{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:17:03Z","timestamp":1787026623586,"version":"build-2736575974"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Multiple sclerosis (MS) is an incurable autoimmune disease marked by heterogeneous progression and a lack of reliable biomarkers, complicating prognosis and individualized care. This study introduces a novel trajectory-based statistical approach designed to identify patterns in patient histories within MS populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using longitudinal clinical data from a real-world cohort of 1,025 MS patients (median follow-up: 6.75 years), two complementary analyses were conducted based on patient trajectory analysis. In the first analysis, the technique is applied to the complete dataset after removal of missing values (n = 985; 11,048 events) to uncover latent progressive trajectories. The second analysis evaluated the techniques\u2019 performance on a smaller, limited-sample cohort (n = 83; 282 events).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across both analyses, the approach revealed previously unrecognized progression patterns, giving rise to new hypotheses, including an effect of Alemtuzumab on the bowel\/bladder function (p\n                      <jats:italic>&amp;lt;<\/jats:italic>\n                      0.01, RR = 2.83) and glatiramer acetate on the occurrence of relapses (p\n                      <jats:italic>&amp;lt;<\/jats:italic>\n                      0.01, RR = 1.49). Known associations were also confirmed, such as the relationship between relapse activity and brain lesions (p\n                      <jats:italic>&amp;lt;<\/jats:italic>\n                      0.01, RR = 1.20).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results demonstrate the method\u2019s robustness across varying dataset sizes, highlight its methodological limitations, and show its potential to uncover previously unseen relationships among MS-specific diagnostic events. These findings provide a foundation for generating novel hypotheses relevant to biomarker discovery and therapeutic optimization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1758416","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T04:20:50Z","timestamp":1776140450000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Data-driven hypothesis discovery from disease trajectories in multiple sclerosis"],"prefix":"10.3389","volume":"17","author":[{"given":"Niels","family":"Jodts","sequence":"first","affiliation":[{"name":"IDLab, Universiteit Gent - Imec","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorin","family":"Werthen-Brabants","sequence":"additional","affiliation":[{"name":"IDLab, Universiteit Gent - Imec","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofie","family":"Aerts","sequence":"additional","affiliation":[{"name":"Universitair MS Centrum (UMSC) Hasselt-Pelt","place":["Pelt, Belgium"]},{"name":"Biomedisch Onderzoeksinstituut (BIOMED), Universiteit Hasselt","place":["Diepenbeek, Belgium"]},{"name":"Rehabilitation Research Center, Universiteit Hasselt","place":["Diepenbeek, Belgium"]},{"name":"Revalidatie en MS, Noorderhart","place":["Pelt, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liesbet M.","family":"Peeters","sequence":"additional","affiliation":[{"name":"Universitair MS Centrum (UMSC) Hasselt-Pelt","place":["Pelt, Belgium"]},{"name":"Biomedisch Onderzoeksinstituut (BIOMED), Universiteit Hasselt","place":["Diepenbeek, Belgium"]},{"name":"Data Science Institute (DSI), Universiteit Hasselt","place":["Diepenbeek, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart","family":"Van Wijmeersch","sequence":"additional","affiliation":[{"name":"Universitair MS Centrum (UMSC) Hasselt-Pelt","place":["Pelt, Belgium"]},{"name":"Biomedisch Onderzoeksinstituut (BIOMED), Universiteit Hasselt","place":["Diepenbeek, Belgium"]},{"name":"Rehabilitation Research Center, Universiteit Hasselt","place":["Diepenbeek, Belgium"]},{"name":"Revalidatie en MS, Noorderhart","place":["Pelt, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlotte","family":"Herzeel","sequence":"additional","affiliation":[{"name":"AI & Algorithms (AIA) Department, Imec","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christel","family":"Meertens","sequence":"additional","affiliation":[{"name":"AI & Algorithms (AIA) Department, Imec","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roel","family":"Wuyts","sequence":"additional","affiliation":[{"name":"AI & Algorithms (AIA) Department, Imec","place":["Leuven, Belgium"]},{"name":"Declarative Language and Artificial Intelligence (DTAI), KU Leuven","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Dhaene","sequence":"additional","affiliation":[{"name":"IDLab, Universiteit Gent - 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