Managing relapsing multiple sclerosis requires precise evaluation of therapeutic efficacy and inflammatory disease control. In routine neurological practice, clinicians primarily rely on magnetic resonance imaging and relapse occurrence to gauge therapeutic response. However, these conventional markers often reflect damage that has already occurred. Consequently, there is an urgent demand for sensitive fluid biomarkers that capture subclinical axonal injury in real time. Serum neurofilament light chain monitoring provides a quantifiable measurement of neuroaxonal damage across diverse disease stages. By adjusting raw biomarker concentrations into age- and body mass index-adjusted z-scores, clinicians can standardize values across heterogeneous adult cohorts. This analytical evolution significantly enhances our capability to detect persistent inflammatory disease activity early during disease-modifying therapy.

Understanding Biomarkers and Neurofilament Light Chain Monitoring

Neurofilaments represent vital structural components of the neuronal cytoskeleton. When inflammatory demyelination or acute axonal injury strikes the central nervous system, these structural proteins release into the cerebrospinal fluid and subsequently diffuse into peripheral blood. Previously, technical barriers restricted the precise quantification of blood neurofilaments. Fortunately, ultra-sensitive Single Molecule Array technology has transformed clinical research by enabling reliable peripheral detection. Nevertheless, baseline concentrations naturally elevate with physiological aging and fluctuate based on metabolic variables. Therefore, neurofilament light chain monitoring utilizes normative reference databases to convert absolute serum levels into standardized z-scores. This standardization eliminates physiological confounding factors and isolates genuine pathological fluctuations.

Furthermore, longitudinal shifts in these values offer deeper prognostic insight than isolated cross-sectional measurements. When clinicians initiate disease-modifying therapies, tracking longitudinal z-score alterations reveals whether therapy successfully dampens ongoing neuroaxonal destruction. Consequently, assessing dynamic changes over the first year of treatment establishes a robust biological benchmark for subsequent clinical stability.

Study Design and Longitudinal Cohort Analysis

To establish the real-world utility of longitudinal biomarker changes, researchers at the Multiple Sclerosis Center of Catalonia conducted a prospective cohort investigation. The study enrolled adult patients diagnosed with relapsing multiple sclerosis who initiated disease-modifying therapies. Investigators gathered serum samples at baseline before starting therapy and systematically collected follow-up samples after twelve months. The primary exposure variable was the change in serum neurofilament light chain z-scores, denoted as ΔzNfL, calculated by subtracting the baseline score from the one-year score.

Additionally, the investigators categorized patients using data-derived thresholds to discern meaningful biological responses. They defined distinct cohorts based on whether patients demonstrated a reduction in z-scores or exhibited persistent stability and elevation. Subsequently, the research team tracked all participants over the second year of therapy to evaluate the emergence of evidence of disease activity. This comprehensive composite endpoint incorporated clinical relapses, confirmed disability progression, and new or enlarging T2 and gadolinium-enhancing brain lesions on magnetic resonance imaging. Thus, the prospective design ensured rigorous clinical correlation across diverse therapeutic classes in clinical practice.

Prognostic Value of sNfL Z-Score Dynamics

The study results revealed that dynamic alterations in biomarker z-scores during the first year strongly predicted disease outcomes throughout the subsequent year. Specifically, patients who failed to achieve a reduction in z-scores showed significantly higher odds of experiencing breakthrough inflammatory disease activity. Even among individuals who appeared clinically stable on standard evaluations, stable or rising z-scores signaled underlying neuroaxonal injury.

Moreover, the researchers observed that a marked decrease in z-score values reflected effective suppression of active central nervous system inflammation. When therapies effectively halted focal inflammatory insults, biomarker levels rapidly declined toward the healthy population average. Conversely, individuals maintaining elevated z-scores despite twelve months of treatment faced a heightened risk of developing new magnetic resonance imaging lesions. These individuals also experienced higher rates of clinical relapses during the second year of follow-up. Therefore, longitudinal biomarker trajectory acts as a sensitive early indicator of subclinical treatment failure, allowing timely therapeutic re-evaluation before irreversible disability accumulates.

Integrating Biomarkers With Treatment Response Scores

Standard neurological monitoring frequently utilizes established multi-parameter algorithms such as the Rio Score, modified Rio Score, and MAGNIMS criteria. These scoring systems categorize treatment response using early clinical relapses and neuroimaging lesion accrual. However, a significant clinical challenge arises when managing patients who show intermediate or low scores on these conventional scales. In the study, investigators evaluated whether adding ΔzNfL measurements improved the predictive accuracy of these traditional scoring systems.

Remarkably, integrating biomarker trajectories provided substantial incremental value. Among patients classified into low-risk categories by standard scores, those who lacked a meaningful decrease in biomarker z-scores exhibited more than double the risk of subsequent disease activity compared to counterparts with robust biomarker reductions. Hence, serum neurofilament light chain monitoring successfully unmasks hidden therapeutic failure within seemingly responsive cohorts. By refining risk stratification, this composite approach allows clinicians to distinguish truly stable patients from individuals experiencing ongoing subclinical disease progression.

Clinical Implications for Real-World Disease Management

Implementing longitudinal biomarker testing offers immediate advantages for real-world disease-modifying treatment management. Because therapeutic choices have expanded to include oral immunomodulators, high-efficacy monoclonal antibodies, and immune reconstitution therapies, choosing the optimal agent requires nuanced monitoring tools. Relying entirely on annual magnetic resonance imaging can lead to delays in identifying non-responders. In contrast, peripheral blood draws represent a minimally invasive, cost-effective, and highly accessible adjunct in outpatient settings.

Furthermore, early identification of non-responders enables proactive treatment switching. When a patient demonstrates stagnant or increasing z-scores after twelve months of moderate-efficacy therapy, clinicians obtain objective biological rationale to escalate to high-efficacy disease-modifying agents. This swift escalation mitigates permanent neurological damage and preserves long-term functional independence. Consequently, incorporating standardized biomarker z-score dynamics into regular clinical practice bridges the gap between active focal inflammation and diffuse neurodegeneration, elevating the overall quality of multiple sclerosis care.

Future Directions and Practical Implementation

Although the clinical utility of biomarker z-scores is evident, widespread clinical adoption requires standardized laboratory assays and automated electronic health record integration. Automated calculators that adjust raw values for age, sex, and body mass index must become universally accessible to practicing neurologists. Additionally, future research should explore optimal sampling intervals, especially during the initial six months of starting high-efficacy therapies.

Nevertheless, current evidence confirms that longitudinal biomarker evaluation enhances clinical decision-making. As personalized medicine continues to advance, combining blood biomarkers with advanced neuroimaging and digital clinical metrics will form the cornerstone of multiple sclerosis monitoring. Clinicians can confidently use these dynamic measurements to tailor individual therapeutic regimens, optimize therapeutic safety, and improve long-term patient outcomes across health systems worldwide.

Frequently Asked Questions

How do sNfL z-scores improve standard multiple sclerosis monitoring?

Standard multiple sclerosis monitoring often detects disease activity only after clinical relapses or new neuroimaging lesions occur. Standardized z-scores adjust for age and body mass index, providing an objective measure of subclinical axonal damage. Tracking longitudinal z-score changes helps clinicians detect ongoing subclinical inflammation and assess therapeutic efficacy early.

What clinical threshold of ΔzNfL indicates ongoing disease activity?

In prospective clinical evaluation, an absence of z-score reduction (ΔzNfL ≥ 0) after one year of disease-modifying treatment indicates persistent neuroaxonal injury. Patients who fail to demonstrate a decrease in z-scores face significantly higher odds of experiencing breakthrough disease activity and magnetic resonance imaging lesions in subsequent years.

Can sNfL dynamics replace routine neuroimaging assessments in practice?

Serum biomarker dynamics do not completely replace routine neuroimaging. Instead, they provide vital complementary biological information that enhances clinical scoring systems like the MAGNIMS criteria. Combining fluid biomarkers with regular magnetic resonance imaging scans offers superior risk stratification and guides timely treatment optimization in relapsing multiple sclerosis.

Disclaimer: This content is for informational and educational purposes only. It is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Refer to the latest local and national guidelines for clinical practice.

References

1. Fernández V et al. Refining Treatment Monitoring in Patients With Relapsing Multiple Sclerosis Based on Changes on Neurofilament Light Chain Z-Scores. Neurology. 2026 Sep 22. doi: 10.1212/WNL.0000000000218451. PMID: 42623577.

2. Kuhle J et al. Blood neurofilament light chain as a biomarker of disease activity and treatment response in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. 2022;21(11):978-989.

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4. Sormani MP et al. Assessing response to interferon-beta in a multicenter dataset of patients with MS. Neurology. 2016;87(2):134-140.