Researchers have made a significant advancement in the understanding of multiple sclerosis (MS) that could lead to more targeted treatments. Utilizing artificial intelligence (AI), scientists analyzed brain scans and tested a blood marker associated with nerve cell injury, discovering two new biological sub-types of MS. This research, conducted by a team from the University College London (UCL) and published in the journal Brain, marks a potential turning point in how the condition is diagnosed and treated.
Multiple sclerosis affects the brain and spinal cord, resulting in a range of symptoms including fatigue, vision issues, numbness, and muscle cramps. Currently, there is no cure for MS, but existing treatments focus on managing symptoms. The new findings may facilitate the development of more personalized treatment options, according to the study’s lead author, Dr. Arman Eshaghi from the UCL Queen Square Institute of Neurology.
Identifying New Biological Sub-Types
In their study, researchers examined the levels of the serum neurofilament light chain (sNfL) in 634 patients diagnosed with MS. They identified two distinct biological trajectories:
1. **Early-sNfL**: Patients in this group displayed high levels of the blood biomarker early in the disease progression, along with damage to the corpus callosum—a brain area crucial for cognitive functions and motor coordination.
2. **Late-sNfL**: This sub-type exhibited a delayed increase in sNfL levels, associated with early volume loss in both cortical and deep grey matter regions.
Dr. Eshaghi emphasized that these findings help explain the varied experiences of individuals living with MS. Traditional classifications like relapsing-remitting, secondary progressive, and primary progressive do not adequately reflect the biological realities of the disease.
Implications for Future Treatment
Caitlin Astbury, senior research communications manager at the MS Society, highlighted the importance of this study in advancing the understanding of MS. She stated, “This study used machine learning to analyze 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.”
Astbury noted that while the understanding of the biology of MS has improved, current definitions largely rely on clinical symptoms, which do not always accurately represent the underlying processes in the body. This discrepancy can complicate effective treatment strategies.
As research continues to evolve, there is hope that a deeper understanding of MS will pave the way for treatments capable of halting the progression of this complex condition. The study underscores a promising step towards refining the diagnosis and management of multiple sclerosis, offering new hope for those affected by the disease.
