Study Rationale:
We have identified small molecules associated with altered odds of having Parkinson's disease (PD) among hundreds of metabolites measured in metabolomic analyses of brain fluid or blood samples from Parkinson’s Progression Markers Initiative (PPMI) participants. Some were identified in individuals who carry disease-facilitating variants of the GBA and LRRK2 genes, but most of those that were higher or lower in PD were linked to PD independent of gene status. If these or other analytes measured through metabolomic analysis play a direct role in the disease process, then they their levels may also predict the rate of PD progression.
Hypothesis:
Molecules or a set of molecules flowing through the body (in blood) or through the brain (in cerebrospinal fluid, a.k.a. CSF) can help identify people with PD who will progress at a faster or slower pace in the years after the blood or CSF samples were collected. Some of these predictors will help identify who with PD will progress (i.e., worsen) at a slower rate than others, and whether that prediction differs depending on whether one carries LRRK2 genes linked to PD.
Study Design:
Hundreds of previously measured levels of metabolites (i.e., small molecules in life chemical pathways) in blood and CSF samples donated by PPMI study volunteers with PD will be analyzed for whether they predict progression of PD, and whether that prediction differs among those with or without a known PD-associated mutation in the LRRK2 or GBA gene.
Impact on Diagnosis/Treatment of Parkinson’s disease:
This metabolomic analysis may help find new targets for treatment aimed and slowing worsening of disability in PD, and may also improve the design of future clinical trials by better understanding what molecular factors contribute to the pression outcomes in these studies.
Next Steps for Development:
Markers of PD validated through this metabolomics analysis will be used to improve the design of clinical trials of potential therapies to slow or prevent PD 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).