Funded Studies
Study Rationale: Although Parkinson’s disease (PD) affects individuals differently, there are groups of people with PD who share similar features and whose disease changes similarly over time. Recognizing and characterizing these groups could allow physicians to tailor treatment, researchers to improve study design and individuals and their families to optimize support. This project will apply a unique approach to identifying groups of individuals with PD who have distinct clinical features and biomarker profiles (such as brain images or measurements of specific proteins in cerebrospinal fluid and blood) that change in a similar pattern over time.
Hypothesis: We hypothesize that our computer model will successfully identify groups of individuals with PD who have a unique pattern of biological measures, including brain images based on dopamine transport and measures of proteins in cerebrospinal fluid and blood.
Study Design: We will apply our computational technique to participants in ongoing PD studies, including the Parkinson’s Progression Markers Initiative (PPMI). Using this computer model, we will identify subsets of individuals with similar biomarker profiles. We will then apply a new statistical method to identify clusters of proteins in the blood and cerebrospinal fluid, an approach that could help us discover new biological pathways that contribute to disease progression in the different groups.
Impact on Diagnosis/Treatment of Parkinson’s disease: Accurately identifying people who share similar features and disease progression allows individuals with PD and their families to better understand their prognosis and what to expect moving forward. For researchers, the ability to stratify people with PD facilitates the design of studies that evaluate new treatments.
Next Steps for Development: We will make our computer model available to the research community, promoting its broad application to data from diverse samples of individuals with PD. Such a tool should allow doctors to provide more personalized prognoses and researchers to evaluate more targeted treatments in clinical trials.