CSF proteomic signatures show potential for MS diagnosis and prognosis in cohort studyCould a spinal fluid test predict how multiple sclerosis will progress?

medRxiv Published April 4, 2026 Study authors: Bastide, L.; Imbault, V.; Perrotta, G.; Borrelli, S.; Elands, S.; van Pesch, V.; Borras, E.; Sabido,… DOI ↗ Editorial oversight: Dr. Ji-eun Park, MD · Brain, Mind & Pain

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Key Takeaway

Consider CSF proteomic signatures as potential research biomarkers for MS diagnosis and prognosis, pending validation.

A multicentric cohort study analyzed cerebrospinal fluid proteomic signatures in 120 participants: 62 with multiple sclerosis, 15 with clinically isolated syndrome, and 43 healthy controls. The study aimed to identify signatures associated with diagnosis and short- to mid-term prognosis, with follow-up at 2 and 5 years. Comparisons were made between healthy controls and between patients with no evidence of disease activity versus those with evidence of disease activity.

For diagnosis of multiple sclerosis compared with clinically isolated syndrome, multivariate models based on proteins achieved good accuracy, with area under the receiver operating characteristics curve up to 80%. For prognosis (no evidence of disease activity vs. evidence of disease activity), models achieved AUROC up to 96% at both 2 and 5 years. The study identified a set of ten proteins associated with diagnosis and prognosis. Absolute numbers, p-values, and confidence intervals were not reported.

Safety and tolerability data were not reported. The authors note that results will require further investigation to validate the new biomarkers. As an observational cohort study, this research shows association rather than causation. Practice relevance was not reported, and clinical utility should not be overstated based on these preliminary findings.

For people living with multiple sclerosis, one of the hardest questions is: what will my future look like? A new study looked for clues in spinal fluid, searching for patterns of proteins that might give doctors a clearer picture. Researchers analyzed samples from 120 people—some with MS, some with early symptoms called clinically isolated syndrome, and some healthy controls. They found a set of ten proteins that, when analyzed together, could distinguish MS from early symptoms with good accuracy. More strikingly, the same protein patterns showed promise in predicting which patients would have active disease versus no evidence of disease activity two and five years later. The models for this long-term forecast were particularly strong. It's important to know this is a small, early study. The results are a promising signal, but they absolutely need to be confirmed in much larger groups of people before we can know if this approach is reliable enough to help guide real treatment decisions.

What this means for you:

Spinal fluid proteins may help forecast MS progression, but the finding needs validation.

Study Details

Study typeCohort

Sample sizen = 120

EvidenceLevel 3

PublishedMar 2026

View Original Abstract ↓

Despite important advances in understanding the etiopathology of multiple sclerosis, factors determining disease progression remain partially understood and often difficult to predict. Specific diagnostic and prognostic biomarkers are needed to optimize the risk-benefit ratio of treatment for each patient. The aim of our study was to identify a cerebrospinal fluid proteomic signature associated with diagnosis and short- to mid-term prognosis across the multiple sclerosis continuum. Our multicentric cohort study analyzed CSF samples from 120 patients using a proteomics data-independent acquisition strategy. Differentially expressed proteins were identified across diagnostic groups: 62 patients with multiple sclerosis, 15 patients with clinically isolated syndrome, and 43 healthy controls. We also compared the CSF of patients with no evidence of disease activity with those with disease activity at 2 and 5 years of follow-up. A diagnostic and prognostic classification model was built using iterative cross-validated logistic regression models on shared differentially expressed proteins across these two comparisons. A total of 1,257 proteins were quantified, and 162 differentially expressed proteins were identified across comparisons. We identified a set of ten proteins associated with the diagnosis and prognosis of multiple sclerosis, including previously identified potential biomarkers (CH3L2, IGHG1, IGKC, LAMP2, ADA2), proteins known to be involved in the pathophysiology of multiple sclerosis (A0A8J8YUT9, AT2A2, CO3A1) and two yet unreported proteins (DSC2 and MMRN2). Multivariate models based on these proteins achieved good accuracy for the diagnosis of MS compared with CIS (area under the receiver operating characteristics curve [AUROC] up to 80% using 3 proteins) and prognosis (NEDA vs. EDA; AUROC up to 96% at 2 and 5 years; using 5 proteins). These results, which will require further investigation to validate the new biomarkers, open new perspectives on multiple sclerosis pathophysiology and therapeutic targets.

CSF proteomic signatures show potential for MS diagnosis and prognosis in cohort studyCould a spinal fluid test predict how multiple sclerosis will progress?

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CSF proteomic signatures show potential for MS diagnosis and prognosis in cohort studyCould a spinal fluid test predict how multiple sclerosis will progress?

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Study Details

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Clinical research that matters. Delivered to your inbox.

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