Background and objectives: In multiple sclerosis (MS), neurodegeneration results from the interplay between disease-specific pathology and normal aging. Conventional MRI captures morphologic changes in neurodegeneration, while quantitative MRI (qMRI) provides biophysical measures of microstructural alterations. Combining these modalities may reveal how aging and pathology interact and contribute to disability progression in people with MS. Methods: We analyzed cross-sectional and longitudinal morphometry data from 1,353 patients with MS and 3,462 healthy controls (HCs). In addition, cross-sectional qMRI data, available for 378 HCs and 169 patients with MS, were analyzed separately. Morphometric measures and quantitative metrics were used to estimate brain-predicted age differences (brain-PADs) with machine learning. We assessed the added value of quantitative metrics over a model based exclusively on morphometric measures in brain age prediction. We also investigated the associations of brain-PADs derived from conventional and qMRI-based predictive models with clinical disability, serum inflammatory biomarkers of neuroaxonal and astrocytic injury, and lesion burden. Discussion: Integrating qMRI improves brain age predictions. Brain-PAD serves as an imaging biomarker to quantify MS-associated aging and track disability and neuroinflammation progression.
Chen et al. (Mon,) studied this question.