to make it available within the next two years.
“As new therapies become available to treat Parkinson’s, we need to diagnose patients before they have developed the symptoms,” study senior author Professor Kevin Mills, of UCL, said.
"We cannot regrow our brain cells and therefore we need to protect those that we have. Therefore, we set out to use state-of-the-art technology to find new and better biomarkers for Parkinson’s disease and develop them into a test that we can translate into any large NHS laboratory.
"With sufficient funding, we hope that this may be possible within two years.”
Parkinson’s disease is a form of dementia, and is a progressive disorder that is caused by the death of nerve cells in the part of the brain that controls movement.
There is currently no cure for Parkinson’s, and the disease affects around 153,000 people in the UK, with two people diagnosed every hour.
Parkinson’s patients are currently treated with dopamine replacement therapy, but only after symptoms such as tremors, slowness of movement, and memory problems have already developed.
Which is why the researchers believe that an early prediction of the disease could help to slow or stop the condition developing by protecting dopamine-producing brain cells.
The study, published in the journal Nature Communications, showed that when a branch of AI, called machine learning, analysed a panel of eight blood based biomarkers whose concentrations are altered in patients with Parkinson’s, it could provide a diagnosis with 100% accuracy.
The team then experimented to see whether the test could predict the likelihood that a person would go on to develop Parkinson’s.
They did so by analysing blood from 72 participants with Rapid Eye Movement Behaviour Disorder (iRBD) which results in patients physically acting out their dreams without knowing. When the machine learning tool analysed the blood of the iRBD patients it identified that 79% had the same profile as someone with Parkinson’s.
The patients were followed over the course of 10 years and the AI predictions have so far matched the clinical conversion rate – with the researchers correctly predicting 16 patients as going on to develop Parkinson’s and being able to do so up to seven years before the onset of any symptoms.
The team are now continuing to follow those predicted to develop Parkinson’s, to further verify the accuracy of the test.
"By determining eight proteins in the blood, we can identify potential Parkinson's patients several years in advance,” co-first author Dr Michael Bartl, of University Medical Centre Goettingen, said.
"This means that drug therapies could potentially be given at an earlier stage, which could possibly slow down disease progression or even prevent it from occurring.
“We have not only developed a test, but can diagnose the disease based on markers that are directly linked to processes such as inflammation and degradation of non-functional proteins. So these markers represent possible targets for new drug treatments.”
Professor David Dexter, Director of Research at Parkinson’s UK which co-funded the study, called the research a ‘major step forward’ in the search for a patient friendly diagnostic test for Parkinson’s.
"Finding biological markers that can be identified and measured in the blood is much less invasive than a lumbar puncture, which is being used more and more in clinical research,” he explained. “With more work, it may be possible that this blood based test could distinguish between Parkinson’s and other conditions that have some early similarities, such as Multiple Systems Atrophy or Dementia with Lewy Bodies.
“The findings add to an exciting flurry of recent activity towards finding a simple way to test for and measure Parkinson’s.”