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Integrated Biomarker-Volumetric Profiling Defines Neurodegenerative Subtypes and Predicts Neuroaxonal Injury in

Alin Ciubotaru1, Roxana Covali2, Cristina Grosu1

  • 1Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania.

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Summary
This summary is machine-generated.

Serum neurofilament light chain (sNfL) and brain volumetry identify distinct multiple sclerosis (MS) subtypes. Machine learning accurately predicts neuroaxonal injury using MRI-derived volumes, aiding personalized MS treatment.

Keywords:
Bayesian analysisbrain volumetryendophenotypesmachine learningmultiple sclerosisneurodegenerationpredictive modellingserum neurofilament light chain

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Radiology

Background:

  • The clinical-radiological paradox in multiple sclerosis (MS) highlights the need for biomarkers reflecting neurodegeneration.
  • Serum neurofilament light chain (sNfL) indicates neuroaxonal injury, while brain volumetry assesses structural damage.
  • The relationship between sNfL, regional atrophy, and patient stratification requires further investigation.

Purpose of the Study:

  • To develop a multimodal biomarker framework integrating sNfL and volumetric MRI in MS.
  • To define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning.
  • To explore the combined utility of sNfL and volumetric data for patient stratification and prediction.

Main Methods:

  • Measured sNfL in 57 MS patients using Simoa technology.
  • Quantified brain volumes for 42 regions using automated deep learning segmentation.
  • Employed Bayesian correlation, mediation analysis, K-means clustering, and supervised machine learning (Elastic Net, Random Forest).

Main Results:

  • Strong evidence linked sNfL to reduced grey matter volume and increased ventricular volume.
  • Grey matter atrophy significantly mediated the relationship between Expanded Disability Status Scale (EDSS) and sNfL.
  • Three patient subtypes were identified: 'High Neurodegeneration,' 'Moderate Injury,' and 'Benign Volumetry.'
  • Supervised models accurately predicted sNfL (R²=0.65) using grey matter volume, ventricular volume, and age.

Conclusions:

  • sNfL is robustly associated with global grey matter and ventricular volumes in MS.
  • These integrated measures define clinically meaningful neurodegenerative subtypes.
  • Volumetric MRI features can predict neuroaxonal injury, supporting prognosis and personalized therapy.