Clinical trajectories of patients with multiple sclerosis from onset and their relationship with serum neurofilament light chain levels
Serum neurofilament light chain (sNfL) is a biomarker of neuroaxonal destruction that correlates with acute inflammation (AI) in multiple sclerosis (MS). However, in the treatment era, progression without AI is the main driver of long-term disability. sNfL may provide added value in detecting ongoin...
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Veröffentlicht in: | Frontiers in neurology 2024-10, Vol.15, p.1477335 |
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Zusammenfassung: | Serum neurofilament light chain (sNfL) is a biomarker of neuroaxonal destruction that correlates with acute inflammation (AI) in multiple sclerosis (MS). However, in the treatment era, progression without AI is the main driver of long-term disability. sNfL may provide added value in detecting ongoing axonal damage and neurological worsening in patients without AI. We conducted a prospective three-year study on patients with a first MS relapse to evaluate the basal cut-off value predicting early increased disability unrelated to relapses.
sNfL levels and AI presence were measured every 6 months during the first year and the Expanded Disability Status Scale (EDSS) was monitored until the third year. Baseline cohorts were stratified by sNfL levels, using a cut-off derived from patients without AI (absence of clinical relapses, new/enlarging T2 lesions, or gadolinium enhancement in magnetic resonance imaging) at year one.
Fifty-one patients were included. A sNfL cut-off of 11 pg/mL predicted sustained neurological worsening independent of AI. Patients exceeding this threshold exhibited features of highly active MS (higher proportion of AI, oligoclonal M bands and higher EDSS). Despite AI ablation, sNfL levels persisted elevated and were significantly associated with increased EDSS at baseline and year 3. Patients with low sNfL and concurrent AI (
= 8) experienced relapses in the optic nerve, brainstem, and spinal cord topographies.
sNfL elevation may detect patients with increased disability even when AI is controlled. This may reveal mechanisms associated with early axonal degeneration and help identify patients at higher risk of progression. |
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ISSN: | 1664-2295 1664-2295 |
DOI: | 10.3389/fneur.2024.1477335 |