An international research team led by Tel Aviv University has developed a new technology that can detect Parkinson’s disease 20 years before symptoms begin to appear.
Parkinson’s disease is a neurodegenerative disorder that occurs when patients gradually lose dopamine-producing nerve cells in the brain. When diagnosing the disease, doctors typically rely on observable symptoms such as tremors in the hands or feet and gait issues. However, these signs only manifest after the patient has already lost a significant number of nerve cells.
Parkinson’s disease is a neurodegenerative disorder that occurs when patients gradually lose dopamine-producing nerve cells in the brain (illustrative image).
In the study, Israeli scientists utilized super-resolution microscopy and computational analysis to accurately map specific protein aggregates—a key indicator of Parkinson’s disease—in human skin biopsies.
According to the researchers, the process of protein aggregation begins about 15 years before symptoms appear, and the cell death process occurs 5 to 10 years before current diagnostic methods can detect it. Therefore, their new technology opens a significant 20-year window for early diagnosis and intervention for Parkinson’s disease.
The scientists tested their new technology on skin biopsy samples from 7 Parkinson’s patients and 7 individuals without the disease, sourced from three leading medical centers in Israel. They successfully mapped and identified more protein aggregates in those with Parkinson’s disease.
This new technology, detailed in the journal Frontiers in Molecular Neuroscience, can early identify cellular signs of Parkinson’s disease, opening the potential for earlier treatment or even prevention of the disease. According to the researchers, the new technology may also aid in the early detection of other neurodegenerative diseases, such as Alzheimer’s disease.
Following this breakthrough, the research team aims to develop a machine learning algorithm to find correlations between motor and cognitive test results and micro findings, with the goal of predicting disease progression in the future.
