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Lookup NU author(s): Dr Rachael LawsonORCiD, Professor Alison YarnallORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Background: Prediction models for dementia in Parkinson disease (PD) are needed to better identify high-risk patients, but existing risk models often lack validation in early-stage PD, when prognosis is most challenging. Objective: This study aims to validate the Montreal Parkinson Risk of Dementia Scale (MoPaRDS) in six population-based cohorts of newly diagnosed PD and to evaluate if incorporating genetic factors (GBA1 and APOE-ε4) enhances its performance. Methods: We calculated MoPaRDS scores for 1108 newly diagnosed PD patients, and MoPaRDS + GBA1 + APOE for the 941 patients with complete genetic data. We assessed the scores' performance in predicting dementia diagnosed over 10 years using time-dependent receiver operating characteristic (ROC) curves. Results: Of the 1108 patients (mean age 69.5 ± 10.0 years; 61.0% men), 350 (31.6%) developed dementia. The area under the time-dependent ROC curve (AUC) was 0.79 for MoPaRDS and 0.80 for MoPaRDS + GBA1 + APOE. Subdividing patients based on their MoPaRDS scores revealed annual observed risks of PDD of 39.4% (n = 8; high risk-), 11.4% (n = 176; intermediate risk-), and 5.0% (n = 942; low risk-group). With the suggested cutoff of ≥4, MoPaRDS had a sensitivity of 21.7% and specificity of 94.9%. Including the genetic items improved the sensitivity to 36.4% while maintaining comparable performance for specificity (91.5%). Conclusions: MoPaRDS demonstrates high specificity but limited sensitivity in early PD, highlighting that a one-size-fits-all approach is inadequate for predicting dementia risk in PD across different disease stages. Integrating genetic items increases sensitivity and identifies more newly diagnosed patients at higher risk of dementia, and may be a useful approach to assist dementia risk assessment in early-stage PD. Many people with Parkinson's disease (PD) are at risk of developing dementia but predicting who will develop dementia early in the disease remains a challenge. Tools like the Montreal Parkinson Risk of Dementia Scale (MoPaRDS) aim to help identify patients at higher risk, but their accuracy in early-stage PD is not well established. This study tested the MoPaRDS tool in six large groups of newly diagnosed PD patients and explored whether adding genetic information (GBA1 and APOE-ε4 genes) could make predictions more accurate. The study included 1108 people with newly diagnosed PD, of whom 350 (31.6%) developed dementia within 10 years. MoPaRDS was highly specific, meaning it could reliably identify people who were unlikely to develop dementia. However, it had limited sensitivity, identifying only 22% of those who later developed dementia. Adding genetic data improved sensitivity to 36%, meaning more at-risk individuals were flagged without losing significant accuracy. Further, the MoPaRDS tool can be used to group patients into low, intermediate, and high-risk categories. Patients in the high-risk group had a 39% annual chance of developing dementia, compared to only 5% in the low-risk group. Adding genetic information improved the tool's ability to identify more high-risk patients. These findings suggest that MoPaRDS is more appropriate for later-stage PD patients, and that incorporating genetic testing could improve existing tools for identifying those at higher risk of dementia, especially in the early stages of PD when intervention may be most beneficial. Such an approach could facilitate more tailored care for PD patients.
Author(s): Szwedo AA, Dalen I, Lawson RA, Yarnall AJ, Pedersen KF, Macleod AD, Counsell CE, Backstrom D, Forsgren L, Camacho M, Williams-Gray CH, Tysnes O-B, Alves G, Maple-Grodem J
Publication type: Article
Publication status: Published
Journal: Journal of Parkinson's Disease
Year: 2025
Volume: 15
Issue: 4
Pages: 868-878
Print publication date: 01/06/2025
Online publication date: 29/04/2025
Acceptance date: 04/03/2025
Date deposited: 09/07/2025
ISSN (print): 1877-7171
ISSN (electronic): 1877-718X
Publisher: Sage Publications Ltd
URL: https://doi.org/10.1177/1877718X251329857
DOI: 10.1177/1877718X251329857
Data Access Statement: The data supporting the findings of this study are available on application to the PICC steering committee via the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
PubMed id: 40302388
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