An Approach to Early Detection of Parkinson's Disease via Analysis of Skin Biopsies
Neurodegenerative conditions driven by aggregation of misfolded or otherwise pathologically altered proteins can take a very long time to develop to the point of causing evident symptoms. There is a window of a decade or two in which researchers might develop sufficiently sensitive tests to detect the earliest stages of harmful protein aggregation. Demonstration technologies for early detection of Alzheimer's disease already exist. Here, researchers do much the same for Parkinson's disease, a condition characterized by the prion-like spread of misfolded α-synuclein through the central nervous system, but also into other, more readily accessible tissues such as skin.
"One known feature of Parkinson's is cell death resulting from aggregates of the alpha-synuclein protein. The protein begins to aggregate about 15 years before symptoms appear, and cells begin to die 5-10 years before diagnosis is possible with the means available today. This means that we have an extensive time window of up to 20 years for diagnosis and prevention, before symptoms appear. If we can identify the process at an early stage, in people who are 30, 40, or 50 years old, we may be able to prevent further protein aggregation and cell death."
Past studies have shown that alpha-synuclein aggregates form in other parts of the body as well, such as the skin and digestive system. In the current work the researchers examined skin biopsies from 7 people with and 7 people without Parkinson's disease. "We examined the samples using super-resolution imaging, combined with advanced computational analysis - enabling us to map the aggregates and distribution of alpha-synuclein molecules. As expected, we found more protein aggregates in people with Parkinson's compared to people without the disease. We also identified damage to nerve cells in the skin, in areas with a large concentration of the pathological protein. In future studies we will increase the number of samples and develop a machine learning algorithm to spot relatively young individuals at risk for Parkinson's."