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"... For decades, multiple sclerosis (MS) has been defined primarily by its symptoms, rather than its underlying biology. Now, a new study aims to challenge that approach, presenting evidence that MS may actually follow two distinct biological pathways. ...
To do that, the team paired two complementary sources of information. One came from blood measurements of serum neurofilament light chain (sNfL), a protein released when nerve cells are damaged and widely used as a marker of disease activity. The other came from MRI scans that captured how structural degeneration spread through the brain over time.
Rather than examining each dataset in isolation, the researchers analyzed them together using a machine-learning system developed at UCL called SuStaIn (Subtype and Stage Inference). The model is designed to detect subtle disease patterns and map how they evolve, allowing the team to test whether MS follows a single biological trajectory or something more complex. ...
Instead of detecting a smooth disease spectrum, two distinct structural patterns emerged. The team found that patients clustered into separate groups that reflected different underlying pathways of neurodegeneration.
One subtype was marked by early damage concentrated in the brain’s cortex, while the other was dominated by degeneration in white matter regions. Although both patterns ultimately produced the symptoms associated with multiple sclerosis, the location of tissue damage and the path it followed through the brain differed substantially between the two groups. ..."
From the abstract:
"Multiple sclerosis (MS) is a highly heterogeneous disease in its clinical manifestation and progression. Predicting individual disease courses is key for aligning treatments with underlying pathobiology.
We developed an unsupervised machine learning model integrating MRI-derived measures with serum neurofilament light chain (sNfL) levels to identify biologically informed MS subtypes and stages. ...
In comparison to MRI-only models, incorporating sNfL with MRI improved correlations of data-derived stages with the Expanded Disability Status Scale in the training (Spearman’s ρ = 0.420 versus MRI-only ρ = 0.231, P = 0.001) and external test sets (ρ = 0.163 for MRI–sNfL, versus ρ = 0.067 for MRI-only).
The early-sNfL subtype showed elevated sNfL, corpus callosum injury and early lesion accrual, reflecting more active inflammation and neurodegeneration, whereas the late-sNfL group showed early volume loss in the cortical and deep grey matter volumes, with later sNfL elevation.
Cross-sectional subtyping predicted longitudinal radiological activity: the early-sNfL group showed a 144% increased risk of new lesion formation (hazard ratio = 2.44, 95% confidence interval 1.38–4.30, P < 0.005) compared with the late-sNfL group. Baseline subtyping, over time, predicted treatment effect on new lesion formation on the external test set (faster lesion accrual in early-sNfL compared with late-sNfL, P = 0.01), in addition to treatment effects on brain atrophy (early sNfL average percentage brain volume change: −0.41, late-sNfL = −0.31, P = 0.04).
Integration of sNfL provides an improved framework in comparison to MRI-only subtyping of MS to stage disease progression and inform prognosis. Our model predicted treatment responsiveness in early, more active disease states. This approach offers a powerful alternative to conventional clinical phenotypes and supports future efforts to refine prognostication and guide personalized therapy in MS."
Combined magnetic resonance imaging and serum analysis reveals distinct multiple sclerosis types (open access)
Fig. 1 Overview of the study
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