Frontiers in Neurology: New model can accurately predict early Parkinson's disease

May 11, 2020 /PRNewswire/---How do you smell? Do you find yourself dozing off during the day or dreaming at night? Often, early Parkinson's disease is not associated with typical motor disorder symptoms, thus presenting challenges for early diagnosisNow, neuroscientists at the University of York have developed five different models that use non-kinetic clinical and biological indicators to more accurately predict early Parkinson's diseasetheir five-pattern analysis was one of the first to use non-motor clinical and biological variablesAccording to the study, published in the journal Frontiersin Neurology, these models may help to make diagnosis and interventional treatment more timelyThis new approach is a less invasive alternative than traditional radiotracor scans (DaTscan), which are commonly used to assess a patient's symptoms(photo source:the study's lead author, Charles Leger, an associate professor in the Department of Psychology at the University of York, hopes the model will be used to predict patients with no obvious symptoms of Parkinson's disease in the early stages of the disease"There is no cure for Parkinson's disease, " said De Souza, author of thearticleAll we know now is signs and symptoms, and it can only be treatedTherefore, these models are useful for distinguishing between patients with suspected illness and those who actually get sickDeSouza believes that easier and more accurate early Parkinson's disease predictions can lead to early action, such as regular physical activity, in patients who receive accurate diagnosis to improve activity and balanceresearchers used cross-sectional baseline data from the Parkinson's Progressive Marker Program (PPMI)The PPMI data used are limited to non-sporting clinical variables (e.g., smell, daytime drowsiness, rapid eye movement disorders, age, etc.) and biological variables (e.g., cerebrospinal fluid alpha-synaptic nucleoproteins, tau proteins, beta-amyloid proteins, etc.)(BioValleyBioon.com)Source:' Predictmodelscanscanmoremorepreodetecti-stageParkinson's
original origin: Charles Legeretal, Non-Motor Clinicaland BioPredictmarkers EnableHighCross-EdAccuracyDetectOfDetectP Frontiersin Neurology (2020) DOI: 10.3389/fneur.2020.00364