A Panel of Just 11 Proteins Can Predict Long-Term Outcomes in Multiple Sclerosis
Researchers at Linköping University use machine learning to predict disease progression in multiple sclerosis.
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A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The identified proteins could be used to tailor treatments to the individual based on the expected severity of the disease.
In multiple sclerosis, the immune system attacks the person’s own body, damaging nerves in the brain and in the spinal cord. What is attacked primarily is a fatty compound called myelin, which surrounds and insulates the nerve axons so that signals can be transmitted. When myelin is damaged, transmission becomes less efficient.
“I think we’ve come one step closer to an analysis tool for selecting which patients would need more effective treatment in an early stage of the disease. But such a treatment may have side effects and be relatively expensive, and some patients don’t need it,” says Mika Gustafsson, professor of bioinformatics at the Department of Physics, Chemistry and Biology at Linköping University, who led the study, which has been published in the journal Nature Communications.
From 1500 to 11 proteins
“Having a panel consisting of only 11 proteins makes it easy should anyone want to develop analysis for this. It won’t be as costly as measuring 1,500 proteins, so we’ve really narrowed it down to make it useful for others wanting to take this further,” says Sara Hojjati, doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University.
The research team also found that a specific protein, leaking from damaged nerve axons, is a reliable biomarker for disease activity in the short term. This protein is called neurofilament light chain, NfL. These findings confirm earlier research on the use of NfL to identify nerve damage and also suggest that the protein indicates how active the disease is.
Study with several strengths
One of the main strengths of the study is that the combination of proteins found in the patient group from which samples were taken at Linköping University Hospital was later confirmed in a separate group consisting of 51 MS patients sampled at the Karolinska University Hospital in Stockholm.
This study is the first to measure such a large amount of proteins with a highly sensitive method, proximity extension assay, combined with next-generation sequencing, PEA-NGS. This technology allows for high-accuracy measuring also of very small amounts, which is important as these proteins are often present in very low levels.
Reference: Åkesson J, Hojjati S, Hellberg S, et al. Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Nat Commun. 2023;14(1):6903. doi: 10.1038/s41467-023-42682-9
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