Objective: We aim to create a longitudinal dataset of clinical, molecular, imaging, and continuous wearable sensor-based data from a representative Parkinson’s disease (PD) cohort. Data will be made available to researchers worldwide to accelerate the discovery of novel etiological insights, development of new therapeutic approaches, and personalized disease management. For this purpose, we aim to create an extensible norm for sharing research data, meeting the latest data privacy and security standards.

Background: Our understanding of PD has stagnated, partly due to the limited patient diversity and brief follow-up captured in most study cohorts. Additionally, potentially valuable biomarkers derived from different types of measurements are rarely analyzed in an integrated fashion.

Methods: Supported by a multinational, public-private partnership, we designed a prospective cohort study to include 650 representative PD patients (disease duration <5 years). Comprehensive follow-up for at least 2 years includes: (1) annual assessment at the study center for acquisition of detailed clinimetric data, magnetic resonance imaging, and biospecimens (plasma, serum, CSF, stool) and (2) collection of data from the home environment, using self-assessments and an advanced wrist-worn wearable device to continuously measure biological and environmental signals. Collection, storage, and sharing of these research data will be facilitated by a new method to protect privacy and enhance security using polymorphic encryption and pseudonymization (PEP), a methodology that combines advanced encryption with distributed pseudonymization and data access management.

Results: The research infrastructure is in place and patient recruitment is underway.

Conclusions: This study is unique, as it includes a cohort of unbiased subjects with recently diagnosed PD, creating an unprecedented dataset that combines longitudinally collected clinical, molecular, imaging, and wearable sensor-based data using state of the art technology. The single-center study design minimizes measurement variability. Finally, the innovative methodology for data privacy and protection might serve as a new international standard for sharing research data.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/study-design-for-a-multi-modal-approach-to-understanding-parkinsons-disease-the-personalized-parkinson-project/