Neuronal alpha-synuclein diseases (NSD), such as Parkinson’s disease, progress slowly and affect people differently, making it hard to predict when symptoms will appear or worsen. Current methods for staging these diseases use broad categories, which do not capture the full picture of how the disease develops over time.
This project aims to create advanced computer models that use data from the Parkinson’s Progression Markers Initiative (PPMI) study to better understand how NSD progresses. These models will combine information from clinical tests and biological markers to estimate each person’s “disease age”—a measure of how far along they are in the disease process. By doing this, researchers can track changes more precisely and identify patterns in how symptoms and biomarkers evolve.
The benefits of this approach include:
- Improved disease staging: Moving from simple categories to a continuous scale for more accurate tracking.
- Better trial design: Helping researchers select participants who are at the right stage for a study, which can make trials more efficient and powerful.
- Predictive tools: Creating simulations of virtual patients to test how different trial designs might work before running real studies.
Ultimately, this work will help scientists design smarter clinical trials and speed up the development of treatments that can slow or prevent NSD.