The Evolution of Precision Monitoring in Multiple Sclerosis

Managing multiple sclerosis (MS) has historically relied on clinical relapses and magnetic resonance imaging (MRI) to gauge disease activity. However, these traditional measures often fail to capture the subtle, ongoing neuroaxonal damage and astrocytic activation that drive long-term disability. Recent advancements have highlighted serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) as critical tools for clinicians. Understanding MS biomarker Z scores has become essential for providing a standardized, objective window into the biological state of the central nervous system. These biomarkers offer a more nuanced perspective on treatment efficacy than clinical observation alone. Specifically, sNfL serves as a sensitive indicator of acute neuroaxonal injury, while sGFAP reflects astrocytic involvement and chronic neurodegeneration. Despite their potential, the clinical application of these biomarkers has faced significant hurdles. Inter-assay variability and the influence of biological confounders like age and body mass index (BMI) often complicate the interpretation of raw data. This research by Inojosa and colleagues addresses these challenges by employing a robust normalization framework. Their findings provide a roadmap for using these molecular signals to refine treatment strategies for patients receiving ocrelizumab.

Harmonizing Assays: The Power of Z Score Normalization

A major obstacle in integrating fluid biomarkers into routine MS care is the discrepancy between different testing platforms. For instance, laboratories may use different assays such as the Elecsys or Simoa platforms, which often yield varying absolute values for the same sample. This variability makes it nearly impossible to compare results across different clinical centers or longitudinal studies. To overcome this, researchers utilized covariate-adjusted Z scores to harmonize data across different assays. By converting raw concentrations into MS biomarker Z scores, the team successfully accounted for the physiological effects of age and BMI. This normalization process ensures that an elevated score reflects actual disease-related pathology rather than mere biological aging or metabolic factors. Consequently, this platform-agnostic approach allows for a unified interpretation of biomarker trajectories regardless of the specific assay used. This methodological breakthrough is particularly relevant for large-scale clinical practice where patients may undergo testing at different facilities. Moreover, the use of Z scores simplifies the communication of risk to both clinicians and patients. It provides a clear, standardized metric that identifies how far a patient’s biomarker levels deviate from a healthy reference population. This foundation is crucial for making informed decisions regarding treatment escalation or maintenance.

Deciphering Trajectories: sNfL vs sGFAP in RMS and PPMS

The study analyzed 430 people with MS, including those with relapsing MS (RMS) and primary progressive MS (PPMS), to track biomarker changes during ocrelizumab therapy. At the start of the study, patients with RMS exhibited significantly higher baseline sNfL Z scores compared to those with PPMS, highlighting the intense inflammatory activity typical of the relapsing phase. In contrast, sGFAP Z scores were relatively similar across both phenotypes at baseline, suggesting that astrocytic activation is a common feature regardless of the clinical presentation. Following the initiation of ocrelizumab, distinct trajectories emerged for each biomarker. In patients with RMS, sNfL levels showed a marked and consistent decrease, reflecting the drug’s powerful effect on suppressing acute focal inflammation. However, sGFAP levels remained relatively stable over the 24-month period in the overall cohort. This stability suggests that while ocrelizumab is highly effective at reducing neuroaxonal injury, its impact on underlying astrocytic processes may be more gradual or limited. Interestingly, patients with RMS who had concurrent elevations in both biomarkers at baseline maintained the highest overall levels over time. Their trajectories diverged significantly from the more stable profile seen in the PPMS group, indicating that high baseline activity predicts a more challenging biological course.

The 12-Month Threshold: Evaluating MS Biomarker Z Scores for Success

One of the most clinically significant findings of this research is the identification of the 12-month mark as a critical window for assessment. While baseline measurements and 6-month checks provide early data, the study found that the 12-month combined biomarker status was the superior predictor of long-term outcomes. Specifically, failing to achieve a relative reduction in MS biomarker Z scores at this timepoint served as a strong warning sign. The researchers established an exploratory responder threshold of a 50% relative Z score reduction. Patients who did not reach this 50% reduction in sNfL by month 12 had a 7.14-fold increased risk of persistent elevation at 24 months. Even more striking was the data for sGFAP, where failing to meet the reduction threshold was associated with a 32.08-fold higher risk of persisting elevation. These odds ratios emphasize that the biological response in the first year is highly indicative of the treatment's long-term impact on the underlying disease process. Identifying these "non-responders" early allows clinicians to adjust their surveillance strategies before significant clinical worsening occurs. This predictive power transforms sNfL and sGFAP from retrospective markers of damage into proactive tools for clinical management.

Clinical Implications: Refining Surveillance and Intervention

Integrating these standardized biomarker trajectories into clinical practice offers a new level of precision in MS management. For patients who show a rapid and sustained decrease in Z scores, clinicians can have greater confidence in the current treatment regimen. Conversely, those who fail to reach the 50% reduction threshold at month 12 require much closer clinical and radiological surveillance. In an environment where the goal is "No Evidence of Disease Activity" (NEDA), these biomarkers provide a more sensitive metric than relapses or new lesions alone. For instance, a patient might appear clinically stable but continue to show elevated sGFAP Z scores, indicating ongoing subclinical neurodegeneration or astrocytic activation. In such cases, the clinician might consider more frequent MRI monitoring or a more rigorous assessment of cognitive function. Furthermore, the ability to use different laboratory platforms interchangeably via Z score normalization removes a significant logistical barrier for Indian healthcare providers who may work with multiple diagnostic partners. This flexibility ensures that the monitoring of MS patients remains consistent even if they relocate or change insurance providers. Ultimately, this framework empowers neurologists to transition from a reactive to a proactive care model, tailoring interventions to the specific biological needs of the individual patient.

Future Outlook: Moving Toward Biomarker-Informed Precision Medicine

The success of the platform-agnostic Z score framework marks a pivotal shift in the landscape of neuroimmunology. As we move forward, the integration of MS biomarker Z scores into routine diagnostic workflows will likely become the standard of care for monitoring all high-efficacy disease-modifying therapies. This study provides the evidence base needed to justify the broader use of sNfL and sGFAP in clinical decision-making. Future research will likely focus on whether these 12-month thresholds can also predict the risk of progression independent of relapse activity (PIRA), which remains one of the most significant challenges in MS care. Additionally, as more real-world data becomes available, the Z score reference databases will continue to grow, further refining the accuracy of these assessments. There is also potential for these biomarkers to play a role in clinical trials as primary or secondary endpoints, potentially shortening the time needed to evaluate new therapeutic candidates. For now, the practical 12-month threshold identified in this study offers an immediate and actionable tool for neurologists. By identifying patients who remain at high biological risk despite treatment, we can better allocate resources and focus our clinical efforts where they are needed most. This approach brings us one step closer to the ideal of personalized, precision medicine for every person living with multiple sclerosis.

Frequently Asked Questions

Why is Z score normalization better than using raw biomarker values?

Raw biomarker levels for sNfL and sGFAP are heavily influenced by age and BMI, making it difficult to distinguish disease activity from normal biological factors. Z score normalization adjusts for these confounders and harmonizes data across different assay platforms, providing a standardized, platform-agnostic metric that is reliable for long-term clinical monitoring.

How does ocrelizumab affect sNfL and sGFAP levels differently in patients?

Ocrelizumab typically induces a significant reduction in sNfL Z scores, especially in relapsing MS patients, by suppressing acute neuroaxonal injury. However, sGFAP Z scores often remain more stable or decrease more slowly, as they reflect astrocytic activation and chronic processes that may be less responsive to B-cell depletion in the short term.

What is the significance of the 50% relative Z score reduction at month 12?

A 50% relative reduction in Z scores at 12 months is a powerful predictor of long-term treatment response. Patients failing to meet this threshold have a significantly higher risk of persistent biomarker elevation at 24 months, suggesting a suboptimal biological response that warrants closer clinical surveillance and potential reconsideration of the management plan.

Disclaimer: This content is for informational and educational purposes only and does not constitute 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

Inojosa H et al. From measurement to biomarker trajectories: platform-agnostic Z score analysis of serum NfL and GFAP in ocrelizumab-treated multiple sclerosis. J Neurol. 2026 Jul 10. doi: 10.1007/s00415-026-13986-9. PMID: 42429990.

Kuhle J, et al. Blood neurofilament light chain as a biomarker of MS disease activity and treatment response. Lancet Neurol. 2024;23(3):305-316.

Abdelhak A, et al. Glial Fibrillary Acidic Protein (GFAP) as a Biomarker of Neurodegeneration and Treatment Response in Multiple Sclerosis. Cells. 2023;12(10):1441.