Researchers identify key protein biomarkers in MS patients, paving way for personalized treatment strategies

In a recent article featured in Nature Communications, researchers aimed to identify dependable protein biomarkers for diagnosing and predicting the short- and long-term disease trajectory of multiple sclerosis (MS) by utilizing proximity-extension assay (PEA) in tandem with next-generation sequencing (NGS) technology.

Study: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Image Credit: New Africa/Shutterstock.comStudy: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Image Credit: New Africa/Shutterstock.com

Background

MS is a chronic degenerative ail that affects the central nervous system (CNS), so it likely alters the composition of circulating body fluids, such as blood plasma and cerebrospinal fluid (CSF).

Consequently, fluctuations in protein levels within the plasma and CSF might serve as credible biomarkers for MS diagnosis, monitoring disease activity, and tracking the progression of neurological disability.

The researchers speculate that empirically testing MS biomarkers in paired CSF and plasma samples from early-stage MS patients and healthy controls could pave the way for initiating high-efficacy therapies sooner. This could delay MS onset and progression, mitigate unnecessary treatment, and facilitate personalized treatment strategies.

Historically, predicting MS-related disability using the Expanded Disability Status Scale (EDSS) scores was challenging due to the short duration of follow-ups. While some research hinted at neurofilament light chain (NfL) in CSF as a promising biomarker, these studies lacked corroborative data.

Study design and methodology

The current study focused on two Swedish cohorts: 143 MS patients and 43 healthy controls. These cohorts, named the discovery and replication cohorts, supplied paired CSF and plasma samples from two different hospitals: Linköping and Karolinska University.

The team profiled 1463 proteins in these samples using the Olink Explore platform. They also measured NfL levels in the CSF samples and standardized the data for consistency.

With a follow-up period extending to 13 years, the researchers utilized a two-sided linear model t-test (Limma analysis) for differential expression analysis (DEA).

The DEA revealed 52 differentially expressed proteins (DEPs) in the discovery cohort, 40 of which were similarly expressed in the replication cohort. Notably, all protein expression levels were higher in MS patients than in the controls.

The study also employed the no evident disease activity (NEDA-3) concept, relying on three specific parameters to ascertain the absence of MS activity. Additionally, a normalized age-related MS score (nARMSS) was calculated for each participant to indicate overall disability progression.

Logistic regression models assessed binary outcomes, and a linear regression model forecasted the nARMSS score using baseline protein expressions. Both models also considered age and gender as potential influencing factors.

Results

Of the 52 DEPs identified in the discovery cohort's CSF, NfL and interleukin-18 (IL-18) exhibited the most robust correlation. Even after two years, lower NfL levels in the CSF accurately predicted the absence of disease activity.

Throughout an average six-year follow-up, EDSS scores effectively predicted long-term disability. The nARMSS score, which considered disease duration and age, facilitated a comparative analysis of disability progression across cohorts.

The researchers noted that MS-induced disability often manifested independently from relapse-related inflammation. Thus, understanding disability development and pinpointing its trustworthy biomarkers is paramount.

Given the inclusion of a replication cohort for results validation, the linear regression model could distinguish between MS patients with varying likelihoods of a high nARMSS score. Therefore, protein biomarkers emerge as potential predictive tools to discern different clinical trajectories in MS.

Initiating a high-efficacy treatment early on can profoundly influence the long-term progression of MS disability. Furthermore, it could usher in personalized MS treatments while preventing unnecessary drug administration, considering the associated costs and potential side effects.

Conclusions

This research identified 52 robust biomarker candidates in the CSF of early-stage MS patients when compared to healthy controls. Given their significance in MS progression, these protein biomarkers can reliably forecast the disease's progression, especially disability outcomes, based on nARMSS scores.

In future endeavors, these protein biomarkers could guide optimal individualized treatment strategies for those battling multiple sclerosis.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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