A research published in the journal Nature Communications suggests that a blood test could potentially identify Parkinson's disease up to seven years before the onset of symptoms.
Parkinson's disease is a progressive neurological condition characterised by symptoms such as tremors, slow movement, gait issues, and memory problems.
By utilising machine learning techniques, scientists examined blood samples from 72 individuals with Rapid Eye Movement Behaviour Disorder (iRBD), a condition characterised by unknowingly physically acting out dreams.
Machine learning, a form of artificial intelligence, involves learning from historical data to predict future outcomes.
The group of scientists, headed by those at the University College London, UK, stated that it is recognised that roughly 75-80 per cent of individuals with iRBD progress to having an abnormal accumulation of alpha-synuclein protein in their brains, which is also observed in individuals with Parkinson's disease.
After examining the blood samples, the researchers' machine learning tool revealed that nearly 80 per cent of the 72 iRBD patients exhibited the same characteristics as someone with a neurodegenerative disease related to ageing.
The researchers also assessed whether the tool could forecast the likelihood of the patient developing Parkinson's. To do this, the iRBD patients were monitored for ten years.
The researchers discovered that the tool accurately anticipated 16 patients developing the neurodegenerative condition and could do so up to seven years prior to the onset of any symptoms.
"By determining eight proteins in the blood, we can identify potential Parkinson's patients several years in advance. 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," said first author Michael Bartl, University Medical Center Goettingen, Germany.
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