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Study Identifies Biologically Distinct Subgroups of Parkinson’s Disease in Fruit Fly Models
Belgian researchers used machine learning on fruit fly models with 24 Parkinson’s-linked gene mutations to identify two main groups and five subgroups. The findings indicate that treatments effective in one subgroup often fail in others, pointing toward tailored therapies. Patrik Verstreken and Natalie Kaempf led the work at VIB and KU Leuven.
May 7, 1:00 AM·2m read1 source
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Researchers at Vlaams Instituut voor Biotechnologie (VIB) and KU Leuven in Belgium conducted a study suggesting Parkinson’s disease may consist of several biologically distinct conditions. The study used machine learning to identify two main groups and five subgroups of Parkinson’s disease in fruit fly models.
Parkinson’s disease involves symptoms such as movement difficulties and progressive neurological decline.
It can be caused by mutations in many different genes. According to the World Health Organization, death and disability due to Parkinson’s disease are rapidly increasing. 5 million individuals have Parkinson’s disease.
Patrik Verstreken, head of the research group of molecular neurobiology at VIB-KU Leuven, said the clinical symptoms unify patients even as molecular differences divide them. “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.
To examine those differences, researchers used fruit flies carrying mutations in 24 genes linked to Parkinson’s disease. Natalie Kaempf, first author of the study and a researcher at VIB-KU Leuven Center for Brain & Disease Research, said the team approached the work without assumptions. “We came in without any preconceived notions of how a specific mutation would affect our animal model.
The results suggest that different genetic forms of Parkinson’s naturally fall into separate groups. Euronews reported that this classification could allow scientists to identify biomarkers specific to each subgroup and design more precise interventions.
