Funded Studies
Study Rationale:
Mutations in a gene called LRRK2 can cause Parkinson's disease but only a quarter to half of the people who carry one of these mutations will actually develop Parkinson’s in their lifetimes. This suggests counter-balancing protective influences of genetic and environmental factors. Characterizing the mix of molecules in people with LRRK2+ Parkinson’s (in comparison to those without Parkinson’s despite a LRRK2 mutation) can identify candidate biomarkers and potentially mediators of disease resistance, as well as of disease progression.
Hypothesis:
A signature set of molecules flowing through the body (in blood) or through the brain (in cerebrospinal fluid, also known as CSF) can help identify people who are resistant to Parkinson’s, particularly if due to a LRRK2 gene mutation compared to those without a known genetic risk factor or one in a different gene like GBA. Similarly, this kind of molecular signature may help predict or track progression of genetic and typical forms of Parkinson’s.
Study Design:
Hundreds of metabolites (small molecules in life chemical pathways) in blood and CSF samples donated by Parkinson’s Progression Markers Initiative (PPMI) study volunteers — with or without Parkinson’s, and with or without a known Parkinson’s-associated mutation in the LRRK2 or GBA gene — will be measured using a ‘metabolomics’ approach, which is a powerful bio-analytical platform tailored to such metabolites.
Impact on Diagnosis/Treatment of Parkinson’s Disease:
This broad systematic metabolomics analysis will efficiently elucidate and differentiate the small molecule signatures of genetic and typical forms of Parkinson’s, thereby informing the development of clinical trials.
Next Steps for Development:
Markers of Parkinson’s validated through this metabolomics analysis will be used to improve the design of clinical trials of potential therapies to slow or prevent Parkinson’s by selecting more responsive subpopulations to enroll (toward greater ‘precision medicine’), by reducing the variability of progression measurements (allowing for smaller, faster trials), or by identifying candidate neuroprotective treatments (targeting natural pathways).