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A recent study discovered a combination of 11 proteins that can predict long-term disability in patients with multiple sclerosis (MS). The study, conducted by researchers from Linköping University, the Karolinska Institute, and the University of Skövde, was recently published in the journal Nature Communications.1
As one of the most widespread neurological conditions in young adults,2 MS causes the immune system to attack the patient’s own body, targeting a fatty compound called myelin. This damages the compound leading to interference in nerve signal transmission.1
According to recent findings from the National MS Society, there are an estimated 1 million people in the United States living with MS, which is double the last reported number in 1975.2
“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,” said study lead Mika Gustafsson, professor of bioinformatics at the Department of Physics, Chemistry, and Biology at Linköping University, in the press release.
As noted in the press release, researchers analyzed nearly 1,500 proteins in samples from 92 people with suspected MS. The data were combined with information from the patients’ journals, such results of MRI scans and treatment history. Then, using machine learning, the researchers identified proteins that could predict disease progression.
Of the 11, a specific protein that was found leaking from damaged nerves can serve as a reliable biomarker for disease activity in the short term.
This study is also the first to measure a large amount of proteins utilizing proximity extension assay, combined with next-generation sequencing.
“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,” said Sara Hojjati, doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University, in the release.
References
- Science Daily. (2024, January 9). Severe MS predicted using machine learning. Retrieved from https://www.sciencedaily.com/releases/2024/01/240109121200.htm
- Healthline. Multiple Sclerosis: Facts, Statistics, and You. Retrieved from https://www.healthline.com/health/multiple-sclerosis/facts-statistics-infographic