In multiple sclerosis (MS), there is a critical need for non-invasive biomarkers to concurrently classify disease subtypes, evaluate 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 and assessed the clinical utility of cfDNA methylation as a single assay for distinguishing MS patients from non-MS controls, identifying 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 regulatory elements (e.g., active promoters and enhancers) and contained motifs associated with neuronal function and T-cell differentiation. To distinguish MS subtypes and severity groups, we achieved area-under-the-curve (AUC) values ranging from 0.67 to 0.81 using DMRs and 0.70 to 0.82 using inferred tissue-of-origin patterns from cfDNA methylation, significantly outperforming benchmark neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in the same cohort. Finally, a linear mixed-effects model identified prognostic regions where baseline cfDNA methylation levels were associated with disease progression and predicted future disability severity (AUC=0.81) within a 4-year evaluation window. As we plan to generate higher-depth WGBS data and validation in independent cohorts, the present findings suggest the potential clinical utility of circulating cfDNA methylation profiles as promising noninvasive biomarkers in MS diagnosis and prognosis.

Y.L. and Z.X. filed a provisional patent. Y.L. owns stocks from Freenome Inc. The remaining authors declare no competing interests.

This research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. This research was also supported in part through the computational resources and staff contributions provided by the Genomics Compute Cluster, which is jointly supported by the Feinberg School of Medicine, the Center for Genetic Medicine, and Feinberg's Department of Biochemistry and Molecular Genetics, the Office of the Provost, the Office for Research, and Northwestern Information Technology. The Genomics Compute Cluster is part of Quest, Northwestern University's high performance computing facility, with the purpose to advance research in genomics. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562. This work used the XSEDE at the Pittsburgh Supercomputing Center (PSC) through allocation MCB190124P and MCB190006P. L.W. and Y.L. are supported by the startup grant to Y.L. from Northwestern University, Robert H. Lurie Comprehensive Cancer Center of Northwestern University, and the iDEA-TECH award from Sanofi Inc. (to Y.L. and Z.X.). Z.X. is supported by NINDS (R01NS098023 and R01NS124882).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Board of the University of Pittsburgh gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The raw WGBS sequencing data generated in this study have been deposited in the Sequence Read Archive with controlled access from dbGaP under accession code (pending). The data are available under restricted access, and access can be obtained by contacting the Data Access Committee in dbGaP. The raw sequencing data are protected by data privacy laws. Processed DNA methylation level is available at Gene Expression Omnibus (GEO) with access ID (pending). The processed and de-identified data are available at zenodo.org (doi: https://doi.org/10.5281/zenodo.14803482). The remaining data are available within the Article, Supplementary Information, and Source Data file.