Personalised Parkinson’s treatments are a step closer after researchers identified distinct molecular subtypes of the disease.
Using a machine learning-driven analysis, researchers identified two main groups and five subgroups, helping explain why a single treatment does not work for all patients.
The findings could open the door to more targeted treatments for specific patient subgroups, the team says.
The study was led by researchers from VIB and KU Leuven in Belgium, with the work headed by Prof Patrik Verstreken of the VIB-KU Leuven Center for Neuroscience.
Prof Verstreken said: “We discovered two broad subgroups that can be divided into five smaller groups of parkinsonism.”
Parkinson’s affects millions of people worldwide and is traditionally defined by clinical symptoms including movement difficulties and progressive neurological decline.
However, despite being grouped as a single disorder, it 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 research suggests these genetically different forms of Parkinson’s can be organised into distinct molecular subtypes, highlighting the need to rethink the disease as a collection of related conditions.
Verstreken said: “When clinicians or patients are looking at the disease, they see the clinical symptoms, which unifies people with Parkinson’s disease.
“But when you look under the hood at the molecular level, then you see that they fall into subcategories. And that’s important because one drug to target the different molecular dysfunctions in all Parkinson’s disease essentially doesn’t exist.”
Rather than starting with assumptions about how different genetic mutations could affect the disease, the researchers monitored the behaviour of fruit fly models carrying mutations in Parkinson’s-related genes over time.
They then used computational and machine learning-based methods to identify patterns.
By allowing the data to guide the analysis, the team uncovered natural groupings of the disease in these animals that would not have been evident using traditional hypothesis-driven methods.
Dr Natalie Kaempf, first author of the study, said: “We came in without any preconceived notions of how a specific mutation would affect our animal model. We took animals with mutations in any of those 24 different genes that are causing the disease, and we just monitored their behaviour over periods of time.”
Researchers managed to cure the Parkinson’s phenotype in animal models by testing compounds in different subgroups. They also observed that different subgroups respond differently to different compounds.
Verstreken said: “When we took a first compound that cured subgroup A and tested it in subgroup B, the latter wasn’t rescued. Our study shows that you can make subgroup-specific drugs that have positive effects and are really specific to that subgroup.”
He added that the same strategy could also be applied to other diseases caused by mutations in multiple genes.
Verstreken said: “The same principle can be applied to other types of diseases.
“Diseases that are caused by mutations in a variety of different genes or environmental factors could be classified according to this principle.”
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