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Researchers have identified a series of blood markers that betray the presence of Parkinson's disease up to seven years before most symptoms present. If findings from this small study can be replicated in larger populations, a simple blood test could be developed to identify those at risk.. One of the reasons this has proved challenging is our inability to identify people at risk of Parkinson's early enough to trial mitigation strategies.
So, University College London biochemist Jenny Hällqvist and colleagues used machine learning models to find eight proteins in our blood that change as Parkinson's disease progresses.Comparing the blood from 99 people with recently diagnosed Parkinson's, 72 people with REM sleep behavior disorder, and 26 healthy controls, the researchers identified 23 potential biomarkers.
They then narrowed these candidates down to the most reliable combination of blood markers with the help of the machine learning model. Combined, the resulting eight biomarkers allowed the researchers to predict which patients with REM sleep behavior disorders would go on to develop Parkinson's disease with nearly 80 percent accuracy – well before they started to have visibly physical symptoms.
The biomarkers the researchers identified are proteins involved in inflammation, blood clotting, and cell developmental biochemical pathways. Some of them increased along with symptom severity and reduced cognitive performance. "This powerful combination of multiple well-selected biomarkers with state-of-the-art machine-learning bioinformatics, allowed us to use a panel of eight biomarkers that could distinguish early Parkinson's disease from healthy controls," Hällqvist and teamsigns of Parkinson's disease early on, this requires an invasive procedure that is not easily accessible.
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