The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Association of Cerebrospinal Fluid Neurofilament Light Concentration With Alzheimer Disease Progression
Open accessAbstract
IMPORTANCE: The extent to which large-caliber axonal degeneration contributes to Alzheimer disease (AD) progression is unknown. Cerebrospinal fluid (CSF) neurofilament light (NFL) concentration is a general marker of damage to large-caliber myelinated axons. OBJECTIVE: To test whether CSF NFL concentration is associated with cognitive decline and imaging evidence of neurodegeneration and white matter change in AD. DESIGN, SETTING, AND PARTICIPANTS: A commercially available immunoassay was used to analyze CSF NFL concentration in a cohort of patients with AD (n = 95) or mild cognitive impairment (MCI) (n = 192) and in cognitively normal individuals (n = 110) from the Alzheimer's Disease Neuroimaging Initiative. The study dates were January 2005 to December 2007. The NFL analysis was performed in November 2014. MAIN OUTCOMES AND MEASURES: Correlation was investigated among baseline CSF NFL concentration and longitudinal cognitive impairment, white matter change, and regional brain atrophy within each diagnostic group. RESULTS: Cerebrospinal fluid NFL concentration (median [interquartile range]) was higher in the AD dementia group (1479 [1134-1842] pg/mL), stable MCI group (no progression to AD during follow-up; 1182 [923-1687] pg/mL), and progressive MCI group (MCI with progression to AD dementia during follow-up; 1336 [1061-1693] pg/mL) compared with control participants (1047 [809-1265] pg/mL) (P < .001 for all) and in the AD dementia group compared with the stable MCI group (P = .01). In the MCI group, a higher CSF NFL concentration was associated with faster brain atrophy over time as measured by changes in whole-brain volume (β = -4177, P = .003), ventricular volume (β = 1835, P < .001), and hippocampus volume (β = -54.22, P < .001); faster disease progression as reflected by decreased Mini-Mental State Examination scores (β = -1.077, P < .001) and increased Alzheimer Disease Assessment Scale cognitive subscale scores (β = 2.30, P < .001); and faster white matter intensity change (β = 598.7, P < .001). CONCLUSIONS AND RELEVANCE: Cerebrospinal fluid NFL concentration is increased by the early clinical stage of AD and is associated with cognitive deterioration and structural brain changes over time. This finding corroborates the contention that degeneration of large-caliber axons is an important feature of AD neurodegeneration.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
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?
The cited source says what the claim attributes to it.
In the citing paper
“CSF neurogranin is presumed to measure synaptic degeneration and loss [170,171], and neurofilament light chain [172] is presumed to measure axonal injury.”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [172]
The cited source says what the claim attributes to it.
In the citing paper
“Furthermore, several studies describe association between cognitive impairment and increased CSF (Zetterberg et al., 2016) and plasma NFL (Lin et al., 2018) in AD.”
From Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis (2020) · cites this paper as Zetterberg et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Self-citationAutomatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208
Blennow, K., de Leon, M. J., & Zetterberg, H. (2006) · The Lancet · doi:10.1016/s0140-6736(06)69113-7 · Cited by 3,134
Self-citationCerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects
Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Blennow, K., Soares, H., Simon, A., Lewczuk, P., et al. (2009) · Annals of Neurology · doi:10.1002/ana.21610 · Cited by 2,065
Self-citationHua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D. S., Calabresi, P. A., Pekar, J. J., van Zijl, P. C. M., & Mori, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.053 · Cited by 1,507
A white matter disorder in dementia of the Alzheimer type: A pathoanatomical study
Brun, A., & Englund, E. (1986) · Annals of Neurology · doi:10.1002/ana.410190306 · Cited by 1,014
Alzheimer' Disease as a Disconnection Syndrome?
Delbeuck, X., der Linden, M. V., & Collette, F. (2003) · Neuropsychology Review · doi:10.1023/a:1023832305702 · Cited by 705
White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study
Medina, D., deToledo-Morrell, L., Urresta, F., Gabrieli, J. D. E., Moseley, M., Fleischman, D., Bennett, D. A., Leurgans, S., Turner, D. A., & Stebbins, G. T. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.03.026 · Cited by 492
Rose, S. (2000) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.69.4.528 · Cited by 465
Cited by
Sorted by citations
Items 401–490 of 490
Sun, X., Xia, X., Liu, Y., Qi, X., & Xu, T. (2026) · Advanced Sustainable Systems · doi:10.1002/adsu.70442 · Cited by 2
“Many studies have shown that NfL concentrations in AD patients are significantly higher than those in HC, and this increase is detectable at the stage of MCI [91–93].”
Zhai, Y., Nevian, T., Finn, D. P., & Kwakowsky, A. (2026) · Molecular Neurodegeneration Advances · doi:10.1186/s44477-026-00025-0 · Cited by 2
Ly, M. T., Altaras, C., Tripodis, Y., Adler, C. H., Balcer, L. J., Bernick, C. B., Zetterberg, H., Blennow, K., Peskind, E. R., Banks, S. J., et al. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2451828 · Cited by 2
“Higher levels of CSF NfL have been associated with cognitive impairment in small vessel disease, early-onset dementia, AD, and cognitively normal older adults (Kartau et al., 2022; Osborn et al., 2019; Walia et al., 2022; Zetterberg et al., 2016).”
Self-citationSingh, R. K., Bekena, S., Damera, N., Zhu, Y., Trani, J., & Babulal, G. M. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251365219 · Cited by 4
“31,32 Unlike amyloid and tau biomarkers, which are primarily associated with AD-speci fi c pathology, 31 CSF NfL remains strongly linked to MCI even when CSF A β 42 levels are not reduced.”
Fluid Biomarkers in Alzheimer’s Disease and Frontotemporal Dementia
Mattsson, N., Grigoriou, S., & Zetterberg, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-72938-1_11 · Cited by 2
Self-citationBiomarqueurs du liquide cérébrospinal dans la maladie d’Alzheimer
Hugon, J., Dumurgier, J., Cognat, E., & Paquet, C. (2018) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/s0001-4079(19)30359-0 · Cited by 2
Artificial Intelligence in the Detection of Alzheimer's Disease
Galety, M. G., & Gupta, S. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-9534-3.ch009 · Cited by 2
Moore, E., Khan, O., Shashikumar, N., Pechman, K., Liu, D., Bell, S., Nair, S., Terry, J., Gifford, K., Anderson, A., et al. (2022) · UCL Discovery (University College London) · Cited by 2
Self-citationBrown, Q. L., Nicholson, E. M., Wang, C., Greenlee, J. J., Seger, H., Veneziano, S. E., & Cassmann, E. D. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299038 · Cited by 1
Barsanele, P. S., de Assis, L. V. M., da Silva, J. E., Furtado, E. M. D. O., Fernandes, P., Cipolla‐Neto, J., Poletini, M. O., & Moraes, M. N. (2024) · The FASEB Journal · doi:10.1096/fj.202401105r · Cited by 2
“40 40 Enrichment analysis of downregulated genes enriched for processes associated with circadian rhythm, including circadian regulation of gene expression, rhythmic processes, and regulation of circadian rhythm (Figure 2D; Table S1).”
Biomarkers for Alzheimer's Disease
Panwar, D., Sharma, P., Sharma, S., Malik, M., & Kaur, J. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-6442-0.ch003 · Cited by 1
Cui, R., Hu, H., Han, S., Liang, S., Liu, M., Gao, P., Chen, Y., Wang, Y., Guo, F., Xue, R., et al. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70076 · Cited by 1
“…for neurodegenerative pathologies, including Alzheimer's disease (AD), frontotemporal dementia, amyotrophic lateral sclerosis, and traumatic brain injury (Ashton et al. 2020; Ashton et al. 2021; Khalil et al. 2018; Gaetani et al. 2019; Zetterberg et al. 2016; Qu et al. 2021), among others.”
Ahn, S., Prock, M., Seo, J.-W., Han, S., O'Sullivan, D. M., & Song, W. J. (2025) · Sports Medicine and Health Science · doi:10.1016/j.smhs.2025.07.003 · Cited by 1
Moreira, I. P., Serrão, P., Sequeira, L., Sá, M. J., Vieira‐Coelho, M. A., & Guimarães, J. (2025) · Neurological Research · doi:10.1080/01616412.2025.2542944 · Cited by 1
“…for neuronal injury, NF levels correlate with and are predictive of brain atrophy and worsening of cognition independently of Aβ pathology [25–27], are associated with a greater risk of development of MCI in cognitively unimpaired individuals, with faster rates of cognitive decline in MCI,…”
Massa, F., Orso, B., Cesari, F. D., Pelagotti, V., Garbarino, S., Raffa, S., Mattioli, P., Argenti, L., Lombardo, L., Losa, M., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01893-6 · Cited by 3
“NFL levels increase in AD, already at the MCI stage, and correlate with cognitive impairment, amyloid burden, cortical atrophy and transition time from MCI to dementia [22, 26, 27].”
Gibson, M., O’Brien, W. T., Rowsthorn, E., Franks, K. H., Cribb, L., Cavuoto, M. G., Campbell‐Brown, B., Harding, I. H., Law, M., Chong, T. T. ‐., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02220-z · Cited by 1
Wang, Y., Zhu, H.-Y., Miao, W., Wang, Z., Qin, J.-J., Song, X.-H., Wang, Y.-H., Sun, Z.-W., Zhou, X., Yu, X.-F., et al. (2026) · Psychological Medicine · doi:10.1017/s003329172610484x · Cited by 2
“…biomarker of neuroaxonal damage across neurological diseases (Gaetani et al., 2019; Khalil et al., 2018; Lewczuk et al., 2018; Olsson et al., 2019; Zetterberg et al., 2016), is detectable in both CSF and peripheral blood following neuroinflammatory processes (Gaetani et al., 2019; Khalil et al.,…”
InfluentialMaladies neurodégénératives et biomarqueurs du liquide céphalorachidien
Delaby, C., & Lehmann, S. (2023) · Revue Francophone des Laboratoires · doi:10.1016/s1773-035x(23)00156-9 · Cited by 1
Fluid-Based Biomarkers of Alzheimer’s Disease
Rocha, N. P., Teixeira, A. L., & de Souza, L. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-43356-6_10 · Cited by 1
Biomarkers used in Alzheimer's disease diagnosis
Mishra, S. K., Singh, R., Chang, H., Obaid, A. A., & Singh, S. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15540-6.00013-6 · Cited by 1
Lee, A. J., Howard, E., Saltiel, N. A., Hayes, J. P., & Hayes, S. M. (2024) · Brain Communications · doi:10.1093/braincomms/fcaf068 · Cited by 1
A Review of Clinical Biomarker Insights: Advances in Diagnosis and Prognosis of Early Alzheimer
Rajagopalan, R., Hariharan, S., Akshaya, N. R., & Senthamarai, R. (2025) · Global Journal of Medical Pharmaceutical and Biomedical Update · doi:10.25259/gjmpbu_38_2024 · Cited by 1
Maliukova, P., & Karelina, T. (2025) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70062 · Cited by 3
Delineating blood DNA methylation biomarkers for Alzheimer's disease
Pishva, E., Bertram, L., & Lunnon, K. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70646
Laboratory diagnostics in dementia
Weber, T. (2018) · Journal of Laboratory Medicine · doi:10.1515/labmed-2018-0047 · Cited by 1
Neuroimaging and plasma marker evidence for white matter macrostructure loss in Parkinson’s diseasePreprint
Zarkali, A., Hannaway, N., McColgan, P., Heslegrave, A., Veleva, E., Laban, R., Zetterberg, H., Lees, A. J., Fox, N. C., & Weil, R. (2023) · bioRxiv · doi:10.1101/2023.09.22.558937 · Cited by 3
Self-citationImpaired glymphatic clearance independently contributes to poor outcomes in Parkinson’s diseasePreprint
Zarkali, A., Thomas, G. E. C., Paterson, R. W., Hannaway, N., Dobreva, I., Heslegrave, A., Veleva, E., Zetterberg, H., & Weil, R. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.15.633106 · Cited by 1
Self-citationThe Alzheimer disease continuum : findings from monogenic Alzheimer disease
Johansson, C. (2025) · doi:10.69622/28639856 · Cited by 3
Bos, I., Vos, S. J. B., Verhey, F. R. J., & Visser, P. J. (2019) · Neuropraxis · doi:10.1007/s12474-019-00215-5
“Tot nu toe worden hoge concentraties NFL geassocieerd met axonale schade in voornamelijk subcorticale hersengebieden [16, 17], terwijl hoge concentraties YKL40 worden geassocieerd met astrogliale activiteit, wat waarschijnlijk een inflammatoire respons is op neurodegeneratievemechanismen [18].”
BackgroundNeurofilaments as biomarkers of neuronal damage
Pujol-Calderón, F. (2019)
Potential of Blood- and CSF-Based Biomarkers for Alzheimer’s Disease Diagnostics
Goldwaser, E. L., & Nagele, R. G. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190634230.003.0016
Robertson, E. L., Boehnke, S. E., Silva, N. M. L. E., Armitage‐Brown, B., Winterborn, A., Cook, D. J., Felice, F. G. D., & Munoz, D. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.01.433384
Cerebrospinal fluid biomarkers and cognition in older adults
Rådestig, M. A. (2021)
Characterizing Neurodegenerative Disease Biomarkers in Cerebrospinal Fluid of Non-human Primates
Robertson, E. (2021) · QSpace (Queen's University Library)
An overview of Alzheimer's disease and its diagnosis using conventional and novel methods
Zhao, X. (2021) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3498731.3498761
“More recent investigations on the CSF show that AD patients have a relatively high neurofilament light (NFL), but the diagnostic accuracy cannot be guaranteed because other neurodegenerative diseases might also experience a high level of NFL, making this biomarker unable to be added into the core CSF biomarkers [33].”
BackgroundAshford, J. W. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734280398.793553881
Rosenberg, G. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823455-6.00006-7
Srikrishna, M., Ashton, N. J., Moscoso, A., Pereira, J. B., Heckemann, R. A., van Westen, D., Volpe, G., Simrén, J., Zettergren, A., Kern, S., et al. (2022) · medRxiv · doi:10.1101/2022.12.19.22282455
Self-citationRole of fluid biomarkers in the early diagnosis of Alzheimer’s disease
Vo, G. V., Nguyen, M. N., & Toi, V. V. (2021) · Science & Technology Development Journal - Health Sciences · doi:10.32508/stdjhs.v2i1.458
Jing, X., Wang, L., Song, M. L., Geng, H., Li, W., Huo, Y., Huang, A., Wang, X., & An, C. (2024) · Research Square · doi:10.21203/rs.3.rs-3828911/v1
Remnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150846
Self-citationWeiner, S., Sauer, M., Visser, P. J., Tijms, B. M., Vorontsov, E. A., Blennow, K., Zetterberg, H., & Gobom, J. (2022) · Figshare · doi:10.6084/m9.figshare.19768000
Self-citationShang, H., Che, N.-N., Huang, J., Wang, S., Jiang, Q., Yang, T., Xiao, Y., Lin, J., Fu, J., Ou, R., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4425827/v1
Rymo, I., Zetterberg, H., Blennow, K., Kern, S., Skoog, I. G., Sacuiu, S. F., & Wærn, M. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.11.044
Self-citationFazeli, B., Botzenhardt, S., Bachhuber, F., Klassen, P., Klose, V., Dorst, J., Wiesenfarth, M., Uzelac, Ž., Jesse, S., Brenner, D., et al. (2025) · medRxiv · doi:10.1101/2025.01.20.25320650
New Research on Biomarkers in Alzheimer’s Continuum
Vecchio, I., & Colica, C. (2025) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871331138250114052615 · Cited by 2
Fluid Markers of Axonal and Neuronal Injury
Arslan, B., & Zetterberg, H. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_5
Self-citationBiomarkers as Outcome Measures in Clinical Trials of Alzheimer Disease
Tarawneh, R. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_15
Pitra, D. A. H., Syafrita, Y., Susanti, R., & Rita, R. S. (2025) · International Journal of Social health · doi:10.58860/ijsh.v4i7.322
“Similarly, pNfH and NSE also function as biomarkers indicating neuronal damage and can provide prognostic information about clinical outcomes (Zetterberg et al., 2016).”
Mild Cognitive Impairment across the Dementias
Petersen, R. C. (2025) · Cambridge University Press eBooks · doi:10.1017/9781108985055.014
Yin, C., Kindt, A. S. D., Harms, A. C., Hartman, R., Hankemeier, T., & de Lange, E. C. M. (2025) · Metabolomics · doi:10.1007/s11306-025-02350-z
“…older individuals; TCA, taurocholic acid; DCA, deoxycholic acid; TDCA, taurodeoxycholic acid; TCDCA, taurochenodeoxycholic acid; GCA, glycocholic acid; GDCA, glycodeoxycholic acid; GCDCA, glycochenodeoxycholic acid; GUDCA, glycoursodeoxycholic acid; EPA, eicosapentaenoic acid; AA, arachidonic acid.”
Remnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150870
Self-citationRemnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150861
Self-citationRemnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150852
Self-citationXia, D., Lianoglou, S., Sandmann, T., Calvert, M., Suh, J. H., Thomsen, E. R., Dugas, J. C., Pizzo, M. E., DeVos, S. L., Earr, T., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20056383
Oosthoek, M., Vermunt, L., de Wilde, A., Bongers, B., Antwi‐Berko, D., Scheltens, P., van Bokhoven, P., Vijverberg, E. G. B., & Teunissen, C. E. (2024) · Figshare · doi:10.6084/m9.figshare.26713487
Weiner, S., Sauer, M., Visser, P. J., Tijms, B. M., Vorontsov, E. A., Blennow, K., Zetterberg, H., & Gobom, J. (2022) · Figshare · doi:10.6084/m9.figshare.19768009
Self-citationXia, D., Lianoglou, S., Sandmann, T., Calvert, M., Suh, J. H., Thomsen, E. R., Dugas, J. C., Pizzo, M. E., DeVos, S. L., Earr, T., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20056359
Remnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150858
Self-citationRemnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150843
Self-citationRemnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150855
Self-citationRemnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150849
Self-citationWeiner, S., Sauer, M., Visser, P. J., Tijms, B. M., Vorontsov, E. A., Blennow, K., Zetterberg, H., & Gobom, J. (2022) · Figshare · doi:10.6084/m9.figshare.19768006
Self-citationOosthoek, M., Vermunt, L., de Wilde, A., Bongers, B., Antwi‐Berko, D., Scheltens, P., van Bokhoven, P., Vijverberg, E. G. B., & Teunissen, C. E. (2024) · Figshare · doi:10.6084/m9.figshare.26713496
Weiner, S., Sauer, M., Visser, P. J., Tijms, B. M., Vorontsov, E. A., Blennow, K., Zetterberg, H., & Gobom, J. (2022) · Figshare · doi:10.6084/m9.figshare.19768003
Self-citationOosthoek, M., Vermunt, L., de Wilde, A., Bongers, B., Antwi‐Berko, D., Scheltens, P., van Bokhoven, P., Vijverberg, E. G. B., & Teunissen, C. E. (2024) · Figshare · doi:10.6084/m9.figshare.26713493
Xia, D., Lianoglou, S., Sandmann, T., Calvert, M., Suh, J. H., Thomsen, E. R., Dugas, J. C., Pizzo, M. E., DeVos, S. L., Earr, T., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20056422
Tang, R., Buchholz, E. J., Dale, A. M., Rissman, R. A., Fennema‐Notestine, C., Gillespie, N. A., Hagler, D. J., Lyons, M. J., Neale, M. C., Panizzon, M. S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25698219
Remnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150864
Self-citationHalbgebauer, S., Steinacker, P., Riedel, D., Oeckl, P., Anderl-Straub, S., Lombardi, J., Arnim, C. A. F. V., Nagl, M., Giese, A., Ludolph, A. C., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21622142
Remnestål, J., Bergström, S., Olofsson, J., Sjöstedt, E., Uhlén, M., Blennow, K., Zetterberg, H., Zettergren, A., Kern, S., Skoog, I. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14150867
Self-citationOosthoek, M., Vermunt, L., de Wilde, A., Bongers, B., Antwi‐Berko, D., Scheltens, P., van Bokhoven, P., Vijverberg, E. G. B., & Teunissen, C. E. (2024) · Figshare · doi:10.6084/m9.figshare.26713490
Cortical asymmetry in autosomal dominant Alzheimer’s disease progression
Pérez‐Millan, A., Falgàs, N., Bosch, B., Borrego‐Écija, S., Antonell, A., Fernández‐Villullas, G., Esteller, D., Tort‐Merino, A., Bargalló, N., Balasa, M., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf488 · Cited by 1
Xu, J., Chen, H., Wang, Y., Hu, Z., Ke, Z., Huang, L., Mo, Y., Yang, D., Mao, C., Chen, Y., et al. (2026) · Brain Communications · doi:10.1093/braincomms/fcag080 · Cited by 1
“52,53 The Vanderbilt Memory & Aging Project found that NfL was negatively associated with FA and positively associated with MD, axial diffusivity and radial diffusivity.”
Vieillissement et maladies neurodégénératives : nouvelles contraintes apportées par la métallomique
Sauzéat, L. (2018) · theses.fr (ABES) · doi:10.70675/6f22af22z8cfcz4a9fzb647zf065e4131641
Velmurugan, G., Upadhyay, R., Sevanan, M., Francis, D., Bougea, A. M., & Chinnathambi, S. (2026) · Advances in pharmacology · doi:10.1016/bs.apha.2026.01.001
Role of T-cell immunity in the pathophysiology of Alzheimer’s disease and other Tauopathies
Matta, K. (2024) · theses.fr (ABES) · doi:10.70675/cea4fe5ez67c3z4445z92f7z39af62ba059b
Cannon, M. K., Fojtik, A., White, C. M., Swetenburg, R. L., Stice, S. L., Savitz, S. I., & Baker, E. W. (2026) · Translational Stroke Research · doi:10.1007/s12975-026-01445-6
“In addition, ICAM-1 plays a key role in blood–brain barrier disruption and leukocyte infiltration, supporting its rele - vance as a candidate biomarker given our data showing that NSC EV treatment mitigates hemorrhagic transformation in a pig stroke model [5 ].”
Gonzalez, A. I., Martinez, J. E., Giudicessi, A., Rowe, M., Ku, V., Tristão‐Pereira, C., He, B., Malotaux, V., & Quiroz, Y. T. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100604
“Previous research has recognized NfL as a core blood biomarker of neurodegeneration related to AD and a possible tool for monitoring disease progression [40 – 42].”
Lynch, D. S., Wade, C., Schöls, L. J., Hayer, S. N., Gelfand, J. M., Köhler, W., Bergner, C., de Souza, P. V. S., Finger, E. C., Wolf, N. I., et al. (2026) · Brain Communications · doi:10.1093/braincomms/fcag271
Relationships of CSF biomarkers to cortical atrophy in early‐onset Alzheimer's disease
Touroutoglou, A., Katsumi, Y., Hensel, J. A., Schindler, S. E., Ibáñez, L., Brickhouse, M. J., Eloyan, A., Eckbo, R., Zaitsev, A., Joie, R. L., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71479
Magalhães, T. N. C., Casseb, R. F., Moraes, A. S., Teixeira, C. V. L., Carletti, A. M., de Rezende, T. J. R., Joaquim, H. P. G., Talib, L. L., Forlenza, O., Cendes, F., et al. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261467065
Oosthoek, M., Leistra, A., Hok‐A‐Hin, Y. S., Tanck, M. W. T., Okuda, T., Veld, L. I. ‘., Aladdin, A., van Bokhoven, P., Tijms, B. M., Jutten, R. J., et al. (2026) · medRxiv · doi:10.64898/2026.08.27.26361425
Rizzo, M., Teunissen, C. E., Zetterberg, H., Blennow, K., Kollmorgen, G., Krüger, R., Fernandes, S. B. G., Aarsland, D., Borejko, O., Hu, O. M., et al. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70498
“33,34 It must be noted that our PD group was on average younger, mostly cognitively unimpaired, and with mild motor deficits.”
Self-citationKim, B., Lee, S., & Jeong, H.-J. (2026) · Applied Sciences · doi:10.3390/app162010240
Páez, A., Piñol‐Ripoll, G., Dakterzada, F., & Dang-Vu, T. T. (2026) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-026-01528-9
“Alzheimer’s disease likely reflects multiple interacting pathologies and risk factors [ 53], and sleep disruption has also been associated with markers of neuroinflammation, synaptic dysfunction, and neuroaxonal damage, including YKL-40, neurogranin, and neurofila - ment light chain [54–57].”
Jianjing, Z., Wang, X., Yang, L., Lv, Z., Jia, S., & Ye, T. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1826215
Wang, T., Ji, Y., Liu, S., Wu, H., Gu, L., Guo, M., Wang, P., & Xu, G. (2026) · Behavioural Brain Research · doi:10.1016/j.bbr.2026.116536
Metadata
- Authors
- Zetterberg, H., Skillbäck, T., Mattsson, N., Trojanowski, J. Q., Portelius, E., Shaw, L. M., Weiner, M. W., Blennow, K., & Initiative, F. T. A. D. N.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- University of Gothenburg (SE)
- University of California, San Francisco (US)
- University of Pennsylvania (US)
- Corresponding author
- Zetterberg, Henrik
- ORCID
- Venue
- JAMA Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 2016-01-01
- Volume
- 73
- Issue
- 1
- Pages
- 60
- ISSN
- 2168-6149 (ISSN-L)
- PMC
- PMC5624219
- OpenAlex
- W1934117767
- Cited by
- 487
- References
- 31
- Citations by year
- 2026: 20 · 2025: 38 · 2024: 62 · 2023: 42 · 2022: 65 · 2021: 60 · 2020: 62 · 2019: 41 · 2018: 48 · 2017: 35 · 2016: 14
- FWCI
- 26.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Neurology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, AG024904)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-)
- National Institutes of Health (5u01ag024904-10, W81XWH-12-2-0012, U01-AG024904, AG024904)
- Canadian Institutes of Health Research
- National Institute on Aging (U01-AG-024904, P30AG010129, K01AG030514, P30AG013846, U24AG021886)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- neurofilament light chain, cerebrospinal fluid biomarkers, mild cognitive impairment, cognitive decline, axonal degeneration, brain atrophy, hippocampal volume, ventriculomegaly, neurodegeneration, Alzheimer's Disease Neuroimaging Initiative, biomarker validation, white matter hyperintensities, amyloid PET, tau PET
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Follow-Up Studies, Humans, Male, Cohort Studies, Biomarkers, Neurofilament Proteins, Disease Progression, Cognitive Dysfunction
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