The Personalized Parkinson Project (PPP)

Background Our understanding of PD has stagnated, partly due to the limited patient diversity and brief followup captured in most study cohorts. Additionally, potentially valuable biomarkers derived from different types of measurements are rarely analyzed in an integrated fashion. Objective This study aims 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, an extensible norm for sharing research data will be developed, meeting the latest data privacy and security standards. Methods Supported by a multinational, public-private partnership, a prospective cohort study was designed 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, cerebrospinal fluid (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. Conclusion 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 data from wearable sensors 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.