Machine learning reveals two main Parkinson’s types and five subgroups

A new study led by researchers from VIB and KU Leuven shows that Parkinson's disease can be divided into distinct subtypes, helping explain why a single treatment does not work for all patients. Using a machine-learning-driven analysis, the team identified two main groups and five subgroups of the disease, marking an important step toward more personalized therapies. The findings are published in Nature Communications.

“We discovered two broad subgroups that can be divided into five smaller groups of parkinsonism,” says Prof. Patrik Verstreken (VIB-KU Leuven Center for Neuroscience).

Parkinson’s disease affects millions of people worldwide and is traditionally defined by its clinical symptoms, including movement difficulties and progressive neurological decline. However, despite being grouped as a single disorder, Parkinson’s can be caused by mutations in many different genes, leading to diverse underlying biological mechanisms. This complexity has challenged the development of effective treatments, as therapies targeting one pathway may not work for all patients.

The new study reveals that these genetically different forms of Parkinson’s can be organized into distinct molecular subtypes, highlighting the need to rethink the disease as a collection of related conditions and opening the door to more targeted therapeutic approaches.

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