Sandor Lab

New ways to detect and monitor Parkinson’s disease

The Sandor Lab aims to develop new ways to detect and monitor Parkinson’s disease (PD) earlier, even before the typical motor symptoms, like tremors, appear. By the time these symptoms show up, a large portion of brain cells responsible for movement has already been damaged, making it harder to treat the disease effectively. The team want to find clues that show the disease is developing much earlier, which could help intervene sooner.

To do this, the Sandor Lab will use data from smartwatches that track non-motor symptoms of PD, such as sleep problems, depression, or changes in blood pressure, which often appear years before the disease is diagnosed. They will also study specific markers in the blood that may indicate early changes related to PD. Finally, we’ll use electronic health records to explore whether any common drugs taken for other conditions might slow down the progression of PD.

This research is important because it could lead to earlier diagnosis, more effective treatments, and even new drugs that slow the disease’s progression, improving the quality of life for millions of people affected by Parkinson’s worldwide.

Harnessing digital biomarkers, molecular markers, and Big Data to unlock insights in early detection and progression in Parkinson’s 

Currently, there is no cure or treatment available to slow the progression of Parkinson’s disease (PD). Research has primarily focused on individuals with a clinical diagnosis of PD, which is contingent on the presence of motor symptoms. By the time these symptoms appear, up to 50% of dopaminergic neurons—essential for movement—are already lost. Various non-motor symptoms, such as REM Sleep Behavior Disorder, depression, orthostatic hypotension, anosmia, and constipation, have been identified up to 10 years before diagnosis, in what is known as the prodromal phase. 

The goal of this research program is to understand the molecular mechanisms underlying these early symptoms, which could pave the way for neuroprotective treatments. We will use large-scale data, including Electronic Health Records (EHR), deeply phenotyped cohorts different omics dataset, digital biomarkers, while leveraging advanced computional approach methods to take avantage of these dataset such as large language models or transfer learning.

Research objectives:

1. Identify early non-motor symptoms in the general population using digital biomarkers.

   This research will focus on developing digital markers that can predict these non-motor symptoms, leveraging data from smartwatch data. The Sandor Lab have shown it is possible to identify such symptoms using one week of accelerometer data, and their objective is to further refine this approach.

2. Identify specific blood molecular markers that precede a clinical diagnosis. 

   There is growing evidence that PD pathology may begin in the enteric or peripheral autonomic nervous system and then spread to the brain. This suggests that peripheral immune system changes may precede brain involvement. The goal of the team is to establish blood-based immune markers that correlate with early non-motor symptoms, which could help identify PD earlier. The Sandor Lab will use omics data from both human and mouse models, including bulk and single-cell transcriptomics as well as proteomics, to assess how these blood signatures relate to neurodegeneration.

3. Identify non-Parkinson’s drugs that alter PD progression using EHR.

   A promising approach to discovering new treatments is identifying non-Parkinson’s medications that may modify the disease through off-target effects. To explore this, the Sandor Lab will analyse EHR data from the Clinical Practice Research Datalink and the Parkinson’s Progression Marker Initiative. Since neither dataset directly measures PD progression, they will use the Levodopa Equivalent Daily Dose (LEDD) as a proxy for disease progression. This will allow the team to investigate whether any coincident non-Parkinson’s medications slow PD progression.