Polygenic risk score association with cognitive decline in Parkinson's Disease. (20th December 2022)

• Record Type: Journal Article
• Authors: Harvey, Joshua; Reijnders, Rick A; Shireby, Gemma; Duits, Annelien; Köhler, Sebastian; Creese, Byron; Lunnon, Katie; Pishva, Ehsan
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.069246
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Cognitive impairment is a common and debilitating symptom in Parkinson's disease (PD) with high variability in individual trajectory of decline. We sought to explore heterogeneity in the trajectory of individual cognitive change in a cohort of early stage PD patients and test association to cumulative genetic risk identified in large scale Genome Wide Association Studies (GWAS). Method: Using longitudinal measures of the Montreal Cognitive Assessment (MoCA) we employed latent class mixed modelling (LCMM) to identify and investigate unknown populations in the Parkinson's Progression Markers Inititative (PPMI) de‐novo PD cohort. Tranformed MoCA scores were modelled as a quadratic function of years from baseline, controlling for age, gender and motor symptom severity. Optimal group number was identified and determined using standardly advised model fit metrics. Polygenic risk scores (PRS) for five GWAS were calculated using PRSice‐2 applied to genotyping array data. Association of PRS with cognitive groups was tested using linear models and ANOVA tests. Result: LCMM showed optimal fit statistics for three classes (lowest BIC, high entropy) and these groups were retained for further analysis The largest identified class (n = 240) on average, presented at baseline with higher MoCA scores and remained stable over time. The second class (n = 132) presented with lower MoCA scores and showed a slow declining trajectory whilst the smallest class (n = 13) presented with lower MoCA scores but declined at a rapid rate. Educational attainment and Alzheimer's disease (AD) GWAS derived PRS were significantly associated with cognitive class and explained the highest amount of phenotypic variance. For PD case‐control status, only the PD PRS was significantly associated with Parkinson's status and explained a similar level of phenotypic variation. Conclusion: Latent class analysis may provide utility in subsetting longitudinal cognitive outcome groups for use in groupwise comparisons. Using this method we show evidence for association of educational attainment and AD cumulative genetic risk and worse cognitive outcomes in early PD.