Objective: To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including additional genetic risk on the performance of prediction.

Background: Early identification of individuals at high-risk of being diagnosed with PD is an important step towards developing therapies which could prevent or slow neurodegeneration. Understanding the interplay between genetic and environmental risk factors for PD may shed light on disease biology and help to risk-stratify individuals for enrolment in prevention studies.

Method: We identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously developed prediction algorithm for finding incident cases.

Results: Strong evidence of association (Pcorr<0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, and daytime somnolence, and novel associations with epilepsy and earlier menarche. Individuals with the highest 10% of PRS scores had increased risk of PD (OR=3.30, 95% CI 2.57-4.24) compared to the lowest risk decile. Higher PRS scores were associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm improved model performance (Nagelkerke pseudo-R2 0.0053, p=6.87×10-14). We found evidence of negative interaction between the PRS and diabetes, which may act as a protective factor among individuals at high genetic risk (OR=0.28, 95% CI 0.07-1.15, p=0.078), but as a risk factor for individuals with low genetic risk (OR=2.76, 95% CI 1.22-6.27, p=0.015).

Conclusion: Here we used UK Biobank data to reproduce several well-known associations with PD and report the novel association of earlier menarche and PD. We demonstrate the validity and predictive power of a genome-wide PRS and a novel genome-wide gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/parkinsons-disease-determinants-prediction-and-gene-environment-interactions-in-the-uk-biobank/