2024-07-25 17:00:43
François Lehn, science/health journalist and author for 20 years, renowned “feather” and assistant to Professor David Servan-Schreiber.
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Parkinson’s disease is a complex neurodegenerative disease with considerable heterogeneity in symptoms and progression. Parkinson’s disease has long been thought to be a single entity, but new research using machine learning has discovered three distinct subtypes of Parkinson’s disease. This innovative classification opens the way for more personalized and targeted care for patients, with the potential to significantly improve their quality of life and treatment prospects.
Parkinson’s disease heterogeneity
Parkinson’s disease has long been considered a holistic disease, with all patients having similar symptoms and course. However, recent research advances reveal a more nuanced reality. Indeed, Parkinson’s disease is highly variable in its clinical manifestations, rate of progression, and response to treatment.
Disease process is highly variable
In some patients, the disease progresses rapidly, with rapid deterioration in motor and cognitive function. However, some exhibit a slower, more insidious form with symptoms that progress slowly. Between these two extremes, we also find intermediate cases in which the disease progresses at a moderate rate.
This heterogeneity of Parkinson’s disease highlights the importance of a personalized treatment approach adapted to each patient’s specific needs. In fact, treatment strategies that are effective for patients with rapid disease may not be suitable for patients with slower disease.
Identification of 3 subtypes of Parkinson’s disease
It is against this background that researchers at Cornell University in New York conducted a study using artificial intelligence to identify different subtypes of Parkinson’s disease. By analyzing data from existing studies, the team was able to classify patients into three distinct categories based on how quickly their disease progressed.
Rapidly progressive subtype (PD-R)
The first subtype identified is characterized by rapid progression of symptoms. These patients, who comprised 13.3% of the study cohort, experienced accelerated deterioration in motor and cognitive function.
slowly progressive subtype (PD-I)
In contrast, the second subtype is characterized by relatively mild baseline symptoms and slow disease progression. This “slow progression” group of patients, 35.7%, could benefit from lighter care focused on maintaining quality of life.
Moderately progressive subtype (PD-M)
Finally, the third subtype is the most numerous (50.9% of the cohort) and is characterized by mild underlying symptoms and moderate disease progression. This group may require a treatment approach that combines symptom management and slowing disease progression.
The meaning of this classification
Classification of three subtypes of Parkinson’s disease opens new perspectives in patient care. In fact, it makes it possible to consider treatment strategies that are better adapted to the specific needs of each group.
For example, Patients with rapidly developing disease (PD-R) may benefit from more aggressive therapeutic intervention and closer monitoring, to slow down the progression of the condition as quickly as possible. Conversely, those with the chronic form (PD-I) may be content with less intensive care and focus on maintaining their quality of life.
More relevant clinical trials
This subtype classification also makes it possible to conduct more targeted clinical trials of new treatments. In fact, rather than assessing a drug’s effectiveness for all Parkinson’s patients, it would be better to focus on the subgroups most likely to benefit from it.
Better allocation of resources
Finally, knowledge of the different subtypes of Parkinson’s disease should help allocate health resources more efficiently. Therefore, high-risk patients (PD-R) may benefit from more intensive intensive care, while slower-moving patients (PD-I) require less follow-up.
Challenges to be addressed
Although promising, this classification of Parkinson’s disease subtypes also presents some challenges that need to be overcome before large-scale implementation.
First, the researchers emphasize that the results obtained in this study, although very interesting, are still preliminary and need to be verified in a larger population. In fact, the original sample included only 406 participants, which can be considered a relatively small sample for establishing clear categories.
Additionally, the use of artificial intelligence tools to identify disease subtypes raises accessibility issues. Not all patients necessarily have access to these cutting-edge technologies, especially in less privileged areas. It is therefore necessary to ensure that these advances benefit all people, not just the elite.
Ethical issues related to data
Finally, using large amounts of patient data to train these AI models raises ethical questions about privacy and information security. Strong safeguards must be in place to ensure data privacy is respected.
The use of artificial intelligence to identify three different subtypes of Parkinson’s disease represents a major advance in the understanding and management of this complex neurodegenerative disease. This classification paves the way for precision medicine, making treatments more tailored to each patient’s specific needs. Although challenges remain, particularly in large-scale validation and equitable access to diagnostic tools, these results constitute a critical step towards significantly improving the quality of life of people with Parkinson’s disease.
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