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A team of researchers led by Anthony Lang of the University Health Network and the University of Toronto have proposed a novel classification system for Parkinson’s disease that considers biological features and not just clinical symptoms.

The "SynNeurGe" system, described by Lang and collaborators in a paper , classifies Parkinson’s disease based on three biomarkers: presence or absence of misfolded alpha synuclein protein, which is believed to cause or contribute to the underlying neurodegeneration; evidence of neurodegeneration using imaging techniques; and presence of gene variants that increase disease risk.

The researchers argue that such a classification system is necessary to advance the development of tailored treatments for Parkinson’s disease.

“This is a complex group of disorders that may cause similar symptoms, but biologically they're very different,” says Lang, a senior scientist and Lily Safra Chair in Movement Disorders at UHN and a professor in the department of medicine and the Tanz Centre for Research in Neurodegenerative Disease at �Թϱ���’s Temerty Faculty of Medicine, where he holds the Jack Clark Chair for Parkinson’s Disease Research

“If we cannot find ways to subdivide patients biologically, then applying a therapy designed to affect one biological pathway may not be effective in another group of patients that doesn't have that same pathway involved – and we won’t really have precision or personalized medicine for Parkinson’s disease.”

Currently, Parkinson’s disease is classified based on clinical presentation and symptoms, but the disease can affect the brain for years, possibly even decades, before symptoms appear. For future therapies to treat the underlying disease rather than just the symptoms, patients will need early intervention and treatments tailored to the biological features of the disease, researchers say.

Similar approaches are being used for other diseases – cancer treatments vary not only by the location of tumors but also their molecular features, and the development of drugs for Alzheimer’s disease is increasingly guided by the specific biological mechanisms involved in the disease.

The SynNeurGe classification system, while based on the three key biomarkers, also considers whether clinical features are present. The different combinations of biomarkers classify the disease into various sub-types.

Lang and co-authors note that such a classification should only be used for research at present, although it will almost certainly have clinical applications.

“Eventually we will see a biological approach influencing clinical care, particularly when we finally have effective disease-modifying therapies,” says Lang. “We currently don’t know how important these biomarkers actually are.

"We need large-scale prospective studies of biomarkers, imaging and clinical features to interpret the results, give patients accurate information about their diagnosis and provide appropriate treatment.”

Lang’s team plans to start conducting such studies of cerebrospinal fluid, skin and blood to look for biomarkers of different sub-types of Parkinson’s disease that will help inform the classification system and the development of tailored therapies.

“Now is the time to think about these diseases not solely based on their clinical manifestations, but to look at the biology and try to separate different biological subtypes so we can ultimately improve treatment for this disease,” Lang says.

Professor Graham Collingridge, director of the Tanz Centre, says Lang and his team’s “landmark paper” is poised to have a significant impact on clinical practice around Parkinson's. “I am delighted that our researchers have played such a key role in this important biological classification,” Collingridge says.

Lang says research by Tanz Centre scholars has contributed significantly to the body of knowledge used to develop the proposed biological classification.

For example, Professor Ekaterina Rogaeva’s research on the genetics and epigenetics of Parkinson’s disease has shown that multiple genes and environments can influence Parkinson’s risk, highlighting the need to tailor therapies based on a patient’s genetic makeup.

Other researchers – including Anurag Tandon, Joel Watts, Martin Ingelsson and Gabor Kovacs – have been studying the role of misfolded alpha synuclein in neurodegeneration as well as cases of Parkinson’s disease where alpha synuclein is absent – which informed how Lang’s team included the protein in the classification.