Two new subtypes of MS found in ‘exciting’ breakthrough | Multiple sclerosis

Scientists have discovered two new subtypes of multiple sclerosis using artificial intelligence, paving the way for personalized treatments and better patient outcomes.

Millions of people have the disease worldwide, but treatments are mostly selected based on symptoms and may not be effective because they do not target the patient’s underlying biology.

Today, scientists have detected two new biological aspects of MS using AI, a simple blood test and MRI. Experts said the “exciting” breakthrough could revolutionize the treatment of the disease worldwide.

In research involving 600 patients, led by University College London (UCL) and Queen Square Analytics, researchers looked at blood levels of a special protein called serum neurofilament light chain (sNfL). The protein may help indicate levels of nerve cell damage and signal disease activity.

The sNfL results and scans of the patients’ brains were interpreted by a machine learning model, called SuStaIn. The findings, published in the medical journal Brain, revealed two distinct types of MS: early-onset sNfL and late-onset sNfL.

In the first subtype, patients had high levels of sNfL early in the disease, with visible damage in a part of the brain called the corpus callosum. They also quickly developed brain damage. This type appears to be more aggressive and active, according to scientists.

In the second subtype, patients showed brain shrinkage in areas such as the limbic cortex and deep gray matter before sNfL levels increased. This type appears to be slower, with obvious damage occurring later.

Researchers say this advance will allow doctors to more precisely understand which patients are at higher risk of different complications, paving the way for more personalized care.

Lead author of the study, Dr Arman Eshaghi from UCL, said: “MS is not a single disease and current subtypes fail to describe the underlying tissue changes, which we need to know to treat it.

“Using an AI model combined with a highly available blood marker with MRI, we were able to show two clear biological patterns of MS for the first time. This will help clinicians understand where a person is in the disease journey and who may need closer monitoring or earlier targeted treatment.”

In the future, when the AI ​​tool suggests that a patient has early sNfL MS, they could become eligible for more effective treatments and be monitored more closely, Eshaghi said.

In contrast, people with late-onset sNfL may be offered different types of treatments, such as personalized therapies to protect brain cells or neurons. “The new developments will therefore be twofold: transforming clinical and neurological examinations, which have not changed for centuries, using AI algorithms, and offering personalized treatments according to the profile of the disease.

Caitlin Astbury, senior manager of research communications at the MS Society, a charity, said: “This is an exciting development in our understanding of MS.

“This study used machine learning to examine MRI and biomarker data from people with relapsing and secondary progressive MS. By combining this data, they were able to identify two new biological subtypes of MS.

“In recent years, we have developed a better understanding of the biology of the disease. But currently, definitions are based on the clinical symptoms a person experiences. MS is complex and these categories often do not accurately reflect what is happening in the body, which can make it difficult to treat effectively.”

There are about 20 treatment options for people with relapsing MS and some are starting to emerge for progressive MS, but for many there are no options, Astbury said. “The more we learn about the disease, the more we will be able to find treatments that can stop the progression of the disease.

“This research adds to growing evidence for moving away from existing descriptors of MS (like ‘relapsing’ and ‘progressive’) and towards terms that reflect the underlying biology of the disease. This could help identify people at increased risk of progression – and allow people to be offered more personalized treatment.”