Abstract
Progressive multiple sclerosis poses a considerable challenge in the evaluation of disease progression and treatment response owing to its multifaceted pathophysiology. Traditional clinical measures such as the Expanded Disability Status Scale are limited in capturing the full scope of disease and treatment effects. Advanced imaging techniques, including MRI and PET scans, have emerged as valuable tools for the assessment of neurodegenerative processes, including the respective role of adaptive and innate immunity, detailed insights into brain and spinal cord atrophy, lesion dynamics and grey matter damage. The potential of cerebrospinal fluid and blood biomarkers is increasingly recognized, with neurofilament light chain levels being a notable indicator of neuro-axonal damage. Moreover, patient-reported outcomes are crucial for reflecting the subjective experience of disease progression and treatment efficacy, covering aspects such as fatigue, cognitive function and overall quality of life. The future incorporation of digital technologies and wearable devices in research and clinical practice promises to enhance our understanding of functional impairments and disease progression. This Review offers a comprehensive examination of these diverse evaluation tools, highlighting their combined use in accurately assessing disease progression and treatment efficacy in progressive multiple sclerosis, thereby guiding more effective therapeutic strategies.
Key points
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Effective treatment of progressive multiple sclerosis (MS) remains an urgent medical need.
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The recent approvals of treatments for progressive forms of MS highlight the importance of better disease monitoring measures in clinical trials and practice.
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Traditional MRI biomarkers do not adequately track progressive MS. Advances in MRI, such as brain atrophy and lesion volume analysis, show promise in assessing disease progression and response to treatment.
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Remyelination is key in MS neuroprotection. MRI techniques such as magnetization transfer and myelin water fraction imaging, alongside PET scans, provide deeper insights into myelin repair and inflammation.
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Changes in optical coherence tomography, a non-invasive imaging modality that measures retinal layer thickness, reflect brain atrophy and MS progression, offering a valuable window into neurodegeneration and treatment efficacy.
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Body fluid biomarkers, such as neurofilament light in blood, and immune activation and neuronal damage markers in cerebrospinal fluid are emerging as important tools for assessing disease activity and treatment response in progressive MS.
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Patient-reported outcomes capture the unique experience of individuals with MS, which is of particular importance in progressive forms of the disease. These assessments can help evaluate hidden symptoms such as fatigue and cognitive impairment and are becoming vital in clinical trials and routine practice.
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References
Lublin, F. D. et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 83, 278–286 (2014).
Kappos, L. et al. Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. 77, 1132–1140 (2020).
Lublin, F. D. et al. How patients with multiple sclerosis acquire disability. Brain 145, 3147–3161 (2022).
Lassmann, H. Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8, a028936 (2018).
Kuhlmann, T. et al. Multiple sclerosis progression: time for a new mechanism-driven framework. Lancet Neurol. 22, 78–88 (2023).
Ocrevus (ocrelizumab) product information. https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/761053s012lbl.pdf (Genentech, Inc., Roche Group, 2018).
European Medicines Agency (EMA). Assessment Report: Ocrevus. https://www.ema.europa.eu/en/documents/variation-report/ocrevus-h-c-004043-x-0039-epar-assessment-report-variation_en.pdf (2017).
Mayzent (siponimod) product information. https://www.novartis.com/us-en/sites/novartis_us/files/mayzent.pdf (Novartis Pharmaceuticals Corporation, 2023).
Novartis Pharma GmbH. Mayzent summary of product characteristics. ema.europa.eu/en/documents/product-information/mayzent-epar-product-information_en.pdf (2023).
Montalban, X. et al. Ocrelizumab versus placebo in primary progressive multiple sclerosis. N. Engl. J. Med. 376, 209–220 (2017).
Kappos, L. et al. Siponimod versus placebo in secondary progressive multiple sclerosis (EXPAND): a double-blind, randomised, phase 3 study. Lancet 391, 1263–1273 (2018).
Dalla Costa, G. et al. A wearable device perspective on the standard definitions of disability progression in multiple sclerosis. Mult. Scler. 30, 103–112 (2024).
Hechenberger, S. et al. Information processing speed as a prognostic marker of physical impairment and progression in patients with multiple sclerosis. Mult. Scler. Relat. Disord. 57, 103353 (2022).
Koch, M. W. et al. Reliability of outcome measures in clinical trials in secondary progressive multiple sclerosis. Neurology 96, e111–e120 (2021).
Lublin, F. et al. Oral fingolimod in primary progressive multiple sclerosis (INFORMS): a phase 3, randomised, double-blind, placebo-controlled trial. Lancet 387, 1075–1084 (2016).
LaRocca, N. G. et al. The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability. Mult. Scler. 24, 1469–1484 (2018).
Zhang, T. et al. CIHR Team in the Epidemiology and Impact of Comorbidity on Multiple Sclerosis. Effects of physical comorbidities on disability progression in multiple sclerosis. Neurology 90, e419–e427 (2018).
Salter, A., Lancia, S., Kowalec, K., Fitzgerald, K. C. & Marrie, R. A. Investigating the prevalence of comorbidity in multiple sclerosis clinical trial populations. Neurology 102, e209135 (2024).
Marrie, R. A. et al. Etiology, effects and management of comorbidities in multiple sclerosis: recent advances. Front. Immunol. 14, 1197195 (2023).
Chard, D. & Trip, S. A. Resolving the clinico-radiological paradox in multiple sclerosis. F1000Res 6, 1828 (2017).
Jacobsen, C. et al. Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J. Neurol. Neurosurg. Psychiatry 85, 1109–1115 (2014).
Ghione, E. et al. Brain atrophy is associated with disability progression in patients with MS followed in a clinical routine. Am. J. Neuroradiol. 39, 2237–2242 (2018).
Zivadinov, R. et al. Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Rev. Neurother. 16, 777–793 (2016).
Rocca, M. A. et al. Brain MRI atrophy quantification in MS: from methods to clinical application. Neurology 88, 403–413 (2017).
Kim, Y., Varosanec, M., Kosa, P. & Bielekova, B. Confounder-adjusted MRI-based predictors of multiple sclerosis disability. Front. Radiol. 2, 971157 (2022).
Ingle, G. T. et al. Primary progressive multiple sclerosis: a 5-year clinical and MR study. Brain 126, 2528–2536 (2003).
University of California SFMSETet al. Silent progression in disease activity-free relapsing multiple sclerosis. Ann. Neurol. 85, 653–666 (2019).
Sprenger, T. et al. Association of brain volume loss and long-term disability outcomes in patients with multiple sclerosis treated with teriflunomide. Mult. Scler. 26, 1207–1216 (2020).
Tsivgoulis, G. et al. The effect of disease modifying therapies on disease progression in patients with relapsing–remitting multiple sclerosis: a systematic review and meta-analysis. PLoS ONE 10, e0144538 (2015).
Sormani, M. P., Arnold, D. L. & De Stefano, N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann. Neurol. 75, 43–49 (2014).
Guevara, C. et al. Prospective assessment of no evidence of disease activity-4 status in early disease stages of multiple sclerosis in routine clinical practice. Front. Neurol. 10, 788 (2019).
Rovira, À. et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat. Rev. Neurol. 11, 471–482 (2015).
Hanninen, K. et al. Thalamic atrophy without whole brain atrophy is associated with absence of 2-year NEDA in multiple sclerosis. Front. Neurol. 10, 459 (2019).
van de Pavert, S. H. P. et al. DIR-visible grey matter lesions and atrophy in multiple sclerosis: partners in crime? J. Neurol. Neurosurg. Psychiatry 87, 461–467 (2016).
Calabrese, M. et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing–remitting multiple sclerosis. Arch. Neurol. 66, 1144–1150 (2009).
Popescu, B. F. G. et al. What drives MRI-measured cortical atrophy in multiple sclerosis. Mult. Scler. 21, 1280–1290 (2015).
De Stefano, N. et al. Evidence of early cortical atrophy in MS. Relevance to white matter changes and disability. Neurology 60, 1157–1162 (2013).
Steenwijk, M. D. et al. What explains gray matter atrophy in long-standing multiple sclerosis? Radiology https://doi.org/10.1148/radiol.14132708 (2014).
Calabrese, M. et al. Exploring the origins of grey matter damage in multiple sclerosis. Nat. Rev. Neurosci. https://doi.org/10.1038/nrn3900 (2015).
Sepulcre, J. et al. Contribution of white matter lesions to gray matter atrophy in multiple sclerosis: evidence from voxel-based analysis of T1 lesions in the visual pathway. Arch. Neurol. 66, 173–179 (2009).
Mühlau, M. et al. White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI. Mult. Scler. 19, 1485–1492 (2013).
Geurts, J. J. G. et al. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 11, 1082–1092 (2012).
Zivadinov, R. et al. A serial 10-year follow-up study of atrophied brain lesion volume and disability progression in patients with relapsing–remitting MS. Am. J. Neuroradiol. 40, 446–452 (2019).
Genovese, A. V. et al. Atrophied brain T2 lesion volume at MRI is associated with disability progression and conversion to secondary progressive multiple sclerosis. Radiology 293, 424–433 (2019).
Dal-Bianco, A. et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol. 133, 25–42 (2017).
Zhang, Y. et al. Quantitative susceptibility mapping and R2* measured changes during white matter lesion development in multiple sclerosis: myelin breakdown, myelin debris degradation and removal, and iron accumulation. Am. J. Neuroradiol. 37, 1629–1635 (2016).
Dal-Bianco, A. et al. Long-term evolution of multiple sclerosis iron rim lesions in 7 T MRI. Brain 144, 833–847 (2021).
Ng Kee Kwong, K. C. et al. The prevalence of paramagnetic rim lesions in multiple sclerosis: a systematic review and meta-analysis. PLoS ONE 16, e0256845 (2021).
Absinta, M. et al. Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol. 76, 1474–1483 (2019).
Hemond, C. C. et al. Paramagnetic rim lesions are associated with pathogenic CSF profiles and worse clinical status in multiple sclerosis: a retrospective cross-sectional study. Mult. Scler. 28, 2046–2056 (2022).
Altokhis, A. I. et al. Longitudinal clinical study of patients with iron rim lesions in multiple sclerosis. Mult. Scler. 28, 2202–2211 (2022).
Absinta, M. et al. Identification of chronic active multiple sclerosis lesions on 3 T MRI. Am. J. Neuroradiol. 39, 1233–1238 (2018).
Renner, B. et al. S1.5: Diagnostic potential of paramagnetic rim lesions for MS in a multicenter setting. In Proc. ACTRIMS Forum 2022 - Invited Program. Mult. Scler. J. 28 (suppl), 3–19 (2022).
Elliott, C. et al. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis. Brain 142, 2787–2799 (2019).
Harrison, D. M. et al. Lesion heterogeneity on high-field susceptibility MRI is associated with multiple sclerosis severity. Am. J. Neuroradiol. 37, 1447–1453 (2016).
Elliott, C. et al. Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Mult. Scler. 25, 1915–1925 (2019).
Beynon, V. et al. Chronic lesion activity and disability progression in secondary progressive multiple sclerosis. BMJ Neurol. Open 4, e000240 (2022).
Elliott, C. et al. MRI characteristics of chronic MS lesions by phase rim detection and/or slowly expanding properties (4101). Neurology 96, 4101 (2021).
Calvi, A. et al. Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis. Mult. Scler. 29, 352–362 (2023).
Elliott, C. et al. Lesion-level correspondence and longitudinal properties of paramagnetic rim and slowly expanding lesions in multiple sclerosis. Mult. Scler. 29, 680–690 (2023).
Harrison, D. M. et al. Leptomeningeal enhancement at 7 T in multiple sclerosis: frequency, morphology, and relationship to cortical volume. J. Neuroimaging 27, 461–468 (2017).
Zivadinov, R. et al. Leptomeningeal contrast enhancement is associated with progression of cortical atrophy in MS: a retrospective, pilot, observational longitudinal study. Mult. Scler. 23, 1336–1345 (2017).
Ighani, M. et al. No association between cortical lesions and leptomeningeal enhancement on 7-tesla MRI in multiple sclerosis. Mult. Scler. 26, 165–176 (2020).
Bevan, R. J. et al. Meningeal inflammation and cortical demyelination in acute multiple sclerosis. Ann. Neurol. 84, 829–842 (2018).
Bergsland, N. et al. Leptomeningeal contrast enhancement is related to focal cortical thinning in relapsing–remitting multiple sclerosis: a cross-sectional MRI study. Am. J. Neuroradiol. 40, 620–625 (2019).
Makshakov, G. et al. Leptomeningeal contrast enhancement is associated with disability progression and grey matter atrophy in multiple sclerosis. Neurol. Res. Int. 2017, 8652463 (2017).
Müller, J. Choroid plexus volume in multiple sclerosis vs neuromyelitis optica spectrum disorder: a retrospective, cross-sectional analysis. Neurol. Neuroimmunol. Neuroinflamm. 9, e1147 (2022).
Klistorner, S. et al. Choroid plexus volume in multiple sclerosis predicts expansion of chronic lesions and brain atrophy. Ann. Clin. Transl. Neurol. 9, 1528–1537 (2022).
Eden, D. et al. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord. Brain 142, 633–646 (2019).
Casserly, C. et al. Spinal cord atrophy in multiple sclerosis: a systematic review and meta-analysis. J. Neuroimaging 28, 556–586 (2018).
Singhal, T. et al. The effect of glatiramer acetate on spinal cord volume in relapsing–remitting multiple sclerosis. J. Neuroimaging 27, 33–36 (2017).
Dupuy, S. L. et al. The effect of intramuscular interferon beta-1a on spinal cord volume in relapsing–remitting multiple sclerosis. BMC Med. Imaging 16, 56 (2016).
Cawley, N. et al. Spinal cord atrophy as a primary outcome measure in phase II trials of progressive multiple sclerosis. Mult. Scler. 24, 932–941 (2018).
Allen, I., McQuaid, S., Mirakhur, M. & Nevin, G. Pathological abnormalities in the normal-appearing white matter in multiple sclerosis. J. Neurol. Sci. 22, 141–144 (2001).
Zeis, T., Graumann, U., Reynolds, R. & Schaeren-Wiemers, N. Normal-appearing white matter in multiple sclerosis is in a subtle balance between inflammation and neuroprotection. Brain 131, 288–303 (2007).
Dutta, D. J. et al. Regulation of myelin structure and conduction velocity by perinodal astrocytes. Proc. Natl Acad. Sci. USA 115, 11832–11837 (2018).
Mesaros, S. et al. Thalamic damage predicts the evolution of primary-progressive multiple sclerosis at 5 years. Am. J. Neuroradiol. 32, 1016–1020 (2011).
Kolasa, M. et al. Diffusion tensor imaging and disability progression in multiple sclerosis: a 4-year follow-up study. Brain Behav. 9, e01194 (2019).
Bodini, B. et al. Corpus callosum damage predicts disability progression and cognitive dysfunction in primary-progressive MS after five years. Hum. Brain Mapp. 34, 1163–1172 (2013).
Franklin, R. J. M. & Simons, M. CNS remyelination and inflammation: from basic mechanisms to therapeutic opportunities. Neuron 110, 3549–3565 (2022).
Patrikios, P. et al. Remyelination is extensive in a subset of multiple sclerosis patients. Brain 129, 3165–3172 (2006).
Lubetzki, C. et al. Remyelination in multiple sclerosis: from basic science to clinical translation. Lancet Neurol. 19, 678–688 (2020).
Schultz, V. et al. Acutely damaged axons are remyelinated in multiple sclerosis and experimental models of demyelination. Glia 65, 1350–1360 (2017).
Irvine, K. A. & Blakemore, W. F. Remyelination protects axons from demyelination-associated axon degeneration. Brain 131, 1464–1477 (2008).
Kornek, B. et al. Multiple sclerosis and chronic autoimmune encephalomyelitis: a comparative quantitative study of axonal injury in active, inactive, and remyelinated lesions. Am. J. Pathol. 157, 267–276 (2000).
Ricigliano, V. A. G. et al. Spontaneous remyelination in lesions protects the integrity of surrounding tissues over time in multiple sclerosis. Eur. J. Neurol. 29, 1719–1729 (2022).
Stephan Bramow, J. M. et al. Demyelination versus remyelination in progressive multiple sclerosis. Brain 133, 2983–2998 (2010).
Van der Weijden, C. W. J. et al. Myelin quantification with MRI: a systematic review of accuracy and reproducibility. NeuroImage 226, 117561 (2021).
Moccia, M. et al. Pathologic correlates of the magnetization transfer ratio in multiple sclerosis. Neurology 95, e2965–e2976 (2020).
Mancini, M. et al. An interactive meta-analysis of MRI biomarkers of myelin. eLife 9, e61523 (2020).
Petiet, A. et al. Ultrahigh field imaging of myelin disease models: toward specific markers of myelin integrity? J. Comp. Neurol. 527, 2179–2189 (2019).
Hertanu, A. et al. Inhomogeneous magnetization transfer (ihMT) imaging in the acute cuprizone mouse model of demyelination/remyelination. NeuroImage 265, 11978 (2023).
Alsop, D. C. et al. Inhomogeneous magnetization transfer imaging: concepts and directions for further development. NMR Biomed. 36, e4543 (2023).
Chen, J. T. et al. Magnetization transfer ratio evolution with demyelination and remyelination in multiple sclerosis lesions. Ann. Neurol. 63, 254–262 (2008).
Lazzarotto, A. et al. Clinically relevant profiles of myelin content changes in patients with multiple sclerosis: a multimodal and multicompartment imaging study. Mult. Scler. 28, 1881–1890 (2022).
Chen, J. T. et al. Clinically feasible MTR is sensitive to cortical demyelination in MS. Neurology 80, 246–252 (2013).
Kolb, H. et al. 7 T MRI differentiates remyelinated from demyelinated multiple sclerosis lesions. Ann. Neurol. 90, 612–626 (2021).
Rahmanzadeh, R. et al. A new advanced MRI biomarker for remyelinated lesions in multiple sclerosis. Ann. Neurol. 92, 486–502 (2022).
Eshaghi, A. et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat. Commun. 12, 2078 (2021).
Ananthavarathan, P., Sahi, N. & Chard, D. T. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev. Neurother. 24, 201–216 (2024).
Stankoff, B. et al. Imaging of CNS myelin by positron-emission tomography. Proc. Natl Acad. Sci. USA 103, 9304–9309 (2006).
Stankoff, B. et al. Imaging central nervous system myelin by positron emission tomography in multiple sclerosis using [methyl-11C]-2-(4′-methylaminophenyl)-6-hydroxybenzothiazole. Ann. Neurol. 69, 673–680 (2011).
Wu, C. et al. Longitudinal positron emission tomography imaging for monitoring myelin repair in the spinal cord. Ann. Neurol. 74, 688–698 (2013).
Auvity, S. et al. Repurposing radiotracers for myelin imaging: a study comparing 18F-florbetaben, 18F-florbetapir, 18F-flutemetamol, 11C-MeDAS, and 11C-PiB. Eur. J. Nucl. Med. Mol. Imaging 47, 490–501 (2020).
Bodini, B. et al. Positron emission tomography in multiple sclerosis — straight to the target. Nat. Rev. Neurol. 17, 663–675 (2021).
Bodini, B. et al. Dynamic imaging of individual remyelination profiles in multiple sclerosis. Ann. Neurol. 79, 726–738 (2016).
Pytel, V. et al. Amyloid PET findings in multiple sclerosis are associated with cognitive decline at 18 months. Mult. Scler. Relat. Disord. 39, 101926 (2020).
Carotenuto, A. et al. [18F]florbetapir PET/MR imaging to assess demyelination in multiple sclerosis. Eur. J. Nucl. Med. Mol. Imaging 47, 366–378 (2020).
Zhang, M. et al. 18F-florbetapir PET/MRI for quantitatively monitoring myelin loss and recovery in patients with multiple sclerosis: a longitudinal study. eClinicalMedicine 37, 100982 (2021).
Van der Weijden, C. W. J. et al. Quantitative assessment of myelin density using [11C]MeDAS PET in patients with multiple sclerosis: a first-in-human study. Eur. J. Nucl. Med. Mol. Imaging 49, 3492–3507 (2022).
Ricigliano, V. A. G. & Stankoff, B. Choroid plexuses at the interface of peripheral immunity and tissue repair in multiple sclerosis. Curr. Opin. Neurol. 36, 214–221 (2023).
Wei, W. et al. Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis. Med. Image Anal. 58, 101546 (2019).
Wei, W. et al. Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis. NeuroImage 223, 117308 (2020).
Lazzarotto, A. et al. Time is myelin: early cortical myelin repair prevents atrophy and clinical progression in multiple sclerosis. Brain 147, 1331–1343 (2024).
El Behi, M. et al. Adaptive human immunity drives remyelination in a mouse model of demyelination. Brain 140, 967–980 (2017).
Heß, K. et al. Lesion stage-dependent causes for impaired remyelination in MS. Acta Neuropathol. 140, 359–375 (2020).
Yong, H. Y. F. & Yong, V. W. Mechanism-based criteria to improve therapeutic outcomes in progressive multiple sclerosis. Nat. Rev. Neurol. 18, 40–55 (2022).
Nutma, E. et al. A quantitative neuropathological assessment of translocator protein expression in multiple sclerosis. Brain 142, 3440–3455 (2019).
Hamzaoui, M. et al. Positron emission tomography with [18F]-DPA-714 unveils a smoldering component in most multiple sclerosis lesions which drives disease progression. Ann. Neurol. 94, 366–383 (2023).
Rissanen, E. et al. In vivo detection of diffuse inflammation in secondary progressive multiple sclerosis using PET imaging and the radioligand ¹¹C-PK11195. J. Nucl. Med. 55, 939–944 (2014).
Sucksdorff, M. et al. Brain TSPO-PET predicts later disease progression independent of relapses in multiple sclerosis. Brain 143, 3318–3330 (2020).
Datta, G. et al. 11C-PBR28 and 18F-PBR111 detect white matter inflammatory heterogeneity in multiple sclerosis. J. Nucl. Med. 58, 1477–1482 (2017).
Herranz, E. et al. Neuroinflammatory component of gray matter pathology in multiple sclerosis. Ann. Neurol. 80, 776–790 (2016).
Bodini, B. et al. Individual mapping of innate immune cell activation is a candidate marker of patient-specific trajectories of worsening disability in multiple sclerosis. J. Nucl. Med. 61, 1043–1049 (2020).
Nylund, M. et al. Phenotyping of multiple sclerosis lesions according to innate immune cell activation using 18 kDa translocator protein-PET. Brain Commun. 4, fcab301 (2021).
Poirion, E. et al. Structural and clinical correlates of a periventricular gradient of neuroinflammation in multiple sclerosis. Neurology 96, e1865–e1875 (2021).
Sucksdorff, M. et al. Natalizumab treatment reduces microglial activation in the white matter of the MS brain. Neurol. Neuroimmunol. Neuroinflamm. 6, e574 (2019).
Sucksdorff, M. et al. Evaluation of the effect of fingolimod treatment on microglial activation using serial PET imaging in multiple sclerosis. J. Nucl. Med. 58, 1646–1651 (2017).
Petzold, A. et al. Retinal layer segmentation in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. 16, 797–812 (2017).
Saidha, S. et al. Optical coherence tomography reflects brain atrophy in multiple sclerosis: a four-year study. Ann. Neurol. 78, 801–813 (2015).
Guerrieri, S., Comi, G. & Leocani, L. Optical coherence tomography and visual evoked potentials as prognostic and monitoring tools in progressive multiple sclerosis. Front. Neurosci. 15, 692599 (2021).
Pulicken, M. et al. Optical coherence tomography and disease subtype in multiple sclerosis. Neurology 69, 2085–2092 (2007).
Henderson, A. P. et al. An investigation of the retinal nerve fibre layer in progressive multiple sclerosis using optical coherence tomography. Brain 131, 277–287 (2008).
Gelfand, J. M. et al. Retinal axonal loss begins early in the course of multiple sclerosis and is similar between progressive phenotypes. PLoS ONE 7, e36847 (2012).
Albrecht, P. et al. Degeneration of retinal layers in multiple sclerosis subtypes quantified by optical coherence tomography. Mult. Scler. J. 18, 1422–1429 (2012).
Balk, L. J. et al. Disease course heterogeneity and OCT in multiple sclerosis. Mult. Scler. 20, 1198–1206 (2014).
Cellerino, M. et al. Relationship between retinal inner nuclear layer, age, and disease activity in progressive MS. Neurol. Neuroimmunol. Neuroinflamm. 6, e596 (2019).
Martinez-Lapiscina, E. H. et al. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study. Lancet Neurol. 15, 574–584 (2016).
Bsteh, G. et al. Peripapillary retinal nerve fibre layer thinning rate as a biomarker discriminating stable and progressing relapsing–remitting multiple sclerosis. Eur. J. Neurol. 26, 865–871 (2019).
Bsteh, G. et al. Macular ganglion cell-inner plexiform layer thinning as a biomarker of disability progression in relapsing multiple sclerosis. Mult. Scler. 27, 684–694 (2020).
Sotirchos, E. S. et al. Progressive multiple sclerosis is associated with faster and specific retinal layer atrophy. Ann. Neurol. 87, 885–896 (2020).
Ratchford, J. N. et al. Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning. Neurology 80, 47–54 (2013).
El Ayoubi, N. K. et al. Rate of retinal layer thinning as a biomarker for conversion to progressive disease in multiple sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 9, e200030 (2022).
Brown, J. A. et al. Patient-tailored, connectivity-based forecasts of spreading brain atrophy. Neuron 104, 856–868.e5 (2019).
Lie, I. A. et al. Relationship between white matter lesions and gray matter atrophy in multiple sclerosis: a systematic review. Neurology 98, e1562–e1573 (2022).
Comi, G. et al. Measuring evoked responses in multiple sclerosis. Mult. Scler. 5, 263–267 (1999).
Leocani, L., Guerrieri, S. & Comi, G. Visual evoked potentials as a biomarker in multiple sclerosis and associated optic neuritis. J. Neuroophthalmol. 38, 350–357 (2018).
Leocani, L. et al. Multimodal evoked potentials to assess the evolution of multiple sclerosis: a longitudinal study. J. Neurol. Neurosurg. Psychiatry 77, 1030–1035 (2006).
Kira, J., Tobimatsu, S., Goto, I. & Hasuo, K. Primary progressive versus relapsing remitting multiple sclerosis in Japanese patients: a combined clinical, magnetic resonance imaging and multimodality evoked potential study. J. Neurol. Sci. 117, 179–185 (1993).
Stevenson, V. L. et al. Primary and transitional progressive MS: a clinical and MRI cross-sectional study. Neurology 52, 839–845 (1999).
Sater, R. A., Rostami, A. M., Galetta, S., Farber, R. E. & Bird, S. J. Serial evoked potential studies and MRI imaging in chronic progressive multiple sclerosis. J. Neurol. Sci. 171, 79–83 (1999).
Schlaeger, R. et al. Electrophysiological markers and predictors of the disease course in primary progressive multiple sclerosis. Mult. Scler. 20, 51–56 (2014).
Backner, Y. et al. Vision and vision-related measures in progressive multiple sclerosis. Front. Neurol. 10, 455 (2019).
Abalo-Lojo, J. M. et al. Retinal nerve fiber layer thickness, brain atrophy, and disability in multiple sclerosis patients. J. Neuroophthalmol. 34, 23–28 (2014).
Kuhle, J. et al. Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin. Chem. Lab. Med. 54, 1655–1661 (2016).
Barro, C. et al. Serum GFAP and NfL levels differentiate subsequent progression and disease activity in patients with progressive multiple sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 10, e200052 (2022).
Teunissen, C. E. et al. Body fluid biomarkers for multiple sclerosis — the long road to clinical application. Nat. Rev. Neurol. 11, 585–596 (2015).
Romme Christensen, J. et al. CSF inflammation and axonal damage are increased and correlate in progressive multiple sclerosis. Mult. Scler. 19, 877–884 (2013).
Sellebjerg, F. et al. Defining active progressive multiple sclerosis. Mult. Scler. 23, 1727–1735 (2017).
Segal, B. M. Stage-specific immune dysregulation in multiple sclerosis. J. Interferon Cytokine Res. 34, 633–640 (2014).
Komori, M. et al. Cerebrospinal fluid markers reveal intrathecal inflammation in progressive multiple sclerosis. Ann. Neurol. 78, 3–20 (2015).
Lycke, J. & Zetterberg, H. The role of blood and CSF biomarkers in the evaluation of new treatments against multiple sclerosis. Expert Rev. Clin. Immunol. 13, 1143–1153 (2017).
Romme Christensen, J. et al. CSF inflammatory biomarkers responsive to treatment in progressive multiple sclerosis capture residual inflammation associated with axonal damage. Mult. Scler. 25, 937–946 (2019).
Gil-Perotin, S. et al. Combined cerebrospinal fluid neurofilament light chain protein and chitinase-3 like-1 levels in defining disease course and prognosis in multiple sclerosis. Front. Neurol. 10, 1008 (2019).
Martin, S. J., McGlasson, S., Hunt, D. & Overell, J. Cerebrospinal fluid neurofilament light chain in multiple sclerosis and its subtypes: a meta-analysis of case–control studies. J. Neurol. Neurosurg. Psychiatry 90, 1059–1067 (2019).
Petzold, A. et al. Elevated CSF neurofilament proteins predict brain atrophy: a 15-year follow-up study. Mult. Scler. 22, 1154–1162 (2016).
Romme, C. J. et al. Natalizumab in progressive MS: results of an open-label, phase 2A, proof-of-concept trial. Neurology 82, 1499–1507 (2014).
Axelsson, M. et al. Immunosuppressive therapy reduces axonal damage in progressive multiple sclerosis. Mult. Scler. 20, 43–50 (2014).
Disanto, C. et al. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann. Neurol. 81, 857–870 (2017).
Barro, C. et al. Serum neurofilament light as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 141, 2382–2391 (2018).
Kapoor, R. et al. Serum neurofilament light as a biomarker in progressive multiple sclerosis. Neurology 95, 436–444 (2020).
Bar-Or, A. et al. Blood neurofilament light levels predict non-relapsing progression following anti-CD20 therapy in relapsing and primary progressive multiple sclerosis: findings from the ocrelizumab randomised, double-blind phase 3 clinical trials. eBioMedicine 93, 104662 (2023).
Meier, S. et al. Serum glial fibrillary acidic protein compared with neurofilament light chain as a biomarker for disease progression in multiple sclerosis. JAMA Neurol. 80, 287–297 (2023).
Jiang, X. et al. Glial fibrillary acidic protein and multiple sclerosis progression independent of acute inflammation. Mult. Scler. 29, 1070–1079 (2023).
Huss, A. et al. A score based on NfL and glial markers may differentiate between relapsing–remitting and progressive MS course. Front. Neurol. 11, 608 (2020).
Abdelhak, A. et al. Serum glial fibrillary acidic protein and disability progression in progressive multiple sclerosis. Ann. Clin. Transl. Neurol. https://doi.org/10.1002/acn3.51969 (2024).
Shi, T. et al. Metabolomic profiles in relapsing–remitting and progressive multiple sclerosis compared to healthy controls: a five-year follow-up study. Metabolomics 19, 44 (2023).
Lim, C. K. et al. Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression. Sci. Rep. 7, 41473 (2017).
Stilund, M. et al. Soluble CD163 as a marker of macrophage activity in newly diagnosed patients with multiple sclerosis. PLoS ONE 9, e98588 (2014).
Festa, E. D. et al. Serum levels of CXCL13 are elevated in active multiple sclerosis. Mult. Scler. 15, 1271–1279 (2009).
Fissolo, N. et al. Serum levels of CXCL13 are associated with teriflunomide response in patients with multiple sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 10, e200050 (2022).
Sievers, C. et al. Altered microRNA expression in B lymphocytes in multiple sclerosis: towards a better understanding of treatment effects. Clin. Immunol. 144, 70–79 (2012).
Campbell, J. A. et al. SF-6D health state utilities for lifestyle, socio-demographic and clinical characteristics of a large international cohort of people with multiple sclerosis. Qual. Life Res. 29, 2509–2527 (2020).
Castelnovo, G. et al. Safety, patient-reported well-being, and physician-reported assessment of walking ability in patients with multiple sclerosis for prolonged-release fampridine treatment in routine clinical practice: results of the LIBERATE study. CNS Drugs 35, 1009–1022 (2021).
Solaro, C. et al. Italian validation of the 12-item multiple sclerosis walking scale. Mult. Scler. Int. 2015, 540828 (2015).
He, A. et al. Association between early treatment of multiple sclerosis and patient-reported outcomes: a nationwide observational cohort study. J. Neurol. Neurosurg. Psychiatry 94, 284–289 (2023).
Block, V. J. et al. Continuous daily assessment of multiple sclerosis disability using remote step count monitoring. J. Neurol. 264, 316–326 (2017).
Hobart, J. et al. Do clinical trials prepare to fail by failing to prepare? An examination of MS trials and recommendations for patient-reported outcome measure selection. Mult. Scler. Relat. Disord. 76, 104788 (2023).
Abdelhak, A. et al. Patient-reported outcome parameters and disability worsening in progressive multiple sclerosis. Mult. Scler. Relat. Disord. 81, 105139 (2023).
Strijbis, E. et al. The MSIS-29 and SF-36 as outcomes in secondary progressive MS trials. Mult. Scler. J. 28, 1606–1619 (2022).
Oh, J. et al. Use of smartphone-based remote assessments of multiple sclerosis in Floodlight Open, a global, prospective, open-access study. Sci. Rep. 14, 122 (2024).
Maillart, E. et al. MSCopilot, a new multiple sclerosis self-assessment digital solution: results of a comparative study versus standard tests. Eur. J. Neurol. 27, 429–436 (2020).
Green, R., Kalina, J., Ford, R., Pandey, K. & Kister, I. SymptoMScreen: a tool for rapid assessment of symptom severity in MS across multiple domains. Appl. Neuropsychol. Adult 24, 183–189 (2017).
Zaratin, P. et al. The agenda of the global patient reported outcomes for multiple sclerosis (PROMS) initiative: progresses and open questions. Mult. Scler. Relat. Disord. 61, 103757 (2022).
Conway, D. S. et al. Patient reported outcomes and performance metrics at diagnosis of secondary progressive multiple sclerosis. Mult. Scler. 27, 742–754 (2020).
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G.C., G.D.C., B.S., H.-P.H., P.S.S., P.V. and L.L. searched data for the article and made substantial contributions to discussions of the content and each wrote a section. All authors edited and reviewed the document before submission.
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G.C. has received consulting and speaking fees from Bristol Myers, Janssen, Novartis, Rewind, Roche, Sanofi and Squibb. G.D.C. has received funding for research, travel and/or speaker honoraria from Biogen, Celgene, Merck, Novartis, Roche and Teva. B.S. has received research support from Merck, Novartis and Roche and honoraria for lectures from Biogen, Janssen, Novartis, Merck and Sanofi. H.-P.H. received honoraria for serving on steering committees from Roche, Sanofi and TG Therapeutics, for serving on data monitoring committees from Merck KG and Novartis and on the scientific advisory board of Aurinia. P.S.S. has received personal compensation for serving on scientific advisory boards, steering committees or independent data monitoring boards for Biogen, Celgene, Forward Pharma GlaxoSmithKline, Genzyme, MedDay Pharmaceuticals, Merck, Novartis and TEVA and has received speaker honoraria from Biogen, Genzyme Merck, Novartis and Teva. His department has received research support from Biogen, Merck, Novartis, Roche, RoFAR, Sanofi-Aventis/Genzyme and TEVA. P.V. has received honorarium for contribution to meetings from AB Science, Ad Scientiam, Biogen, BMS-Celgene, Imcyse, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva and research support from Merck, Novartis and Sanofi-Genzyme. L.L. has received research support from Almirall, Biogen, Merck and Novartis and consultancy or speaker fees from Almirall, Biogen, Bristol-Myers Squibb, Janssen-Cilag, Merck, Novartis and Roche.
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Comi, G., Dalla Costa, G., Stankoff, B. et al. Assessing disease progression and treatment response in progressive multiple sclerosis. Nat Rev Neurol 20, 573–586 (2024). https://doi.org/10.1038/s41582-024-01006-1
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DOI: https://doi.org/10.1038/s41582-024-01006-1