WISDOM at ECTRIMS 2026: understanding MS earlier through data, AI and genetics
At ECTRIMS 2026, Prof. Ali Manouchehrinia, representing the WISDOM project, presented research across three sessions exploring how AI, routinely collected healthcare data and genetics could help us better understand MS, from the period before clinical onset to the role of comorbidities.
Using AI to identify MS risk earlier
During the session Artificial Intelligence Applications from Diagnosis to Prognosis, Prof. Manouchehrinia presented WISDOM research investigating whether routinely collected GP data could help identify people at increased risk of MS.
Rather than focusing on individual symptoms, the AI model looks at patterns of diagnoses, prescriptions and other healthcare information over time.
In test data of around half a million people, the model identified approximately one in ten people who later developed MS. Results were even stronger among people with a family history of MS, where around one in five future cases could be identified at the defined referral capacity.
The findings highlight the potential of routine healthcare data to contain meaningful signals before recognised MS onset, although the approach still requires prospective testing before considering clinical use.
Connecting the dots in the MS prodrome
In the RIS, Time to Unify session, the focus shifted to temporal clinical graphs, an approach that preserves the relationships and timing between healthcare visits, diagnoses and prescriptions.
Rather than looking at symptoms in isolation, the analysis identified combinations and sequences of clinical events associated with future MS. These included patterns involving neurological symptoms and symptomatic treatments related to areas such as spasticity and bladder problems.
The findings reinforce an important idea for WISDOM: understanding the MS prodrome may require us to connect the dots between multiple signals over time.
Genetics, MS and comorbidities
Finally, during the Comorbidities and Management session, Prof. Manouchehrinia explored how genetics can help explain why MS and other health conditions occur together.
Genetic approaches can help identify shared biological mechanisms, investigate potential causal relationships and potentially support future risk stratification. Examples included evidence supporting a causal relationship between higher BMI and MS risk, as well as research into genetic liability to depression and MS outcomes.
Towards a more complete picture of MS
Together, the three presentations demonstrate WISDOM's ambition to look beyond individual symptoms or risk factors.
By bringing together AI, longitudinal healthcare data and genetics, WISDOM is exploring how we can better understand the earliest stages of MS, recognise meaningful patterns and ultimately contribute to earlier, more personalised approaches to MS care.
