A new mathematical model that reproduces the relapsing-remitting pattern of multiple sclerosis (MS) offers scientists a tool to better understand how the disease progresses.
MS has highly variable and unpredictable course making it difficult to understand
The model shows how biological processes cause cycles of inflammation and damage to nerve myelin
The new model could help build tools to better understand underlying factors of relapse.
The model, developed by QUT researchers from the School of Mathematical Science, is published in the Journal of the Royal Society Interface.
First author Dr Adrianne Jenner, from QUT’s School of Mathematical Sciences, said MS was one of the most challenging neurological diseases to understand because of its highly variable and unpredictable nature.
“Our model shows how relatively simple biological processes can cause the cycles of inflammation and myelin damage, typical of relapsing-remitting MS.
“MS affects the brain and spinal cord and is driven by immune-mediated attacks on myelin – the outer layer insulating nerve cells – which disrupts communication between nerve cells leading to a range of debilitating physical and cognitive symptoms.
“People with relapsing-remitting MS experience periods where their symptoms worsen and then have periods of recovery and remission.
“It is crucial to understand the factors behind the frequency of these relapses as these, in almost half the episodes, cause lasting disability.”
Dr Jenner said the team’s two-variable mathematical model tracked changes in healthy myelin and inflammation over time.
“After mathematical analysis, we found the model naturally shifted through three stages: from a healthy state to a stable disease state to an oscillating state resembling relapse and remission cycle of many MS patients,” she said.
“We found that increasing disease activity or reduction in the body’s resilience to inflammation drove a stable state into recurring cycles of relapse and remission.
“The model also suggests that changes in how long inflammation persists may influence the time between relapses.
“We tested the model by comparing its predictions with existing data from people living with MS and found it accurately reproduced patterns seen in contrast-enhancing lesions, a common MRI marker of inflammatory disease.”
Dr Jenner said the model could help build tools to better understand disease activity and the biological mechanisms underlying relapses.
“It could also help guide future studies investigating biomarkers of MS activity and the biological mechanisms underlying relapses,” she said.
The research team comprised Dr Jenner and Georgia Weatherly (PhD Candidate) from QUT, and Professor Frederico Frascoli from Swinburne University.
The study, A mathematical model for inflammation and demyelination in multiple sclerosis, was published in the Journal of the Royal Society Interface.
