{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T18:56:35Z","timestamp":1784919395980,"version":"3.55.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"ITEA4 SYMPHONY","award":["21026"],"award-info":[{"award-number":["21026"]}]},{"name":"European Union\u2019s Horizon Europe Research and Innovation Actions","award":["101137154 (WISDOM)"],"award-info":[{"award-number":["101137154 (WISDOM)"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Mult Scler"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>The prodromal phase of multiple sclerosis (MS) is increasingly recognized, but most studies have focused on isolated symptoms or static comorbidity counts, leaving the evolving structure of pre-onset disease burden underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To characterize dynamic disease trajectories preceding MS onset through longitudinal network modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Health data from 10,273 MS patients and 47,167 matched controls in Sweden were analyzed. Disease co-occurrence networks were constructed for three pre-onset windows (0\u20135, 5\u201310, 10\u201315 years), with comparisons of centrality, clustering, and path length. Rewiring scores captured structural shifts, while Markov clustering and trajectory mapping identified comorbidity communities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>MS networks were denser, more clustered, and showed shorter path lengths than controls, reflecting higher systemic interconnectivity. Psychiatric and metabolic diagnoses, especially depression, anxiety, diabetes, and abdominal pain, were hubs that gained prominence over time. Distinct clusters, including neuropsychiatric-toxicological and immune-endocrine constellations, were observed only in MS. Rewiring analysis revealed significant topological shifts in key diagnoses, such as inflammatory central nervous system disorders and substance use, as onset approached.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>MS is preceded by dynamic reorganization of the comorbidity landscape, marked by increasing connectivity and rewired hubs. This framework highlights systemic disruption before diagnosis and provides a novel, network-based tool for studying prodromes in complex disorders.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13524585261421480","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:33:19Z","timestamp":1773250399000},"page":"915-926","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A susceptibility network analysis of disease trajectories leading to multiple sclerosis: A nationwide cohort study"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-6205","authenticated-orcid":false,"given":"Ali","family":"Ebrahimi","sequence":"first","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6898-4083","authenticated-orcid":false,"given":"Uffe Kock","family":"Wiil","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2938-1877","authenticated-orcid":false,"given":"Tomas","family":"Olsson","sequence":"additional","affiliation":[{"name":"Karolinska Neuroimmunology and Multiple Sclerosis Centre, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden"},{"name":"Center for Molecular Medicine, Karolinska University Hospital, Stockholm, 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