Analyzing levels of early biomarkers can modestly improve predictive models that contain clinical and magnetic resonance imaging (MRI) variables in multiple sclerosis (MS), according to a new study published in the journal Multiple Sclerosis and Related Disorders.

Accordingly, worse clinical outcomes, secondary progressive MS (SPMS), and Expanded Disability Status Scale (EDSS) are associated with higher levels of serum glial fibrillary acidic proteins and worse MRI outcomes, while the T2-lesion volume and brain parenchymal fraction are associated with higher levels of serum neurofilament, the researchers noted.

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“Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice,” the study authors said. 

It is of great importance to perform early risk stratification in MS to ensure the right treatment is offered to patients, the investigators noted.

Here, a team of researchers from Harvard Medical School and Brigham and Women’s Hospital in Boston, Massachusetts, led by Tanuja Chitnis, MD, developed a predictive clinical model using age, relapse rate, EDSS, pyramidal signs, sex, and spinal cord lesions in 144 patients with MS.

The area under the curve for the predictive clinical model was 0.73 without biomarker data. This value went up to 0.77 when serum levels of neurofilament and glial fibrillary acidic proteins were included in the model. 

Higher levels of serum glial fibrillary acidic proteins at baseline were associated with the development of SPMS, the researchers found.

They reported that when they added 1-year follow-up biomarker levels to the model, the area under the curve went further up to 0.79. However, this change was not statistically significant. 

When baseline biomarker data was added, the R-squared of clinical models for 10-year EDSS also improved, but additional 1-year follow-up levels did not. 

The levels of serum glial fibrillary acidic proteins at baseline were associated with 10-year EDSS, and the levels of biomarker levels at baseline improved R-squared for T2-lesion volume on MRI from 0.12 to 0.27 and brain parenchymal fraction from 0.15 to 0.2.

When 1-year follow-up biomarker data was added, the T2-lesion volume improved further to 0.33 while the brain parenchymal fraction improved to 0.23. 

Serum levels of neurofilament at baseline were associated with the volume of T2 lesions, and the 1-year follow-up serum neurofilament was associated with brain parenchymal fraction. 

“Implementing predictive models into web-based calculators is needed to use early biomarker data for personalized risk estimation and will allow for prospective validation,” the authors concluded.


Bose G, Healy BC, Saxena S, et al. Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes. Mult Scler Relat Disord. Published online April 2, 2023. doi:10.1016/j.msard.2023.104695