{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:45:29Z","timestamp":1788371129070,"version":"build-2803163510"},"posted":{"date-parts":[[2025,2,20]]},"group-title":"Genetic and Genomic Medicine","reference-count":67,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  In chronic neurodegenerative diseases such as multiple sclerosis (MS), there is a critical unmet need for cost-effective non-invasive blood biomarkers to concurrently classify disease subtypes, monitor disability severity, and predict long-term progression. In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples collected from a well-characterized real-world prospective clinic cohort with longitudinal disability outcome measurements. We assessed the clinical utility of cfDNA methylation profile for differentiating MS patients from non-MS controls, classifying MS subtypes, estimating disability severity, and predicting disease trajectories. We identified thousands of differentially methylated CpGs and hundreds of differentially methylated regions (DMRs) that significantly distinguished MS from controls, separated MS subtypes, and stratified disability severity levels. These DMRs were highly enriched in immunologically and neurologically relevant cis-regulatory elements (\n                  <jats:italic>e.g.,<\/jats:italic>\n                  active promoters and enhancers) and enriched in motifs associated with neuronal function and T-cell differentiation, underscoring the interplay between the peripheral immune system and the central nervous system in MS. To distinguish MS subtypes and disability severity levels, models using DMRs achieved area-under-the-curve (AUC) values ranging from 0.67 to 0.81 and models using inferred tissue-of-origin patterns from cfDNA methylation achieved AUC 0.70 to 0.82, outperforming historical benchmark neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in the same cohort. Finally, a linear mixed-effects model identified \u201cprognostic regions\u201d where baseline cfDNA methylation levels were associated with subsequent disability progression and could predict the future disability severity (AUC=0.74) within a 3-year evaluation window. Using these prognostic regions, we generated a cfDNA methylation-based progression risk score for each patient and stratified patient groups with differential progression risks. As we generate higher-depth WGBS data and validate in follow-up studies, the present findings suggest the potential clinical utility of circulating cfDNA methylation profiles as promising MS biomarkers for future validation.\n                <\/jats:p>","DOI":"10.1101\/2025.02.14.25322180","type":"posted-content","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T18:20:28Z","timestamp":1740075628000},"source":"Crossref","is-referenced-by-count":2,"title":["Circulating cell-free DNA methylation profiles as noninvasive multiple sclerosis biomarkers: A proof-of-concept study"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0461-3552","authenticated-orcid":false,"given":"Hailu","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8398-8348","authenticated-orcid":false,"given":"Ravi","family":"Bandaru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shruthi","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Walker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-7693","authenticated-orcid":false,"given":"Yaping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1500-2589","authenticated-orcid":false,"given":"Zongqi","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025070103451072000_2025.02.14.25322180v2.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1401483"},{"key":"2025070103451072000_2025.02.14.25322180v2.2","doi-asserted-by":"publisher","DOI":"10.1177\/135245852097084"},{"key":"2025070103451072000_2025.02.14.25322180v2.3","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000000094"},{"key":"2025070103451072000_2025.02.14.25322180v2.4","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181c5b47f"},{"key":"2025070103451072000_2025.02.14.25322180v2.5","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2217\/nmt-2021-0058","article-title":"Updates and advances in multiple sclerosis neurotherapeutics","volume":"13","year":"2023","journal-title":"Neurodegener. 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