{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:12:23Z","timestamp":1787019143467,"version":"build-2736575974"},"reference-count":34,"publisher":"SAGE Publications","issue":"13","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"NIH\/NIMH","award":["R01MH137075"],"award-info":[{"award-number":["R01MH137075"]}]},{"name":"NIH\/NIDA","award":["R01DA058315"],"award-info":[{"award-number":["R01DA058315"]}]},{"name":"NIH\/NIHR","award":["R56NR019306"],"award-info":[{"award-number":["R56NR019306"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Mult Scler"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>While standard clinical assessments provide great value for people with multiple sclerosis (PwMS), they are limited in their ability to characterize patient perspectives and individual-level symptom heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>To identify PwMS subgroups based on patient-reported outcomes (PROs) of physical, cognitive, and emotional symptoms. We also sought to connect PRO-based subgroups with demographic variables, functional impairment, hypertension and smoking status, traditional qualitative multiple sclerosis (MS) symptom groupings, and neuroperformance measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      Using a cross-sectional design, we applied latent profile analysis (LPA) to a large database of PROs; analytic sample\n                      <jats:italic>N<\/jats:italic>\n                      = 6619).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>We identified nine distinct MS subtypes based on PRO patterns. The subtypes were primarily categorized into low, moderate, and high mobility impairment clusters. Approximately 70% of participants were classified in a low mobility impairment group, 10% in a moderate mobility impairment group, and 20% in a high mobility impairment group. Within these subgroups, several unexpected patterns were observed, such as high mobility impairment clusters reporting low non-mobility impairment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The present study highlights an opportunity to advance precision medicine approaches in MS. Combining PROs with data-driven methodology allows for a cost-effective and personalized characterization of symptom presentations. that can inform clinical practice and future research designs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13524585241282763","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T01:54:51Z","timestamp":1729216491000},"page":"1642-1652","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Multiple sclerosis subgroups: Data-driven clusters based on patient-reported outcomes and a large clinical sample"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7280-576X","authenticated-orcid":false,"given":"Alessandro S","family":"De Nadai","sequence":"first","affiliation":[{"name":"McLean Hospital, Harvard Medical School, Belmont, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan J","family":"Zamora","sequence":"additional","affiliation":[{"name":"McLean Hospital, Harvard Medical School, Belmont, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyse","family":"Finch","sequence":"additional","affiliation":[{"name":"McLean Hospital, Harvard Medical School, Belmont, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8026-1854","authenticated-orcid":false,"given":"Deborah M","family":"Miller","sequence":"additional","affiliation":[{"name":"The Mellen Center for Multiple Sclerosis and Research, Department of Neurology, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA"},{"name":"Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2838-9148","authenticated-orcid":false,"given":"Daniel","family":"Ontaneda","sequence":"additional","affiliation":[{"name":"The Mellen Center for Multiple Sclerosis and Research, Department of Neurology, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA"},{"name":"Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas D","family":"Gunzler","sequence":"additional","affiliation":[{"name":"Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA"},{"name":"Center for Health Care Research and Policy, School of Medicine, Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farren BS","family":"Briggs","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Division of Epidemiology, Miller School of Medicine, University of Miami, Miami, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/1352458519864681"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-041249"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13311-017-0566-3"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-014-1244-9"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2022-ABN.445"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2377-14-58"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2019.05.019"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2017.04.017"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2377-13-119"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpeds.2018.10.019"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27046"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/cells9030586"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.25122\/jml-2020-0182"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000200931"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2021.102912"},{"key":"e_1_3_3_17_2","first-page":"683","article-title":"The knowledge program: An innovative, comprehensive electronic data capture system and warehouse","volume":"2011","author":"Katzan I","year":"2011","unstructured":"Katzan I, Speck M, Dopler C, et al. 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