Plasma Proteins Predict Multiple Sclerosis Risk and Disease Severity

Unraveling Multiple Sclerosis Risk Through Circulating Proteins

Multiple sclerosis (MS) presents a significant clinical challenge due to its prolonged preclinical phase, where neuroinflammatory pathology accumulates for years before the onset of overt symptoms [1, 2, 3]. This asymptomatic window complicates early diagnosis and intervention, which are known to improve long-term outcomes [4]. While diagnosis currently hinges on clinical and imaging findings, there is a pressing need for biomarkers that can identify at-risk individuals far earlier [5]. Given that inflammatory processes involving immune cells and signaling molecules are central to MS pathogenesis [6, 7, 8], investigating circulating proteins offers a potential avenue for developing tools for preclinical risk stratification and targeted intervention [9].

Identifying Protein Signatures of MS Susceptibility

To address the challenge of preclinical detection, this study sought to identify circulating proteins that may be causally linked to multiple sclerosis (MS) development. The researchers employed a large-scale proteogenomic approach, beginning with a cohort of 80,824 individuals. They analyzed genetic variants that directly influence the circulating levels of specific proteins, a technique that identifies what are known as protein quantitative trait loci (pQTLs). This analysis covered 2,545 different plasma proteins. The investigators then used the genetic data to predict protein levels and tested their association with MS risk in a separate dataset of 14,802 MS cases and 26,703 controls. To move beyond simple correlation and infer causality, they applied Mendelian randomization, a statistical method that uses these genetic variants as proxies for protein levels to mitigate confounding from environmental or lifestyle factors. This was combined with colocalization analysis, which confirms that the genetic signal for a protein and the genetic signal for disease risk originate from the same source, strengthening the evidence for a direct biological link.

Key Proteins and Immune Pathways Linked to MS Risk

The analysis successfully pinpointed 39 plasma proteins whose levels are genetically associated with MS risk. Rather than acting independently, most of these proteins formed a densely connected biological network, suggesting a coordinated pathological process. This network was significantly enriched in pathways central to immune function, including B-cell and T-cell costimulation, cytokine signaling, and Epstein-Barr virus-related pathways. The strong representation of these specific pathways reinforces their established roles in MS autoimmunity. When investigating the cellular origins of these protein signals, the study found that the transcriptomic enrichment was strongest in B-cell subsets. This finding provides a genetic underpinning for the pivotal role of B-cells in MS, a role already targeted by highly effective B-cell depleting therapies. The researchers also used splicing data to clarify measurement discrepancies, concluding that differences observed between various laboratory platforms likely reflect the detection of distinct proteoforms, which are different functional versions of a protein produced from a single gene.

Predictive Value for Incident MS and Disease Progression

A crucial next step was to determine if these risk-associated proteins could predict future disease onset. The researchers tested this in a prediagnostic cohort from the United Kingdom Biobank, including 124 individuals who later developed MS and 52,515 controls. Blood samples were collected a median of 5.9 years before diagnosis, providing a true preclinical assessment. Of the 28 genetically implicated proteins that could be measured in these samples, 8 were significantly associated with the time to MS diagnosis (p binom = 4.92 × 10−5). This enrichment was far greater than expected by chance, supporting the potential of a protein-based signature for early risk stratification years before clinical presentation. The study also assessed associations with disease severity in a cohort of 12,584 individuals. One protein, Dickkopf-related protein 1 (DKKL1), stood out for its concordant protective associations across MS risk, incidence, and severity. Such a consistent signal makes DKKL1 a compelling candidate for further investigation as both a prognostic biomarker and a potential therapeutic target.

Refining Risk Loci and Future Directions

This integrated proteogenomic strategy not only identified potential biomarkers but also substantially refined the genetic map of MS. By linking genetic variants to the specific proteins they regulate (pQTLs), the researchers improved the fine-mapping resolution at shared genetic loci by more than 10-fold. This level of precision helps move from a broad genetic region to the specific causal gene, and through this process, the study nominated 13 putative new risk loci for MS. For the clinician, this enhanced genetic understanding provides a more detailed blueprint of the disease's molecular architecture, which is foundational for developing next-generation targeted therapies. The findings support a clear translational path forward: using this genetically anchored framework to develop and validate protein biomarkers for preclinical MS detection. Such tools could one day enable clinicians to identify high-risk individuals and consider interventions at a much earlier stage, potentially altering the natural history of the disease.

References

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