The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimer's disease
Open accessAbstract
Reduced cerebrospinal fluid amyloid-β42 and increased retention of florbetapir positron emission tomography are biomarkers reflecting cortical amyloid load in Alzheimer's disease. However, these measurements do not always agree and may represent partly different aspects of the underlying Alzheimer's disease pathology. The goal of this study was therefore to test if cerebrospinal fluid and positron emission tomography amyloid-β biomarkers are independently related to other Alzheimer's disease markers, and to examine individuals who are discordantly classified by these two biomarker modalities. Cerebrospinal fluid and positron emission tomography amyloid-β were measured at baseline in 769 persons [161 healthy controls, 68 subjective memory complaints, 419 mild cognitive impairment and 121 Alzheimer's disease dementia, mean age 72 years (standard deviation 7 years), 47% females] and used to predict diagnosis, APOE ε4 carriage status, cerebral blood flow, cerebrospinal fluid total-tau and phosphorylated-tau levels (cross-sectionally); and hippocampal volume, fluorodeoxyglucose positron emission tomography results and Alzheimer's Disease Assessment Scale-cognitive subscale scores (longitudinally). Cerebrospinal fluid and positron emission tomography amyloid-β were highly correlated, but adjusting one of these predictors for the other revealed that they both provided partially independent information when predicting diagnosis, APOE ε4, hippocampal volume, metabolism, cognition, total-tau and phosphorylated-tau (the 95% confidence intervals of the adjusted effects did not include zero). Cerebrospinal fluid amyloid-β was more strongly related to APOE ε4 whereas positron emission tomography amyloid-β was more strongly related to tau levels (P < 0.05). Discordance (mainly isolated cerebrospinal fluid amyloid-β positivity) differed by diagnostic group (P < 0.001) and was seen in 21% of cognitively healthy people but only 6% in dementia patients. The finding that cerebrospinal fluid and positron emission tomography amyloid-β provide partially independent information about a wide range of Alzheimer's measures supports the theory that these modalities represent partly different aspects of Alzheimer's pathology. The fact that mismatch, with positive cerebrospinal fluid amyloid-β but normal positron emission tomography amyloid-β, is relatively common in cognitively healthy people may be considered when using these biomarkers to identify early stage Alzheimer's disease. Reduced cerebrospinal fluid amyloid-β may be more strongly related to early stage Alzheimer's disease, whereas increased positron emission tomography amyloid-β may be more strongly related to disease progression.
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
“This may be due to a temporal offset between these two measures [121–123].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [121]
In the citing paper
“Previous cross-sectional studies indicate that Aβ42 levels decrease in the CSF as Aβ starts to accumulate, before fibrillar plaques can be detected with PET7–11.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [10]
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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · cited 1×
“Alzheimer’s disease and biomarker evidence of brain amyloid-b have been included in Alzheimer’s disease research criteria (Albert et al., 2011; McKhann et al., 2011; Sperling et al., 2011; Dubois et al., 2014).”
Self-citationFischl, 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
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Flagged first, then by citations
Research works printed but not linked: 2
- Bates, 2010, lme4: Mixed-effects modeling with R
- Burnham, 2002, Model Selection and Multimodal Inference
Cited by
Sorted by citations
Items 201–237 of 237
The Framework for an Integrative Theory of Alzheimer’s Disease
Zaretsky, D. V., & Zaretskaia, M. V. (2025) · Current Alzheimer Research · doi:10.2174/0115672050381553250425062803 · Cited by 2
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-citationRobust Biomarkers: Methodologically Tracking Causal Processes in Alzheimer’s Measurement
Keyser, V., & Sarry, L. (2020) · Boston studies in the philosophy of science · doi:10.1007/978-3-030-29179-2_13 · Cited by 2
Cerebral amyloid burden and Alzheimer's disease subtypes – does localization information matter?
Zetterberg, H., & Mattsson, N. (2015) · European Journal of Neurology · doi:10.1111/ene.12777 · Cited by 2
Self-citationLiguori, C., Izzi, F., Mercuri, N. B., & Placidi, F. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00262 · Cited by 2
Elmaaty, A. A. A., Eissa, M. A., El-Rashedy, S. K., Zehry, H. I., Mosa, A. A. H., Zarad, C. A., Abdel-Dayem, M. A., Nokrashy, A. E., & Elsherifi, S. S. (2024) · The Egyptian Journal of Neurology Psychiatry and Neurosurgery · doi:10.1186/s41983-024-00912-x · Cited by 1
Lee, S., Lee, S., Park, I., Moon, Y., Yim, Y., Lee, J., Kim, J. S., & Moon, W. (2025) · Brain and Behavior · doi:10.1002/brb3.70521 · Cited by 1
Amyloid spatial extent with florbetapir-PET for early detection of preclinical Alzheimer’s disease
Thibault, E. G., Montenegro, G. A. D. C., Becker, J. A., Price, J. C., Healy, B. C., Hanseeuw, B. J., Buckley, R. F., Jacobs, H. I. L., Properzi, M. J., Sperling, R. A., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100529 · Cited by 1
Brain MRI signatures across sex and CSF Alzheimer’s disease biomarkers
Cheng, Y., He, Y., Gopinath, K. K., Billot, B., Iglesias, J. E., Wu, C., Dodge, H. H., Wills, A.-M. A., Carlyle, B. C., Kivisäkk, P., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf210 · Cited by 1
A blood-based signature of cerebrospinal fluid A β 1–42 statusPreprint
Goudey, B., Fung, B. J., Schieber, C., Initiative, A. D. N., & Faux, N. G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/190207 · Cited by 1
Patients with Alzheimer’s disease have increased cellular amyloid uptakePreprint
Zaretsky, D. V., Zaretskaia, M. V., Molkov, Y. I., & Initiative, A. D. N. (2022) · medRxiv · doi:10.1101/2022.01.12.22269196 · Cited by 1
Lecy, E. E., Min, H.-K. P., Apgar, C. J., Maltais, D. D., Lundt, E. S., Albertson, S. M., Senjem, M. L., Schwarz, C. G., Botha, H., Graff‐Radford, J., et al. (2023) · medRxiv · doi:10.1101/2023.05.14.23289948 · Cited by 1
Connecting genomic and proteomic signatures of amyloid burden in the brainPreprint
Puerta, R., de Rojas, I., García‐González, P., Olivé, C., Sotolongo‐Grau, Ó., García‐Sánchez, A., García‐Gutiérrez, F., Montrreal, L., Tartari, J. P., Sanabria, Á., et al. (2024) · medRxiv · doi:10.1101/2024.09.06.24313124 · Cited by 1
Molecular Imaging in Alzheimer Clinical Trials
Schmidt, M. E., Matthews, D. C., & Staelens, S. G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802173-6.00020-4
Nobili, F. M., Cagnin, A., Calcagni, M. L., Chincarini, A., Guerra, U. P., Morbelli, S. D., Padovani, A., Paghera, B., Pappatà, S., Parnetti, L., et al. (2019) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.18.03069-8
Amyloid-β PET Imaging in Aging and Dementia
Tolboom, N., Ossenkoppele, R., & van Berckel, B. N. M. (2019) · Springer eBooks · doi:10.1007/978-3-030-01523-7_11
Reimand, J., de Wilde, A., Teunissen, C. E., Zwan, M. D., Windhorst, A. D., Boellaard, R., Barkhof, F., van der Flier, W. M., Scheltens, P., van Berckel, B. N. M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/673467
Ruolo dei biomarcatori nella diagnostica liquorale della malattia di Alzheimer
Cinquanta, L., & Brusca, I. (2019) · La Rivista Italiana della Medicina di Laboratorio - Italian Journal of Laboratory Medicine · doi:10.23736/s1825-859x.19.00028-8
Khatun, A., Paterson, R. W., & Schöll, M. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-0583-z
Major, C. K., & Okhravi, H. R. (2021) · Cureus · doi:10.7759/cureus.13481
Jardel, A., Hopes, L., Malaplate, C., Roch, V., Manca, C., Jonveaux, T., & Verger, A. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201435
Piersson, A. D., Mohamad, M., Suppiah, S., & Rajab, N. F. (2021) · Clinical and Translational Imaging · doi:10.1007/s40336-021-00440-1
Kim, M. H., Lee, J., Kim, H. N., Shin, I. J., Lee, K., Kim, Y. S., Lee, S. M., Park, S. A., & Moon, S. Y. (2021) · Journal of the Korean Neurological Association · doi:10.17340/jkna.2021.3.19
Яхьяевич, Я. Н., & Vagabov, N. M. (2009) · VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MARINE ENGINEERING AND TECHNOLOGIES
Amyloid Conversion is Related to Risk Factors For DementiaPreprint
Groechel, R. C., Tripodis, Y., Alosco, M. L., Mez, J., Qiu, W. Q., Mercier, G., Goldstein, L. E., Budson, A. E., Kowall, N. W., & Killiany, R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4161567
Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer’s Disease
Yu, Z., Shi, Z., Dan, T., Dere, M., Kim, M., Li, Q., Guorong Wu, A., & Initiative, A. D. N. (2023) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-230081
Wolff, A. W., Demleitner, A. F., Feneberg, E., & Lingor, P. (2024) · Der Nervenarzt · doi:10.1007/s00115-024-01654-0
Xu, L., Ren, C., Jing, C., Wang, G., Wei, H., Kong, M., Ba, M., & Initiative, A. D. N. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16376
Yang, X., Shang, J., Tong, Q., & Han, Q. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-04693-z
Accrual of Alzheimer's disease pathology as a function of proximity to parental dementia onset
Ziukelis, E. T., Mak, E., Ritchie, C. W., O'Brien, J. T., & Aarsland, D. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70092
An overview of current diagnostic strategies
Cappa, S. F., & Cerami, C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-822240-9.00007-7
Amyloid PET Imaging and Quantification of Amyloid Deposition
Matsuda, H., & Yamao, T. (2025) · Neuromethods · doi:10.1007/978-1-0716-4494-2_5
Yukti, Sharma, A., Dhar, S., Ojha, R., & Prajapati, V. K. (2025) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2025.11.004
Kim, S., Lee, P., Oh, K. T., Byun, M. S., Yi, D., Lee, J. H., Kim, Y. K., Ye, B. S., Yun, M. J., Lee, D. Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14767314
Milà-Alomà, M., Shekari, M., Salvadó, G., Gispert, J. D., Arenaza‐Urquijo, E. M., Operto, G., Falcón, C., Vilor‐Tejedor, N., Grau‐Rivera, O., Sala‐Vila, A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15064593
Self-citationAndersson, E., Schultz, N., Saito, T., Saido, T. C., Blennow, K., Gouras, G. K., Zetterberg, H., & Hansson, O. (2023) · Figshare · doi:10.6084/m9.figshare.22620482
Self-citationAbdelouahab, O. B. (2021) · theses.fr (ABES) · doi:10.70675/97f4885fz3f5cz4234zb8e2ze3fa7c4e3d41
Metadata
- Authors
- Mattsson, N., Insel, P. S., Donohue, M., Landau, S., Jagust, W. J., Shaw, L. M., Trojanowski, J. Q., Zetterberg, H., Blennow, K., & Weiner, M. W.
- Affiliations
- San Francisco VA Medical Center (US)
- University of California San Diego (US)
- University of California, Berkeley (US)
- University of Pennsylvania (US)
- National Hospital for Neurology and Neurosurgery (GB)
- University of Gothenburg (SE)
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-12-24
- Volume
- 138
- Issue
- 3
- Pages
- 772-783
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4339769
- OpenAlex
- W2103861278
- Cited by
- 237
- References
- 60
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 15 · 2023: 12 · 2022: 15 · 2021: 27 · 2020: 25 · 2019: 35 · 2018: 21 · 2017: 23 · 2016: 21 · 2015: 21
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Brain (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Foundation for the National Institutes of Health (P30AG010129, K01 AG030514, U01AG024904, Grant U01 AG024904, AG024904)
- Alzheimer's Association (AG010129, U01 AG024904)
- Bristol-Myers Squibb (U01 AG024904, AG024904)
- Eli Lilly and Company (U01 AG024904, AG024904)
- Pfizer (AG024904, U01 AG024904)
- AstraZeneca
- Novartis
- F. Hoffmann-La Roche (AG024904, U01 AG024904)
- Medpace (U01 AG024904)
- Synarc (U01 AG024904, P30 AG010129, K01 AG030514)
- Alzheimer's Disease Neuroimaging Initiative (U01AG024904, AG024904, P30 AG010129, K01 AG030514)
- Northern California Institute for Research and Education (U01 AG024904, K01 AG030514, P30 AG010129)
- Elan (U01 AG024904)
- Eisai (U01 AG024904, AG024904)
- Vetenskapsrådet
- Sahlgrenska Universitetssjukhuset
- National Institutes of Health (5u01ag024904-10, U01-AG024904, Grant U01 AG024904, P30 AG010129 and K01 AG030514, AG010129, AG024904)
- U.S. Food and Drug Administration
- Genentech (AG024904, U01 AG024904)
- University of California, Los Angeles
- University of California, San Diego
- National Institute on Aging (U01-AG-024904, P30AG010129, AG 024904, K01AG030514, AG010129)
- National Institute of Biomedical Imaging and Bioengineering (U01 AG024904, AG024904)
- 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
- Health Sciences › Medicine › Clinical Neurology › Cerebrospinal fluid and hydrocephalus
- Keywords
- Alzheimer's disease, amyloid-beta, florbetapir PET, amyloid PET, biomarker discordance, APOE ε4, tau protein, phosphorylated tau, hippocampal volume, cerebral blood flow, FDG-PET, mild cognitive impairment, subjective memory complaints, Alzheimer's disease diagnosis
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Aniline Compounds, Cognition Disorders, Ethylene Glycols, Female, Fluorine Radioisotopes, Humans, Magnetic Resonance Imaging, Male, Neuropsychological Tests, Peptide Fragments, Cohort Studies, Amyloid beta-Peptides, Fluorodeoxyglucose F18, Positron-Emission Tomography, Apolipoprotein E4
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
