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NEW YORK – Parkinson’s disease is a complex puzzle for researchers and patients alike. However, thanks to cutting-edge machine learning techniques, scientists at Weill Cornell Medicine have made a breakthrough that could change the way we understand and treat this complex condition. This form of artificial intelligence has helped identify three new types of Parkinson’s, which could lead to new treatments depending on a patient’s symptoms.
In a study published in the journal npj Digital MedicineResearchers have identified three distinct types of Parkinson’s disease based specifically on the progression of symptoms.
"Parkinson’s disease is very heterogeneous, which means that people with the same disease can have very different symptoms," said senior author Dr. Fei Wang, professor of population health science and founding director of the Institute of AI for Digital Health (AIDH). ) in the Department of Population Health Sciences at Weill Cornell Medicine, in a media release. "This indicates that there is no one-size-fits-all approach to its treatment. We may need to consider specific treatment strategies based on the type of disease a patient has."
So what are these new types of seedlings? Let’s read them:
- Speed (PD-I): This type, which affects about 36% of patients, is characterized by mild symptoms that gradually progress over time. Think of it as a turtle in the world of Parkinson’s – slow and steady.
- Average speed (PD-M): Making up about 51% of cases, this type begins with mild symptoms but progresses at a moderate rate. It’s like a steady jog instead of a sprint.
- The rapid response (PD-R): This is the subtype that progresses the fastest, with symptoms worsening more quickly than the other two groups.
The researchers came up with these subtypes in a very specific way. They used deep learning, a type of artificial intelligence that can analyze large amounts of data to find patterns that might not be visible to humans. By looking at anonymized clinical records from two large databases, the team was able to identify these distinct developmental patterns.
The most exciting thing about this new discovery is that it has its own genetic and molecular fingerprint. For example, the Rapid Pace subtype showed increased activity in pathways related to neuroinflammation (brain inflammation), oxidative stress (cell damage caused by unstable molecules), and metabolism.
This is not just academic knowledge – it can have real-world implications in medicine. By understanding the specific biological processes at work in each category, researchers can begin to think about targeting these pathways with new or existing drugs.
In fact, the team has already made promising innovations in this regard. They used their findings to identify potential drug candidates for the treatment of specific types of Parkinson’s. A notable example is metformin, a common diabetes drug.
"By examining these data, we found that people taking metformin for diabetes seemed to improve their symptoms – especially symptoms related to cognitive impairment and falls – while compared to those who did not take metformin," said first author Dr. Chang Su, assistant. professor of population health sciences and AIDH member at Weill Cornell Medicine.
This effect is particularly observed in patients with the Rapid Pace subtype, who may experience cognitive problems at the onset of their disease.
This research clearly shows that big data and artificial intelligence are revolutionizing medical research. By analyzing large amounts of patient data, researchers can see patterns and relationships that might otherwise be missed.
Of course, like all scientific achievements, more research is needed to fully verify these findings. However, the potential consequences are great. Imagine a future where, upon diagnosis, a person with Parkinson’s is immediately classified into one of these categories. Their treatment plans can be adapted to specific subtypes, potentially reducing disease progression and improving quality of life.
Paper summary
method of operation of
A study has shed new light on Parkinson’s disease (PD), a complex neurodegenerative disease. Researchers have developed a sophisticated method to differentiate the various PD progressions by using a multi-database framework.
This approach integrates machine learning and deep learning with advanced network therapy and statistical methods, analyzing large amounts of data, including clinical records, biospecimens, neuroimaging, and information. about genetics. An important innovation is in the development of a deep learning model, called "deep phenotypic progression embedding" (DPPE), which captures the profile of the patient’s progression over time, allowing the identification of distinct PD types.
Basic products
The study identified three unique types of PD, characterized by their pace: the Inching Pace subtype (PD-I), with mild symptom progression; the Moderate Pace subtype (PD-M), with moderate progress; and the Rapid Pace subtype (PD-R), with the most aggressive progression.
Each subtype presents unique clinical and molecular features, potentially leading to more tailored treatment. The researchers also analyzed cerebrospinal fluid biomarkers and neuroimaging findings that are associated with these subtypes, improving the ability to better diagnose and monitor the disease.
Limitations of the study
Despite its progress, the study acknowledges several limitations. The identification of the subtype is mainly based on patients in the early stages of PD, which may not fully represent the disease population. Furthermore, the complexity of the methodology and the need for extensive data may limit the immediate application of these findings in poorly resourced settings.
Conversations & Takeaways
This study marks an important step towards personalized medicine in the treatment of Parkinson’s disease by showing that PD is not a homogeneous entity but rather a disease with a variable rate of progression. The identification of PD subtypes paves the way for more precise and effective treatments tailored to the specific development pattern and molecular profile of each patient.
In addition, studies highlight potential drugs, such as metformin, that can be repurposed to specifically target PD, offering hope for interventions that may slow the progression of the disease. This research not only improves our understanding of Parkinson’s disease but also demonstrates the power of combining multiple sources of information and advanced analysis to unravel the complexities of neurodegenerative disease.
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