Researchers from the Department of Neuroscience at the Central Clinical School have found that no common genetic variation (those found in 5% or more of the population) is strongly linked to disease severity in people with multiple sclerosis relapsing-remitting (RR-MS).
Published in one of the top neuroscience journals, Brain, the multicenter study instead found that multiple genetic loci with small effect sizes were associated with clinical outcomes in people with RRMS. Additionally, a machine learning algorithm using this information, along with clinical and demographic variables available at disease onset, accurately predicted disease severity.
Multiple sclerosis is an autoimmune degenerative neurological disease that interferes with the ability of the central nervous system to send messages along nerves from the brain to the body.
MS is most common in young adults with 2.8 million people diagnosed worldwide. It causes a neurological disability that affects activities of daily living to varying degrees for people with the disease. Relapsing-remitting multiple sclerosis (RRMS) is the most common form of the disease, affecting around 85% of people with MS. Clinicians are able to manage the disease with disease-modifying treatments that target the immune system.
Patients with RRMS accumulate disability at different rates, some very slowly over several decades, others rapidly, within 10 years of diagnosis or less. It is unclear why there is such great variability in rates of disability progression or MS outcomes. Previous studies have shown that common genetic variants are linked to the risk of developing MS. The team therefore wanted to determine if the same was true for disability accumulation.
To better understand the large heterogeneity that occurs with disease outcomes from person to person, the research team used prospectively collected longitudinal data from the largest international registry of clinical outcomes of the disease. SEP, MSBase.
Clinical, demographic and genetic data were collected from 1,813 patients who met study criteria and were further categorized by clinical and demographic characteristics, including age at disease onset. A genome-wide association study was performed to search for new single nucleotide variants associated with the characteristics identified in the study cohort. Additionally, a machine learning algorithm was applied to the data to predict the severity of MS.
First author of the study, Dr Vilija Jokubaitishead of the Neuroimmunology Genomics and Prognostics research group, said: “We have shown that the genetics underlying the severity of MS involve the central nervous system (CNS), in particular synaptic plasticity and myelination pathways – from functionally distinct mechanisms of MS risk.
“Since no genetic variant is strongly associated with disease outcomes, the results suggest that MS outcomes are, to some extent, modifiable with appropriate and early treatment with disease-modifying therapies.
“We were also able to demonstrate, using machine learning, the potential of common genetic variants to serve as prognostic biomarkers when combined with demographic data. Once independently validated, the machine learning algorithm could allow clinicians to provide patients with more accurate prognostic information, which would also help select the most appropriate disease-modifying therapy based on likely long-term outcomes. term of a person’s MS.
Additionally, the machine learning algorithm can also help stratify patients for participation in future randomized controlled drug trials and other clinical studies in which genetic predisposition to severe or mild disease results. can be important.
The study marks an important step in advancing research to better understand the wide variation in disability and long-term outcomes among people living with RRMS.
“The work was a long journey, in fact it took 10 years of preparation from writing the first ethics request to recruiting the participants and carrying out the analyses. This would not have been possible without our amazing team and collaborators, the nurses involved in the research and the people with MS who generously donated their samples,” said Dr. Jokubaitis.
The research team is now excited to validate the machine learning algorithm locally in the multiple sclerosis neuroimmunology clinics at Alfred Hospital, a close health partner of the University’s Central Clinical School. Monash, in hopes of seeing it implemented more widely in the clinic. practice in the future.
This study was funded by MS Australia, MSBase Foundation, RMH Home Lottery and Charity Works for MS.
Vilija G Jokubaitis, Maria Pia Campaign, Omar Ibrahim, Jim StankovitchPavlina Kleinova, Fuencisla Matesanz, Daniel Hui, Sara Eichau, Mark Slee, Jeannette Lechner-Scott, Rodney Lea, Trevor J Kilpatrick, Tomas Kalincik, Philip L De Jager, Ashley Beecham, Jacob L McCauley, Bruce V Taylor, Steve Vucic, Louise Laverick, Karolina Vodehnalova, Maria-Isabel García-Sanchéz, Antonio Alcina, Anne van der WaltEva Kubala Havrdova, William Gauche, Nikolaos Patsopoulos, Dana Horakova, Helmut Butzkueven. Not all roads lead to the immune system: the genetic bases of multiple sclerosis severity, Brain, 2022;, ac449, https://doi.org/10.1093/brain/awac44
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