{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:35:20Z","timestamp":1790566520368,"version":"4.1.0"},"posted":{"date-parts":[[2026,4,20]]},"group-title":"Neurology","reference-count":57,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,4,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Biological aging is accelerated in people with multiple sclerosis, but whether such acceleration occurs during the pre-symptomatic phase or varies by organ system is understudied.<\/jats:p>\n                <jats:p>We analyzed two independent proteomics datasets profiled using distinct platforms: the Johns Hopkins cohort profiled using the SomaScan platform (348 multiple sclerosis\/49 age-matched controls) and the Department of Defense cohort profiled using the Olink platform (134 multiple sclerosis\/79 age-matched controls), including 117 pre-symptomatic samples from people with multiple sclerosis (median lead time: 4.0 years), to estimate systemic and organ-specific proteomic age gaps using established clocks in pre-symptomatic and symptomatic phases, and assess their associations with severity.<\/jats:p>\n                <jats:p>\n                  In the Johns Hopkins cohort, people with multiple sclerosis demonstrated acceleration of systemic (\u03b2=2.2, 95% CI 1.2\u20133.2,\n                  <jats:italic>P<\/jats:italic>\n                  &lt;0.001, FDR&lt;0.001), brain (\u03b2=1.7, 95% CI 0.6\u20132.7,\n                  <jats:italic>P<\/jats:italic>\n                  =0.003, FDR=0.01), muscle (\u03b2=2.5, 95% CI 1.3\u20133.7,\n                  <jats:italic>P<\/jats:italic>\n                  &lt;0.001, FDR&lt;0.001), and immune age (\u03b2=1.8, 95% CI 0.6\u20132.9,\n                  <jats:italic>P<\/jats:italic>\n                  =0.003, FDR=0.01), with findings reproduced in the Department of Defense cohort for systemic (\u03b2=0.7, 95% CI 0.0\u20131.4,\n                  <jats:italic>P<\/jats:italic>\n                  =0.04, FDR=0.34) and brain age (3.2 years, 95% CI 2.1\u20134.3,\n                  <jats:italic>P<\/jats:italic>\n                  &lt;0.001, FDR&lt;0.001). Proteomic age acceleration was evident prior to symptom onset [systemic: (\u03b2=1.0, 95% CI 0.4\u20131.7,\n                  <jats:italic>P<\/jats:italic>\n                  =0.002, FDR=0.02); brain: (\u03b2=2.4, 95% CI 1.2\u20133.7,\n                  <jats:italic>P<\/jats:italic>\n                  &lt;0.001, FDR=0.002)], whereas no immune age acceleration was detected before or after onset. Higher systemic age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score (\u03b2=0.14, 95% CI 0.05\u20130.24,\n                  <jats:italic>P<\/jats:italic>\n                  =0.005, FDR=0.03) and slower walking speed (\u03b2=0.02, 95% CI 0.01\u20130.03,\n                  <jats:italic>P<\/jats:italic>\n                  =0.006, FDR=0.04), while higher muscle age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score (\u03b2=0.17, 95% CI 0.10\u20130.24,\n                  <jats:italic>P<\/jats:italic>\n                  &lt;0.001, FDR&lt;0.001), poorer manual dexterity (\u03b2=0.28, 95% CI 0.04\u20130.52,\n                  <jats:italic>P<\/jats:italic>\n                  =0.03, FDR=0.30), slower walking speed (\u03b2=0.02, 95% CI 0.01\u20130.03,\n                  <jats:italic>P<\/jats:italic>\n                  =0.002, FDR=0.02), lower peripapillary retinal nerve fiber layer (\u03b2= \u22120.26, 95% CI \u22120.41 to \u22120.10,\n                  <jats:italic>P<\/jats:italic>\n                  =0.001, FDR=0.02) and ganglion cell-inner plexiform layer thicknesses (\u03b2= \u22120.35; 95% CI \u22120.65 to \u22120.05;\n                  <jats:italic>P<\/jats:italic>\n                  =0.02, FDR=0.30). Higher brain age gap was associated with several imaging measures, including lower whole-brain (\u03b2= \u22120.002, 95% CI \u22120.003 to \u22120.001,\n                  <jats:italic>P<\/jats:italic>\n                  =0.002, FDR=0.02), and lower peripapillary retinal nerve fiber layer thickness (\u03b2= \u22120.21, 95% CI \u22120.39 to \u22120.03,\n                  <jats:italic>P<\/jats:italic>\n                  =0.02, FDR=0.10).\n                <\/jats:p>\n                <jats:p>Proteomic age acceleration in multiple sclerosis is detectable years before symptom onset and distinct organ-specific aging signatures are associated with disease severity. Proteomic aging may provide a biologically informative marker of early disease processes and a clinically relevant readout of disease heterogeneity.<\/jats:p>","DOI":"10.64898\/2026.04.13.26350634","type":"posted-content","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:00:12Z","timestamp":1776729612000},"source":"Crossref","is-referenced-by-count":1,"title":["Proteomic Age Acceleration in Multiple Sclerosis Precedes Symptom Onset and Associates with Severity"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8562-9523","authenticated-orcid":false,"given":"Fatemeh","family":"Siavoshi","sequence":"first","affiliation":[{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5793-8989","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Candia","sequence":"additional","affiliation":[{"name":"Translational 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Somascan bioinformatics: normalization, quality control, and assessment of Pre-analytical variation. Protein Arrays: Methods and Protocols. 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