Multiple sclerosis: modifiable risk factors and quantifiable outcomes

© 2021 Ai-Lan NguyenMultiple sclerosis (MS) is a chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system. It is one of the most common causes of disability in young adults and represents a significant personal and societal burden. The aetiology of MS is unknown but involves both environmental and genetic risk factors. Women are three times more likely to develop relapsing-remitting MS (RRMS) than men, and sex hormones are hypothesised to be protective in MS. Further understanding the role of these risk factors will advance our knowledge of the aetiology of MS and lead to improved counselling. MS is also a heterogeneous disease and there is currently no good measure of predicting which patients are at highest risk of disability accrual. Brain atrophy is one potential biomarker of neurodegeneration but there are a paucity of studies exploring its application in the real-world clinical setting. This thesis has two parts. In the first part, we selected one modifiable risk factor, pregnancy, to further explore its association with MS. In chapter 1, we reported on the epidemiology of pregnancy in women with MS over the last 15 years using the international MSBase registry. Of 9098 women with RRMS, we found a low but increasing incidence of pregnancy as well as a greater proportion of women who conceived on disease-modifying therapy (DMT) over time. We did not find any differences in pregnancy outcomes between those pregnancies conceived on or off therapy. A higher disability was associated with a reduced pregnancy incidence. In chapter 2, we investigated the association of pregnancy with time to onset of clinically isolated syndrome (CIS), the first clinical demyelinating episode of MS. We recruited 2557 women across 4 sites and found that women with previous pregnancies and childbirths had a later onset of CIS compared to women without pregnancies and childbirths. A higher number of pregnancies and childbirths was not associated with a delayed CIS onset. In the second part, we selected one quantifiable outcome, MRI brain volumetry, and modelled its association with MS disability in a real-world setting. We used an automated volumetry software, icobrain, for centralised analysis of clinical MRIs. In chapter 3, we assessed the variability of percentage brain volume change (PBVC) measurements in a multicentre clinical setting. 6829 brain MRIs acquired on different scanners across 4 sites were analysed by icobrain. We found that after applying strict selection criteria for MRI pairs, which included filtering by image quality and an alignment similarity above the threshold for same-scanner scan-rescan images, only 42% of the original number of consecutive MRI pairs remained. The final MRI pairs had a mean PBVC comparable to single-site centres (-0.26% +/- 0.34). In chapter 4, we determined the association between various MRI metrics and MS disability using the subgroup of MRI pairs with the lowest variance from chapter 3. Our cohort of 260 relapse-onset MS patients were almost all treated, had a mean PBVC in the range for healthy individuals (-0.26% +/- 0.52), and a low disability accrue. We found no association between PBVC and future disability, however, cross-sectional whole brain volume was associated with disability. Our study findings highlight that pregnancy plays an important immunomodulatory role in women predisposed to MS, and whole brain volume may be the best performing MRI outcome to predict future disability in patients with minimal brain atrophy