Connect with us
  • Elysium


Machine learning helps to predict MS outcomes



Image: Thor Balkhed/Linköping University

A combination of 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals, new research has found.

The identified proteins could be used to tailor treatments to patients based on the expected severity of the disease.

Julia Åkesson is doctoral student at Linköping University and the University of Skövde.

The researcher said: “A combination of 11 proteins predicted both short and long-term disease activity and disability outcomes.

“We also 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.”

In MS, the immune system attacks the person’s own body, damaging nerves in the brain and in the spinal cord.

What is attacked primarily is the fatty compound myelin, which surrounds and insulates the nerve axons so that signals can be transmitted.

When the compound is damaged, transmission becomes less efficient.

Disease progression in MS varies considerably from person to person.

To those for whom a more severe disease is predicted, it is vital to get the right treatment quickly.

The researchers behind the new study wanted to find out whether it was possible to detect at an early stage of disease which patients would require a more powerful treatment.

Being able to do so would help both to physicians and those living with MS.

Mika Gustafsson, professor of bioinformatics at the Department of Physics, Chemistry and Biology at Linköping University led the study.

The researcher said: “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.

“But such a treatment may have side effects and be relatively expensive, and some patients don’t need it.”

In the study, the researchers analysed nearly 1,500 proteins in samples from 92 people with suspected or recently diagnosed MS.

Data from the protein analyses were combined with a large trove of information from the patients’ journals, such as disability, results from MRI scans of the nervous system and treatments received.

Using machine learning, the researchers found a number of proteins that could predict MS disease progression.

Sara Hojjati is a doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University.

She said: “Having a panel consisting of only 11 proteins makes it easy should anyone want to develop analysis for this.

“It won’t be as costly as measuring 1,500 proteins, so we’ve really narrowed it down to make it useful for others wanting to take this further.”

The researchers team also found that a specific protein, leaking from damaged nerve axons, is a reliable biomarker for disease activity in the short term.

This protein is called neurofilament light chain, or NfL.

The findings confirm earlier research on the use of NfL to identify nerve damage and also suggest that the protein indicates how active the disease is.

One of the main strengths of the study is that the combination of proteins found in the patient group from which samples were taken at Linköping University Hospital was later confirmed in a separate group comprising 51 MS patients sampled at the Karolinska University Hospital in Stockholm.

This study is the first of its kind to measure such a large amount of proteins with a highly sensitive method, proximity extension assay, combined with next-generation sequencing, PEA-NGS.

The technology allows for high-accuracy measuring also of very small amounts, which is important as these proteins are often present in very low levels.