Two new biological subtypes of multiple sclerosis (MS) have been identified in research that could change how the disease is understood and treated. Scientists used artificial intelligence to combine blood tests measuring serum neurofilament light chain (sNfL), a marker of nerve damage, with MRI brain scans from around 600 people with MS to detect distinct patterns in disease progression. The work was led by University College London and Queen Square Analytics.
The two subtypes discovered are termed early sNfL and late sNfL. In the early sNfL subtype, people have high levels of sNfL at the start of the disease along with rapid lesion growth in the brain, indicating a more aggressive form. In the late sNfL subtype, brain volume loss appears before a rise in sNfL, suggesting slower disease progression.
Researchers say these findings move beyond the traditional clinical categories of MS based on symptom patterns, offering a biology-informed classification that could help tailor monitoring and treatment more precisely. This approach may support earlier targeted interventions and improve outcomes for people living with MS.
The research underscores a shift toward personalised management of MS by identifying underlying disease mechanisms, which may lead to better treatment strategies in the future.
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