MS & Aging: Proteomic Brain Age Predicts Mortality Risk | Healio

New research suggests a disconnect between chronological age and biological age in individuals with multiple sclerosis (MS), with implications for understanding disease progression and mortality risk. A study presented at the American Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) meeting on February 5, 2026, revealed that proteomic analysis can offer insights into how MS affects aging processes at a biological level.

Biological vs. Chronological Age in MS

Researchers found a gap of more than a decade between brain-specific proteomic age and chronological age in patients with MS. This means that, based on protein signatures in the brain, individuals with MS appeared biologically older than their actual age. “We know that aging is the most important prognostic factor for MS because it drives the transition from relapsed-remaining MS to the progressive phase of MS,” explained Dylan Hamitouche, a doctoral student at McGill University and student researcher at Montreal Neurological Institute-Hospital, during his presentation.

This age gap isn’t uniform across all organs. The study pinpointed the brain as exhibiting the most pronounced aging effect, showing a difference of 2.5 years between individuals with MS and healthy controls. Proteomic analysis, which examines the complete set of proteins in a biological sample, allowed researchers to assess organ-specific aging with greater precision.

How the Study Was Conducted

The research team utilized data from the U.K. Biobank, a large-scale biomedical database. They modeled biological age using various biomarkers, including leukocyte telomere length, brain MRI scans, and plasma proteomics. The study included data from 472,266 individuals, with a subset of 2,411 diagnosed with MS. An additional 2,440 MS cases, some with proteomic samples collected up to 15 years before diagnosis, were analyzed alongside age- and sex-matched controls.

By subtracting chronological age from the predicted biological age, researchers calculated the “age gap.” A positive gap indicated accelerated biological aging. Machine learning models were trained to predict proteomic age based on protein signatures and MRI data, and their performance was validated in the MS cohort.

Key Findings and Implications

The study confirmed that proteomic aging is accelerated in MS, with affected individuals appearing almost a full year biologically older than those without the disease. Importantly, this accelerated aging was observed even when accounting for known MS-related biomarkers like neurofilament light chain and glial fibrillary acidic protein.

Brain-specific proteomic aging was found to precede an MS diagnosis by over a decade, correlating with white matter lesions and decreased brain volume observed on MRI. This suggests that changes in brain protein signatures may serve as early indicators of disease development.

Impact on Clinical Outcomes and Mortality

A larger brain-age gap was associated with worse outcomes in MS, including impaired reaction time, decreased grip strength, and reduced overall health. Notably, a higher MRI brain-age gap was linked to increased falls and slower reaction times.

Perhaps most significantly, the study found a strong association between the brain-age gap and mortality risk in MS. Each one-year increase in the gap was associated with a 55% increase in the risk of death (HR = 1.55; 95% CI: 1.2 to 2). However, this association was not observed when the analysis included healthy controls, suggesting that brain proteomic aging affects both populations similarly, but its impact on mortality is more pronounced in the context of MS.

Hamitouche suggested that these findings may have implications for treatment strategies. “We showed that brain-specific aging accelerates 10 years before diagnosis… that the peak difference is 10 years after and this might align with optimal disease-modifying therapy efficacy,” he stated. The research highlights the potential for using proteomic and MRI-based biomarkers to identify individuals at higher risk of disease progression and mortality, and to tailor treatment approaches accordingly.

The study underscores the complex interplay between aging and MS, and the importance of considering biological age alongside chronological age when assessing disease prognosis and guiding clinical management. Further research is needed to fully understand the mechanisms driving accelerated brain aging in MS and to develop interventions that can mitigate its effects.