DOI: 10.1177/17562864261458516 ISSN: 1756-2864

Brain age gap in multiple sclerosis: associated with disability but independent of serum biomarkers

Marc Pawlitzki, Patricia Kirschner, Lars Masanneck, Ramona Hagler, Elias Meier, Marius Vach, Hernan Inojosa, Tjalf Ziemssen, Vivien Lorena Ivan, Shammi More, Kaustubh Patil, Parnian Firouzi-Memarpuri, Julian Caspers, Jonathan Repple, Udo Dannlowski, Michael Khalil, Sven G. Meuth, Christian Rubbert
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Background:

Multiple sclerosis (MS) is influenced by age-related brain alterations and affects cellular aging mechanisms. Machine-learning models can estimate brain-predicted age from magnetic resonance imaging (MRI) to quantify these aging-related changes.

Objectives:

This study examines whether the difference between predicted and chronological age (BrainAGE) relates to clinical disability and biomarkers of neuro-axonal injury in MS.

Design:

This study analyzed brain-predicted age from structural 3D T1-weighted MRI in 82 patients with relapsing MS enrolled in three prospective clinical trials and 30 healthy controls.

Methods:

BrainAGE, calculated as MRI-predicted minus chronological age, was correlated with the Expanded Disability Status Scale (EDSS), MS Functional Composite subtests, and serum neurofilament light chain and glial fibrillary acidic protein.

Results:

The mean chronological age of patients and healthy controls included in this study was 39.2 and 40.9 years, respectively. Patients with MS ( n  = 82) showed a higher BrainAGE (6.48 ± 6.83 years) than controls ( n  = 30; 0.69 ± 6.5 years; p  = 0.0002). BrainAGE increased stepwise from controls to patients with EDSS < 3 and EDSS ⩾3 ( p  < 0.0001). Higher BrainAGE correlated with worse 9-Hole Peg Test (9HPT, ρ = 0.34, p  = 0.002) and Timed 25-Foot Walk performance (T25FW, ρ = 0.23, p  = 0.043), but not with serum neurofilament light chain ( p  = 0.68) or glial fibrillary acidic protein ( p  = 0.33). In multivariable regression models adjusting for chronological age, sex, disease duration, and disease-modifying therapy, BrainAGE remained significantly associated with EDSS, 9HPT, and T25FW performance. sNfL and sGFAP remained nonsignificant after adjustment.

Conclusion:

Our findings suggest that BrainAGE and serum biomarkers capture complementary aspects of MS pathology, supporting a multimodal approach to assess disease progression.

Trial registration:

ClinicalTrials.gov ID: SATURATE: NCT05701423, 360PMS: NCT06501950, SAFEGUIDE-MS: NCT06461481.

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