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

ABSTRACT:

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 function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regres

SUBMITTER: Nalls MA 

PROVIDER: S-EPMC4575273 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature