Summary: Researchers have identified a distinct cluster of blood proteins that change in people who later develop multiple sclerosis (MS). Using genetic-proteomic integration and longitudinal biobank data, the team isolated markers that appear in the bloodstream up to a decade before clinical diagnosis. These findings point toward a potential non-invasive screening approach that could identify high-risk individuals early enough for preventative or protective interventions.
Early detection is critical for neurodegenerative conditions because established brain damage is hard or impossible to reverse. Detecting MS before symptoms appear would allow clinicians to intervene during a preclinical window, potentially delaying or preventing disabling outcomes. This study takes a major step toward that goal by combining large-scale genetic analyses with retrospective blood-sample review.
Key Facts
- Prevention-first approach: For diseases like MS, preventing damage is the most effective strategy. Detecting early biological signals could enable therapies or lifestyle changes before irreversible neurological injury occurs.
- Proteomic screening at scale: More than 2,500 plasma proteins were evaluated through a genetic statistical framework (Mendelian Randomization and colocalization) to identify proteins likely causal for MS risk. The analysis implicated 39 proteins, most concentrated in immune signaling pathways.
- Biobank validation over years: Researchers used UK Biobank samples collected between 2006 and 2010. Among participants, 124 later developed MS, which allowed investigators to analyze blood taken an average of six years—and in some cases over ten years—before diagnosis.
- Early predictive set: In the retrospective analysis, eight proteins were consistently altered in individuals who later developed MS, supporting their role as prediagnostic indicators.
- DKKL1 as a dual marker: One protein, DKKL1, showed a particularly strong, concordant signal: higher DKKL1 levels associated with lower risk of developing MS and, in those who did develop MS, with a milder disease course. This dual association makes DKKL1 promising for both risk screening and prognosis.
- Next steps and scale-up: Led by neurologist Dr. Adil Harroud (The Neuro, McGill University), the team intends to replicate these findings in larger cohorts and explore combining these proteomic markers with other diagnostic tools to create a routine, non-invasive blood test for clinical use.
Source: McGill University
Study overview
The research team, led by Dr. Adil Harroud at The Neuro (Montreal Neurological Institute-Hospital), focused on circulating proteins because proteins execute most biological functions and can serve as accessible indicators of disease biology. Using cis-acting protein quantitative trait loci (pQTLs) for 2,545 plasma proteins from large proteomic datasets (n = 80,824), the investigators applied Mendelian Randomization to test whether genetically predicted protein levels were associated with MS risk in a genetic case-control meta-analysis of 14,802 cases and 26,703 controls.
Colocalization analysis helped prioritize proteins whose genetic signals mapped to the same loci as MS risk. The implicated proteins formed a tightly connected network enriched for immune regulatory pathways, including B- and T-cell costimulation, cytokine signaling, and pathways linked to Epstein–Barr virus biology. Transcriptomic enrichment pointed to B-cell subsets as a key cellular source. Integrating splicing annotations clarified differences across proteomic measurement platforms and suggested distinct proteoforms underlie some platform-specific findings.
To assess whether genetically implicated proteins were detectable before clinical onset, the team measured candidate proteins in prediagnostic samples from the UK Biobank (124 incident MS cases and 52,515 controls, median 5.9 years prediagnosis). Of 28 genetically implicated proteins available in those samples, eight were associated with time to MS diagnosis—an enrichment unlikely to occur by chance. DKKL1 in particular showed protective associations across risk, incidence, and severity analyses.
Genetic integration also improved fine-mapping at associated loci, boosting resolution by more than tenfold in some regions and nominating 13 putative novel risk loci. The combined genetic-proteomic approach therefore both highlights causal proteins and refines genetic risk architecture.
Key Questions Answered
A: Early detection exposes a window of opportunity when interventions can prevent or delay irreversible brain injury. For MS, treating or monitoring high-risk individuals before clinical onset could preserve neurological function and substantially reduce long-term disability.
A: Some proteins reflect core biological mechanisms that influence both whether disease develops and how it progresses. DKKL1 demonstrated such a dual relationship: its levels correlated with a lower likelihood of developing MS and, when MS did occur, with a milder clinical course, making it useful for risk stratification and prognosis.
A: The concept is analogous to cholesterol screening for heart disease. Measuring predictive biomarkers years before clinical events enables physicians to identify and manage at-risk individuals early, shifting care from reactive to preventive—this study proposes a similar model for neurology using blood-based proteomic markers.
Editorial Notes
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full by editorial staff.
- Additional context was provided by the newsroom team.
About this multiple sclerosis research news
Author: Shawn Hayward
Source: McGill University
Contact: Shawn Hayward – McGill University
Image credit: Neuroscience News
Original research: Open access. “Genetic-Proteomic Integration Identifies Predictive Plasma Proteins for Multiple Sclerosis” by Yuan Ding MSc, Dylan Hamitouche, Simon Thebault MD, PhD, Patrick Kearns MBChB, MPH, Ahmed Abdelhak MD, PhD, Adil Harroud MD. Annals of Neurology. DOI: 10.1002/ana.78256
Abstract (condensed)
Objective: To identify circulating plasma proteins that capture preclinical MS biology, improve risk stratification, and guide earlier intervention.
Methods: Using cis-pQTLs for 2,545 plasma proteins, the study applied Mendelian Randomization and colocalization to link genetically predicted protein levels with MS risk across large case-control datasets. Candidates were validated in prediagnostic UK Biobank samples and evaluated for associations with disease severity.
Results: Thirty-nine proteins were genetically associated with MS risk, clustering in immune signaling networks with transcriptomic enrichment in B cells. Of genetically implicated proteins measurable in prediagnostic samples, eight predicted time to MS diagnosis. DKKL1 exhibited consistent protective associations across risk, incidence, and severity. Integrating pQTLs improved fine-mapping and nominated additional risk loci.
Interpretation: The genetically anchored proteomic framework identifies likely causal proteins, sharpens genetic signals, and supports the development of blood-based biomarkers for preclinical MS detection and early intervention.