{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:23:44Z","timestamp":1791433424470,"version":"4.3.3"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009945","name":"Merck KGaA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009945","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Arman Eshaghi"},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR302495"],"award-info":[{"award-number":["NIHR302495"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"name":"United Kingdom Department of Health and Social Care"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multiple sclerosis (MS) is a highly heterogeneous disease in its clinical manifestation and progression. Predicting individual disease courses is key for aligning treatments with underlying pathobiology. We developed an unsupervised machine learning model integrating MRI-derived measures with serum neurofilament light chain (sNfL) levels to identify biologically informed MS subtypes and stages. Using a training cohort of patients with relapsing\u2013remitting and secondary progressive MS (n = 189), with validation on a newly diagnosed population (n = 445), we discovered two distinct subtypes defined by the timing of sNfL elevation and MRI abnormalities (early- and late-sNfL types).<\/jats:p>\n                  <jats:p>In comparison to MRI-only models, incorporating sNfL with MRI improved correlations of data-derived stages with the Expanded Disability Status Scale in the training (Spearman\u2019s \u03c1 = 0.420 versus MRI-only \u03c1 = 0.231, P = 0.001) and external test sets (\u03c1 = 0.163 for MRI\u2013sNfL, versus \u03c1 = 0.067 for MRI-only). The early-sNfL subtype showed elevated sNfL, corpus callosum injury and early lesion accrual, reflecting more active inflammation and neurodegeneration, whereas the late-sNfL group showed early volume loss in the cortical and deep grey matter volumes, with later sNfL elevation. Cross-sectional subtyping predicted longitudinal radiological activity: the early-sNfL group showed a 144% increased risk of new lesion formation (hazard ratio = 2.44, 95% confidence interval 1.38\u20134.30, P &amp;lt; 0.005) compared with the late-sNfL group. Baseline subtyping, over time, predicted treatment effect on new lesion formation on the external test set (faster lesion accrual in early-sNfL compared with late-sNfL, P = 0.01), in addition to treatment effects on brain atrophy (early sNfL average percentage brain volume change: \u22120.41, late-sNfL = \u22120.31, P = 0.04).<\/jats:p>\n                  <jats:p>Integration of sNfL provides an improved framework in comparison to MRI-only subtyping of MS to stage disease progression and inform prognosis. Our model predicted treatment responsiveness in early, more active disease states. This approach offers a powerful alternative to conventional clinical phenotypes and supports future efforts to refine prognostication and guide personalized therapy in MS.<\/jats:p>","DOI":"10.1093\/brain\/awaf331","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T12:27:58Z","timestamp":1761222478000},"page":"4578-4591","source":"Crossref","is-referenced-by-count":17,"title":["Combined magnetic resonance imaging and serum analysis reveals distinct multiple sclerosis types"],"prefix":"10.1093","volume":"148","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6856-9242","authenticated-orcid":false,"given":"Charles","family":"Willard","sequence":"first","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lemuel","family":"Puglisi","sequence":"additional","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]},{"name":"Department of Mathematics and Computer Science, University of Catania , Catania 95124 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele","family":"Ravi","sequence":"additional","affiliation":[{"name":"MIFT Department, University of Messina , Messina 98122 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariia","family":"Dmitrieva","sequence":"additional","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rozemarijn M","family":"Mattiesing","sequence":"additional","affiliation":[{"name":"MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC , Amsterdam ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]},{"name":"Department of Medical Physics and Biomedical Engineering, University College London , London WC1E 6BT ,","place":["UK"]},{"name":"Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam 1081 BT ,","place":["The Netherlands"]},{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London WC1N 3BG ,","place":["UK"]},{"name":"Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London , London WC1N 3BG ,","place":["UK"]},{"name":"UCL Hawkes Institute, University College London , London WC1V 6LJ ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C","family":"Alexander","sequence":"additional","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]},{"name":"UCL Hawkes Institute, University College London , London WC1V 6LJ ,","place":["UK"]},{"name":"Department of Computer Science, University College London , London WC1E 6EA ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle E","family":"Harlow","sequence":"additional","affiliation":[{"name":"Neurology & Immunology Medical Unit, EMD Serono Research & Development Institute, Inc. , Billerica, MA 01821, USA, an affiliate of Merck KGaA, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Piani-Meier","sequence":"additional","affiliation":[{"name":"Neurology & Immunology Medical Unit, Ares Trading SA , Eysins 1262 , Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Eshaghi","sequence":"additional","affiliation":[{"name":"Department of Research and Analysis, Queen Square Analytics Limited , London EC1V 2NX ,","place":["UK"]},{"name":"Department of Medical Physics and Biomedical Engineering, University College London , London WC1E 6BT ,","place":["UK"]},{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London WC1N 3BG ,","place":["UK"]},{"name":"UCL Hawkes Institute, University College London , London WC1V 6LJ ,","place":["UK"]},{"name":"Department of Computer Science, University College London , London WC1E 6EA 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