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Shifting Paradigms: How SynNeurGe Is Redefining Parkinson’s Disease

The SynNeurGe model revolutionizes Parkinson’s disease classification by focusing on biological rather than clinical factors, promising advancements in research and personalized treatment. Credit: SciTechDaily.com

Scientists propose new, biologically-based classification system for Parkinson’s Disease— the fastest-growing neurological disease worldwide.

An international research team led by Krembil Brain Institute Neurologist and Senior Scientist, Dr. Anthony Lang, has proposed a new model for classifying Parkinson’s disease (PD).

In recent decades, researchers have uncovered several biological factors that underlie PD. Key factors include a build up of the protein α-synuclein in the brain, which leads to neuron degeneration, and genetic factors that increase one’s risk of developing the disease. They have also begun to develop reliable methods to test for these factors, called biomarkers, in living patients.

Despite these advancements, doctors still diagnose the disease based on clinical features, such as the presence of tremors and other common motor symptoms.

According to Dr. Lang, who is the Lily Safra Chair in Movement Disorders at the University Health Network (UHN) and the Jack Clark Chair for Parkinson’s Disease Research and a Professor in the Department of Medicine, at the University of Toronto, this traditional approach to diagnosing PD does not account for the complex biological processes at play.

“We know Parkinson’s exists in the brain for one to two decades, or longer, before the clinical manifestations present,” says Dr. Lang. “So, we believe current research must be driven by biological determinants of the disease, rather than limited clinical descriptions of its signs and symptoms.”

He adds: “We need a radically different way of looking at this disease.”

Biologically-Based Model To Classify Parkinson’s Disease

The model emphasizes the important interactions between three biological factors contributing to Parkinson’s: the presence of pathologic α-synuclein in the brain, evidence of neurodegeneration, and the presence of gene variants. Credit: University Health Network

The SynNeurGe Model

In a recent article published in Lancet Neurology, Dr. Lang’s team proposed a new, biologically based model for classifying PD, called SynNeurGe (pronounced “synergy”).

The model emphasizes the important interactions between three biological factors that contribute to the disease: 

  1. the presence of pathologic α-synuclein in the brain (S);
  2. evidence of neurodegeneration, which occurs as the disease progresses (N); and
  3. the presence of gene variants that cause or strongly predispose a person to the disease (G).

According to the team, this “S-N-G” classification system better accounts for the biological heterogeneity of PD and the many ways the condition can present in patients. Consequently, the system could help researchers identify subgroups of patients that have distinct disease processes and develop clinically meaningful disease-modifying therapies.

Anthony Lang

Dr. Anthony Lang, neurologist & senior scientist at UHN’s Krembil Brain Institute in Toronto is leading an international team proposing a new, biologically-based model for classifying Parkinson’s disease. Credit: UHN’s Krembil Brain Institute

Implications for Research and Treatment

“We need to recognize that Parkinson’s can differ dramatically between patients. We are not dealing with a single disorder,” explains Dr. Lang. “Our model provides a much broader, more holistic view of the disease and its causes.”

“With this new model, Dr. Lang is spearheading a truly pivotal international effort to redefine the biological complexity of Parkinson’s Disease, which will lead to more advanced and streamlined research in this area, and ultimately, to precision medicine for patients,” says Dr. Jaideep Bains, co-Director of UHN’s Krembil Brain Institute.

The team is confident that this new way of looking at PD will help researchers study its molecular basis, distinguish it from other neurodegenerative conditions that share common biological features, and identify targets for new therapies.

Despite these potential applications, Dr. Lang cautions that the model is intended for research purposes only and is not ready for immediate application in the clinic. Yet, it is already spurring hope among patients and the medical community.

“The ability to tailor treatments improves when you can identify exactly what is going on in a specific patient like me,” says Hugh Johnston, Founding Chair of The Movement Disorders Patient Advisory Board at UHN’s Krembil Brain Institute, who is currently living with PD. “This new way of thinking is what we have been waiting for. It’s a game changer.”

“Without looking at the biology, you can’t get answers. And without answers, we won’t have much-needed breakthroughs in Parkinson’s,” says Dr. Lang. “This new classification system and the future research project it will inspire, is one of the most exciting things I have worked on in my career.”

Reference: “A biological classification of Parkinson’s disease: the SynNeurGe research diagnostic criteria” by Günter U Höglinger, Charles H Adler, Daniela Berg, Christine Klein, Tiago F Outeiro, Werner Poewe, Ronald Postuma, A Jon Stoessl and Anthony E Lang, February 2024, The Lancet Neurology<em>The Lancet Neurology</em> is a peer-reviewed medical journal that publishes original research articles, reviews, and commentaries on the clinical aspects of neurology, including the diagnosis and treatment of neurological disorders. The journal is published monthly by Elsevier and has been in publication since 2002. <em>The Lancet Neurology</em> covers a wide range of topics related to the field of neurology, including neurodegenerative diseases, epilepsy, stroke, multiple sclerosis, neuroimaging, neuropsychology, and neuropharmacology. The journal aims to provide a platform for the dissemination of high-quality, evidence-based research in the field of neurology, and to promote the translation of scientific knowledge into clinical practice.” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>The Lancet Neurology.
DOI: 10.1016/S1474-4422(23)00404-0

This work was supported by National Institutes of HealthThe National Institutes of Health (NIH) is the primary agency of the United States government responsible for biomedical and public health research. Founded in 1887, it is a part of the U.S. Department of Health and Human Services. The NIH conducts its own scientific research through its Intramural Research Program (IRP) and provides major biomedical research funding to non-NIH research facilities through its Extramural Research Program. With 27 different institutes and centers under its umbrella, the NIH covers a broad spectrum of health-related research, including specific diseases, population health, clinical research, and fundamental biological processes. Its mission is to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability.” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>National Institutes of Health, Canadian Institutes of Health Research, Canada Foundation for Innovation, Michael J. Fox Foundation, Brain Canada, Ontario Brain Institute, Garfield Weston Foundation, Webster Foundation, Edmond J Safra Philanthropic Foundation, Parkinson Foundation, Parkinson Canada, the State of Arizona, Mayo Clinic, Banner Health, Fonds de Recherche du Quebec – Sante, Deutsche Forschungsgemeinschaft (German Research Foundation), German Federal Ministry of Education and Research, EU/EFPIA/Innovative Medicines Initiative, European Joint Programme on Rare Diseases, Niedersächsisches Ministerium für Wissenschaft und Kunst, Volkswagen Foundation, Petermax-Müller Foundation, German Parkinson Society, German Parkinson’s Disease Association, Parkinson Fonds Deutschland gGmbH, Damp Foundation and UHN Foundation.

Source: SciTechDaily