CoChat
Sign Up
Cited by 25Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Real‐world applicability of glial fibrillary acidic protein and neurofilament light chain in Alzheimer's disease

Open access
Parvizi, Tandis, König, Theresa, Wurm, Raphael, Silvaieh, Sara, Altmann, Patrick, Klotz, Sigrid, Rommer, Paulus Stefan, Furtner, Julia +5 more·2022·Frontiers in Aging Neuroscience
DOI 10.3389/fnagi.2022.887498PMC PMC9441692
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Blood-based biomarkers may add a great benefit in detecting the earliest neuropathological changes in patients with Alzheimer’s disease (AD). We examined the utility of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) regarding clinical diagnosis and differentiation between amyloid positive and negative patients. To evaluate the practical application of these biomarkers in a routine clinical setting, we conducted this study in a heterogeneous memory-clinic population. Methods: We included 167 patients in this retrospective cross-sectional study, 123 patients with an objective cognitive decline [mild cognitive impairment (MCI) due to AD, n = 63, and AD-dementia, n = 60] and 44 age-matched healthy controls (HC). Cerebrospinal fluid (CSF) and plasma concentrations of NfL and GFAP were measured with single molecule array (SIMOA®) technology using the Neurology 2-Plex B kit from Quanterix. To assess the discriminatory potential of different biomarkers, age- and sex-adjusted receiver operating characteristic (ROC) curves were calculated and the area under the curve (AUC) of each model was compared. Results: We constructed a panel combining plasma NfL and GFAP with known AD risk factors (Combination panel: age+sex+APOE4+GFAP+NfL). With an AUC of 91.6% (95%CI = 0.85–0.98) for HC vs. AD and 81.7% (95%CI = 0.73–0.90) for HC vs. MCI as well as an AUC of 87.5% (95%CI = 0.73–0.96) in terms of predicting amyloid positivity, this panel showed a promising discriminatory power to differentiate these populations. Conclusion: The combination of plasma GFAP and NfL with well-established risk factors discerns amyloid positive from negative patients and could potentially be applied to identify patients who would benefit from a more invasive assessment of amyloid pathology. In the future, improved prediction of amyloid positivity with a noninvasive test may decrease the number and costs of a more invasive or expensive diagnostic approach.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnagi.2022.887498

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A previous study found a significant correlation in GFAP levels between plasma and CSF (r = 0.40).

    “which is different from a previous study that found a significant correlation in levels (r = 0.40). 43”

    From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [43]

  • Previous studies found plasma NfL correlated relatively strongly with CSF NfL levels (r = 0.64-0.86; sample size 78-188).

    “including some that found plasma NfL correlated relatively strongly with CSF NfL levels (r = 0.64‐0.86; sample size 78‐188), 38 , 40 , 43”

    From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [43]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 48

  1. Abdelhak A. Foschi M. Abu-Rumeileh S. Yue J. K. D’Anna L. Huss A.. (2022). Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat. Rev. Neurol. 18, 158– 172. 10.1038/s41582-021-00616-3 35115728 · DOI · Paper page
  2. Albert M. S. DeKosky S. T. Dickson D. Dubois B. Feldman H. H. Fox N. C.. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dementia 7, 270– 279. 10.1016/j.jalz.2011.03.008 21514249 PMC3312027 · DOI · Paper page
  3. Altmann P. Leutmezer F. Zach H. Wurm R. Stattmann M. Ponleitner M.. (2020). Serum neurofilament light chain withstands delayed freezing and repeated thawing. Sci. Rep. 10: 19982. 10.1038/s41598-020-77098-8 33203974 PMC7672085 · DOI · Paper page
  4. Ashton N. J. Janelidze S. Khleifat A. A. Leuzy A. van der Ende E. L. Karikari T. K.. (2021). A multicentre validation study of the diagnostic value of plasma neurofilament light. Nat. Commun. 12: 3400. 10.1038/s41467-021-23620-z 34099648 PMC8185001 · DOI · Paper page
  5. Asken B. M. Elahi F. M. Joie R. L. Strom A. Staffaroni A. M. Lindbergh C. A.. (2020). Plasma glial fibrillary acidic protein levels differ along the spectra of amyloid burden and clinical disease stage. J. Alzheimers Dis. 78, 265– 276. 10.3233/JAD-200755 32986672 PMC7727314 · DOI
  6. Bateman R. J. Xiong C. Benzinger T. L. S. Fagan A. M. Goate A. Fox N. C.. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New Engl. J. Med. 367, 795– 804. 10.1056/NEJMoa1202753 22784036 PMC3474597 · DOI · Paper page
  7. Benedet A. L. Ashton N. J. Pascoal T. A. Leuzy A. Mathotaarachchi S. Kang M. S.. (2019). Plasma neurofilament light associates with Alzheimer’s disease metabolic decline in amyloid-positive individuals. Alzheimers Dementia (Amst) 11, 679– 689. 10.1016/j.dadm.2019.08.002 31673598 PMC6816316 · DOI
  8. Benedet A. L. Milà-Alomà M. Vrillon A. Ashton N. J. Pascoal T. A. Lussier F.. (2021). Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. JAMA Neurol. 78, 1471– 1483. 10.1001/jamaneurol.2021.3671 34661615 PMC8524356 · DOI · Paper page
  9. Chatterjee P. Pedrini S. Stoops E. Goozee K. Villemagne V. L. Asih P. R.. (2021). Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl. Psychiatry 11: 27. 10.1038/s41398-020-01137-1 33431793 PMC7801513 · DOI · Paper page
  10. Chouliaras L. Thomas A. Malpetti M. Donaghy P. Kane J. Mak E.. (2022). Differential levels of plasma biomarkers of neurodegeneration in Lewy body dementia, Alzheimer’s disease, frontotemporal dementia and progressive supranuclear palsy. J. Neurol. Neurosurg. Psychiatry 93, 651– 658. 10.1136/jnnp-2021-327788 35078917 PMC9148982 · DOI · Paper page
  11. Cicognola C. Janelidze S. Hertze J. Zetterberg H. Blennow K. Mattsson-Carlgren N.. (2021). Plasma glial fibrillary acidic protein detects Alzheimer pathology and predicts future conversion to Alzheimer dementia in patients with mild cognitive impairment. Alzheimers Res. Ther. 13: 68. 10.1186/s13195-021-00804-9 33773595 PMC8005231 · DOI
  12. Delcoigne B. Manouchehrinia A. Barro C. Benkert P. Michalak Z. Kappos L.. (2020). Blood neurofilament light levels segregate treatment effects in multiple sclerosis. Neurology 94, e1201– e1212. 10.1212/WNL.0000000000009097 32047070 PMC7387108 · DOI
  13. DeLong E. R. DeLong D. M. Clarke-Pearson D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837– 845. 3203132
  14. Duits F. H. Prins N. D. Lemstra A. W. Pijnenburg Y. A. L. Bouwman F. H. Teunissen C. E.. (2015). Diagnostic impact of CSF biomarkers for Alzheimer’s disease in a tertiary memory clinic. Alzheimers Dement. 11, 523– 532. 10.1016/j.jalz.2014.05.1753 25156643 · DOI
  15. Ebenau J. L. Pelkmans W. Verberk I. M. W. Verfaillie S. C. J. van den Bosch K. A. van Leeuwenstijn M.. (2022). Association of CSF, plasma and imaging markers of neurodegeneration with clinical progression in people with subjective cognitive decline. Neurology 98, e1315– e1326. 10.1212/WNL.0000000000200035 35110378 PMC8967429 · DOI
  16. Elahi F. M. Casaletto K. B. Joie R. L. Walters S. M. Harvey D. Wolf A.. (2019). Plasma biomarkers of astrocytic and neuronal dysfunction in early- and late-onset Alzheimer’s disease. Alzheimers Dementia 16, 681– 695. 10.1016/j.jalz.2019.09.004 31879236 PMC7138729 · DOI
  17. Frost G. R. Li Y.-M. (2017). The role of astrocytes in amyloid production and Alzheimer’s disease. Open Biol. 7: 170228. 10.1098/rsob.170228 29237809 PMC5746550 · DOI
  18. Heller C. Foiani M. S. Moore K. Convery R. Bocchetta M. Neason M.. (2020). Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 91, 263– 270. 10.1136/jnnp-2019-321954 31937580 · DOI · Paper page
  19. Hulstaert F. Blennow K. Ivanoiu A. Schoonderwaldt H. C. Riemenschneider M. de Deyn P. P.. (1999). Improved discrimination of AD patients using β-amyloid(1-42) and tau levels in CSF. Neurology 52, 1555– 1562. 10.1212/wnl.52.8.1555 10331678 · DOI · Paper page
  20. Kamphuis W. Middeldorp J. Kooijman L. Sluijs J. A. Kooi E.-J. Moeton M.. (2014). Glial fibrillary acidic protein isoform expression in plaque related astrogliosis in Alzheimer’s disease. Neurobiol. Aging 35, 492– 510. 10.1016/j.neurobiolaging.2013.09.035 24269023 · DOI
  21. Katisko K. Cajanus A. Huber N. Jääskeläinen O. Kokkola T. Kärkkäinen V.. (2021). GFAP as a biomarker in frontotemporal dementia and primary psychiatric disorders: diagnostic and prognostic performance. J. Neurol. Neurosurg. Psychiatry 92, 1305– 1312. 10.1136/jnnp-2021-326487 34187866 · DOI · Paper page
  22. Kuhle J. Barro C. Andreasson U. Derfuss T. Lindberg R. Sandelius Å.. (2016). Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin. Chem. Lab. Med. 54, 1655– 1661. 10.1515/cclm-2015-1195 27071153 · DOI · Paper page
  23. Lehrner J. Kogler S. Lamm C. Moser D. Klug S. Pusswald G.. (2015a). Awareness of memory deficits in subjective cognitive decline, mild cognitive impairment, Alzheimer’s disease and Parkinson’s disease. Int. Psychogeriatr. 27, 357– 366. 10.1017/S1041610214002245 25382659 · DOI
  24. Lehrner J. Krakhofer H. Lamm C. Macher S. Moser D. Klug S.. (2015b). Visuo-constructional functions in patients with mild cognitive impairment, Alzheimer’s disease and Parkinson’s disease. Neuropsychiatry 29, 112– 119. 10.1007/s40211-015-0141-2 25861769 · DOI
  25. Lewczuk P. Ermann N. Andreasson U. Schultheis C. Podhorna J. Spitzer P.. (2018). Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer’s disease. Alzheimers Res. Ther. 10: 71. 10.1186/s13195-018-0404-9 30055655 PMC6064615 · DOI
  26. Mattsson N. Andreasson U. Zetterberg H. Blennow K. for the Alzheimer’s Disease Neuroimaging Initiative (2017). Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 74: 557. 10.1001/jamaneurol.2016.6117 28346578 PMC5822204 · DOI · Paper page
  27. Mattsson N. Cullen N. C. Andreasson U. Zetterberg H. Blennow K. (2019). Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 76, 791– 799. 10.1001/jamaneurol.2019.0765 31009028 PMC6583067 · DOI · Paper page
  28. McKhann G. Drachman D. Folstein M. Katzman R. Price D. Stadlan E. M. (1984). Clinical diagnosis of Alzheimer’s disease Report of the NINCDS-ADRDA Work Group* under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 34, 939– 939. 10.1212/wnl.34.7.939 6610841 · DOI · Paper page
  29. McKhann G. M. Knopman D. S. Chertkow H. Hyman B. T. Jack C. R. Kawas C. H.. (2011). The diagnosis of dementia due to Alzheimer’s disease: recommendations from the national institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263– 269. 10.1016/j.jalz.2011.03.005 21514250 PMC3312024 · DOI · Paper page
  30. Moscoso A. Grothe M. J. Ashton N. J. Karikari T. K. Rodriguez J. L. Snellman A.. (2020). Time course of phosphorylated-tau181 in blood across the Alzheimer’s disease spectrum. Brain 144, 325– 339. 10.1093/brain/awaa399 33257949 PMC7880671 · DOI · Paper page
  31. Oeckl P. Halbgebauer S. Anderl-Straub S. Steinacker P. Huss A. M. Neugebauer H.. (2019). Glial fibrillary acidic protein in serum is increased in Alzheimer’s disease and correlates with cognitive impairment. J. Alzheimers Dis. 67, 481– 488. 10.3233/JAD-180325 30594925 · DOI · Paper page
  32. Olsson B. Alberg L. Cullen N. C. Michael E. Wahlgren L. Kroksmark A.-K.. (2019). NFL is a marker of treatment response in children with SMA treated with nusinersen. J. Neurol. 266, 2129– 2136. 10.1007/s00415-019-09389-8 31123861 PMC6687695 · DOI
  33. Philippe C. Haeusler D. Mitterhauser M. Ungersboeck J. Viernstein H. Dudczak R.. (2011). Optimization of the radiosynthesis of the Alzheimer tracer 2-(4-N-[11C]methylaminophenyl).-6-hydroxybenzothiazole ([11C]PIB). Appl. Radiat. Isot. 69, 1212– 1217. 10.1016/j.apradiso.2011.04.010 21550258 · DOI
  34. Preische O. Schultz S. A. Apel A. Kuhle J. Kaeser S. A. Barro C.. (2019). Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277– 283. 10.1038/s41591-018-0304-3 30664784 PMC6367005 · DOI · Paper page
  35. Pusswald G. Moser D. Gleiß A. Janzek-Hawlat S. Auff E. Dal-Bianco P.. (2013). Prevalence of mild cognitive impairment subtypes in patients attending a memory outpatient clinic—comparison of two modes of mild cognitive impairment classification. results of the vienna conversion to dementia study. Alzheimers Dement. 9, 366– 376. 10.1016/j.jalz.2011.12.009 23164551 · DOI
  36. Robin X. Turck N. Hainard A. Tiberti N. Lisacek F. Sanchez J.-C.. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12: 77. 10.1186/1471-2105-12-77 21414208 PMC3068975 · DOI · Paper page
  37. Rojas J. C. Karydas A. Bang J. Tsai R. M. Blennow K. Liman V.. (2016). Plasma neurofilament light chain predicts progression in progressive supranuclear palsy. Ann. Clin. Transl. Neur. 3, 216– 225. 10.1002/acn3.290 27042681 PMC4774256 · DOI · Paper page
  38. Sperling R. A. Aisen P. S. Beckett L. A. Bennett D. A. Craft S. Fagan A. M.. (2011). Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 280– 292. 10.1016/j.jalz.2011.03.003 21514248 PMC3220946 · DOI · Paper page
  39. Tabaraud F. Leman J. P. Milor A. M. Roussie J. M. Barrière G. Tartary M.. (2012). Alzheimer CSF biomarkers in routine clinical setting. Acta Neurol. Scand. 125, 416– 423. 10.1111/j.1600-0404.2011.01592.x 21954973 · DOI
  40. Teunissen C. E. Tumani H. Engelborghs S. Mollenhauer B. (2014). Biobanking of CSF: international standardization to optimize biomarker development. Clin. Biochem. 47, 288– 292. 10.1016/j.clinbiochem.2013.12.024 24389077 · DOI · Paper page
  41. Thebault S. Booth R. A. Freedman M. S. (2020). Blood neurofilament light chain: the neurologist’s troponin. Biomedicines 8: 523. 10.3390/biomedicines8110523 33233404 PMC7700209 · DOI · Paper page
  42. Vanderstichele H. van Kerschaver E. Hesse C. Davidsson P. Buyse M.-A. Andreasen N.. (2009). Standardization of measurement of β-amyloid(1-42) in cerebrospinal fluid and plasma. Amyloid 7, 245– 258. 10.3109/13506120009146438 11132093 · DOI · Paper page
  43. Vanmechelen E. Vanderstichele H. Davidsson P. van Kerschaver E. van der Perre B. Sjögren M.. (2000). Quantification of tau phosphorylated at threonine 181 in human cerebrospinal fluid: a sandwich ELISA with a synthetic phosphopeptide for standardization. Neurosci. Lett. 285, 49– 52. 10.1016/s0304-3940(00)01036-3 10788705 · DOI
  44. Verberk I. M. W. Laarhuis M. B. van den Bosch K. A. Ebenau J. L. van Leeuwenstijn M. Prins N. D.. (2021). Serum markers glial fibrillary acidic protein and neurofilament light for prognosis and monitoring in cognitively normal older people: a prospective memory clinic-based cohort study. Lancet Heal. Longev. 2, e87– e95. 10.1016/s2666-7568(20)30061-1 36098162 · DOI
  45. Verberk I. M. W. Thijssen E. Koelewijn J. Mauroo K. Vanbrabant J. de Wilde A.. (2020). Combination of plasma amyloid β(1-42/1-40) and glial fibrillary acidic protein strongly associates with cerebral amyloid pathology. Alzheimers Res. Ther. 12: 118. 10.1186/s13195-020-00682-7 32988409 PMC7523295 · DOI · Paper page
  46. Verkhratsky A. Olabarria M. Noristani H. N. Yeh C.-Y. Rodriguez J. J. (2010). Astrocytes in Alzheimer’s disease. Neurotherapeutics 7, 399– 412. 10.1016/j.nurt.2010.05.017 20880504 PMC5084302 · DOI
  47. Youden W. J. (1950). Index for rating diagnostic tests. Cancer 3, 32– 35. 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3 15405679 · DOI
  48. Zhu N. Santos-Santos M. Illán-Gala I. Montal V. Estellés T. Barroeta I.. (2021). Plasma glial fibrillary acidic protein and neurofilament light chain for the diagnostic and prognostic evaluation of frontotemporal dementia. Transl. Neurodegener. 10: 50. 10.1186/s40035-021-00275-w 34893073 PMC8662866 · DOI · Paper page

Cited by

Sorted by citations

Authors
Parvizi, T., König, T., Wurm, R., Silvaieh, S., Altmann, P., Klotz, S., Rommer, P. S., Furtner, J., Regelsberger, G., Lehrner, J., Traub-Weidinger, T., Gelpi, E., & Stögmann, E.
Affiliations
  • University of Vienna (AT)
  • Medical University of Vienna (AT)
Corresponding author
Stögmann, Elisabeth
ORCID
Venue
Frontiers in Aging Neuroscience
Publisher
Frontiers Media SA
Published
2022-08-22
Volume
14
Pages
887498-887498
ISSN
1663-4365 (ISSN-L)
DOI
10.3389/fnagi.2022.887498
PMC
PMC9441692
OpenAlex
W4292554576
Cited by
25
References
8
Citations by year
2026: 6 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 1
FWCI
3.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Aging Neuroscience (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
Alzheimer's disease, neurofilament light chain, glial fibrillary acidic protein, blood-based biomarkers, amyloid positivity, mild cognitive impairment, plasma biomarkers, cerebrospinal fluid biomarkers, APOE ε4, diagnostic biomarker panel

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review