Abstract
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10−15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.
Highlights
Multiple sclerosis (MS) is an inflammatory, demyelinating disease of the CNS
At time point 2 and 3, 53 and 44% of the patients were categorized as having NEDA (No Evidence of Disease Activity)−3
Using cross-sectional and longitudinal MRI data as basis for brain age estimation based on machine learning, we tested the hypotheses that patients with MS on average show higher brain age than healthy controls, and that the rate of brain aging is associated with clinical trajectories
Summary
Multiple sclerosis (MS) is an inflammatory, demyelinating disease of the CNS. The pathophysiology of MS can be divided into acute inflammation during a relapse and chronic inflammation thought to continuously perturb neuroaxonal homeostasis and drive neurodegeneration [1]. Utilizing sensitive measures of MRI-based brain morphometry, brain age estimation provides a robust imaging-based biomarker with potential to yield novel insights into similarities and differences of disease pathophysiology across brain disorders [11, 12]. Such imaging-based brain age has been shown to be reliable both within and between MRI scanners, and is a candidate biomarker of an individual’s brain health and integrity [10,11,12]. Only two preprint manuscripts [11, 14] and one abstract [15] have reported brain age estimations in MS, and all reported older appearing brains in patients with MS compared to HC
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