ArticlesDiagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study
Introduction
Accurate diagnosis or prediction of risk by use of simple, non-invasive measures is a rarely realised goal for many complex diseases. For complex progressive diseases such as Parkinson's disease, preclinical diagnosis and low error rates in diagnosis are crucial in clinical trials and the study of disease-altering therapeutic approaches.
Imaging is often deemed the gold standard for identification of typical Parkinson's disease pre-mortem, however, high cost and restricted portability limit the use of this approach. We aimed to develop a portable method to identify patients with Parkinson's disease who show aetiologically typical disease presentation (confirmed by dopamine transporter [DAT] imaging data). We used a combination of factors that vary over the life of an individual, factors that are constant and do not change with time, general indicators of neurodegeneration, and Parkinson's disease-specific measures to create our classification algorithm.
Section snippets
Study design and participants
Figure 1 shows a summary of our workflow. Table 1 describes the cohorts we used and further details are available in the appendix: the Parkinson's Progression Marker Initiative (PPMI), the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). PPMI and PDBP are case-control studies that use a shared set
Results
To accompany this report, and to help with replication and extension of our work, the code and training data for this predictive model and some validation data have been made publicly available online.
Each of five factors that we included made significant contributions to the information content of the integrative predictive model. In comparisons of the standardised beta coefficients within the regression model, the UPSIT score was responsible for 63·1% of the explained variance, followed by
Discussion
We have designed an accurate, non-invasive method to discriminate patients with Parkinson's disease from controls. The studies that we assessed vary in their design, recruitment, and implementation; however, our results and validation suggest that the model might be useful in future. The model we developed includes hyposmia, which is often considered an indicator of neurodegeneration, in addition to genetic, clinical, and demographic data.1 This approach makes use of the growing wealth of data
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