Blood GFAP as an emerging biomarker in brain and spinal cord disorders
Open accessAbstract
Blood-derived biomarkers for brain and spinal cord diseases are urgently needed. The introduction of highly sensitive immunoassays led to a rapid increase in the number of potential blood-derived biomarkers for diagnosis and monitoring of neurological disorders. In 2018, the FDA authorized a blood test for clinical use in the evaluation of mild traumatic brain injury (TBI). The test measures levels of the astrocytic intermediate filament glial fibrillary acidic protein (GFAP) and neuroaxonal marker ubiquitin carboxy-terminal hydrolase L1. In TBI, blood GFAP levels are correlated with clinical severity and extent of intracranial pathology. Evidence also indicates that blood GFAP levels hold the potential to reflect, and might enable prediction of, worsening of disability in individuals with progressive multiple sclerosis. A growing body of evidence suggests that blood GFAP levels can be used to detect even subtle injury to the CNS. Most importantly, the successful completion of the ongoing validation of point-of-care platforms for blood GFAP might ameliorate the decision algorithms for acute neurological diseases, such as TBI and stroke, with important economic implications. In this Review, we provide a systematic overview of the evidence regarding the utility of blood GFAP as a biomarker in neurological diseases. We propose a model for GFAP concentration dynamics in different conditions and discuss the limitations that hamper the widespread use of GFAP in the clinical setting. In our opinion, the clinical use of blood GFAP measurements has the potential to contribute to accelerated diagnosis and improved prognostication, and represents an important step forward in the era of precision medicine.
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Plasma GFAP and NfL are not anticipated to serve as FTD-specific diagnostic markers.
“Given that plasma GFAP and NfL are elevated not only in FTD but also in other neurological diseases such as Alzheimer disease [7], stroke [50, 51], and TBI [6], these biomarkers are not anticipated to serve as FTD-specific diagnostic markers.”
Cites
Neurotoxic reactive astrocytes are induced by activated microglia
Liddelow, S. A., Guttenplan, K. A., Clarke, L. E., Bennett, F. C., Bohlen, C. J., Schirmer, L., Bennett, M. L., Münch, A. E., Chung, W.-S., Peterson, T. C., et al. (2017) · Nature · doi:10.1038/nature21029 · Cited by 7,971 · see citing works
Astrocytes: Biology and pathology
Sofroniew, M. V., & Vinters, H. V. (2010) · Acta Neuropathologica · doi:10.1007/s00401-009-0619-8 · Cited by 5,196 · see citing works
Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research
Maas, A. I. R., Menon, D. K., Adelson, P. D., Andelic, N., Bell, M. J., Belli, A., Bragge, P., Brazinova, A., Büki, A., Chesnut, R. M., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30371-x · Cited by 2,609 · see citing works
Self-citationThe glymphatic system: a beginner's guide
Jessen, N. A., Munk, A. S. F., Lundgaard, I., & Nedergaard, M. (2015) · Neurochemical Research · doi:10.1007/s11064-015-1581-6 · Cited by 2,075 · see citing works
An acidic protein isolated from fibrous astrocytes
Eng, L. F., Vanderhaeghen, J. J., Bignami, A., & Gerstl, B. (1971) · Brain Research · doi:10.1016/0006-8993(71)90668-8 · Cited by 1,230 · see citing works
Wallin, M. T., Culpepper, W. J., Nichols, E., Bhutta, Z. A., Gebrehiwot, T. T., Hay, S. I., Khalil, I. A., Krohn, K. J., Liang, X., Naghavi, M., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30443-5 · Cited by 1,108 · see citing works
Middeldorp, J., & Hol, E. M. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.01.005 · Cited by 1,080 · see citing works
A new look at cerebrospinal fluid circulation
Brinker, T., Stopa, E., Morrison, J., & Klinge, P. (2014) · Fluids and Barriers of the CNS · doi:10.1186/2045-8118-11-10 · Cited by 819 · see citing works
Messing, A., Brenner, M., Feany, M. B., Nedergaard, M., & Goldman, J. E. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5384-11.2012 · Cited by 250 · see citing works
Zhang, Z., Zoltewicz, J. S., Mondello, S., Newsom, K. J., Yang, Z., Yang, B., Kobeissy, F., Guingab, J., Glushakova, O., Robicsek, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092698 · Cited by 200 · see citing works
Ishiki, A., Kamada, M., Kawamura, Y., Terao, C., Shimoda, F., Tomita, N., Arai, H., & Furukawa, K. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13399 · Cited by 156 · see citing works
Hol, E. M., & Capetanaki, Y. (2017) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a021642 · Cited by 144 · see citing works
Benussi, A., Ashton, N. J., Karikari, T. K., Gazzina, S., Premi, E., Benussi, L., Ghidoni, R., Rodriguez, J. L., Emeršič, A., Binetti, G., et al. (2020) · Journal of Alzheimer’s Disease · doi:10.3233/jad-200608 · Cited by 113 · see citing works
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Greene, C., Connolly, R., Brennan, D., Laffan, A., O’Keeffe, E., Zaporojan, L., O’Callaghan, J., Thomson, B., Connolly, E., Argue, R., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01576-9 · Cited by 429 · see citing works
GFAP as a Potential Biomarker for Alzheimer’s Disease: A Systematic Review and Meta-Analysis
Kim, K. Y., Shin, K. Y., & Chang, K.-A. (2023) · Cells · doi:10.3390/cells12091309 · Cited by 309 · see citing works
Diagnosis of Alzheimer's disease using plasma biomarkers adjusted to clinical probability
Therriault, J., Janelidze, S., Benedet, A. L., Ashton, N. J., Arranz Martínez, J., Gonzalez-Escalante, A., Bellaver, B., Alcolea, D., Vrillon, A., Karim, H., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00731-y · Cited by 112 · see citing works
Chadarevian, J. P., Hasselmann, J., Lahian, A., Capocchi, J. K., Escobar, A., Lim, T. E., Le, L., Tu, C., Nguyen, J., Kiani Shabestari, S., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.05.023 · Cited by 83 · see citing works
Lista, S., Imbimbo, B. P., Grasso, M., Fidilio, A., Emanuele, E., Minoretti, P., López-Ortiz, S., Martín-Hernández, J., Gabelle, A., Caruso, G., et al. (2024) · Journal of Neuroinflammation · doi:10.1186/s12974-024-03163-y · Cited by 81 · see citing works
Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer’s disease
Eteleeb, A. M., Novotny, B. C., Tarraga, C. S., Sohn, C., Dhungel, E., Brase, L., Nallapu, A., Buss, J., Farias, F., Bergmann, K., et al. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002607 · Cited by 64 · see citing works
Vollmuth, C., Fiessler, C., Montellano, F. A., Kollikowski, A. M., Essig, F., Oeckl, P., Barba, L., Steinacker, P., Schulz, C., Ungethüm, K., et al. (2024) · European Stroke Journal · doi:10.1177/23969873241234436 · Cited by 38 · see citing works
Self-citationDinkel, L., Hummel, S., Zenatti, V., Malara, M., Tillmann, Y., Colombo, A., Monasor, L. S., Suh, J. H., Logan, T., Roth, S., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adl4616 · Cited by 22 · see citing works
Sheth, U., Öijerstedt, L., Heckman, M. G., White, L. J., Heuer, H. W., Lario Lago, A., Forsberg, L. K., Faber, K. M., Foroud, T. M., Rademakers, R., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00821-4 · Cited by 20 · see citing works
An interpretable deep learning framework identifies proteomic drivers of Alzheimer’s disease
Panizza, E., & Cerione, R. A. (2024) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2024.1379984 · Cited by 11 · see citing works
Borko, T. L., Ritchie, A., Selva, S., Sillau, S., Engebretson, E., Wolf, A. B., Alvarez, E., Gross, R., Gutierrez, E., Corboy, J., et al. (2026) · Multiple Sclerosis Journal – Experimental, Translational and Clinical · doi:10.1177/20552173261466440 · Cited by 1 · see citing works
- Authors
- Abdelhak, A., Foschi, M., Abu-Rumeileh, S., Yue, J. K., D’Anna, L., Huss, A., Oeckl, P., Ludolph, A. C., Kuhle, J., Petzold, A., Manley, G. T., Green, A. J., Otto, M., & Tumani, H.
- Affiliations
- University of California, San Francisco (US)
- Ospedale "Santa Maria delle Croci" di Ravenna (IT)
- Imperial College Healthcare NHS Trust (GB)
- German Center for Neurodegenerative Diseases (DE)
- Moorfields Eye Hospital (GB)
- University Hospital Ulm (DE)
- Fachklinik für Neurologie Dietenbronn (DE)
- Corresponding author
- Tumani, Hayrettin
- ORCID
- Venue
- Nature Reviews Neurology
- Published
- 2022-02-03
- Volume
- 18
- Issue
- 3
- Pages
- 158-172
- ISSN
- 1759-4758 (ISSN-L)
- 1759-4766
- OpenAlex
- W4213416459
- Cited by
- 807
- References
- 13
- Citations by year
- 2026: 155 · 2025: 238 · 2024: 227 · 2023: 134 · 2022: 53
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- green
- research.vumc.nl
- Licence
- Locations
- Nature Reviews Neurology (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- DZNE Pub (submittedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Archivio Istituzionale della Ricerca - Università degli Studi dell Aquila (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Health Sciences › Medicine › Neurology › Traumatic Brain Injury and Neurovascular Disturbances
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › S100 Proteins and Annexins
- Keywords
- glial fibrillary acidic protein, blood-derived biomarkers, mild traumatic brain injury, multiple sclerosis, stroke, point-of-care testing, neuroaxonal damage, outcome prediction, precision medicine
- MeSH terms
- Brain, Glial Fibrillary Acidic Protein, Humans, Intermediate Filaments, Spinal Cord Diseases, Biomarkers
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