Serum GFAP as a biomarker for progression in multiple sclerosis: assay comparison and a large reference database of healthy controls
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Eline A.J. Willemse
, Sofia Sandgren, Pascal Benkert
, Sabine Schaedelin
, Aleksandra Maleska Maceski , Johanna Oechtering , Nafiye Genc , Klaus Berger , Marco Hermesdorf , Stefanie Müller , Sebastian Finkener , Juan F. Vilchez Gomez , Amar Zadic , Giulio Disanto , Marcus D’Souza , Cristina Granziera, Caroline Pot
, Chiara Zecca
, Patrice H. Lalive , Robert Hoepner , Patrick Roth , Marina Herwerth , Claudio Gobbi, David Leppert
, Maximilian Einsiedlerand Jens Kuhle
Abstract
Objectives
Compare Elecsys (Roche) and Simoa (Quanterix) immunoassays for serum glial fibrillary acidic protein (GFAP) using our reference database and Z scores, and evaluate their prognostic value for progression independent of relapse activity (PIRA) in multiple sclerosis (MS).
Methods
Platform correlation was assessed in 612 samples from healthy controls (n=188; median [interquartile range, IQR] age 45.1 [36.4–61.7] years) and people with MS (n=424; 45.3 [35.2–53.9] years). Elecsys values were converted to Z scores via Passing-Bablok-derived regression and validated in fingolimod (n=414), and B-cell depleting therapy (BCDT; n=353) cohorts. Z scores and hazard ratios (HRs) for time-to-PIRA were compared using Cox regression.
Results
GFAPSimoa and GFAPElecsys measurements were correlated (r=0.94), with Elecsys values ∼54 % lower (GFAPElecsys, ng/L=2.847 [95 % confidence interval, CI: 1.335 – 4.98] + 0.457 [0.434 – 0.478] * GFAPSimoa, ng/L). In univariable Cox models, GFAPSimoa and GFAPElecsys Z scores were associated with time-to-PIRA in both validation cohorts. In multivariable Cox models, higher GFAPSimoa Z scores were associated with shorter time-to-PIRA in fingolimod cohort (HR: 1.27 [95 % CI 1.08 – 1.50], p=0.0031) and trended toward significance in BCDT (1.18 [0.99 – 1.41, p=0.0693). In contrast, GFAPElecsys Z scores were associated with time-to-PIRA in both cohorts (fingolimod: 1.27 [1.09 – 1.48], p=0.0023; BCDT: (1.19 [1.00 – 1.40], p=0.0487).
Conclusions
Serum GFAP measured by Elecsys shows a comparable association with time-to-PIRA as Simoa, and GFAPSimoa Z scores can be successfully bridged to GFAPElecsys Z scores, supporting Elecsys`s potential for clinical implementation.
Funding source: Merck
Funding source: Sanofi
Funding source: Novartis
Funding source: The Swiss MS Society
Funding source: Roche
Funding source: Celgene
Funding source: Biogen
Acknowledgments
We express our deep thankfulness to patients and relatives for their participation and support, study nurses in participating centers for their motivated collaboration and recruitment efforts, and the administrative personnel of the SMSC.
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Research ethics: This cohort study, conducted since January 1, 2012, followed the Declaration of Helsinki and was approved by the Ethics Committees of all participating centers.
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Informed consent: Written informed consent was obtained from all HCs and pwMS.
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Author contributions: Significant contribution to: conception and design of the study (E.A.J.W., S.Sa., P.B., S.Sc., A.M.M., J.O., N.G., D.L., M.E., and J.K.), acquisition and analysis of data (E.A.J.W., S.Sa., P.B., S.Sc., A.M.M, J.O., N.G., K.B., M.Herm., S.M., S.F., J.F.V.G., A.Z., G.D., M.D., C.G., C.P.K., C.P., C.Z., P.H.L., R.H., M.Herw., C.G., D.L., M.E. and J.K.), participation in drafting a significant portion of the manuscript or figures (E.A.J.W., S.Sa., P.B., S.Sc., and J.K.). All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: E.A.J.W. is a contractor for Roche Diagnostics as of November 1, 2025. S.Sa. has received compensation for lectures from Novartis and Merck, has served on scientific advisory boards for Merck and Sanofi; and has received grants from the Swedish Foundation for MS Research; NEURO Sweden; the Edit Jacobson Foundation; the Rune and Ulla Amlövs Foundation for Neurological Research; the Göran Jahnsons Foundation; the Gothenburg Foundation for Neurological Research; the Gothenburg Medical Society; the Family Bursies Foundation; the Swedish Society of Medicine (SLS-1019901); the Swedish Federal Government [LUA/ALF agreement, 2025-MC-001011]; the Sparbank Foundation in Varberg; the Scientific Council Region Halland; and an unconditional grant from Sanofi (2025-MC-001011; this funder had no role in study design, data collection, analysis, or interpretation). P.B., S.Sc., A.M.M. report no disclosure. J.O. her employer (University hospital Basel) received research support by the Swiss MS Society, Roche and Novartis. N.G., K.B., M.Herm. report no disclosure. S.M. received honoraria for travel, honoraria for lectures/consulting, and/or grants for studies from Almirall, Biogen, Celgene, Novartis, Teva, Merck Serono, Genzyme, Roche, and Bayer Schweiz. S.F. has received honoraria for lectures and advisory boards as well as research and travel support from Biogen, Novartis, Almirall, Bayer Schweiz AG, Teva, Merck, Sanofi Genzyme, Roche and the Swiss MS Society. J.F.V.G., A.Z. report no disclosure. G.D. received financial support from Teva, Merck Serono, Biogen Idec, Bayer Schering, Genzyme, Roche, and Novartis. The submitted work is not related to any of these agreements. C.Z. received financial support from Teva, Merck Serono, Biogen Idec, Bayer Schering, Genzyme, Roche, and Novartis. The submitted work is not related to any of these agreements. M.D. is CEO of Neurostatus-UHB Ltd. He has received travel support from Bayer AG, Biogen, Teva Pharmaceuticals and Sanofi Genzyme and research support from the University Hospital Basel. C.G. reports that the Ente Ospedaliero Cantonale (employer) received compensation for speaking activities, consulting fees, or research grants from Almirall, Biogen Idec, Bristol Meyer Squibb, Lundbeck, Merck, Novartis, Sanofi, Teva Pharma, Roche. C.P. her institution received financial support and honoraria from Merck Serono, Biogen, Roche and Novartis none related to this work. C.Z. her institution the Department of Neurology, Regional Hospital Lugano (EOC), Lugano, Switzerland receives financial support from Teva, Merck Serono, Biogen, Genzyme, Roche, Celgene, Bayer and Novartis. P.H.L. received honoraria for speaking and or travel expense from Biogen, Merck, Novartis, Roche; consulting fees from Biogen, GeNeuro, Merck, Novartis, Roche; research support from Biogen, Merck, Novartis. None were related to this work. R.H. received speaker/advisor honorary from Merck, Novartis, Roche, Biogen, Alexion, Sanofi, Janssen, Bristol-Myers Squibb, Teva/Mepha and Almirall. He received research support within the last 5 years from Roche, Merck, Sanofi, Biogen, Chiesi, and Bristol-Myers Squibb. He also received research grants from the Swiss MS Society, the SITEM Insel Support Fund and is a member of the Advisory Board of the Swiss and International MS Society. He also serves as deputy editor in chief for Journal of Central Nervous System disease and is part of the ECTRIMS Young Investigator Committee. M.Herw. reports no disclosure. C.G. his instutition the Department of Neurology, Regional Hospital Lugano (EOC), Lugano, Switzerland received financial support from Teva, Merck Serono, Biogen, Genzyme, Roche, Celgene, Bayer and Novartis. D.L. was Chief Medical Officer of GeNeuro until end of 2023; he is a consultant for Rewind Therapeutics. M.E. has received travel support from Roche. J.K. received speaker fees, research support, travel support, and/or served on advisory boards by Swiss MS Society, Swiss National Research Foundation (320030_212534/1), United Kingdom Dementia Research Institute, University of Basel, Progressive MS Alliance, Alnylam, Bayer, Biogen, Bristol Myers Squibb, Celgene, Immunic, Merck, Neurogenesis, Novartis, Octave Bioscience, Quanterix, Roche, Sanofi, Stata DX.
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Research funding: The SMSC study received funding from the Swiss MS Society and grant funding from Biogen, Celgene, Merck, Novartis, Roche, and Sanofi. The funders of the study had no role in study design, data collection, analysis, and interpretation, writing or approval of this report for publication.
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Data availability: Written requests for access to the data reported in this paper will be considered by the corresponding author and a decision made about the appropriateness of the use of the data. If the use is appropriate, a data sharing agreement will be put in place before a fully de-identified version of the dataset used for the analysis with individual participant data is made available. The internet-based application for determination of sGFAP Z scores is available at: https://shiny.dkfbasel.ch/baselgfapreference/.
References
1. Rosengren, LE, Lycke, J, Andersen, O. Glial fibrillary acidic protein in CSF of multiple sclerosis patients: relation to neurological deficit. J Neurol Sci 1995;133:61–5. https://doi.org/10.1016/0022-510x(95)00152-r.Search in Google Scholar PubMed
2. Axelsson, M, Malmestrom, C, Nilsson, S, Haghighi, S, Rosengren, L, Lycke, J. Glial fibrillary acidic protein: a potential biomarker for progression in multiple sclerosis. J Neurol 2011;258:882–8. https://doi.org/10.1007/s00415-010-5863-2.Search in Google Scholar PubMed
3. Abdelhak, A, Huss, A, Kassubek, J, Tumani, H, Otto, M. Serum GFAP as a biomarker for disease severity in multiple sclerosis. Sci Rep 2018;8:14798. https://doi.org/10.1038/s41598-018-33158-8.Search in Google Scholar PubMed PubMed Central
4. Benkert, P, Maleska Maceski, A, Schaedelin, S, Oechtering, J, Zadic, A, Vilchez Gomez, JF, et al.. Serum glial fibrillary acidic protein and neurofilament light chain levels reflect different mechanisms of disease progression under B-cell depleting treatment in multiple sclerosis. Ann Neurol 2024. https://doi.org/10.1002/ana.27096.Search in Google Scholar PubMed PubMed Central
5. Maceski, AM, Benkert, P, Einsiedler, M, Schaedelin, S, Oechtering, J, Melie-Garcia, L, et al.. GFAP and NfL as predictors of disease progression and relapse activity in fingolimod-treated multiple sclerosis. Brain 2025. https://doi.org/10.1093/brain/awaf433.Search in Google Scholar PubMed
6. van Geel, WJ, de Reus, HP, Nijzing, H, Verbeek, MM, Vos, PE, Lamers, KJ. Measurement of glial fibrillary acidic protein in blood: an analytical method. Clin Chim Acta 2002;326:151–4. https://doi.org/10.1016/s0009-8981(02)00330-3.Search in Google Scholar PubMed
7. Papa, L, Lewis, LM, Falk, JL, Zhang, Z, Silvestri, S, Giordano, P, et al.. Elevated levels of serum glial fibrillary acidic protein breakdown products in mild and moderate traumatic brain injury are associated with intracranial lesions and neurosurgical intervention. Ann Emerg Med 2012;59:471–83. https://doi.org/10.1016/j.annemergmed.2011.08.021.Search in Google Scholar PubMed PubMed Central
8. Meier, S, Willemse, EAJ, Schaedelin, S, Oechtering, J, Lorscheider, J, Melie-Garcia, L, et al.. Serum glial fibrillary acidic protein compared with neurofilament light chain as a biomarker for disease progression in multiple sclerosis. JAMA Neurol 2023;80:287–97. https://doi.org/10.1001/jamaneurol.2022.5250.Search in Google Scholar PubMed PubMed Central
9. Abdelhak, A, Antweiler, K, Kowarik, MC, Senel, M, Havla, J, Zettl, UK, et al.. Serum glial fibrillary acidic protein and disability progression in progressive multiple sclerosis. Ann Clin Transl Neurol 2024;11:477–85. https://doi.org/10.1002/acn3.51969.Search in Google Scholar PubMed PubMed Central
10. Zoltewicz, JS, Scharf, D, Yang, B, Chawla, A, Newsom, KJ, Fang, L. Characterization of antibodies that detect human GFAP after traumatic brain injury. Biomark Insights 2012;7:71–9. https://doi.org/10.4137/bmi.s9873.Search in Google Scholar PubMed PubMed Central
11. Gogishvili, D, Honey, MIJ, Verberk, IMW, Vermunt, L, Hol, EM, Teunissen, CE, et al.. The GFAP proteoform puzzle: how to advance GFAP as a fluid biomarker in neurological diseases. J Neurochem 2025;169:e16226. https://doi.org/10.1111/jnc.16226.Search in Google Scholar PubMed PubMed Central
12. The Swiss MS Cohort. RC2NB. The Swiss MS cohort. University Hospital Basel (Coordinating Centre); 2012. [updated 2025-09-22]. Available from: https://smsc.ch/.Search in Google Scholar
13. Disanto, G, Benkert, P, Lorscheider, J, Mueller, S, Vehoff, J, Zecca, C, et al.. The swiss multiple sclerosis cohort-study (SMSC): a prospective swiss wide investigation of key phases in disease evolution and new treatment options. PLoS One 2016;11:e0152347. https://doi.org/10.1371/journal.pone.0152347.Search in Google Scholar PubMed PubMed Central
14. Wersching, H, Berger, K. New cohorts. The BiDirect study. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2012;55:822–3. https://doi.org/10.1007/s00103-012-1491-6.Search in Google Scholar PubMed
15. Conen, D, Schön, T, Aeschbacher, S, Paré, G, Frehner, W, Risch, M, et al.. Genetic and phenotypic determinants of blood pressure and other cardiovascular risk factors (GAPP). Swiss Med Wkly 2013;143:w13728. https://doi.org/10.4414/smw.2013.13728.Search in Google Scholar PubMed
16. Teismann, H, Wersching, H, Nagel, M, Arolt, V, Heindel, W, Baune, BT, et al.. Establishing the bidirectional relationship between depression and subclinical arteriosclerosis--rationale, design, and characteristics of the BiDirect Study. BMC Psychiatry 2014;14:174. https://doi.org/10.1186/1471-244x-14-174.Search in Google Scholar PubMed PubMed Central
17. Krisai, P, Aeschbacher, S, Ruperti Repilado, FJ, Schoen, T, Reusser, A, Meier, M, et al.. Healthy lifestyle and glucagon-like peptide-1 in young and healthy adults: a population-based study. Prev Med 2017;101:72–6. https://doi.org/10.1016/j.ypmed.2017.05.025.Search in Google Scholar PubMed
18. Teuber, A, Sundermann, B, Kugel, H, Schwindt, W, Heindel, W, Minnerup, J, et al.. MR imaging of the brain in large cohort studies: feasibility report of the population- and patient-based BiDirect study. Eur Radiol 2017;27:231–8. https://doi.org/10.1007/s00330-016-4303-9.Search in Google Scholar PubMed
19. Benkert, P, Meier, S, Schaedelin, S, Manouchehrinia, A, Yaldizli, Ö, Maceski, A, et al.. Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study. Lancet Neurol 2022;21:246–57. https://doi.org/10.1016/s1474-4422(22)00009-6.Search in Google Scholar
20. Kurtzke, JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52. https://doi.org/10.1212/wnl.33.11.1444.Search in Google Scholar PubMed
21. McDonald, WI, Compston, A, Edan, G, Goodkin, D, Hartung, HP, Lublin, FD, et al.. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–7. https://doi.org/10.1002/ana.1032.Search in Google Scholar PubMed
22. Kappos, L, Butzkueven, H, Wiendl, H, Spelman, T, Pellegrini, F, Chen, Y, et al.. Greater sensitivity to multiple sclerosis disability worsening and progression events using a roving versus a fixed reference value in a prospective cohort study. Mult Scler 2018;24:963–73. https://doi.org/10.1177/1352458517709619.Search in Google Scholar PubMed PubMed Central
23. Abdelhak, A, Foschi, M, Abu-Rumeileh, S, Yue, JK, D’Anna, L, Huss, A, et al.. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol 2022;18:158–72. https://doi.org/10.1038/s41582-021-00616-3.Search in Google Scholar PubMed
24. Barro, C, Healy, BC, Liu, Y, Saxena, S, Paul, A, Polgar-Turcsanyi, M, et al.. Serum GFAP and NfL levels differentiate subsequent progression and disease activity in patients with progressive multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 2023;10. https://doi.org/10.1212/nxi.0000000000200052.Search in Google Scholar
25. Pereira, JB, Janelidze, S, Smith, R, Mattsson-Carlgren, N, Palmqvist, S, Teunissen, CE, et al.. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease. Brain 2021;144:3505–16. https://doi.org/10.1093/brain/awab223.Search in Google Scholar PubMed PubMed Central
26. Andreasson, U, Perret-Liaudet, A, van Waalwijk van Doorn, LJ, Blennow, K, Chiasserini, D, Engelborghs, S, et al.. A practical guide to immunoassay method validation. Front Neurol 2015;6:179. https://doi.org/10.3389/fneur.2015.00179.Search in Google Scholar PubMed PubMed Central
27. Rabe, C, Thorne, N, Voyle, N, Mertes, M, Mellino, G, Quan, M, et al.. Evaluation of the Elecsys NeuroToolKit panel in early Alzheimer’s disease populations across six clinical trials. Alzheimer’s Dement 2021;17. https://doi.org/10.1002/alz.052033.Search in Google Scholar
28. van Lierop, Z, Verberk, IMW, van Uffelen, KWJ, Koel-Simmelink, MJA, In’t Veld, L, Killestein, J, et al.. Pre-analytical stability of serum biomarkers for neurological disease: neurofilament-light, glial fibrillary acidic protein and contactin-1. Clin Chem Lab Med 2022;60:842–50. https://doi.org/10.1515/cclm-2022-0007.Search in Google Scholar PubMed
29. Verberk, IMW, Misdorp, EO, Koelewijn, J, Ball, AJ, Blennow, K, Dage, JL, et al.. Characterization of pre-analytical sample handling effects on a panel of Alzheimer’s disease-related blood-based biomarkers: results from the Standardization of Alzheimer’s Blood Biomarkers (SABB) working group. Alzheimer’s Dement 2022;18:1484–97. https://doi.org/10.1002/alz.12510.Search in Google Scholar PubMed PubMed Central
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-1480).
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