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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis.

Vinzenz Fleischer1, Tobias Brummer1, Muthuraman Muthuraman1,2

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Summary

Combining MRI, OCT, and blood biomarkers accurately predicts multiple sclerosis (MS) disability progression, outperforming single markers. This multimodal approach offers a more comprehensive view of disease pathology for better patient outcomes.

Keywords:
biomarkerdisease progressionmagnetic resonance imagingmultiple sclerosisneurofilamentoptical coherence tomographypredictionstructural equation modeling

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Clinical Neurology

Background:

  • Predicting multiple sclerosis (MS) disability progression is challenging due to complex neuroinflammation and neurodegeneration.
  • Single biomarker approaches have limited success in forecasting MS disability accrual.

Purpose of the Study:

  • To investigate the prognostic value of single and multimodal biomarker combinations for predicting four-year disability progression in MS patients.
  • To explore the discriminative power of individual and combined biomarkers from MRI, OCT, and blood serum.

Main Methods:

  • Followed 111 MS patients for four years, tracking disability via Expanded Disability Status Scale (EDSS).
  • Assessed 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.
  • Utilized receiver operator characteristic (ROC) analyses and structural equation modeling (SEM) to evaluate biomarker performance and inter-relationships.

Main Results:

  • Baseline gray matter volume (GMV) alone could predict subsequent EDSS progression.
  • No other individual baseline biomarkers discriminated between progressive and non-progressive patients.
  • A tripartite combination of MRI, OCT, and blood biomarkers demonstrated the highest discriminative accuracy.
  • Predictive causal modeling revealed that lesion volume (LV) mediates effects of GMV and sNfL on disability progression.

Conclusions:

  • Multimodal biomarkers derived from MRI, OCT, and blood serum provide insights into different aspects of disease pathology.
  • Combining these modalities offers a more accurate prediction of clinical progression in multiple sclerosis.