{"id":41791,"date":"2024-02-07T16:27:20","date_gmt":"2024-02-07T21:27:20","guid":{"rendered":"https:\/\/www.parkinson.ca\/?p=41791"},"modified":"2024-02-07T16:27:20","modified_gmt":"2024-02-07T21:27:20","slug":"redefining-parkinsons-through-a-new-biological-lens","status":"publish","type":"post","link":"https:\/\/archiveparkinson.thedev.ca\/fr\/redefining-parkinsons-through-a-new-biological-lens\/","title":{"rendered":"Redefining Parkinson’s through a new biological lens"},"content":{"rendered":"

Traditionally, a diagnosis of Parkinson\u2019s Disease relies on a clinical assessment by a doctor based on established clinical criteria. The clinical presentation of Parkinson\u2019s often manifests late in disease progression. By this point, it is believed that significant underlying pathological biological processes and\/or neurodegeneration have occurred.\u00a0<\/span>\u00a0<\/span><\/p>\n

In the dynamic landscape of Parkinson\u2019s research, two groundbreaking papers published in The Lancet Neurology have provided an important shift in the field\u2019s understanding of Parkinson\u2019s from a clinical perspective to a biological perspective. This means that we are now given a clearer window into the processes taking place from the very first stages of the disease, even well before the disease is clinically apparent. The NSD-ISS paper by Simuni and colleagues, titled <\/span>A biological definition of neuronal a-synuclein disease: towards an integrated staging system<\/span><\/i> and the SynNeurGe paper from H\u00f6glinger and colleagues, titled <\/span>A biological classification of Parkinson\u2019s disease: the SynNeurGe research diagnostic criteria, <\/span><\/i>mark a transformative shift in how we are defining Parkinson\u2019s. These papers are a result of collaborative international efforts and the contribution of both new scientific techniques and data contributed by patients.\u00a0<\/span>\u00a0<\/span><\/p>\n

Understanding NSD-ISS and \u00ab\u00a0SynNeurGe\u00a0\u00bb in Parkinson\u2019s<\/span>\u00a0<\/span><\/h3>\n

Developing effective treatments for Parkinson\u2019s disease is challenging due to its multifaceted nature and variability from person to person. Various forms of the disease are influenced by genetics, environmental factors, and more recently understood variations in biological processes, leading to a wide range of presentations. Additionally, the current diagnosis of Parkinson\u2019s often occurs late, after the disease may have been present in the brain for a decade or more.<\/span><\/p>\n

Published in The Lancet Neurology, scientists in both papers argue that the complexity of Parkinson’s demands a new approach to classifying individuals based on various biological markers. This not only helps to advance further research into the disease but can also have important implications in how patients are enrolled and studied in clinical trials for therapies and, further down the road, disease management.<\/span>\u00a0<\/span><\/p>\n

Each paper relies on the use of relatively new techniques, including the alpha-synuclein seed amplification assay and dopamine neuroimaging. These techniques have undergone significant validation to show their effectiveness as early biomarkers for Parkinson\u2019s disease, both independently and through larger studies, including in the Parkinson\u2019s Progressive Marker Initiative (PPMI) and Dementia with Lewy Bodies (DLB) Consortium. Both papers compiled these known biomarkers (alpha-synuclein aggregation, neurodegeneration and genetic background) to highlight how they contribute to disease and disease progression. Both also acknowledge the well-known clinical symptoms of the disease but note that they typically manifest after these biomarkers are able to be detected. These researchers propose the use of these models in identifying subsets of patients for more targeted research studies. With further validation, these models could be used for more targeted clinical trial recruitment, therapeutics designed for specific disease sub-groups and modeling disease progression.<\/span>\u00a0<\/span><\/p>\n

The SynNeurGe paper proposes a new classification framework based on the biological underpinnings of disease. Using these three components (outlined below), in addition to any clinical manifestations of disease, will help determine how someone is categorized for research purposes.<\/span>\u00a0<\/span><\/p>\n

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  1. \u00ab\u00a0Syn\u00a0\u00bb – alpha-synuclein, this component measures whether a patient sample has aggregated alpha-synuclein. This test uses an alpha-synuclein seed amplification assay from a cerebrospinal fluid (collected via spinal tap) sample, or other validated samples for the seed amplification assay.\u00a0<\/span>\u00a0<\/span><\/li>\n
  2. \u00ab\u00a0Neur\u00a0\u00bb – neurodegeneration, this component measures whether a patient has detectable neurodegeneration.<\/span>\u00a0<\/span><\/li>\n
  3. \u00ab\u00a0Ge\u00a0\u00bb – genetics, this component measures whether a patient has a genetic cause or susceptibility associated with Parkinson\u2019s disease.<\/span><\/li>\n<\/ol>\n

    In this model, a person is categorized based on their positive or negative results in the three components and are assigned a distinct disease title. This classification aids in refining research-related studies, including therapeutic development and clinical trials. For instance, one diagnosed with Parkinson\u2019s disease could be categorized as being \u201cSyn\u201d positive and \u201cNeur + Ge\u201d negative, which they call \u2018Sporadic Parkinson\u2019s type synucleinopathy\u2019, while another diagnosed with Parkinson\u2019s disease could be categorized as \u201cSyn + Neur\u201d positive but \u201cGe\u201d negative, which they call \u2018Sporadic Parkinson\u2019s disease.\u2019 Despite both individuals having Parkinson\u2019s disease, but they would be viewed differently for research purposes, allowing for a more targeted approach in studying the disease. This will have downstream effects on therapeutic development as researchers will have a better ability to target specific subsets of disease, which will have better translation from the laboratory to patients.<\/span>\u00a0<\/span><\/p>\n

    The NSD-ISS paper introduces a similar classification framework as a basis for their proposed disease staging framework that includes:<\/span>\u00a0<\/span><\/p>\n