Genetic biomarkers of dementia in Parkinson’s disease

Abstract

Background: Parkinson's disease (PD) is a complex neurodegenerative disorder with heterogeneous clinical presentation and progression. Cognitive decline and dementia in PD (PDD) are common non-motor complications that significantly impact quality of life and socioeconomic burden. Improved understanding of the factors influencing cognitive outcomes and tools for predicting dementia risk are crucial for patient stratification and personalized management. Objectives: To refine the role of common genetic variants implicated in PD risk or other neurodegenerative diseases in modulating cognitive decline in PD and their utility as prognostic biomarkers in a large sample of newly diagnosed patients representative of the general PD population. Methods: All papers in this thesis are based on Parkinson's Incidence Cohorts Collaboration (PICC), a project pooling data from six longitudinal, population-based cohorts of newly diagnosed patients. In this work, we included up to 1108 PD patients which were prospectively followed for up to 10 years. We used longitudinal Unified Parkinson Disease Rating Scale (UPDRS) and Mini-Mental State Examination (MMSE) scores, and PDD diagnosis (according to standardized diagnostic criteria) as the main study outcomes. Participants were genotyped for common variants in APOE, MAPT, and SNCA, and screened for GBA1 mutations. Using linear mixed-effects regression and Cox regression models, we evaluated the impact of five PD-risk SNCA variants on functional, motor, and cognitive decline, and further, the impact of genetic variance in APOE, GBA1, MAPT, and SNCA on cognitive outcomes in PD. Finally, we validated the Montreal Parkinson Risk of Dementia Scale (MoPaRDS), a clinical-based tool for predicting PDD, and assessed the added value of GBA1 and APOE using timedependent receiver operating characteristic (ROC) analysis. Results: Of the SNCA variants, only rs356219-GG showed a minor effect on global cognitive decline, but not progression to PDD. GBA1 mutations and APOE-ε4 were associated with faster decline in MMSE scores and increased risk of developing PDD (HR 1.8 and 3.6, respectively). Moreover, carriers of both GBA1 and APOE-ε4 had a 5-fold higher risk of PDD. The effect of APOE-ε4 was time-dependent, with the greatest impact in the early disease stages. We found no effect of rs356219 or MAPT H1/H2 haplotypes on progression to PDD. The MoPaRDS demonstrated good overall predictive accuracy for PDD over 10 years follow-up from diagnosis (AUC = 0.79) but displayed poor sensitivity (21.7%) at the recommended cutoff. Adding GBA1 and APOE-ε4 to the MoPaRDS improved sensitivity to 36.4% while maintaining specificity. Conclusions: The genetic landscapes driving susceptibility to PD and the PD-related cognitive impairment do not necessarily overlap. GBA1 mutations and APOE-ε4 are key genetic modulators of cognitive decline, putting about a third of the PD population at increased risk of dementia. The incorporation of genetic biomarkers into prognostic tools may enhance predictive accuracy, particularly in early PD. These findings have implications for patient stratification in clinical trials, targeted interventions, and prevention of dementia. Future research should focus on elucidating the genetic architecture of cognitive decline in PD, and optimization of prognostic tools for the early stages of PD.