Brain age gap in multiple sclerosis: associated with disability but independent of serum biomarkers.
Authors
Affiliations (13)
Affiliations (13)
- Department of Neurology, Heinrich-Heine University Duesseldorf, Moorenstraße 5, Duesseldorf 40225, Germany.
- Department of Neurology, Medical Faculty University Hospital Düsseldorf, Düsseldorf, Germany.
- Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Department of Dermatology, University Hospital Düsseldorf, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany.
- Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University Frankfurt, Germany.
- Goethe University Frankfurt, Cooperative Brain Imaging Center - CoBIC, Frankfurt, Germany.
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
- Department of Psychiatry, Medical School and University Center OWL, Protestant Hospital of the Bethel Foundation, Bielefeld University, Bielefeld, Germany.
- Department of Neurology, Medical University of Graz, Graz, Austria.
Abstract
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. This study examines whether the difference between predicted and chronological age (BrainAGE) relates to clinical disability and biomarkers of neuro-axonal injury in MS. 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. 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. The mean chronological age of patients and healthy controls included in this study was 39.2 and 40.9 years, respectively. Patients with MS (<i>n</i> = 82) showed a higher BrainAGE (6.48 ± 6.83 years) than controls (<i>n</i> = 30; 0.69 ± 6.5 years; <i>p</i> = 0.0002). BrainAGE increased stepwise from controls to patients with EDSS < 3 and EDSS ⩾3 (<i>p</i> < 0.0001). Higher BrainAGE correlated with worse 9-Hole Peg Test (9HPT, ρ = 0.34, <i>p</i> = 0.002) and Timed 25-Foot Walk performance (T25FW, ρ = 0.23, <i>p</i> = 0.043), but not with serum neurofilament light chain (<i>p</i> = 0.68) or glial fibrillary acidic protein (<i>p</i> = 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. Our findings suggest that BrainAGE and serum biomarkers capture complementary aspects of MS pathology, supporting a multimodal approach to assess disease progression. ClinicalTrials.gov ID: SATURATE: NCT05701423, 360PMS: NCT06501950, SAFEGUIDE-MS: NCT06461481.