ArticleFrontiers in immunology2025
Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Fifteen-Year Longitudinal Changes in Peripapillary Retinal Nerve Fiber Layer Thickness in Multiple Sclerosis.Journal of clinical medicine · 2026Article
- From Phenotypes to Spectrum: Rethinking RRMS, SPMS and PPMS in the Era of PIRA-A Framework Integrating PIRA, Smouldering-Associated Worsening, and Neurologic Reserve to Facilitate Earlier Recognition of Progression.Neurology international · 2026Review
- Serum miRNA-155 and TREM2 as Non-Invasive Biomarkers for Monitoring Neuroinflammation and Disease Activity in Multiple Sclerosis.Molecular neurobiology · 2026Review
- Neurogenesis and Neuroinflammation in Dialogue: Mapping Gaps, Modulating Microglia, Rewiring Aging.Cells · 2026Review
- Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening.Frontiers in immunology · 2026Article
- Prognostic value of blood-based biomarkers in multiple sclerosis patients in the absence of clinical relapses or new MRI lesions.Therapeutic advances in neurological disorders · 2025Article
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Authors and funding
11 authors.
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Abstract
Objective: Establishing biomarkers to predict multiple sclerosis (MS) disability accrual has been challenging using a single biomarker approach, likely due to the complex interplay of neuroinflammation and neurodegeneration. Here, we aimed to investigate the prognostic value of single and multimodal biomarker combinations to predict four-year disability progression in patients with MS. Methods: In total, 111 MS patients were followed up for four years to track disability accumulation based on the Expanded Disability Status Scale (EDSS). Three clinically relevant modalities (MRI, OCT and blood serum) served as sources of potential predictors for disease worsening. Two key measures from each modality were determined and related to subsequent disability progression: lesion volume (LV), gray matter volume (GMV), retinal nerve fiber layer, ganglion cell-inner plexiform layer, serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein. First, receiver operator characteristic (ROC) analyses were performed to identify the discriminative power of individual biomarkers and their combinations. Second, we applied structural equation modeling (SEM) to the single biomarkers in order to determine their causal inter-relationships. Results: Baseline GMV on its own allowed identification of subsequent EDSS progression based on ROC analysis. All other individual baseline biomarkers were unable to discriminate between progressive and non-progressive patients on their own. When comparing all possible biomarker combinations, the tripartite combination of MRI, OCT and blood biomarkers achieved the highest discriminative accuracy. Finally, predictive causal modeling identified that LV mediates significant parts of the effect of GMV and sNfL on disability progression. Conclusion: Multimodal biomarkers, i.e. different major surrogates for pathology derived from MRI, OCT and blood, inform about different parts of the disease pathology leading to clinical progression.
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