Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

Publication date: Available online 10 August 2015 Source:The Lancet Neurology Author(s): Mike A Nalls, Cory Y McLean, Jacqueline Rick, Shirley Eberly, Samantha J Hutten, Katrina Gwinn, Margaret Sutherland, Maria Martinez, Peter Heutink, Nigel M Williams, John Hardy, Thomas Gasser, Alexis Brice, T Ryan Price, Aude Nicolas, Margaux F Keller, Cliona Molony, J Raphael Gibbs, Alice Chen-Plotkin, Eunran Suh, Christopher Letson, Massimo S Fiandaca, Mark Mapstone, Howard J Federoff, Alastair J Noyce, Huw Morris, Vivianna M Van Deerlin, Daniel Weintraub, Cyrus Zabetian, Dena G Hernandez, Suzanne Lesage, Meghan Mullins, Emily Drabant Conley, Carrie A M Northover, Mark Frasier, Ken Marek, Aaron G Day-Williams, David J Stone, John P A Ioannidis, Andrew B Singleton Background Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. Methods We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory fu...
Source: The Lancet Neurology - Category: Neurology Source Type: research