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The finding could lead to new treatments for multiple sclerosis patients
Scientists have discovered made a breakthrough discovery about multiple sclerosis (MS) which could pave the way for new treatments.
Using artificial intelligence (AI), researchers looked at brain scans and tested for a blood marker for nerve cell injury and found two new sub-types of the condition. MS affects the brain and spinal cord and causes symptoms such as fatigue, eye problems, numbness and tingling, balance problems and muscle cramps.
MS cannot currently be cured, but treatment can often help manage it – and new research may help to identify ‘more personalised’ treatment, scientists say.
Lead author of the study Dr Arman Eshaghi, from the UCL Queen Square Institute of Neurology and UCL Hawkes Institute at Department of Computer Science, said: “Using routine brain images and a blood marker of nerve-cell injury (neurofilament light), we identified two distinct biological trajectories in multiple sclerosis.
“This helps explain why people living with MS can follow different paths and it’s a step toward more personalised monitoring and treatment.
“Current labels of relapsing-remitting, secondary progressive and primary progressive fail to provide this stratification, and this work at University College London and Queen Square Analytics, amongst others, is helping to change our understanding and definition of MS types and their treatment in the near future.”
The study looked at levels of a blood marker for nerve cell injury known as serum neurofilament light chain (sNfL). Assessments of 634 patients with MS enabled experts from Queen Square Analytics and University College London to identify two ‘biologically informed MS sub-types’.
One sub-type identified, known as early-sNfL, saw patients have high levels of the blood biomarker early on in the disease coupled with damage to part of the brain called the corpus callosum which plays a role in how people think, remember and co-ordinate their movements.
The other sub-type, late-sNfL, showed a later rise in sNfL and is coupled with ‘early volume loss in the cortical and deep grey matter volumes’, the authors wrote.
Caitlin Astbury, senior research communications manager at the MS Society, told the Guardian: “This study used machine learning to look at MRI and biomarker data from people with relapsing-remitting and secondary progressive MS. By combining this data, they were able to identify two new biological subtypes of MS.
“Over recent years, we’ve developed a better understanding of the biology of the condition. But, currently, definitions are based on the clinical symptoms a person experiences. MS is complex and these categories often don’t accurately reflect what is going on in the body, which can make it difficult to treat effectively.”
She added: “The more we learn about the condition, the more likely we will be able to find treatments that can stop disease progression.”
The study was published in the journal Brain.
