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TRUVACE RECORD VERSION
record: TRV-2026-1187
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-26T06:53:32.525675Z
status: published
lens: p_space
sector: health
headline: Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis
dek: Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately. We therefore aimed to use Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, to identify biologically meaningful MRI subtypes of multiple sclerosis and determine their associations with disabi…
gain_title: (none)
problem_title: Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis: Cognitive performance and genetic risk scores for multiple sclerosis severity did not differ across subtypes, but within each subtype advancing SuStaIn stage was associated with worse global and domain-specific cognitive performance (all p≤0.030).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis: Cognitive performance and genetic risk scores for multiple sclerosis severity did not differ across subtypes, but within each subtype advancing SuStaIn stage was associated with worse global and domain-specific cognitive performance (all p≤0.030).
problem_evidence: (none)
quick_read: Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately.

We therefore aimed to use Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, to identify biologically meaningful MRI subtypes of multiple sclerosis and determine their associations with disability, age at onset, cognition, genetic susceptibility to more severe disease, and relapse-independent progression. Cognitive testing was available in 501 patients, genetic profiling in 650, and longitudinal clinical follow-up in 645 (median follow-up=6.57 years).
limitation: 
tag: Evidence-backed problem
key_points: Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. | MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately. | We therefore aimed to use Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, to identify biologically meaningful MRI subtypes of multiple sclerosis and determine their associations with disability, age at onset, cognition, genetic susceptibility to more severe disease, and relapse-independent progression.
rundown: Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately.

We therefore aimed to use Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, to identify biologically meaningful MRI subtypes of multiple sclerosis and determine their associations with disability, age at onset, cognition, genetic susceptibility to more severe disease, and relapse-independent progression. We applied SuStaIn to multimodal 3T brain MRI data from 1017 multiple sclerosis patients and 548 healthy controls.
sources:
- peer_reviewed | Brain | https://doi.org/10.1093/brain/awag320 | 2026-09-23
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