Multiple sclerosis and related disorders · 2025
A systematic review and meta-analysis of real-world data predictors for conversion to progressive multiple sclerosis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
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Solís-Tarazona Luis Rafael, Molina-Castaño Maria, Barea-Moya Lucas, Galan Joana, Raket Lars Lau, Gil-Perotin Sara
Аннотация
BACKGROUND: Available fluid biomarkers, neuroimaging techniques, and clinical assessments may enhance prediction of conversion to secondary progressive multiple sclerosis (SPMS).
OBJECTIVES: To identify robust real-world predictors of MS progression in the literature.
METHODS: A systematic review was conducted in EMBASE. A total of 20,338 MS patients from 64 eligible studies were included. A p-value meta-analysis and a tipping point analysis were performed to assess associations with MS progression and EDSS.
RESULTS: Among CSF biomarkers, GFAP (p = 6.2 × 10⁻¹⁰) and CHI3L1 (p = 1.7 × 10⁻¹¹) demonstrated consistent aggregated associations with disease progression. In serum, NfL (p = 2.5 × 10⁻13) and GFAP (p = 1.4 × 10-8) showed strong association with concurrent EDSS and disease progression. Spinal cord lesions (p = 9.4 × 10⁻¹¹) and iron rim lesions (p = 4 × 10⁻⁶) were the strongest radiological predictors. Among clinical assessment tools, significant associations were found with concurrent EDSS, but prediction of progression was limited.
CONCLUSION: Fluid biomarkers, particularly CSF GFAP, CHI3L1, and serum NfL, along with spinal cord lesions and iron rim lesions on MRI, provide consistent evidence for predicting MS progression. There is a need for harmonized and accessible outcome measures in real-world datasets.
OBJECTIVES: To identify robust real-world predictors of MS progression in the literature.
METHODS: A systematic review was conducted in EMBASE. A total of 20,338 MS patients from 64 eligible studies were included. A p-value meta-analysis and a tipping point analysis were performed to assess associations with MS progression and EDSS.
RESULTS: Among CSF biomarkers, GFAP (p = 6.2 × 10⁻¹⁰) and CHI3L1 (p = 1.7 × 10⁻¹¹) demonstrated consistent aggregated associations with disease progression. In serum, NfL (p = 2.5 × 10⁻13) and GFAP (p = 1.4 × 10-8) showed strong association with concurrent EDSS and disease progression. Spinal cord lesions (p = 9.4 × 10⁻¹¹) and iron rim lesions (p = 4 × 10⁻⁶) were the strongest radiological predictors. Among clinical assessment tools, significant associations were found with concurrent EDSS, but prediction of progression was limited.
CONCLUSION: Fluid biomarkers, particularly CSF GFAP, CHI3L1, and serum NfL, along with spinal cord lesions and iron rim lesions on MRI, provide consistent evidence for predicting MS progression. There is a need for harmonized and accessible outcome measures in real-world datasets.