Pascal Benkert1, Stephanie Meier2, Sabine Schaedelin1, Ali Manouchehrinia3, Özgür Yaldizli2, Aleksandra Maceski2, Johanna Oechtering2, Lutz Achtnichts4, David Conen5, Tobias Derfuss2, Patrice H Lalive6, Christian Mueller7, Stefanie Müller8, Yvonne Naegelin2, Jorge R Oksenberg9, Caroline Pot10, Anke Salmen11, Eline Willemse2, Ingrid Kockum3, Kaj Blennow12, Henrik Zetterberg13, Claudio Gobbi14, Ludwig Kappos2, Heinz Wiendl15, Klaus Berger16, Maria Pia Sormani17, Cristina Granziera18, Fredrik Piehl19, David Leppert2, Jens Kuhle20. 1. Department of Clinical Research, Clinical Trial Unit, University Hospital Basel, University of Basel, Basel, Switzerland. 2. Neurologic Clinic and Policlinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel, University of Basel, Basel, Switzerland. 3. Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden. 4. Department of Neurology, Cantonal Hospital Aarau, Aarau, Switzerland. 5. Population Health Research Institute, McMaster University, Hamilton, ON, Canada. 6. Department of Neurosciences, Division of Neurology, Unit of Neuroimmunology and Neuromuscular Diseases, University Hospital of Geneva and Faculty of Medicine, Geneva, Switzerland. 7. Department of Medicine, Cardiology Division, Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland. 8. Department of Neurology, Cantonal Hospital St Gallen, St Gallen, Switzerland. 9. Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA. 10. Department of Clinical Neurosciences, Service of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. 11. Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland. 12. Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden. 13. Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; UK Dementia Research Institute at UCL, London, UK. 14. Department of Neurology, Multiple Sclerosis Center, Neurocenter of Southern Switzerland, Lugano, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland. 15. Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany. 16. Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany. 17. Department of Health Sciences, University of Genoa, Genoa, Italy. 18. Neurologic Clinic and Policlinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel, University of Basel, Basel, Switzerland; Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel, University of Basel, Basel, Switzerland. 19. Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Center for Neurology, Academic Specialist Center, Stockholm Health Services, Stockholm, Sweden. 20. Neurologic Clinic and Policlinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: jens.kuhle@usb.ch.
Abstract
BACKGROUND: Serum neurofilament light chain (sNfL) is a biomarker of neuronal damage that is used not only to monitor disease activity and response to drugs and to prognosticate disease course in people with multiple sclerosis on the group level. The absence of representative reference values to correct for physiological age-dependent increases in sNfL has limited the diagnostic use of this biomarker at an individual level. We aimed to assess the applicability of sNfL for identification of people at risk for future disease activity by establishing a reference database to derive reference values corrected for age and body-mass index (BMI). Furthermore, we used the reference database to test the suitability of sNfL as an endpoint for group-level comparison of effectiveness across disease-modifying therapies. METHODS: For derivation of a reference database of sNfL values, a control group was created, comprising participants with no evidence of CNS disease taking part in four cohort studies in Europe and North America. We modelled the distribution of sNfL concentrations in function of physiological age-related increase and BMI-dependent modulation, to derive percentile and Z score values from this reference database, via a generalised additive model for location, scale, and shape. We tested the reference database in participants with multiple sclerosis in the Swiss Multiple Sclerosis Cohort (SMSC). We compared the association of sNfL Z scores with clinical and MRI characteristics recorded longitudinally to ascertain their respective disease prognostic capacity. We validated these findings in an independent sample of individuals with multiple sclerosis who were followed up in the Swedish Multiple Sclerosis registry. FINDINGS: We obtained 10 133 blood samples from 5390 people (median samples per patient 1 [IQR 1-2] in the control group). In the control group, sNfL concentrations rose exponentially with age and at a steeper increased rate after approximately 50 years of age. We obtained 7769 samples from 1313 people (median samples per person 6·0 [IQR 3·0-8·0]). In people with multiple sclerosis from the SMSC, sNfL percentiles and Z scores indicated a gradually increased risk for future acute (eg, relapse and lesion formation) and chronic (disability worsening) disease activity. A sNfL Z score above 1·5 was associated with an increased risk of future clinical or MRI disease activity in all people with multiple sclerosis (odds ratio 3·15, 95% CI 2·35-4·23; p<0·0001) and in people considered stable with no evidence of disease activity (2·66, 1·08-6·55; p=0·034). Increased Z scores outperformed absolute raw sNfL cutoff values for diagnostic accuracy. At the group level, the longitudinal course of sNfL Z score values in people with multiple sclerosis from the SMSC decreased to those seen in the control group with use of monoclonal antibodies (ie, alemtuzumab, natalizumab, ocrelizumab, and rituximab) and, to a lesser extent, oral therapies (ie, dimethyl fumarate, fingolimod, siponimod, and teriflunomide). However, longitudinal sNfL Z scores remained elevated with platform compounds (interferons and glatiramer acetate; p<0·0001 for the interaction term between treatment category and treatment duration). Results were fully supported in the validation cohort (n=4341) from the Swedish Multiple Sclerosis registry. INTERPRETATION: The use of sNfL percentiles and Z scores allows for identification of individual people with multiple sclerosis at risk for a detrimental disease course and suboptimal therapy response beyond clinical and MRI measures, specifically in people with disease activity-free status. Additionally, sNfL might be used as an endpoint for comparing effectiveness across drug classes in pragmatic trials. FUNDING: Swiss National Science Foundation, Progressive Multiple Sclerosis Alliance, Biogen, Celgene, Novartis, Roche.
BACKGROUND: Serum neurofilament light chain (sNfL) is a biomarker of neuronal damage that is used not only to monitor disease activity and response to drugs and to prognosticate disease course in people with multiple sclerosis on the group level. The absence of representative reference values to correct for physiological age-dependent increases in sNfL has limited the diagnostic use of this biomarker at an individual level. We aimed to assess the applicability of sNfL for identification of people at risk for future disease activity by establishing a reference database to derive reference values corrected for age and body-mass index (BMI). Furthermore, we used the reference database to test the suitability of sNfL as an endpoint for group-level comparison of effectiveness across disease-modifying therapies. METHODS: For derivation of a reference database of sNfL values, a control group was created, comprising participants with no evidence of CNS disease taking part in four cohort studies in Europe and North America. We modelled the distribution of sNfL concentrations in function of physiological age-related increase and BMI-dependent modulation, to derive percentile and Z score values from this reference database, via a generalised additive model for location, scale, and shape. We tested the reference database in participants with multiple sclerosis in the Swiss Multiple Sclerosis Cohort (SMSC). We compared the association of sNfL Z scores with clinical and MRI characteristics recorded longitudinally to ascertain their respective disease prognostic capacity. We validated these findings in an independent sample of individuals with multiple sclerosis who were followed up in the Swedish Multiple Sclerosis registry. FINDINGS: We obtained 10 133 blood samples from 5390 people (median samples per patient 1 [IQR 1-2] in the control group). In the control group, sNfL concentrations rose exponentially with age and at a steeper increased rate after approximately 50 years of age. We obtained 7769 samples from 1313 people (median samples per person 6·0 [IQR 3·0-8·0]). In people with multiple sclerosis from the SMSC, sNfL percentiles and Z scores indicated a gradually increased risk for future acute (eg, relapse and lesion formation) and chronic (disability worsening) disease activity. A sNfL Z score above 1·5 was associated with an increased risk of future clinical or MRI disease activity in all people with multiple sclerosis (odds ratio 3·15, 95% CI 2·35-4·23; p<0·0001) and in people considered stable with no evidence of disease activity (2·66, 1·08-6·55; p=0·034). Increased Z scores outperformed absolute raw sNfL cutoff values for diagnostic accuracy. At the group level, the longitudinal course of sNfL Z score values in people with multiple sclerosis from the SMSC decreased to those seen in the control group with use of monoclonal antibodies (ie, alemtuzumab, natalizumab, ocrelizumab, and rituximab) and, to a lesser extent, oral therapies (ie, dimethyl fumarate, fingolimod, siponimod, and teriflunomide). However, longitudinal sNfL Z scores remained elevated with platform compounds (interferons and glatiramer acetate; p<0·0001 for the interaction term between treatment category and treatment duration). Results were fully supported in the validation cohort (n=4341) from the Swedish Multiple Sclerosis registry. INTERPRETATION: The use of sNfL percentiles and Z scores allows for identification of individual people with multiple sclerosis at risk for a detrimental disease course and suboptimal therapy response beyond clinical and MRI measures, specifically in people with disease activity-free status. Additionally, sNfL might be used as an endpoint for comparing effectiveness across drug classes in pragmatic trials. FUNDING: Swiss National Science Foundation, Progressive Multiple Sclerosis Alliance, Biogen, Celgene, Novartis, Roche.
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