Neurology(R) neuroimmunology & neuroinflammation · 2025
У пациентов с множественным склерозом, у которых прогресс не зависит от обратной активности.
СтатьяНазвание и аннотация: Машинный перевод · не проверен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ocampo-Pineda Mario, Cagol Alessandro, Benkert Pascal, Barakovic Muhamed, Lu Po-Jui, Müller Jannis, Schaedelin Sabine Anna, Melie-Garcia Lester, Weigel Matthias, Sormani Maria Pia, Kappos Ludwig, Kuhle Jens, Granziera Cristina
Аннотация
СПРАВОЧНАЯ ИНФОРМАЦИЯ И ЦЕЛИ: Прогресс, не зависящий от рецидива (ИПРА), связан с худшими результатами у людей с множественным склерозом (pwMS). Хотя в ходе предыдущих исследований ПИРА увязывалась с ускоренной атрофией мозга и спинного мозга и разрозненной хронической воспалением, роль дегенерации тракта белых веществ (ВМ) остается неясной. Цель этого исследования заключалась в изучении взаимосвязи между ПИРА и целостностью основных трактов WM с использованием диффузных настольных изображений (DTI). МЕТОДЫ: КРУГ ВИДОВ ДЕЯТЕЛЬНОСТИ 258 ПМС была стратифицирована на основе наличия или отсутствия ПИРА по сравнению с ПИРА. 4-year последующий период. В конце последующей работы метрики DTI сравнивались между группами, использующими линейные регрессионные модели, взвешенные на основе баллов, для учета потенциальных сомнителей. РЕЗУЛЬТАТЫ: ПУТМ с ≥1 Мероприятие PIRA (n = 39) продемонстрировали значительное сокращение доли анизотропии и увеличение радиальной, осевой и средней диффузности в корпусе каллосума и моторных трактах (ложная скорость обнаружения скорректирована p) ≤ 0.04) по сравнению с теми, которые не имеют PIRA, что свидетельствует о более явном повреждении WM. Дискуссия: Наши выводы указывают на связь между ПИРА и микроструктурным повреждением в ключевых трактатах WM. Наблюдаемые изменения DTI, вероятно, отражают такие процессы, как дегенерация в Уоллериане, и способствуют росту данных, связывающих PIRA с нейродегенерацией.
Оригинал публикации
White Matter Tract Degeneration in Multiple Sclerosis Patients With Progression Independent of Relapse Activity.
BACKGROUND AND OBJECTIVES: Progression independent of relapse activity (PIRA) is associated with worse outcomes in people with multiple sclerosis (pwMS). Although previous research has linked PIRA to accelerated brain and spinal cord atrophy and compartmentalized chronic inflammation, the role of white matter (WM) tract degeneration remains unclear. This study aimed to explore the relationship between PIRA and the integrity of major WM tracts using diffusion tensor imaging (DTI).
METHODS: A cohort of 258 pwMS was stratified based on the presence or absence of PIRA over a 4-year follow-up period. At the end of follow-up, DTI metrics were compared between groups using propensity score-weighted linear regression models to account for potential confounders.
RESULTS: PwMS with ≥1 PIRA event (n = 39) exhibited significant reductions in fractional anisotropy and increases in radial, axial, and mean diffusivity within the corpus callosum and motor tracts (false discovery rate-adjusted p ≤ 0.04) compared with those without PIRA, indicating more pronounced WM damage.
DISCUSSION: Our findings highlight an association between PIRA and microstructural damage in key WM tracts. The observed DTI changes likely reflect processes such as Wallerian degeneration and contribute to the growing evidence linking PIRA to neurodegeneration.
Перевод полного текста: Перевод в очереди
Читать найденный полный текст
This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal
Introduction
In people with multiple sclerosis (pwMS), disability can accumulate because of incomplete recovery from relapses or through progression independent of relapse activity (PIRA). 1 A growing body of evidence highlights PIRA as a frequent phenomenon across all MS phenotypes, 2 including the typical relapsing-remitting multiple sclerosis (RRMS) form. 2
PIRA is associated with unfavorable clinical outcomes, 3 emphasizing the critical need to effectively address it for improving long-term prognosis in pwMS. Achieving this goal necessitates a comprehensive understanding of the pathophysiologic mechanisms underlying PIRA. In addition, identifying biomarkers sensitive to the degenerative changes linked to PIRA is crucial for guiding personalized therapeutic strategies.
Previous investigations have linked PIRA to accelerated brain and spinal cord atrophy, 2,4-7 and compartmentalized chronic inflammation indicated by brain paramagnetic rim lesions (PRLs). 5 Although it is plausible that degeneration in white matter (WM) tracts may also be implicated in the pathogenesis of PIRA, data on this aspect are currently lacking. To address this gap, we aimed to investigate the relationship between PIRA and the integrity of major WM tracts by conducting a diffusion tensor imaging (DTI) study, using a magnetic resonance imaging (MRI) protocol compatible with clinical practice. DTI is a quantitative technique based on diffusion-weighted MRI (DW-MRI) that allows for exploring the magnitude and direction of water diffusion in biological tissues, providing insights into their microstructural architecture. 8 In addition, we explored DTI changes in progression independent of relapse and MRI activity (PIRMA), a recent concept introduced to specifically reflect disability accumulation in the absence of both relapses and focal MRI activity. 2
Methods
Participants
From the Swiss Multiple Sclerosis Cohort study, 9 we selected individuals with (1) a diagnosis of RRMS according to the 2017 revised McDonald criteria, (2) availability of a brain MRI scan including a standardized protocol suitable for DTI reconstruction, and (3) availability of regular clinical follow-up over a predefined 4-year period preceding the DTI scan.
The study follows the Strengthening the Reporting of Observational Studies guideline.
Standard Protocol Approvals, Registrations, and Patient Consents
The local ethics committee approved the study, and all patients provided written informed consent.
Clinical Data
All patients underwent standardized neurologic assessments, using the Expanded Disability Status Scale (EDSS) score. 10 The occurrence of PIRA during follow-up was defined as an event of EDSS increase (≥1.5/≥1.0/≥0.5 points if baseline EDSS was 0/1.0–5.5/>5.5, respectively) using a roving reference, confirmed at least after 6 months, in the absence of relapses (1) between the EDSS increase and the precedent reference visit (performed ≥90 days before the EDSS increase) and (2) between the EDSS increase and its confirmation. 2,5
MRI Acquisition and Analysis
MRI scans were obtained with a 3T magnetic resonance system (Siemens Skyra); the protocol included (1) 3D, 1 mm isotropic fluid-attenuated inversion recovery; (2) 3D, 1 mm isotropic magnetization prepared rapid gradient echo; and (3) single-shell DW-MRI (resolution: 1.8 × 1.8 × 1.8 mm 3 ; 20 b-value = 1,000 seconds/mm 2 and 10 b-value = 0 seconds/mm 2 ).
WM lesions were identified automatically 11 and manually corrected. DW-MRI images were denoised and corrected for ringing artifacts, eddy current distortions, misaligning artifacts, and the bias field. Maps of fractional anisotropy (FA), radial diffusivity (RD), mean diffusivity (MD), and axial diffusivity (AD) were reconstructed with MRtrix (version: 3.0.3). 12 Mean FA, RD, MD, and AD values were quantified in 7 regions of interest (ROIs), which were identified using the Johns Hopkins University DTI-based WM atlas 13 : the genu, body, and splenium of the corpus callosum (CC); the superior corona radiata, the posterior limb of internal capsule, cerebral peduncle, and the corticospinal tract (CST). DTI metrics were extracted after registering the DTI maps to the MNI152 space. WM lesions were also registered to the MNI152 space and used to exclude lesional tissue from the ROIs, limiting the analysis to the normal-appearing WM (NAWM).
A subgroup of patients with PIRMA was identified by assessing the absence of new/enlarging lesions during the 4-year follow-up. 14 Concomitant absence of contrast-enhancing lesions was also confirmed in cases where gadolinium was administered (86.7% of total scans).
Statistical Analysis
The statistical analysis was conducted in R .
DTI metrics were compared between patients with and without episodes of PIRA during clinical follow-up, after propensity score matching of the groups. Specifically, a full propensity score weighting was obtained using age, sex, disease duration, annualized relapse rate in the prior 2 years, use of disease-modifying therapies, and total WM lesion volume as criteria. Between-group differences were then assessed with linear regression models, including the weights obtained from propensity score matching. Results were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate.
In addition, we conducted 3 separate sensitivity analyses: (1) including EDSS as an additional criterion for propensity score matching; (2) including the lesion load in each ROI as an additional covariate in the regression models; and (3) restricting the PIRA group to the subset of patients with PIRMA.
Data Availability
The data supporting this study's findings are available on reasonable request.
Results
In total, 258 pwMS followed up between 2016 and 2022 were included in the study. Patients who had experienced PIRA during the preceding 4-year follow-up (n = 39) exhibited higher disability ( p < 0.001) and T2-lesion load ( p = 0.01) at MRI compared with those without, while showing no significant differences in age, sex distribution, disease duration, relapse rate, and treatment regimen ( Table 1 ).
Table 1 Demographic and Clinical Characteristics of Patients With and Without PIRA Before and After Matching Before matching After matching Patients with PIRA Patients without PIRA SMD Patients with PIRA Patients without PIRA SMD Main analysis Participants, n 39 219 Age, mean (SD), y 50.3 (12.1) 48.0 (11.3) 0.19 50.3 49.9 0.03 Female, n (%) 29 (74.4) 140 (63.9) 0.24 (74.4) (73.1) 0.03 Disease duration, mean (SD), y 18.8 (9.7) 16.5 (8.6) 0.24 18.8 18.4 0.04 ARR previous 2 y, median (IQR) 0 (0; 0) 0 (0; 0) 0.18 0 (0; 0) 0 (0; 0) 0.06 DMT class Platform, n (%) 2 (5.1) 20 (9.1) Oral, n (%) 17 (43.6) 115 (52.5) mAb, n (%) 14 (35.9) 62 (28.3) Untreated, n (%) 6 (15.4) 22 (10.0) T2-lesion volume, median (IQR), ml 9.3 (4.0; 24.5) 4.8 (1.4; 15.8) 0.31 9.3 (4.0; 24.2) 9.4 (2.1; 23.6) −0.08 Sensitivity analysis including EDSS EDSS, median (IQR) 3.5 (2.75; 4.25) 2.0 (1.5; 3.0) 0.83 3.5 (2.5; 4.0) 3.5 (2.5; 4.5) −0.08 Abbreviations: ARR = annualized relapse rate; DMT = disease-modifying therapy; EDSS = Expanded Disability Status Scale; IQR = interquartile range; mAb = monoclonal antibodies; PIRA = progression independent of relapse activity; SMD = standardized mean difference. Platform treatments included glatiramer acetate and interferon preparations; oral treatments included cladribine, dimethyl fumarate, fingolimod, ozanimod, and teriflunomide; monoclonal antibody treatments included natalizumab, ocrelizumab, and rituximab.
Demographic and Clinical Characteristics of Patients With and Without PIRA Before and After Matching
Abbreviations: ARR = annualized relapse rate; DMT = disease-modifying therapy; EDSS = Expanded Disability Status Scale; IQR = interquartile range; mAb = monoclonal antibodies; PIRA = progression independent of relapse activity; SMD = standardized mean difference.
Platform treatments included glatiramer acetate and interferon preparations; oral treatments included cladribine, dimethyl fumarate, fingolimod, ozanimod, and teriflunomide; monoclonal antibody treatments included natalizumab, ocrelizumab, and rituximab.
Patients with PIRA displayed alterations in DTI metrics indicative of more severe WM damage than those without PIRA. Significant between-group differences were observed for FA in the genu of CC; RD in the genu and splenium of CC; MD in the genu of CC, splenium of CC, CST, and SCR; AD in the splenium of CC and CST ( Figure ).
Comparison of DTI Metrics Between Patients With and Without PIRA
DTI metrics in each group are displayed as the estimated marginal mean (and associated 95% CI) derived from the regression models. Significant differences are presented in bold. CC = corpus callosum; CST = corticospinal tract; CP = cerebral peduncle; DTI = diffusion tensor imaging; PIRA = progression independent of relapse activity; PLIC = posterior limb of internal capsule; SCR = superior corona radiata.
Consistent results were observed in the sensitivity analyses ( Table 2 , eTables 1 and 2).
Table 2 Sensitivity Analysis: Comparisons of DTI Metrics Between Patients With and Without PIRMA DTI metric ROI Patients with PIRMA (n = 19) Patients without PIRMA (n = 219) p Value Fractional anisotropy Genu CC 0.502 (0.480–0.524) 0.535 (0.521–0.548)
0.0090
Body CC 0.568 (0.547–0.589) 0.586 (0.573–0.599) 0.2455 Splenium CC 0.562 (0.537–0.587) 0.592 (0.577–0.608)
0.0382
CST 0.457 (0.441–0.473) 0.457 (0.447–0.467) 0.9910 CP 0.543 (0.528–0.558) 0.552 (0.543–0.561) 0.4788 PLIC 0.594 (0.581–0.606) 0.589 (0.581–0.597) 0.6540 SCR 0.469 (0.453–0.486) 0.471 (0.461–0.481) 0.8676 Radial diffusivity (mm 2 /second) Genu CC 6.55*10 −4 (6.18*10 −4 –6.91*10 −4 ) 5.91*10 −4 (5.69*10 −4 –6.14*10 −4 )
0.0024
Body CC 5.54*10 −4 (5.16*10 −4 –5.92*10 −4 ) 5.31*10 −4 (5.08*10 −4 –5.54*10 −4 ) 0.4650 Splenium CC 5.67*10 −4 (5.33*10 −4 –6.01*10 −4 ) 4.98*10 −4 (4.78*10 −4 –5.19*10 −4 )
0.0003
CST 5.18*10 −4 (5.02*10 −4 –5.35*10 −4 ) 5.13*10 −4 (5.03*10 −4 –5.23*10 −4 ) 0.6540 CP 5.72*10 −4 (5.48*10 −4 –5.96*10 −4 ) 5.62*10 −4 (5.47*10 −4 –5.76*10 −4 ) 0.6540 PLIC 4.13*10 −4 (4.02*10 −4 –4.25*10 −4 ) 4.21*10 −4 (4.14*10 −4 –4.28*10 −4 ) 0.3826 SCR 5.01*10 −4 (4.86*10 −4 –5.17*10 −4 ) 4.97*10 −4 (4.88*10 −4 –5.06*10 −4 ) 0.6540 Mean diffusivity (mm 2 /second) Genu CC 9.37*10 −4 (9.04*10 −4 –9.70*10 −4 ) 8.86*10 −4 (8.66*10 −4 –9.06*10 −4 )
0.0070
Body CC 8.72*10 −4 (8.37*10 −4 –9.07*10 −4 ) 8.60*10 −4 (8.38*10 −4 –8.81*10 −4 ) 0.6540 Splenium CC 8.83*10 −4 (8.57*10 −4 –9.09*10 −4 ) 8.24*10 −4 (8.09*10 −4 –8.40*10 −4 )
<0.0001
CST 7.02*10 −4 (6.88*10 −4 –7.16*10 −4 ) 6.96*10 −4 (6.88*10 −4 –7.05*10 −4 ) 0.6540 CP 8.26*10 −4 (8.06*10 −4 –8.46*10 −4 ) 8.22*10 −4 (8.10*10 −4 –8.35*10 −4 ) 0.7524 PLIC 6.80*10 −4 (6.71*10 −4 –6.89*10 −4 ) 6.86*10 −4 (6.81*10 −4 –6.92*10 −4 ) 0.3634 SCR 6.95*10 −4 (6.81*10 −4 –7.10*10 −4 ) 6.90*10 −4 (6.82*10 −4 –6.99*10 −4 ) 0.6540 Axial diffusivity (mm 2 /second) Genu CC 1.50*10 −3 (1.46*10 −3 –1.54*10 −3 ) 1.47*10 −3 (1.45*10 −3 –1.50*10 −3 ) 0.3634 Body CC 1.51*10 −3 (1.47*10 −3 –1.55*10 −3 ) 1.52*10 −3 (1.49*10 −3 –1.54*10 −3 ) 0.7524 Splenium CC 1.52*10 −3 (1.49*10 −3 –1.55*10 −3 ) 1.48*10 −3 (1.46*10 −3 –1.49*10 −3 )
0.0260
CST 1.07*10 −3 (1.05*10 −3 –1.09*10 −3 ) 1.06*10 −3 (1.05*10 −3 –1.08*10 −3 ) 0.6540 CP 1.34*10 −3 (1.31*10 −3 –1.36*10 −3 ) 1.35*10 −3 (1.33*10 −3 –1.36*10 −3 ) 0.6540 PLIC 1.21*10 −3 (1.19*10 −3 –1.23*10 −3 ) 1.22*10 −3 (1.21*10 −3 –1.23*10 −3 ) 0.7524 SCR 1.08*10 −3 (1.06*10 −3 –1.11*10 −3 ) 1.08*10 −3 (1.06*10 −3 –1.09*10 −3 ) 0.7524 Abbreviations: CC = corpus callosum; CP = cerebral peduncle; CST = corticospinal tract; DTI = diffusion tensor imaging; PLIC = posterior limb of internal capsule; PIRMA= progression independent of relapse and MRI activity; ROI = regions of interest; SCR = superior corona radiata. The estimates within each group are the marginal means (95% CIs) derived from the regression models. Reported p values are false discovery rate adjusted. Significant differences are presented in bold.
Sensitivity Analysis: Comparisons of DTI Metrics Between Patients With and Without PIRMA
Abbreviations: CC = corpus callosum; CP = cerebral peduncle; CST = corticospinal tract; DTI = diffusion tensor imaging; PLIC = posterior limb of internal capsule; PIRMA= progression independent of relapse and MRI activity; ROI = regions of interest; SCR = superior corona radiata.
The estimates within each group are the marginal means (95% CIs) derived from the regression models. Reported p values are false discovery rate adjusted. Significant differences are presented in bold.
Discussion
Our study showed that pwMS experiencing PIRA exhibit increased damage in major WM tracts compared with counterparts without PIRA, matched for relevant demographic, clinical, and conventional MRI characteristics. Between-group differences were evident across all the examined DTI-derived quantitative maps, encompassing WM structures spanning both the CC and motor tracts.
DTI enables straightforward mathematical representation of diffusion signals, providing measures sensitive to tissue integrity at both macrostructural and microstructural levels. Although pathologic studies have reported associations between myelin content and FA, AD, and RD, as well as between MD and FA and axonal content, 15,16 inherent limitations of DTI—such as its inability to accurately represent complex tissue architecture—pose challenges in interpreting DTI metrics at the microstructural level. Nonetheless, DTI demonstrates good sensitivity to tissue injury associated with MS, with reported correlations between the severity of changes and both neurologic and cognitive disability. 17 Previous research has also indicated associations between DTI metrics and disability progression 18 ; our study extends these findings by demonstrating that DTI is sensitive to the specific component of disability progression attributable to PIRA.
Our results collectively indicate microstructural damage in major WM tracts as a pathologic correlate of PIRA, adding to previously described findings of enhanced brain and spinal cord atrophy and nonresolving compartmentalized inflammation in PRLs. 4-7 The observed DTI changes suggest the potential involvement of neurodegenerative processes including Wallerian degeneration, in the NAWM of pwMS experiencing PIRA. The simultaneous increases in AD and RD point to co-occurring axonal and myelin damage, reflecting more extensive WM disruption in line with progressive neurodegeneration in MS. Remarkably, the pattern of NAWM damage was confirmed in the subgroup of patients with PIRMA, where any detectable focal inflammatory activity was excluded.
Our study has some limitations. First, although our focus was on a clinically compatible MRI approach, the use of DTI may introduce challenges in interpreting findings at the microstructural level, particularly in areas with crossing tracts. To mitigate this bias, we restricted our analyses to major WM tracts with a single predominant orientation. Second, DTI metrics were available for a single time-point after the 4-year follow-up, which was defined a priori as a plausible time interval to assess the relationship between PIRA occurrence and DTI alterations. 5 Third, although our cohort consisted of patients with a typical RRMS course, their relatively long disease duration precluded the investigation of WM degeneration in early stages of the disease.
In conclusion, our findings demonstrated that patients with PIRA are characterized by significant degeneration of major WM tracts, which can be identified using a clinically compatible DTI protocol.
Acknowledgment
The authors thank patients and relatives for their participation and support, study nurses in participating centers for their motivated collaboration and recruitment efforts, and the administrative personnel of the Swiss Multiple Sclerosis Cohort study.
Glossary
axial diffusivity
corpus callosum
corticospinal tract
diffusion tensor imaging
diffusion-weighted MRI
Expanded Disability Status Scale
fractional anisotropy
mean diffusivity
normal-appearing WM
progression independent of relapse activity
progression independent of relapse and MRI activity
paramagnetic rim lesion
people with multiple sclerosis
radial diffusivity
regions of interest
relapsing-remitting multiple sclerosis
University Hospital Basel
white matter
Author Contributions
M. Ocampo-Pineda: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. A. Cagol: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. P. Benkert: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M. Barakovic: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. P-J. Lu: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. J. Müller: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. S.A. Schaedelin: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. L. Melie-Garcia: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M. Weigel: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M.P. Sormani: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. L. Kappos: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; analysis or interpretation of data. J. Kuhle: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. C. Granziera: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data.
Study Funding
The authors report no targeted funding.
Disclosure
A. Cagol was supported by EUROSTAR E!113682 HORIZON2020 and received speaker honoraria from Novartis; M. Barakovic is an employee of Hays plc and a consultant for F. Hoffmann-La Roche Ltd; M. Weigel has received research funding by Biogen for developing spinal cord MRI; J. Kuhle received speaker fees, research support, travel support, and/or served on advisory boards by Swiss MS Society, Swiss National Research Foundation (320030_189140/1), University of Basel, Progressive MS Alliance, Bayer, Biogen, Bristol Myers Squibb, Celgene, Merck, Novartis, Octave Bioscience, Roche, and Sanofi; L. Kappos has received no personal compensation. His institutions (University Hospital Basel [USB]/Foundation Clinical Neuroimmunology and Neuroscience Basel) have received and used exclusively for research support: payments for steering committee and advisory board participation, consultancy services, and participation in educational activities from Actelion, Bayer, Bristol Myers Squibb (BMS), df-mp Molnia & Pohlmann, Celgene, Eli Lilly, Emmanuel Merck, Darmstadt (EMD) Serono, Genentech, Glaxo Smith Kline, Janssen, Japan Tobacco, Merck, MH Consulting, Minoryx, Novartis, F. Hoffmann-La Roche Ltd, Senda Biosciences Inc., Sanofi, Santhera, Shionogi BV, TG Therapeutics, and Wellmera, and license fees for Neurostatus-UHB products; grants from Novartis, Innosuisse, and Roche. C. Granziera: The USB, as the employer of Cristina Granziera, has received the following fees which were used exclusively for research support: (1) advisory board and consultancy fees from Actelion, Genzyme-Sanofi, Novartis, GeNeuro, and Roche; (2) speaker fees from Genzyme-Sanofi, Novartis, GeNeuro, and Roche; (3) research support from Siemens, GeNeuro, and Roche. C. Granziera was supported by the Swiss National Science Foundation grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung, and the EUROSTAR E!113682 HORIZON2020. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NN .
References
Associated Data
Data Availability Statement
The data supporting this study's findings are available on reasonable request.
In people with multiple sclerosis (pwMS), disability can accumulate because of incomplete recovery from relapses or through progression independent of relapse activity (PIRA). 1 A growing body of evidence highlights PIRA as a frequent phenomenon across all MS phenotypes, 2 including the typical relapsing-remitting multiple sclerosis (RRMS) form. 2
PIRA is associated with unfavorable clinical outcomes, 3 emphasizing the critical need to effectively address it for improving long-term prognosis in pwMS. Achieving this goal necessitates a comprehensive understanding of the pathophysiologic mechanisms underlying PIRA. In addition, identifying biomarkers sensitive to the degenerative changes linked to PIRA is crucial for guiding personalized therapeutic strategies.
Previous investigations have linked PIRA to accelerated brain and spinal cord atrophy, 2,4-7 and compartmentalized chronic inflammation indicated by brain paramagnetic rim lesions (PRLs). 5 Although it is plausible that degeneration in white matter (WM) tracts may also be implicated in the pathogenesis of PIRA, data on this aspect are currently lacking. To address this gap, we aimed to investigate the relationship between PIRA and the integrity of major WM tracts by conducting a diffusion tensor imaging (DTI) study, using a magnetic resonance imaging (MRI) protocol compatible with clinical practice. DTI is a quantitative technique based on diffusion-weighted MRI (DW-MRI) that allows for exploring the magnitude and direction of water diffusion in biological tissues, providing insights into their microstructural architecture. 8 In addition, we explored DTI changes in progression independent of relapse and MRI activity (PIRMA), a recent concept introduced to specifically reflect disability accumulation in the absence of both relapses and focal MRI activity. 2
Methods
Participants
From the Swiss Multiple Sclerosis Cohort study, 9 we selected individuals with (1) a diagnosis of RRMS according to the 2017 revised McDonald criteria, (2) availability of a brain MRI scan including a standardized protocol suitable for DTI reconstruction, and (3) availability of regular clinical follow-up over a predefined 4-year period preceding the DTI scan.
The study follows the Strengthening the Reporting of Observational Studies guideline.
Standard Protocol Approvals, Registrations, and Patient Consents
The local ethics committee approved the study, and all patients provided written informed consent.
Clinical Data
All patients underwent standardized neurologic assessments, using the Expanded Disability Status Scale (EDSS) score. 10 The occurrence of PIRA during follow-up was defined as an event of EDSS increase (≥1.5/≥1.0/≥0.5 points if baseline EDSS was 0/1.0–5.5/>5.5, respectively) using a roving reference, confirmed at least after 6 months, in the absence of relapses (1) between the EDSS increase and the precedent reference visit (performed ≥90 days before the EDSS increase) and (2) between the EDSS increase and its confirmation. 2,5
MRI Acquisition and Analysis
MRI scans were obtained with a 3T magnetic resonance system (Siemens Skyra); the protocol included (1) 3D, 1 mm isotropic fluid-attenuated inversion recovery; (2) 3D, 1 mm isotropic magnetization prepared rapid gradient echo; and (3) single-shell DW-MRI (resolution: 1.8 × 1.8 × 1.8 mm 3 ; 20 b-value = 1,000 seconds/mm 2 and 10 b-value = 0 seconds/mm 2 ).
WM lesions were identified automatically 11 and manually corrected. DW-MRI images were denoised and corrected for ringing artifacts, eddy current distortions, misaligning artifacts, and the bias field. Maps of fractional anisotropy (FA), radial diffusivity (RD), mean diffusivity (MD), and axial diffusivity (AD) were reconstructed with MRtrix (version: 3.0.3). 12 Mean FA, RD, MD, and AD values were quantified in 7 regions of interest (ROIs), which were identified using the Johns Hopkins University DTI-based WM atlas 13 : the genu, body, and splenium of the corpus callosum (CC); the superior corona radiata, the posterior limb of internal capsule, cerebral peduncle, and the corticospinal tract (CST). DTI metrics were extracted after registering the DTI maps to the MNI152 space. WM lesions were also registered to the MNI152 space and used to exclude lesional tissue from the ROIs, limiting the analysis to the normal-appearing WM (NAWM).
A subgroup of patients with PIRMA was identified by assessing the absence of new/enlarging lesions during the 4-year follow-up. 14 Concomitant absence of contrast-enhancing lesions was also confirmed in cases where gadolinium was administered (86.7% of total scans).
Statistical Analysis
The statistical analysis was conducted in R .
DTI metrics were compared between patients with and without episodes of PIRA during clinical follow-up, after propensity score matching of the groups. Specifically, a full propensity score weighting was obtained using age, sex, disease duration, annualized relapse rate in the prior 2 years, use of disease-modifying therapies, and total WM lesion volume as criteria. Between-group differences were then assessed with linear regression models, including the weights obtained from propensity score matching. Results were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate.
In addition, we conducted 3 separate sensitivity analyses: (1) including EDSS as an additional criterion for propensity score matching; (2) including the lesion load in each ROI as an additional covariate in the regression models; and (3) restricting the PIRA group to the subset of patients with PIRMA.
Data Availability
The data supporting this study's findings are available on reasonable request.
Results
In total, 258 pwMS followed up between 2016 and 2022 were included in the study. Patients who had experienced PIRA during the preceding 4-year follow-up (n = 39) exhibited higher disability ( p < 0.001) and T2-lesion load ( p = 0.01) at MRI compared with those without, while showing no significant differences in age, sex distribution, disease duration, relapse rate, and treatment regimen ( Table 1 ).
Table 1 Demographic and Clinical Characteristics of Patients With and Without PIRA Before and After Matching Before matching After matching Patients with PIRA Patients without PIRA SMD Patients with PIRA Patients without PIRA SMD Main analysis Participants, n 39 219 Age, mean (SD), y 50.3 (12.1) 48.0 (11.3) 0.19 50.3 49.9 0.03 Female, n (%) 29 (74.4) 140 (63.9) 0.24 (74.4) (73.1) 0.03 Disease duration, mean (SD), y 18.8 (9.7) 16.5 (8.6) 0.24 18.8 18.4 0.04 ARR previous 2 y, median (IQR) 0 (0; 0) 0 (0; 0) 0.18 0 (0; 0) 0 (0; 0) 0.06 DMT class Platform, n (%) 2 (5.1) 20 (9.1) Oral, n (%) 17 (43.6) 115 (52.5) mAb, n (%) 14 (35.9) 62 (28.3) Untreated, n (%) 6 (15.4) 22 (10.0) T2-lesion volume, median (IQR), ml 9.3 (4.0; 24.5) 4.8 (1.4; 15.8) 0.31 9.3 (4.0; 24.2) 9.4 (2.1; 23.6) −0.08 Sensitivity analysis including EDSS EDSS, median (IQR) 3.5 (2.75; 4.25) 2.0 (1.5; 3.0) 0.83 3.5 (2.5; 4.0) 3.5 (2.5; 4.5) −0.08 Abbreviations: ARR = annualized relapse rate; DMT = disease-modifying therapy; EDSS = Expanded Disability Status Scale; IQR = interquartile range; mAb = monoclonal antibodies; PIRA = progression independent of relapse activity; SMD = standardized mean difference. Platform treatments included glatiramer acetate and interferon preparations; oral treatments included cladribine, dimethyl fumarate, fingolimod, ozanimod, and teriflunomide; monoclonal antibody treatments included natalizumab, ocrelizumab, and rituximab.
Demographic and Clinical Characteristics of Patients With and Without PIRA Before and After Matching
Abbreviations: ARR = annualized relapse rate; DMT = disease-modifying therapy; EDSS = Expanded Disability Status Scale; IQR = interquartile range; mAb = monoclonal antibodies; PIRA = progression independent of relapse activity; SMD = standardized mean difference.
Platform treatments included glatiramer acetate and interferon preparations; oral treatments included cladribine, dimethyl fumarate, fingolimod, ozanimod, and teriflunomide; monoclonal antibody treatments included natalizumab, ocrelizumab, and rituximab.
Patients with PIRA displayed alterations in DTI metrics indicative of more severe WM damage than those without PIRA. Significant between-group differences were observed for FA in the genu of CC; RD in the genu and splenium of CC; MD in the genu of CC, splenium of CC, CST, and SCR; AD in the splenium of CC and CST ( Figure ).
Comparison of DTI Metrics Between Patients With and Without PIRA
DTI metrics in each group are displayed as the estimated marginal mean (and associated 95% CI) derived from the regression models. Significant differences are presented in bold. CC = corpus callosum; CST = corticospinal tract; CP = cerebral peduncle; DTI = diffusion tensor imaging; PIRA = progression independent of relapse activity; PLIC = posterior limb of internal capsule; SCR = superior corona radiata.
Consistent results were observed in the sensitivity analyses ( Table 2 , eTables 1 and 2).
Table 2 Sensitivity Analysis: Comparisons of DTI Metrics Between Patients With and Without PIRMA DTI metric ROI Patients with PIRMA (n = 19) Patients without PIRMA (n = 219) p Value Fractional anisotropy Genu CC 0.502 (0.480–0.524) 0.535 (0.521–0.548)
0.0090
Body CC 0.568 (0.547–0.589) 0.586 (0.573–0.599) 0.2455 Splenium CC 0.562 (0.537–0.587) 0.592 (0.577–0.608)
0.0382
CST 0.457 (0.441–0.473) 0.457 (0.447–0.467) 0.9910 CP 0.543 (0.528–0.558) 0.552 (0.543–0.561) 0.4788 PLIC 0.594 (0.581–0.606) 0.589 (0.581–0.597) 0.6540 SCR 0.469 (0.453–0.486) 0.471 (0.461–0.481) 0.8676 Radial diffusivity (mm 2 /second) Genu CC 6.55*10 −4 (6.18*10 −4 –6.91*10 −4 ) 5.91*10 −4 (5.69*10 −4 –6.14*10 −4 )
0.0024
Body CC 5.54*10 −4 (5.16*10 −4 –5.92*10 −4 ) 5.31*10 −4 (5.08*10 −4 –5.54*10 −4 ) 0.4650 Splenium CC 5.67*10 −4 (5.33*10 −4 –6.01*10 −4 ) 4.98*10 −4 (4.78*10 −4 –5.19*10 −4 )
0.0003
CST 5.18*10 −4 (5.02*10 −4 –5.35*10 −4 ) 5.13*10 −4 (5.03*10 −4 –5.23*10 −4 ) 0.6540 CP 5.72*10 −4 (5.48*10 −4 –5.96*10 −4 ) 5.62*10 −4 (5.47*10 −4 –5.76*10 −4 ) 0.6540 PLIC 4.13*10 −4 (4.02*10 −4 –4.25*10 −4 ) 4.21*10 −4 (4.14*10 −4 –4.28*10 −4 ) 0.3826 SCR 5.01*10 −4 (4.86*10 −4 –5.17*10 −4 ) 4.97*10 −4 (4.88*10 −4 –5.06*10 −4 ) 0.6540 Mean diffusivity (mm 2 /second) Genu CC 9.37*10 −4 (9.04*10 −4 –9.70*10 −4 ) 8.86*10 −4 (8.66*10 −4 –9.06*10 −4 )
0.0070
Body CC 8.72*10 −4 (8.37*10 −4 –9.07*10 −4 ) 8.60*10 −4 (8.38*10 −4 –8.81*10 −4 ) 0.6540 Splenium CC 8.83*10 −4 (8.57*10 −4 –9.09*10 −4 ) 8.24*10 −4 (8.09*10 −4 –8.40*10 −4 )
<0.0001
CST 7.02*10 −4 (6.88*10 −4 –7.16*10 −4 ) 6.96*10 −4 (6.88*10 −4 –7.05*10 −4 ) 0.6540 CP 8.26*10 −4 (8.06*10 −4 –8.46*10 −4 ) 8.22*10 −4 (8.10*10 −4 –8.35*10 −4 ) 0.7524 PLIC 6.80*10 −4 (6.71*10 −4 –6.89*10 −4 ) 6.86*10 −4 (6.81*10 −4 –6.92*10 −4 ) 0.3634 SCR 6.95*10 −4 (6.81*10 −4 –7.10*10 −4 ) 6.90*10 −4 (6.82*10 −4 –6.99*10 −4 ) 0.6540 Axial diffusivity (mm 2 /second) Genu CC 1.50*10 −3 (1.46*10 −3 –1.54*10 −3 ) 1.47*10 −3 (1.45*10 −3 –1.50*10 −3 ) 0.3634 Body CC 1.51*10 −3 (1.47*10 −3 –1.55*10 −3 ) 1.52*10 −3 (1.49*10 −3 –1.54*10 −3 ) 0.7524 Splenium CC 1.52*10 −3 (1.49*10 −3 –1.55*10 −3 ) 1.48*10 −3 (1.46*10 −3 –1.49*10 −3 )
0.0260
CST 1.07*10 −3 (1.05*10 −3 –1.09*10 −3 ) 1.06*10 −3 (1.05*10 −3 –1.08*10 −3 ) 0.6540 CP 1.34*10 −3 (1.31*10 −3 –1.36*10 −3 ) 1.35*10 −3 (1.33*10 −3 –1.36*10 −3 ) 0.6540 PLIC 1.21*10 −3 (1.19*10 −3 –1.23*10 −3 ) 1.22*10 −3 (1.21*10 −3 –1.23*10 −3 ) 0.7524 SCR 1.08*10 −3 (1.06*10 −3 –1.11*10 −3 ) 1.08*10 −3 (1.06*10 −3 –1.09*10 −3 ) 0.7524 Abbreviations: CC = corpus callosum; CP = cerebral peduncle; CST = corticospinal tract; DTI = diffusion tensor imaging; PLIC = posterior limb of internal capsule; PIRMA= progression independent of relapse and MRI activity; ROI = regions of interest; SCR = superior corona radiata. The estimates within each group are the marginal means (95% CIs) derived from the regression models. Reported p values are false discovery rate adjusted. Significant differences are presented in bold.
Sensitivity Analysis: Comparisons of DTI Metrics Between Patients With and Without PIRMA
Abbreviations: CC = corpus callosum; CP = cerebral peduncle; CST = corticospinal tract; DTI = diffusion tensor imaging; PLIC = posterior limb of internal capsule; PIRMA= progression independent of relapse and MRI activity; ROI = regions of interest; SCR = superior corona radiata.
The estimates within each group are the marginal means (95% CIs) derived from the regression models. Reported p values are false discovery rate adjusted. Significant differences are presented in bold.
Discussion
Our study showed that pwMS experiencing PIRA exhibit increased damage in major WM tracts compared with counterparts without PIRA, matched for relevant demographic, clinical, and conventional MRI characteristics. Between-group differences were evident across all the examined DTI-derived quantitative maps, encompassing WM structures spanning both the CC and motor tracts.
DTI enables straightforward mathematical representation of diffusion signals, providing measures sensitive to tissue integrity at both macrostructural and microstructural levels. Although pathologic studies have reported associations between myelin content and FA, AD, and RD, as well as between MD and FA and axonal content, 15,16 inherent limitations of DTI—such as its inability to accurately represent complex tissue architecture—pose challenges in interpreting DTI metrics at the microstructural level. Nonetheless, DTI demonstrates good sensitivity to tissue injury associated with MS, with reported correlations between the severity of changes and both neurologic and cognitive disability. 17 Previous research has also indicated associations between DTI metrics and disability progression 18 ; our study extends these findings by demonstrating that DTI is sensitive to the specific component of disability progression attributable to PIRA.
Our results collectively indicate microstructural damage in major WM tracts as a pathologic correlate of PIRA, adding to previously described findings of enhanced brain and spinal cord atrophy and nonresolving compartmentalized inflammation in PRLs. 4-7 The observed DTI changes suggest the potential involvement of neurodegenerative processes including Wallerian degeneration, in the NAWM of pwMS experiencing PIRA. The simultaneous increases in AD and RD point to co-occurring axonal and myelin damage, reflecting more extensive WM disruption in line with progressive neurodegeneration in MS. Remarkably, the pattern of NAWM damage was confirmed in the subgroup of patients with PIRMA, where any detectable focal inflammatory activity was excluded.
Our study has some limitations. First, although our focus was on a clinically compatible MRI approach, the use of DTI may introduce challenges in interpreting findings at the microstructural level, particularly in areas with crossing tracts. To mitigate this bias, we restricted our analyses to major WM tracts with a single predominant orientation. Second, DTI metrics were available for a single time-point after the 4-year follow-up, which was defined a priori as a plausible time interval to assess the relationship between PIRA occurrence and DTI alterations. 5 Third, although our cohort consisted of patients with a typical RRMS course, their relatively long disease duration precluded the investigation of WM degeneration in early stages of the disease.
In conclusion, our findings demonstrated that patients with PIRA are characterized by significant degeneration of major WM tracts, which can be identified using a clinically compatible DTI protocol.
Acknowledgment
The authors thank patients and relatives for their participation and support, study nurses in participating centers for their motivated collaboration and recruitment efforts, and the administrative personnel of the Swiss Multiple Sclerosis Cohort study.
Glossary
axial diffusivity
corpus callosum
corticospinal tract
diffusion tensor imaging
diffusion-weighted MRI
Expanded Disability Status Scale
fractional anisotropy
mean diffusivity
normal-appearing WM
progression independent of relapse activity
progression independent of relapse and MRI activity
paramagnetic rim lesion
people with multiple sclerosis
radial diffusivity
regions of interest
relapsing-remitting multiple sclerosis
University Hospital Basel
white matter
Author Contributions
M. Ocampo-Pineda: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. A. Cagol: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. P. Benkert: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M. Barakovic: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. P-J. Lu: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. J. Müller: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. S.A. Schaedelin: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. L. Melie-Garcia: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M. Weigel: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. M.P. Sormani: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. L. Kappos: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; analysis or interpretation of data. J. Kuhle: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. C. Granziera: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data.
Study Funding
The authors report no targeted funding.
Disclosure
A. Cagol was supported by EUROSTAR E!113682 HORIZON2020 and received speaker honoraria from Novartis; M. Barakovic is an employee of Hays plc and a consultant for F. Hoffmann-La Roche Ltd; M. Weigel has received research funding by Biogen for developing spinal cord MRI; J. Kuhle received speaker fees, research support, travel support, and/or served on advisory boards by Swiss MS Society, Swiss National Research Foundation (320030_189140/1), University of Basel, Progressive MS Alliance, Bayer, Biogen, Bristol Myers Squibb, Celgene, Merck, Novartis, Octave Bioscience, Roche, and Sanofi; L. Kappos has received no personal compensation. His institutions (University Hospital Basel [USB]/Foundation Clinical Neuroimmunology and Neuroscience Basel) have received and used exclusively for research support: payments for steering committee and advisory board participation, consultancy services, and participation in educational activities from Actelion, Bayer, Bristol Myers Squibb (BMS), df-mp Molnia & Pohlmann, Celgene, Eli Lilly, Emmanuel Merck, Darmstadt (EMD) Serono, Genentech, Glaxo Smith Kline, Janssen, Japan Tobacco, Merck, MH Consulting, Minoryx, Novartis, F. Hoffmann-La Roche Ltd, Senda Biosciences Inc., Sanofi, Santhera, Shionogi BV, TG Therapeutics, and Wellmera, and license fees for Neurostatus-UHB products; grants from Novartis, Innosuisse, and Roche. C. Granziera: The USB, as the employer of Cristina Granziera, has received the following fees which were used exclusively for research support: (1) advisory board and consultancy fees from Actelion, Genzyme-Sanofi, Novartis, GeNeuro, and Roche; (2) speaker fees from Genzyme-Sanofi, Novartis, GeNeuro, and Roche; (3) research support from Siemens, GeNeuro, and Roche. C. Granziera was supported by the Swiss National Science Foundation grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung, and the EUROSTAR E!113682 HORIZON2020. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NN .
References
Associated Data
Data Availability Statement
The data supporting this study's findings are available on reasonable request.