A groundbreaking study conducted by researchers from Linköping University, the Karolinska Institute, and the University of Skövde has identified a set of just 11 proteins that can accurately predict long-term disability outcomes in multiple sclerosis (MS) patients. This discovery could revolutionize the way treatments are tailored for individuals based on the expected severity of the disease.
The scientists analyzed samples from 92 individuals with suspected or recently diagnosed MS for up to 13 years. By combining protein data with information from medical records and imaging scans, the researchers employed machine learning algorithms to identify proteins that could predict disease progression.
Lead author Julia Åkesson explains, “We concluded that it’s important to measure these proteins in cerebrospinal fluid, which better reflects what’s going on in the central nervous system, compared with measuring in the blood.”
Multiple sclerosis is an autoimmune condition where the immune system mistakenly attacks the protective myelin sheath surrounding nerve cells. This results in compromised transmission of nerve signals. The progression and severity of the disease can vary greatly among individuals, underscoring the need for personalized treatment strategies.
The team’s objective was to detect at an early stage of the disease those individuals who would likely require more aggressive treatment options. This would enable healthcare providers to intervene more effectively and prevent long-term disability.
Lead researcher Mika Gustafsson notes, “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.”
Furthermore, the study confirmed the reliability of a specific protein called neurofilament light chain (NfL) as a biomarker for disease activity in the short term. This protein, which leaks from damaged nerve axons, can indicate how active the disease is for up to two years.
The strength of this study lies in the extensive protein analysis performed using a highly sensitive method known as proximity extension assay combined with next-generation sequencing (PEA-NGS). This technology allows for the accurate measurement of proteins even in very small quantities.
The findings from this study not only provide valuable insights into the prediction of individual disease outcomes in multiple sclerosis but also hold promise for the development of personalized treatment approaches. By narrowing down the analysis to just 11 proteins, researchers hope to make the process more accessible and cost-effective for further research and clinical application.
The source of the article is from the blog kunsthuisoaleer.nl