Objective: To examine the potential of a data-driven approach to identify incident cases of neurodegenerative parkinsonian disorders (PD) in a large observational cohort of individuals at risk for PD.

Background: Individuals at-risk for Parkinson’s disease and other neurodegenerative parkinsonian disorders who do not yet exhibit the motor syndrome and other features that fulfill clinical diagnostic criteria for one of these disorders serve as a prime population for disease-prevention therapies. The PPMI at-risk cohort was assembled to identify and characterize such individuals. Accurately identifying incident cases of neurodegenerative parkinsonism is critical to create predictive models and to design PD-prevention clinical trials.

Method: The at-risk PPMI cohort consists of individuals with one or more of the following features: idiopathic REM sleep behavior disorder (RBD, n=39), hyposmia (n=24) or non-manifesting carriers (NMC) of PD-pathogenic mutations (Leucine-rich repeat kinase 2 (LRRK2) gene (n=106) and glucocerebrosidase (GBA) gene (n=53) mutations)). Inclusion criteria enriched the RBD and hyposmia cohorts, but not the NMC cohort, with individuals with dopamine transporter binding deficit. A data-driven definition approach used rating scale and questionnaire responses indicating presence of bradykinesia and either rigidity or tremor to identify possible cases. The accuracy of this approach was compared to gold-standard of neurologist diagnosis based on at-least annual assessment. Data from a 2-year period of follow-up were used.

Results: The data-driven approach identified 32 possible cases (9 in the RBD group, 4 hyposmics, and 19 NMC carriers). Eight of these had received a clinical diagnosis of a neurodegenerative disorder: 7 with PD, 1 with MSA. Another 8 had been designated by the study site neurologist as having parkinsonism but had not yet received a specific diagnosis.

Conclusion: We demonstrate a sensitive method of using data collected during research study assessments to identify incident cases of PD in a large observational study of individuals at-risk for PD. Additional data from motor, non-motor, imaging, and biologic fluid biomarker assessments may be used to improve specificity.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/identifying-incident-cases-of-neurodegenerative-parkinsonism-in-a-large-observational-at-risk-cohort-the-ppmi-study/