A machine-learning study conducted at Weill Cornell Medicine classified Parkinson’s disease into three subgroups, allowing for more targeted treatments specific to each patient’s disease progression. The researchers analyzed data from the Parkinson’s Progression Markers Initiative and identified three subtypes – Rapid Pace, Inching Pace, and Moderate Pace – based on the pace of the disease’s progression. The study, published in npj Digital Medicine, aims to help researchers and clinicians tailor treatments to individual subtypes of Parkinson’s.
Experts view the findings as promising, but emphasize the need for larger populations to be studied in order to create more accurate models. The study’s deep-learning model, known as deep phenotypic progression embedding (DPPE), was able to holistically model participants’ multidimensional, longitudinal progression data. This approach recognizes that Parkinson’s disease is not uniform and that different patients may experience varying symptoms and progression rates.
The goal of precision medicine is to predict disease course in patients and intervene early to prevent complications. Identifying specific Parkinson’s subtypes can help clinicians develop targeted therapeutics. For example, patients with mutations in the GBA gene may benefit from targeted therapies. Recognizing different subtypes allows for tailored treatment plans and better allocation of healthcare resources.
Subgrouping Parkinson’s patients allows for more systematic treatment approaches. Patients with Rapid Pace subtype may benefit from more aggressive therapies, while those with Inching Pace subtype may require less intensive management. The study suggests that existing drugs like metformin may be particularly beneficial for certain subtypes. Subtyping patients can guide medication selection and facilitate early intervention for more effective symptom management.
Identifying different Parkinson’s subgroups enables medical professionals to design specific treatment plans for each subtype. Patients with Inching Pace subtype may benefit from lifestyle modifications and physical therapy, while those with Moderate Pace may require a combination of medications to manage symptoms and slow progression. Patients in the Rapid Pace subtype, who exhibit quick progression and cognitive deficits, may benefit from early intervention with metformin and other neuroprotective agents to manage their symptoms effectively.
While machine learning technology has the potential to predict disease subtypes and progression, accessibility and data privacy remain concerns. Not all patients may have access to advanced diagnostic tools or treatments derived from AI research. Additionally, ensuring that AI models are not biased towards specific populations and validating them across diverse cohorts is essential for their widespread use. More research and trials on larger, high-quality datasets are needed to further harness the power of artificial intelligence for precise medical treatments in Parkinson’s disease.
