Improved method to predict Parkinson’s disease
Researchers from the Wolfson’s PNU have developed a more accurate method to identify people at higher risk of developing Parkinson’s disease.
PREDICT-PD study researchers used their previously developed ‘basic’ algorithm to identify individuals at increased risk of Parkinson’s disease, and assessed speed on a keyboard tapping task, decreased sense of smell, and REM sleep behaviour disorder as ‘intermediate’ markers of early disease. Diagnoses of Parkinson’s disease among participants in the pilot study have now allowed the study authors to incorporate these intermediate markers into ‘enhanced’ risk estimates, which represent an improvement in the algorithm. A comparison of risk estimates using the basic and enhanced algorithms in the PREDICT-PD pilot cohort suggests a meaningful improvement in risk estimation. The PREDICT-PD study is currently recruiting 10,000 people aged 60-80 in the UK to create better prediction models for Parkinson’s disease. Corresponding author Dr Alastair Noyce said: “If we can accurately identify who is at higher risk of Parkinson’s, we hope to soon enrol them in prevention studies.”
Jonathan P Bestwick, Stephen D Auger, Cristina Simonet, Richard N Rees, Daniel Rack, Mark Jitlal, Gavin Giovannoni, Andrew J Lees , Jack Cuzick, Anette E Schrag, Alastair J Noyce. Improving estimation of Parkinson’s disease risk—the enhanced PREDICT-PD algorithm. npj Parkinson’s Disease 2021.
The PREDICT-PD Study is funded by Parkinson’s UK and is co-led by QMUL and UCL. The Preventive Neurology Unit is funded by the Barts Charity.