The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
Concordance between different amyloid immunoassays and visual amyloid positron emission tomographic assessment.
Open accessAbstract
Importance: Visual assessment of amyloid positron emission tomographic (PET) images has been approved by regulatory authorities for clinical use. Several immunoassays have been developed to measure β-amyloid (Aβ) 42 in cerebrospinal fluid (CSF). The agreement between CSF Aβ42 measures from different immunoassays and visual PET readings may influence the use of CSF biomarkers and/or amyloid PET assessment in clinical practice and trials. Objective: To determine the concordance between CSF Aβ42 levels measured using 5 different immunoassays and visual amyloid PET analysis. Design, Setting, and Participants: The study included 262 patients with mild cognitive impairment or subjective cognitive decline from the Swedish BioFINDER (Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably) cohort (recruited from September 1, 2010, through December 31, 2014) who had undergone flutemetamol F 18 ([18F]flutemetamol)-labeled PET. Levels of CSF Aβ42 were analyzed using the classic INNOTEST and the newer modified INNOTEST, fully automated Lumipulse (FL), EUROIMMUN (EI), and Meso Scale Discovery (MSD) assays. Concentrations of CSF Aβ were assessed using an antibody-independent mass spectrometry-based reference measurement procedure. Main Outcomes and Measures: The concordance of CSF Aβ42 levels and Aβ42:Aβ40 and Aβ42:tau ratios with visual [18F]flutemetamol PET status. Results: Of 262 participants (mean [SD] age, 70.9 [5.5] years), 108 were women (41.2%) and 154 were men (58.8%). The mass spectrometry-derived Aβ42 values showed higher correlations with the modified Aβ42-INNOTEST (r = 0.97), Aβ42-FL (r = 0.93), Aβ42-EI (r = 0.93), and Aβ42-MSD (r = 0.95) assays compared with the classic Aβ42-INNOTEST assay (r = 0.88; P ≤ .01). The signal in the classic Aβ42-INNOTEST assay was partly quenched by recombinant Aβ1-40 peptide. However, the classic Aβ42-INNOTEST assay showed better concordance with visual [18F]flutemetamol PET status (area under the receiver operating characteristic curve [AUC], 0.92) compared with the newer assays (AUCs, 0.87-0.89; P ≤ .01). The accuracies of the newer assays improved significantly when Aβ42:Aβ40 (AUCs, 0.93-0.95; P ≤ .01), Aβ42 to total tau (T-tau) (AUCs, 0.94; P ≤ .05), or Aβ42 to phosphorylated tau (P-tau) (AUCs, 0.94-0.95; P ≤ .001) ratios were used. A combination of the Aβ42:Aβ40 ratio and T-tau or P-tau level did not improve the accuracy compared with the ratio alone. Conclusions and Relevance: Concentrations of CSF Aβ42 derived from the new immunoassays (modified INNOTEST, FL, EI, and MSD) may correlate better with the antibody-independent mass spectrometry-based reference measurement procedure and may show improved agreement with visual [18F]flutemetamol PET assessment when using the Aβ42:Aβ40 or Aβ42:tau ratios. These findings suggest the benefit of implementing the CSF Aβ42:Aβ40 or Aβ42:tau ratios as a biomarker of amyloid deposition in clinical practice and trials.
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?
In the citing paper
“Recently, fully automated immunoassays have been developed by several different vendors with improved reliability and precision for CSF Aβ and tau species.23,24,25”
From Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease–Related β-Amyloid Status (2019) · cites this paper as [25]
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
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Practical Statistics for Medical Research
Altman, D. G. (1990) · doi:10.1201/9780429258589 · Cited by 10,668
Practical Statistics for Medical Research.
Bartko, J. J., & Altman, D. G. (1992) · Journal of the American Statistical Association · doi:10.2307/2290246 · Cited by 4,834
Comparing correlated correlation coefficients
Meng, X.-L., Rosenthal, R., & Rubin, D. B. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.1.172 · Cited by 2,472
Cerebrospinal fluid and plasma biomarkers in Alzheimer disease
Blennow, K., Hampel, H., Weiner, M., & Zetterberg, H. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2010.4 · Cited by 1,863
Self-citationmixtools : An R Package for Analyzing Finite Mixture Models
Benaglia, T., Chauveau, D., Hunter, D. R., & Young, D. S. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i06 · Cited by 1,391
Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease
Ikonomovic, M. D., Klunk, W. E., Abrahamson, E. E., Mathis, C. A., Price, J. C., Tsopelas, N. D., Lopresti, B. J., Ziolko, S., Bi, W., Paljug, W. R., et al. (2008) · Brain · doi:10.1093/brain/awn016 · Cited by 933
Tapiola, T., Alafuzoff, I., Herukka, S.-K., Parkkinen, L., Hartikainen, P., Soininen, H., & Pirttilä, T. (2009) · Archives of Neurology · doi:10.1001/archneurol.2008.596 · Cited by 896
Clark, C. M., Pontecorvo, M. J., Beach, T. G., Bedell, B. J., Coleman, R. E., Doraiswamy, P. M., Fleisher, A. S., Reiman, E. M., Sabbagh, M. N., Sadowsky, C. H., et al. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70142-4 · Cited by 855
Buchhave, P. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.155 · Cited by 664
Flagged first, then by citations
1 of the 36 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Janelidze S, Zetterberg H, Mattsson N, et al; Swedish BioFINDER study group. CSF Aβ42:Aβ40 and Aβ42:Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol. 2016;3 (3):154-165.
Cited by
Sorted by citations
Items 101–147 of 147
Challenges in Alzheimer’s Disease Diagnostic Work-Up: Amyloid Biomarker Incongruences
Lombardi, G., Pupi, A., Bessi, V., Polito, C., Padiglioni, S., Ferrari, C., Lucidi, G., Berti, V., Cristofaro, M. T. D., Piaceri, I., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200119 · Cited by 5
Data‐Independent Acquisition Mass Spectrometry in Tumor Classification and Cancer Biomarker Research
Simonik, J., Lapčík, P., Bouchalová, P., & Bouchal, P. (2025) · Mass Spectrometry Reviews · doi:10.1002/mas.70014 · Cited by 4
Roles of nano medicine in diagnosis of Alzheimer's disease
Kaushal, M. A., Patel, N., Xavier, G., & Prajapati, B. G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13205-6.00017-0 · Cited by 4
Dakterzada, F., Cipriani, R., Ortega, R. L., Arias, A. M., Llena, I. R., Ruiz-Julián, M., Huerto, R. P., Tahan, N., Matute, C., Capetillo‐Zarate, E., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3887496/v1 · Cited by 4
Blood-brain barrier water exchange in relation to amyloid, cognition and cerebrovascular burden
Padrela, B. E., Tecelão, S. R. R., Kirsebom, B., Geier, O. M., Tranfa, M., Masserini, F., Sneve, M. H., Slivka, M., FALCH, E., Pålhaugen, L., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103926 · Cited by 3
Potential biomarkers of early diagnosis of Alzheimer’s disease
Bogolepova, A. N., Makhnovich, E. V., Коваленко, Е. А., & Osinovskaya, N. A. (2022) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro20221220917 · Cited by 3
Lorenzini, L., Ingala, S., Wottschel, V., Wink, A. M., Mutsaerts, H. J. M. M., Haller, S., Blennow, K., O'Brien, J. T., Frisoni, B. G., Chételat, G., et al. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100054 · Cited by 3
Han, J., Feng, G., Liu, H.-W., Yi, J., Wu, J., & Yao, X. (2022) · PubMed Central · Cited by 3
Where Should I Draw the Line: PET-Driven, Data-Driven, or Manufacturer Cut-Off?
Sánchez‐Soblechero, A., López‐García, S., Lage, C., Fernández‐Matarrubia, M., Irure, J., López‐Hoyos, M., Jiménez-Bonilla, J. F., Quirce, R., de Arcocha-Torres, M., Cuenca-Vera, O., et al. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-230678 · Cited by 2
Allué, J. A., Sarasa, L., Fandos, N., Gonzalo, R., Sabido-Vera, R., Loscos, J., Romero, J., Sánchez, A., Terencio, J. V., Matías‐Guiu, J. A., et al. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1681516 · Cited by 2
Schinke, H., Jonsson, M. F., Gummesson, M., Nilsson, R., Gaupp, S., Manuilova, E., McIlwrick, S., Weinberger, J.-P., Rutz, S., Carboni, M., et al. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2024-1476 · Cited by 2
Zou, Y., Ma, X., Mao, C., Chu, S., Wang, T., Jin, W., Wang, Y., Yu, S., Gao, J., & Qiu, L. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120352 · Cited by 1
Mao, C., Zou, Y., Wang, T., Sha, L., Wu, M., Chu, S., Jin, W., Li, B., Huang, Y., Qiu, Y., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251379084 · Cited by 1
The Utility of CSF Biomarkers in Diagnosing Alzheimer’s Disease: A Thai Cohort Study
Nanthasi, W., Rattanabannakit, C., Wongkom, N., Dujada, P., Raksthaput, A., Chaichanettee, S., Phoyoo, P., Wachirutmanggur, L., An, S. S. A., & Senanarong, V. (2025) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000548539 · Cited by 1
Ham, L., Villers, O., Lobo, J. D., Bell, T. R., Cookson, D., Galasko, D. R., Letendre, S. L., Bondi, M. W., Moore, D. J., & Sundermann, E. E. (2026) · Journal of NeuroVirology · doi:10.1007/s13365-026-01309-7 · Cited by 1
Yang, D., Ke, Z., Chen, N., Yue, L., Chen, S., Jiang, M., Hu, Z. D., Xie, C., Zhu, W., Xu, J., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02058-9 · Cited by 1
Alzheimer's disease—Biomarkers, clinical evaluation or both?
Simrén, J., Ashton, N. J., Suárez‐Calvet, M., & Zetterberg, H. (2024) · Journal of Neuropsychology · doi:10.1111/jnp.12401 · Cited by 1
Sandoval, C., Lee, J., Tóth, B. I., Nagaraj, R., Schauer, S. P., Hoffman, J. C., Kollmorgen, G., Monteiro, C., Teng, E., Hanson, J. E., et al. (2024) · medRxiv · doi:10.1101/2024.04.13.24305768 · Cited by 1
Potential Biomarkers for Early Diagnosis of Alzheimer’s Disease and Primary Open-Angle Glaucoma
Bogolepova, A. N., Makhnovich, E. V., Коваленко, Е. А., & Osinovskaya, N. A. (2023) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-023-01449-x
Appropriate Use of Biomarkers in Suspected Neurodegenerative Diseases
Lamotte, G. J., & Foster, N. L. (2023) · Springer eBooks · doi:10.1007/978-3-031-35098-6_2
Li, Y., Zhu, Y., & Yao, M. (2023) · Human Brain · doi:10.37819/hb.2.1770
Chong, J. R., Hye, A., Boada, M., Svenningsson, P., Fladby, T., & Aarsland, D. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251360404
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538839
Lehmann, S., Dumurgier, J., Ayrignac, X., Marelli, C., Alcolea, D. A., Fortea, J., Thouvenot, É., Delaby, C., Hirtz, C., Vialaret, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13047440
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538842
Motta, C., Donna, M. G. D., Bonomi, C. G., Assogna, M., Chiaravalloti, A., Mercuri, N. B., Koch, G., & Martorana, A. (2024) · Figshare · doi:10.6084/m9.figshare.26613760
Motta, C., Donna, M. G. D., Bonomi, C. G., Assogna, M., Chiaravalloti, A., Mercuri, N. B., Koch, G., & Martorana, A. (2024) · Figshare · doi:10.6084/m9.figshare.26613757
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538845
Moon, S., Kim, S., Mankhong, S., Choi, S. H., Vandijck, M. L. J., Kostanjevečki, V., Jeong, J. H., Yoon, S. J., Park, K. W., Kim, E.-J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13565702
Motta, C., Donna, M. G. D., Bonomi, C. G., Assogna, M., Chiaravalloti, A., Mercuri, N. B., Koch, G., & Martorana, A. (2024) · Figshare · doi:10.6084/m9.figshare.26613754
Lehmann, S., Dumurgier, J., Ayrignac, X., Marelli, C., Alcolea, D. A., Fortea, J., Thouvenot, É., Delaby, C., Hirtz, C., Vialaret, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13047446
Motta, C., Donna, M. G. D., Bonomi, C. G., Assogna, M., Chiaravalloti, A., Mercuri, N. B., Koch, G., & Martorana, A. (2024) · Figshare · doi:10.6084/m9.figshare.26613763
Bellomo, G., Cataldi, S., Paciotti, S., Paolini Paoletti, F., Chiasserini, D., & Parnetti, L. (2020) · Figshare · doi:10.6084/m9.figshare.13023822
Lehmann, S., Dumurgier, J., Ayrignac, X., Marelli, C., Alcolea, D. A., Fortea, J., Thouvenot, É., Delaby, C., Hirtz, C., Vialaret, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13047443
Lehmann, S., Dumurgier, J., Ayrignac, X., Marelli, C., Alcolea, D. A., Fortea, J., Thouvenot, É., Delaby, C., Hirtz, C., Vialaret, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13047449
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538848
Lehmann, S., Dumurgier, J., Ayrignac, X., Marelli, C., Alcolea, D. A., Fortea, J., Thouvenot, É., Delaby, C., Hirtz, C., Vialaret, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13047452
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538851
Motta, C., Donna, M. G. D., Bonomi, C. G., Assogna, M., Chiaravalloti, A., Mercuri, N. B., Koch, G., & Martorana, A. (2024) · Figshare · doi:10.6084/m9.figshare.26613751
Yamashita, K., Miura, M., Watanabe, S., Ishiki, K., Arimatsu, Y., Kawahira, J., Kubo, T., Sasaki, K., Arai, T., Hagino, K., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20141125
Verma, K., & Kumar, S. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261430858
Real-world clinical profile of individuals with cerebrospinal fluid Aβ – /pTau181 +
Keret, O., Xia, T., Wilkins, S., Ncube, L., Smaw, B., Xiong, Y., Gandy, S., Pereira, A., Naasan, G., & Elahi, F. M. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261445561
Nilsson, M. H., Lindh-Rengifo, M., Rochester, L., van Westen, D., Smith, R., Palmqvist, S., Stomrud, E., Mattsson, N., & Hansson, O. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag114
Yamada, K., Kurihara, M., Akitomi, Y., Sato, K., Shimasaki, R., Hatano, K., Bannai, T., Suzuki, F., Furuta, K., Tokumaru, A. M., et al. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70374
Thalamic nuclei insights into Alzheimer’s diseasePreprint
Vidal, J. P. C., Myall, D. J., Pariente, J., Pitcher, T. L., Roberts, R. P., Cawston, E. E., Heron, C. J. L., Anderson, T., Morgan, C., Melzer, T. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.26.728015
Advances in the Diagnosis and Management of Alzheimer’s Disease
Joshi, P., Tsai, P., Alix, A., & Gopalakrishna, G. (2026) · Advances in Psychiatry and Behavioral Health · doi:10.1016/j.ypsc.2026.05.004
Benussi, A., Agosta, F., Agnello, L., Alfano, A. R., Altini, M., Antico, A., Basilico, S., Bellelli, G., Bonanni, L., Bottini, G., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100685
Metadata
- Authors
- Janelidze, S., Pannee, J., Mikulskis, A., Chiao, P., Zetterberg, H., Blennow, K., & Hansson, O.
- Affiliations
- Lund University (SE)
- University of Gothenburg (SE)
- Biogen (United States) (US)
- Sahlgrenska University Hospital (SE)
- Corresponding author
- Janelidze, Shorena
- Venue
- JAMA Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 2017-12-01
- Volume
- 74
- Issue
- 12
- Pages
- 1492
- ISSN
- 2168-6149 (ISSN-L)
- PMC
- PMC5822196
- OpenAlex
- W2767595531
- Cited by
- 147
- References
- 36
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 19 · 2023: 13 · 2022: 19 · 2021: 24 · 2020: 23 · 2019: 15 · 2018: 12
- FWCI
- 11.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Neurology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Australian Government
- Vetenskapsrådet
- Fujirebio Europe
- Marcus och Amalia Wallenbergs minnesfond
- 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 › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Keywords
- amyloid PET, amyloid-beta 42, cerebrospinal fluid biomarkers, 18F-flutemetamol, Meso Scale Discovery, Aβ42/Aβ40 ratio, mild cognitive impairment
- MeSH terms
- Aged, Amyloid, Aniline Compounds, Cerebral Cortex, Female, Humans, Immunoassay, Male, Peptide Fragments, ROC Curve, Sweden, Cohort Studies, Amyloid beta-Peptides, tau Proteins, Radiopharmaceuticals, Area Under Curve, Positron-Emission Tomography, Benzothiazoles, 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
