Quantitative T1 brain mapping in early relapsing-remitting multiple sclerosis: longitudinal changes, lesion heterogeneity and disability

Objectives To quantify brain microstructural changes in recently diagnosed relapsing-remitting multiple sclerosis (RRMS) using longitudinal T1 measures, and determine their associations with clinical disability. Methods Seventy-nine people with recently diagnosed (< 6 months) RRMS were recruited from a single-centre cohort sub-study, and underwent baseline and 1-year brain MRI, including variable flip angle T1 mapping. Median T1 was measured in white matter lesions (WML), normal-appearing white matter (NAWM), cortical/deep grey matter (GM), thalami, basal ganglia and medial temporal regions. Prolonged T1 (≥ 2.00 s) and supramedian T1 (relative to cohort WML values) WML voxel counts were also measured. Longitudinal change was assessed with paired t-tests and compared with Bland-Altman limits of agreement from healthy control test-retest data. Regression analyses determined relationships with Expanded Disability Status Scale (EDSS) score and dichotomised EDSS outcomes (worsening or stable/improving). Results Sixty-two people with RRMS (mean age 37.2 ± 10.9 [standard deviation], 48 female) and 11 healthy controls (age 44 ± 11, 7 female) contributed data. Prolonged and supramedian T1 WML components increased longitudinally (176 and 463 voxels, respectively; p < .001), and were associated with EDSS score at baseline (p < .05) and follow-up (supramedian: p < .01; prolonged: p < .05). No cohort-wide median T1 changes were found; however, increasing T1 in WML, NAWM, cortical/deep GM, basal ganglia and thalami was positively associated with EDSS worsening (p < .05). Conclusion T1 is sensitive to brain microstructure changes in early RRMS. Prolonged WML T1 components and subtle changes in NAWM and GM structures are associated with disability. Clinical relevance statement MRI T1 brain mapping quantifies disability-associated white matter lesion heterogeneity and subtle microstructural damage in normal-appearing brain parenchyma in recently diagnosed RRMS, and shows promise for early objective disease characterisation and stratification. Key Points • Quantitative T 1 mapping detects brain microstructural damage and lesion heterogeneity in recently diagnosed relapsing-remitting multiple sclerosis. • T 1 increases in lesions and normal-appearing parenchyma, indicating microstructural damage, are associated with worsening disability. • Brain T 1 measures are objective markers of disability-relevant pathology in early multiple sclerosis. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00330-023-10351-6.


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Multi     1, Table S3 (b) Indicate number of participants with missing data for each variable of interest NA -all complete (c) Summarise follow-up time (eg, average and total amount) Table 1 Outcome data 15* Report numbers of outcome events or summary measures over time Tables 1, 2 and 4; Table S7; p10 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval).Make clear which confounders were adjusted for and why they were included

Figure S2 :
Figure S2: One-year changes in median normal-appearing white matter T1 (top) and median white matter lesion T1 in relapsing-remitting multiple sclerosis (RRMS) group.The Bland-Altman limits of agreement, as calculated on healthy control white matter data, are superimposed on both plots using a green dashed line.The confidence intervals have been similarly superimposed using a red dashed line.Each blue dot represents one RRMS participant in the study cohort.

Figure S3 : 1 (
Figure S3: histogram showing the one-year change in Expanded Disability Status Scale (EDSS) scores of this study's cohort.Despite a median longitudinal increase of +0.5, many participants show clinical improvement (as shown by a decrease in EDSS score) and significant variability in one-year change in EDSS score was observed across our early relapsing-remitting multiple sclerosis cohort.

Table S2 :
Overview of regression models to determine the relationship between tissue microstructure and disability in recently diagnosed relapsing-remitting multiple sclerosis.Continuous variables were centred and scaled for regression analyses.

Table S3 :
treatment with disease-modifying therapies (DMTs) at one-year follow-up.All participants were untreated with DMTs at baseline.

Table S4 :
Brain tissue median T1 test-retest results for healthy control participants (n=11).95 % of median T1 measurements in healthy participants would be expected to fall within Bland-Altman limits of agreements.Svalues are given for one-sample sign tests.

Table S5 :
Results of cross-sectional ordinal logistic regression models investigating the relationship between median baseline T1 in each tissue and baseline Expanded Disability Status Scale (EDSS) scores.
Age and lesion load were included as covariates.Adjusted odds ratios are for standardized data; β: standardized beta coefficient; CI: confidence interval; FDR: False Detection Rate.

Table S6 :
Results of the binomial logistic regression models investigating the relationship between the median baseline T1 measures in each tissue and one-year change in Expanded Disability Status Scale (EDSS) score (stable/improving vs worsening EDSS >0.5 points over one year).

Table S7 :
Brain tissue median T1 summary statistics, grouped by dichotomised Expanded Disability Status Scale (EDSS) change over one year (worsening EDSS: ≥0.5 points).matter lesions: a includes any voxels reclassified as lesional at one-year follow-up; b only includes lesions present at baseline.CI: confidence interval; SD: standard deviation.

Table 3
Funding22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting.The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/,Annals of Internal Medicine at http://www.annals.org/,and Epidemiology at http://www.epidem.com/).Information on the STROBE Initiative is available at http://www.strobe-statement.org.
*Give information separately for exposed and unexposed groups.Note: