Scientists Propose New Biological Model for Classifying Parkinson's Disease

Parkinson’s disease is notorious for its intricate nature, with various forms influenced by a combination of genetic and environmental factors.

Scientists have proposed a revolutionary approach to classifying Parkinson’s disease for research purposes. Published in The Lancet Neurology, their paper introduces the “SynNeurGe” biological model, aiming to redefine how we understand and diagnose this complex neurological disorder.

Parkinson’s disease is notorious for its intricate nature, with various forms influenced by a combination of genetic and environmental factors. Patients exhibit a diverse range of symptoms, making timely diagnosis challenging, often occurring after the disease has silently advanced in the brain for a decade or more.

The SynNeurGe model takes a departure from traditional clinical diagnosis methods, focusing on the biological underpinnings of Parkinson’s. The acronym reflects its key components: “Syn” for alpha-synuclein, a protein associated with abnormal deposits called Lewy bodies; “Neur” for neurodegeneration, encompassing breakdowns in the function of neurons throughout the brain; and “Ge” for genetics, acknowledging the intricate interplay between genetic predisposition and environmental exposures.

The emphasis on alpha-synuclein is pivotal, as this protein plays a significant role in the degenerative changes observed in Parkinson’s patients. Abnormalities in alpha-synuclein are linked to the deterioration of brain functions, impacting movement, thinking, behavior, and mood.

Traditionally, the diagnosis of Parkinson’s has relied on identifying specific neurons in the dopamine system. However, the SynNeurGe model broadens the scope, incorporating neurodegeneration across all areas of the brain into its classification criteria. This holistic approach recognizes the multifaceted nature of the disease and aims to provide a more comprehensive understanding.

Genetics, another crucial component, adds a layer of complexity to Parkinson’s. Mutations in various genes have been identified as predisposing factors, and the likelihood of developing the disease depends on the specific gene, mutation, and environmental influences. The SynNeurGe model suggests that, for research purposes, patients should be classified based on the presence or absence of these three factors.

What sets this model apart is its potential to identify Parkinson’s patients before symptoms manifest. The conventional diagnostic approach relies on visible signs and symptoms, often occurring late in the disease progression.

By shifting the classification criteria to a biological basis, researchers aim to detect the disease earlier, possibly even before individuals experience noticeable symptoms. This early identification could be a game-changer, enabling the development of tailored treatments aligned with the unique biology of each patient.

Dr. Ron Postuma, a clinician-scientist at The Neuro of McGill University and one of the study’s authors, emphasizes the significance of this shift in thinking. Comparing it to the diagnostic methods used for other diseases, he highlights the unusual practice of waiting for Parkinson’s patients to exhibit symptoms before making a diagnosis. The SynNeurGe model, he argues, represents a crucial step forward in bringing Parkinson’s research into the 21st century.

“A biological classification of Parkinson’s disease: the SynNeurGe research diagnostic criteria,” published in The Lancet Neurology on January 22, 2024, marks a milestone in Parkinson’s research. The senior author, Dr. Anthony Lang, the Lily Safra Chair in Movement Disorders at UHN’s Krembil Brain Institute, and the Jack Clark Chair for Parkinson’s Disease Research at the University of Toronto, underscores the importance of this research classification as a critical step towards modernizing our understanding of Parkinson’s.

As researchers delve into the potential applications of the SynNeurGe model, the hope is that it will pave the way for earlier and more accurate diagnoses, ultimately facilitating the development of targeted treatments for Parkinson’s patients. This innovative approach not only challenges existing norms in Parkinson’s research but also opens up new avenues for exploration, offering renewed hope for those affected by this debilitating neurological condition.