Cited by 141Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

The National Institute on Aging—Alzheimer's Association Framework on Alzheimer's disease: Application to clinical trials

Open access

Cummings, Jeffrey·2019·Alzheimer's & Dementia

DOI 10.1016/j.jalz.2018.05.006PMC PMC6417790

Open this paper in CoChat — free account Open PDF

Abstract

Introduction: The National Institute on Aging—Alzheimer's Association Research Framework on Alzheimer's disease (AD) represents an important advance in the biological characterization of the AD spectrum. Methods: The National Institute on Aging—Alzheimer's Association Framework is considered as it applies to clinical trials. Results: Using the combination of amyloid (A), tau (T), and neurodegeneration (N) biomarkers, the Framework provides a means of defining the state of patients with regard to Alzheimer pathologic change. The Framework is relevant to clinical trials of disease‐modifying agents, allowing participants to be characterized biologically at baseline. The ATN Framework can also inform trial outcomes. The preclinical phase of the disease after amyloid deposition is defined by A+T−N−, and the transition to prodromal disease and dementia is characterized by the addition of T and N. Most symptomatic patients in clinical trials are in the class of A+T+N− and A+T+N+. Discussion: The National Institute on Aging—Alzheimer's Association Framework on AD represents progress in providing biomarker profiles of participants in the AD spectrum that can be used to help design clinical trials.

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

Crossref · 10.1016/j.jalz.2018.05.006

Cites

55 of 55 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.

Self-check

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

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

NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537
A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., Hampel, H., Jagust, W. J., Johnson, K. A., Knopman, D. S., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000002923 · Cited by 1,864
Tau positron emission tomographic imaging in aging and early Alzheimer disease
Johnson, K. A., Schultz, A., Betensky, R. A., Becker, J. A., Sepulcre, J., Rentz, D., Mormino, E., Chhatwal, J., Amariglio, R., Papp, K., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24546 · Cited by 986
The neuropathology of probable Alzheimer disease and mild cognitive impairment
Schneider, J. A., Arvanitakis, Z., Leurgans, S. E., & Bennett, D. A. (2009) · Annals of Neurology · doi:10.1002/ana.21706 · Cited by 935
Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease
Brier, M. R., Gordon, B., Friedrichsen, K., McCarthy, J., Stern, A., Christensen, J., Owen, C., Aldea, P., Su, Y., Hassenstab, J., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf2362 · Cited by 734
Amyloid β oligomers in Alzheimer’s disease pathogenesis, treatment, and diagnosis
Viola, K. L., & Klein, W. L. (2015) · Acta Neuropathologica · doi:10.1007/s00401-015-1386-3 · Cited by 628
Effects of Aβ immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease
Fox, N. C., Black, R. S., Gilman, S., Rossor, M. N., Griffith, S. G., Jenkins, L., & Koller, M. (2005) · Neurology · doi:10.1212/01.wnl.0000159743.08996.99 · Cited by 550
Person-specific contribution of neuropathologies to cognitive loss in old age
Boyle, P. A., Yu, L., Wilson, R. S., Leurgans, S. E., Schneider, J. A., & Bennett, D. A. (2018) · Annals of Neurology · doi:10.1002/ana.25123 · Cited by 508
Association Between Elevated Brain Amyloid and Subsequent Cognitive Decline Among Cognitively Normal Persons
Donohue, M. C., Sperling, R. A., Petersen, R., Sun, C.-K., Weiner, M. W., Aisen, P. S., & Initiative, F. T. A. D. N. (2017) · JAMA · doi:10.1001/jama.2017.6669 · Cited by 452
The BACE1 inhibitor verubecestat (MK-8931) reduces CNS β-amyloid in animal models and in Alzheimer’s disease patients
Kennedy, M. E., Stamford, A. W., Chen, X., Cox, K., Cumming, J. N., Dockendorf, M. F., Egan, M. F., Ereshefsky, L., Hodgson, R. A., Hyde, L. A., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad9704 · Cited by 441

Show all 54 references

Research works printed but not linked: 2

U.S. Food and Drug Administration, 2018, Early Alzheimer's Disease: Developing Drugs for Treatment; Draft Guidance for Industry, Federal Register, 7060
Cummings JL, 2017, Defining disease modification for Alzheimer's disease clinical trials, J Prev Alzheimers Dis., 4, 109

Sorted by citations

Current and Future Treatments in Alzheimer Disease: An Update
Yiannopoulou, K. G., & Papageorgiou, S. G. (2020) · Journal of Central Nervous System Disease · doi:10.1177/1179573520907397 · Cited by 966
Alzheimer's disease drug development pipeline: 2019
Cummings, J., Lee, G., Ritter, A., Sabbagh, M., & Zhong, K. (2019) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2019.05.008 · Cited by 762
Self-citation
Alzheimer's Disease: Treatment Strategies and Their Limitations.
Passeri, E., Elkhoury, K., Morsink, M., Broersen, K., Linder, M., Tamayol, A., Malaplate, C., Yen, F. T., & Arab-Tehrany, E. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232213954 · Cited by 689
“Moreover, topographical markers for AD include volume modifications in the brain (temporoparietal, hippocampal atrophy, cortical thickness) assessed by magnetic resonance imaging (MRI) and glucose hypometabolism measured by fluorodeoxyglucose (FDG)-PET [73].”
Method
The degree of frailty as a translational measure of health in aging
Howlett, S. E., Rutenberg, A. D., & Rockwood, K. J. (2021) · Nature Aging · doi:10.1038/s43587-021-00099-3 · Cited by 357
Developing the ATX(N) classification for use across the Alzheimer disease continuum
Hampel, H. J., Cummings, J. L., Blennow, K., Gao, P., Jack, C. R., & Vergallo, A. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00520-w · Cited by 351
Self-citation
Reasons for failed trials of disease-modifying treatments for alzheimer disease and their contribution in recent research
Yiannopoulou, K. G., Anastasiou, A. I., Zachariou, V., & Pelidou, S.-H. (2019) · Biomedicines · doi:10.3390/biomedicines7040097 · Cited by 330
The “rights” of precision drug development for Alzheimer’s disease
Cummings, J. L., Feldman, H., & Scheltens, P. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0529-5 · Cited by 252
Self-citation
Designing the next-generation clinical care pathway for Alzheimer’s disease
Hampel, H., Au, R., Mattke, S., van der Flier, W. M., Aisen, P., Apostolova, L., Chen, C., Cho, M., De Santi, S., Gao, P., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00269-x · Cited by 191
Self-citation
Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review
Khoury, R., & Ghossoub, E. (2019) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2019.100005 · Cited by 200
Heterogeneity in Alzheimer’s Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials
Duara, R., & Barker, W. (2022) · Neurotherapeutics · doi:10.1007/s13311-022-01185-z · Cited by 187
Targeting autophagy, oxidative stress, and ER stress for neurodegenerative disease treatment
Esmaeili, Y., Yarjanli, Z., Paknia, F., Bidram, E., Łoś, M., Eshraghi, M., Klionsky, D. J., Ghavami, S., & Zarrabi, A. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.03.001 · Cited by 184
The Role of Biomarkers in Alzheimer’s Disease Drug Development
Cummings, J. L. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-05542-4_2 · Cited by 153
Self-citation
Hippocampal vascular reserve associated with cognitive performance and hippocampal volume
Perosa, V., Priester, A., Ziegler, G., Cárdenas‐Blanco, A., Dobisch, L., Spallazzi, M., Assmann, A., Maaß, A., Speck, O., Oltmer, J., et al. (2019) · Brain · doi:10.1093/brain/awz383 · Cited by 147
Plasma Glial Fibrillary Acidic Protein in the Alzheimer Disease Continuum: Relationship to Other Biomarkers, Differential Diagnosis, and Prediction of Clinical Progression
Shen, X., Huang, S., Cui, M., Zhao, Q., Guo, Y., Huang, Y., Zhang, W., Ma, Y., Chen, S.-D., Zhang, Y.-R., et al. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvad018 · Cited by 99
Plasma p‐tau immunoassays in clinical research for Alzheimer's disease
Teunissen, C. E., Kolster, R., Triana‐Baltzer, G., Janelidze, S., Zetterberg, H., & Kolb, H. C. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14397 · Cited by 96
Frequency and longitudinal clinical outcomes of Alzheimer's AT(N) biomarker profiles: A longitudinal study
Yu, J., Li, J., Suckling, J., Feng, L., Pan, A., Wang, Y., Song, B., Zhu, S., Li, D., Wang, H.-F., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.05.006 · Cited by 72
Female specific risk factors for the development of Alzheimer’s disease neuropathology and cognitive impairment: Call for a precision medicine approach
Udeh-Momoh, C., & Watermeyer, T. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101459 · Cited by 67
Machine Learning Framework for Implementing Alzheimer’s Disease
Sivakani, R., & Ansari, G. A. (2020) · doi:10.1109/iccsp48568.2020.9182220 · Cited by 63
Recommendations for reproducibility of cerebrospinal fluid extracellular vesicle studies
Sandau, U. S., Magaña, S. M., Costa, J., Nolan, J. P., Ikezu, T., Vella, L. J., Jackson, H. K., Moreira, L. R., Palacio, P. L., Hill, A. F., et al. (2023) · Journal of Extracellular Vesicles · doi:10.1002/jev2.12397 · Cited by 60
Omics-based biomarkers discovery for Alzheimer's disease
Aerqin, Q., Wang, Z.-T., Wu, K., He, X., Dong, Q., & Yu, J. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04614-6 · Cited by 56
Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery
Hampel, H. J., Vergallo, A., Caraci, F., Cuello, A. C., Lemercier, P., Vellas, B. J., Giudici, K. V., Baldacci, F., Hänisch, B., Haberkamp, M., et al. (2020) · Neuropharmacology · doi:10.1016/j.neuropharm.2020.108081 · Cited by 52
From beta amyloid to altered proteostasis in Alzheimer’s disease
Bruni, A. C., Bernardi, L., & Gabelli, C. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101126 · Cited by 47
The Neuroprotective and Neurodegeneration Effects of Heme Oxygenase-1 in Alzheimer’s Disease
Si, Z., & Wang, X. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200720 · Cited by 46
Interaction between Alzheimer’s Disease and Cerebral Small Vessel Disease: A Review Focused on Neuroimaging Markers
Kim, S. E., Kim, H. J., Jang, H., Weiner, M. W., DeCarli, C. S., Na, D. L., & Seo, S. W. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810490 · Cited by 42
A machine learning approach to screen for preclinical Alzheimer's disease
Gaubert, S., Houot, M., Raimondo, F., Ansart, M., Corsi, M., Naccache, L., Sitt, J. D., Habert, M., Dubois, B., Fallani, F. D. V., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.04.024 · Cited by 43
The factors contributing to cognitive dysfunction in type 2 diabetic patients
Xia, S.-S., Xia, W.-L., Huang, J., Zou, H., Tao, J., & Yang, Y. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.113 · Cited by 43
Associations of [18F]-APN-1607 Tau PET Binding in the Brain of Alzheimer’s Disease Patients With Cognition and Glucose Metabolism
Lu, J., Bao, W., Li, M., Li, L., Zhang, Z., Alberts, I., Brendel, M., Cumming, P. D., Lu, H., Xiao, Z., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00604 · Cited by 41
Stem Cell Therapies in Alzheimer’s Disease: Applications for Disease Modeling
Si, Z., & Wang, X. (2021) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.120.000324 · Cited by 42
Association of oxidative stress and inflammatory metabolites with Alzheimer’s disease cerebrospinal fluid biomarkers in mild cognitive impairment
Ahmad, S., Yang, W., Orellana, A., Frölich, L., de Rojas, I., Cano, A. C., Boada, M., Hernández, I., Hausner, L., Harms, A. C., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01542-4 · Cited by 41
Genome-wide profiling and predicted significance of post-mortem brain microRNA in Alzheimer’s disease
Henriques, A. D., Machado‐Silva, W., Leite, R. E. P., Suemoto, C. K., Leite, K. R. M., Srougi, M., Pereira, A. C., Filho, W. J., & Nóbrega, O. D. T. (2020) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2020.111352 · Cited by 36
Bridging the rodent to human translational gap: Marmosets as model systems for the study of Alzheimer's disease
Rizzo, S. J. S., Homanics, G. E., Schaeffer, D. J., Schaeffer, L. K. H., Park, J. E., Oluoch, J., Zhang, T., Haber, A., Seyfried, N. T., Paten, B., et al. (2023) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12417 · Cited by 40
Choroid plexus free‐water correlates with glymphatic function in Alzheimer's disease
Xu, X., Yang, X., Zhang, J., Wang, Y., Selim, M. H., Zheng, Y., Shen, R., Sun, L., Huang, Q., Wang, W., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70239 · Cited by 34
Using multivariate base rates of low scores to understand early cognitive declines on the uniform data set 3.0 Neuropsychological Battery.
Kiselica, A. M., Webber, T. A., & Benge, J. F. (2020) · Neuropsychology · doi:10.1037/neu0000640 · Cited by 32
Value-Generating Exploratory Trials in Neurodegenerative Dementias
Friedman, L. G., McKeehan, N., Hara, Y., Cummings, J. L., Matthews, D. C., Zhu, J., Mohs, R. C., Wang, D., Hendrix, S. B., Quintana, M., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000011774 · Cited by 32
Self-citation
Microglia show differential transcriptomic response to Aβ peptide aggregates ex vivo and in vivo
McFarland, K. N., Ceballos, C., Rosario, A. M., Ladd, T. B., Moore, B. D., Golde, G., Wang, X., Allen, M., Ertekin‐Taner, N., Funk, C. C., et al. (2021) · Life Science Alliance · doi:10.26508/lsa.202101108 · Cited by 31
Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer’s Disease
Colvee-Martin, H., Parra, J. R., Gonzalez, G. A., Barker, W., & Duara, R. (2024) · Diagnostics · doi:10.3390/diagnostics14070704 · Cited by 32
Effects of Hypertension on Alzheimer’s Disease and Related Disorders
Malone, J. E., Elkasaby, M., & Lerner, A. J. (2022) · Current Hypertension Reports · doi:10.1007/s11906-022-01221-5 · Cited by 26
Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer’s disease
Hampel, H. J., Vergallo, A., Afshar, M., Akman-Anderson, L., Arenas, J., Benda, N., Batrla, R., Broich, K., Caraci, F., Cuello, A. C., et al. (2019) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2019.21.2/hhampel · Cited by 27
RETRACTED AND REPLACED: Development and Preliminary Validation of Standardized Regression-Based Change Scores as Measures of Transitional Cognitive Decline
Kiselica, A. M., Kaser, A. N., Webber, T. A., Small, B. J., & Benge, J. F. (2020) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acaa042 · Cited by 26
Comparing ATN-T designation by tau PET visual reads, tau PET quantification, and CSF PTau181 across three cohorts
Provost, K., Iaccarino, L., Soleimani‐Meigooni, D. N., Baker, S. L., Edwards, L., Eichenlaub, U., Hansson, O., Jagust, W. J., Janabi, M., Joie, R. L., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05152-8 · Cited by 27
Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial‐Ready Cohort study
Sato, K., Ihara, R., Suzuki, K., Niimi, Y., Toda, T., Jimenez‐Maggiora, G. A., Langford, O., Donohue, M., Raman, R., Aisen, P. S. S., et al. (2021) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12135 · Cited by 25
Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework
Xiong, X., He, H., Ye, Q., Qian, S., Zhou, S., Feng, F., Fang, E. F., & Xie, C. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14357 · Cited by 24
Staging the cognitive continuum in prodromal Alzheimer's disease with episodic memory
Moscoso, A., Silva‐Rodríguez, J., Aldrey, J. M., Cortés, J., Fernández‐Ferreiro, A., Gómez‐Lado, N., Ruibal, Á., & Aguiar, P. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.07.014 · Cited by 23
Episodic Memory–Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer’s Disease: A Multicenter Study Based on Machine Learning
Shi, Y., Wang, Z., Chen, P., Cheng, P., Zhao, K., Zhang, H., Shu, H., Gu, L., Gao, L., Wáng, Q., et al. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.12.007 · Cited by 22
A passive and objective measure of recognition memory in Alzheimer’s disease using Fastball memory assessment
Stothart, G., Smith, L. J., Milton, A., & Coulthard, E. J. (2021) · Brain · doi:10.1093/brain/awab154 · Cited by 22
Blood Pressure Control and Protection of the Aging Brain
Wahidi, N., & Lerner, A. J. (2019) · Neurotherapeutics · doi:10.1007/s13311-019-00747-y · Cited by 24
Default Mode Network Connectivity Moderates the Relationship Between the APOE Genotype and Cognition and Individualizes Identification Across the Alzheimer’s Disease Spectrum
Zhu, Y., Initiative, O. B. O. A. D. N., Gong, L., He, C., Wang, Q., Ren, Q., & Xie, C. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190254 · Cited by 20
Distinct spatiotemporal subtypes of amyloid deposition are associated with diverging disease profiles in cognitively normal and mild cognitive impairment individuals
Sun, Y., Zhao, Y., Hu, K., Wang, M., Liu, Y., Liu, B., & Initiative, A. D. N. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02328-2 · Cited by 24
Early molecular events of autosomal‐dominant Alzheimer's disease in marmosets with PSEN1 mutations
Homanics, G. E., Park, J. E., Bailey, L. S., Schaeffer, D. J., Schaeffer, L. K. H., He, J., Li, S., Zhang, T., Haber, A., Spruce, C. A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13806 · Cited by 20
Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases
Wu, J., Dong, Q., Gui, J., Zhang, J., Su, Y., Chen, K., Thompson, P. M., Caselli, R. J., Reiman, E. M., Ye, J., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.669595 · Cited by 19
The Healthy Brain Initiative (HBI): A prospective cohort study protocol
Besser, L. M., Chrisphonte, S., Kleiman, M. J., O’Shea, D. M., Rosenfeld, A., Tolea, M. I., & Galvin, J. E. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293634 · Cited by 20
Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings
Amato, L. G., Vergani, A. A., Lassi, M., Fabbiani, C., Mazzeo, S., Burali, R., Nacmias, B., Sorbi, S., Mannella, R., Grippo, A., et al. (2024) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12526 · Cited by 17
Performance of Validated MicroRNA Biomarkers for Alzheimer’s Disease in Mild Cognitive Impairment
Sandau, U. S., Wiedrick, J. T., Smith, S. J., McFarland, T. J., Lusardi, T. A., Lind, B., Harrington, C. A., Lapidus, J., Galasko, D. R., Quinn, J. F., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200396 · Cited by 16
Neuroimaging biomarkers: a critical review of their role in neurological disease diagnosis and management
Wankhede, N. L., Koppula, S., Umare, P. D., Umare, M. D., Lokhande, S. K., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., Kopalli, S. R., & Kale, M. B. (2025) · Brain Disorders · doi:10.1016/j.dscb.2025.100222 · Cited by 18
State, trait, and accumulated features of the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS‐Cog) in mild Alzheimer's disease
Cogo‐Moreira, H., Krance, S. H., Wu, C., Lanctôt, K. L., Herrmann, N., Black, S. E., MacIntosh, B. J., Rabin, J. S., Eid, M., Swardfager, W., et al. (2023) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12376 · Cited by 18
Developing robust biomarkers for vascular cognitive disorders: adding ‘V’ to the AT(N) research framework
Sachdev, P. S. (2020) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000577 · Cited by 13
Cingulate white matter mediates the effects of fecal Ruminococcus on neuropsychiatric symptoms in patients with amyloid-positive amnestic mild cognitive impairment
Hung, C.-C., Chao, Y., Lee, Y., Huang, C., Huang, S., Chang, C., & Cheng, C. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-04417-9 · Cited by 13
Attempt to Predict A/T/N-Based Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Using a Peripheral Blood DNA Methylation Clock
Sato, K., Mano, T., Suzuki, K., Toda, T., Iwatsubo, T., Iwata, A., & Initiative, A. D. N. (2020) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-200205 · Cited by 13
Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer’s Disease
Shvetcov, A., Thomson, S., Spathos, J., Cho, A.-N., Wilkins, H. M., Andrews, S. J., Delerue, F., Couttas, T. A., Issar, J. K., Isik, F., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241915011 · Cited by 12
Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data
Sun, J., Xie, Z., Sun, Y., Shen, A., Li, R., Xiao, Y., Lu, B., & Li, Y. (2024) · MedComm · doi:10.1002/mco2.532 · Cited by 12
Strain specific effects of probiotic supplementation on serum amino acid profiles in Alzheimer’s disease: a randomized, double blind, placebo controlled trial
Jouni, N., Akhgarjand, C., Vahabi, Z., Shab‐Bidar, S., Khalifeh, H., & Djafarian, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-15355-4 · Cited by 14
The de-Alzheimerization of age-related dementias: implications for drug targets and approaches to effective therapeutics
Mullane, K., & Williams, M. J. (2019) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2019.01.004 · Cited by 13
Cerebrospinal fluid in the differential diagnosis of Alzheimer’s disease: an update of the literature
Milos, T., Vuić, B., Balic, N., Farkaš, V., Erjavec, G. N., Štrac, D. Š., Perković, M. N., & Pivac, N. (2024) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2024.2400683 · Cited by 11
Association of AK4 Protein From Stem Cell–Derived Neurons With Cognitive Reserve
Yu, L., Hsieh, Y., Pearse, R. V., Wang, Y., Petyuk, V., Schneider, J. A., Buchman, A. S., Seyfried, N. T., Jager, P. L. D., Young‐Pearse, T. L., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201120 · Cited by 13
Associations Between Multimorbidity and Cerebrospinal Fluid Amyloid: A Cross-Sectional Analysis of the European Prevention of Alzheimer’s Dementia (EPAD) V500.0 Cohort
Stirland, L., Russ, T. C., Ritchie, C. W., & Terrera, G. M. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190222 · Cited by 10
Amyloid and tau positive mild cognitive impairment: clinical and biomarker characteristics of dementia progression
Wei, H., Li, B., Ng, K. P., Fu, Q., Dong, S., Ba, M., & Kong, M. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001496 · Cited by 11
Plasma protein risk scores for mild cognitive impairment and Alzheimer's disease in the Framingham heart study
Rehman, H., Ang, T. F. A., Tao, Q., Au, R., Farrer, L. A., Qiu, W. Q., Zhang, X., & Initiative, A. D. N. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70066 · Cited by 9
Identifying Distinct Spatiotemporal Patterns of Juxtacortical Microstructure in Alzheimer Disease Using Diffusion MRI–derived Free Water Fraction
Zhang, J., Yang, X., Wang, Y., Xu, X., Zheng, Y., Huang, Q., Wang, W., Xu, W., Guan, Y., Liu, J., et al. (2025) · Radiology · doi:10.1148/radiol.243423 · Cited by 10
The Association Between Plasma α-Synuclein (α-syn) Protein, Urinary Alzheimer-Associated Neuronal Thread Protein (AD7c-NTP), and Apolipoprotein Epsilon 4 (ApoE ε4) Alleles and Cognitive Decline in 60 Patients with Alzheimer’s Disease Compared with 28 Age-Matched Normal Individuals
Lv, S., Zhou, X., Li, Y., Zhang, S., Wang, Y., Jia, S., Niu, X., Wang, L., & Peng, D. (2021) · Medical Science Monitor · doi:10.12659/msm.932998 · Cited by 9
Biomarkers of Inflammation Increase with Tau and Neurodegeneration but not with Amyloid-β in a Heterogenous Clinical Cohort
Michopoulou, S. K., Prosser, A. M. J., Kipps, C., Dickson, J. C., Guy, M., & Teeling, J. L. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220523 · Cited by 10
Efficacy and Cost-effectiveness of Promotion Methods to Recruit Participants to an Online Screening Registry for Alzheimer Disease Prevention Trials: Observational Study
Sato, K., Niimi, Y., Ihara, R., Suzuki, K., Toda, T., Iwata, A., & Iwatsubo, T. (2021) · Journal of Medical Internet Research · doi:10.2196/26284 · Cited by 9
Harmonized Cognitive Assessment Protocol in the English Longitudinal Study of Ageing: Contrasting approaches to evaluation of factor structure
Liu, Y., Hayat, S. A., Assaad, S., Cadar, D., Steptoe, A., Lee, J., & Gross, A. L. (2025) · International Psychogeriatrics · doi:10.1016/j.inpsyc.2025.100042 · Cited by 9
Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer’s disease
Niu, X., Wang, Y., Zhang, X., Wang, Y., Shao, W., Chen, L., Yang, Z., & Peng, D. (2023) · Neurological Sciences · doi:10.1007/s10072-023-07028-9 · Cited by 10
Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer’s Disease Pathology
Shi, L., Winchester, L. M., Liu, B. Y., Killick, R., Ribé, E. M., Westwood, S., Baird, A. L., Buckley, N. J., Hong, S., Dobričić, V., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200208 · Cited by 8
COVID-19 and geriatric clinical trials research
Rhodus, E. K., Bardach, S. H., Abner, E. L., Gibson, A., & Jicha, G. A. (2020) · Aging Clinical and Experimental Research · doi:10.1007/s40520-020-01705-x · Cited by 8
A Multi-Dimensional Comparison of Alzheimer’s Disease Neurodegenerative Biomarkers
Liu, Y., Initiative, A. D. N., Han, P.-R., Hu, H., Wang, Z.-T., Guo, Y., Ou, Y.-N., Cao, X.-P., Tan, L., & Yu, J.-T. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215724 · Cited by 7
Development and Preliminary Validation of Standardized Regression-Based Change Scores as Measures of Transitional Cognitive Decline
Kiselica, A. M., Kaser, A. N., Webber, T. A., Small, B. J., & Benge, J. F. (2025) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acaf015 · Cited by 8
Predicting conversion to Alzheimer's disease among individual high‐risk patients using the Characterizing AD Risk Events index model
Lu, X., Chen, J., Shu, H., Wang, Z., Shi, Y., Yuan, Y., Xie, C., Liao, W., Su, F., Shi, Y., et al. (2020) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13371 · Cited by 6
Comparison of commonly measured plasma and cerebrospinal fluid proteins and their significance for the characterization of cognitive impairment status
Rehman, H., Initiative, F. T. A. D. N., Ang, T. F. A., Tao, Q., Espenilla, A. L., Au, R., Farrer, L. A., Zhang, X., & Qiu, W. Q. (2024) · Journal of Alzheimer's Disease · doi:10.3233/jad-230837 · Cited by 7
Clinical translation of fluid, imaging, and digital biomarkers for Alzheimer’s disease
Bacci, J. R., Karagianni, S., Alexopoulou, Z.-S., Moukaled, S., Tato-Fernández, C., Arunachalam, P., Aslanyan, A., Aye, S., Rocha, A. S. L., Crugel, M., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01921-5 · Cited by 8
Cohort-Specific Optimization of Models Predicting Preclinical Alzheimer's Disease, to Enhance Screening Performance in the Middle of Preclinical Alzheimer's Disease Clinical Studies
Sato, K., Mano, T., Ihara, R., Suzuki, K., Niimi, Y., Toda, T., Iwatsubo, T., & Iwata, A. (2021) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2021.39 · Cited by 6
Genome-wide association study identifies CBFA2T3 affecting the rate of CSF Aβ42 decline in non-demented elders
Dou, K., Initiative, A. D. N., Zhang, C. M., Tan, C., Xu, W., Li, J., Cao, X., Tan, L., & Yu, J. (2019) · Aging · doi:10.18632/aging.102125 · Cited by 6
Facing the Complexity of Alzheimer's Disease
Dallemagne, P., & Rochais, C. (2019) · Future Medicinal Chemistry · doi:10.4155/fmc-2019-0310 · Cited by 7
Pathway enrichment in genome‐wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes
Rosewood, T. J., Nho, K. T., Risacher, S. L., Liu, S., Gao, S., Shen, L., Foroud, T., Saykin, A. J., & Initiative, A. D. N. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14308 · Cited by 6
Randomized controlled trial of Bushen Yinao pill combined with conventional therapy on intestinal flora and cognitive function in older patients with Alzheimer's disease
Wang, W., Zhang, J., & Li, C. (2024) · The International Journal of Psychiatry in Medicine · doi:10.1177/00912174241290962 · Cited by 6
Characteristics of Severe Late-Life Depression in the Prodromal Phase of Neurodegenerative Dementia
Nishida, H., Takamiya, A., Kudo, S., Uchida, T., Yamagata, B., Bun, S., Uchida, H., Jessen, F., Mimura, M., & Hirano, J. (2024) · The American Journal of Geriatric Psychiatry Open Science Education and Practice · doi:10.1016/j.osep.2024.10.003 · Cited by 4
Optimization of solid-phase extraction (SPE) in the preparation of [18F]D3FSP: A new PET imaging agent for mapping Aβ plaques
Yao, X., Zha, Z., Zhao, R., Choi, S., Plöessl, K., Liu, F., Zhu, L., & Kung, H. F. (2019) · Nuclear Medicine and Biology · doi:10.1016/j.nucmedbio.2019.05.002 · Cited by 4
Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Methodology for Research and Clinical Brain PET Applications
Raman, F., Grandhi, S., Murchison, C., Kennedy, R., Landau, S. M., Roberson, E. D., McConathy, J., & Initiative, A. D. N. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190329 · Cited by 4
Endocytosis-pathway polygenic scores affects the hippocampal network connectivity and individualized identification across the high-risk of Alzheimer’s disease
Zhu, Y., Zang, F., Liu, X., Fan, D., Zhang, Q., Ren, Q., Xie, C., & Initiative, A. D. N. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00316-4 · Cited by 4
Fluid Biomarkers in Clinical Trials for Alzheimer’s Disease: Current and Future Application
Yang, J., Jia, L., Li, Y., Qiu, Q., Quan, M., & Jia, J. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201068 · Cited by 4
Predicting Brain Amyloid using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals from the ADNI and OASIS DatabasesPreprint
Wu, J., Dong, Q., Gui, J., Zhang, J., Su, Y., Chen, K., Thompson, P. M., Caselli, R. J., Reiman, E. M., Ye, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.16.343137 · Cited by 5
Perfusion Imaging and Inflammation Biomarkers Provide Complementary Information in Alzheimer’s Disease
Michopoulou, S. K., Prosser, A. M. J., Dickson, J. C., Guy, M., Teeling, J. L., & Kipps, C. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230726 · Cited by 3
Factors affecting participation in web-based Alzheimer's questionnaire surveys: Lessons from the Japanese trial-ready cohort
Nakashima, S., Sato, K., Niimi, Y., Toda, T., & Iwatsubo, T. (2025) · Journal of Aging Research and Lifestyle · doi:10.1016/j.jarlif.2025.100008 · Cited by 3
ATN profile classification across two independent prospective cohorts
Peretti, D. E., Ribaldi, F., Scheffler, M., Mu, L., Treyer, V., Gietl, A. F., Höck, C., Frisoni, G. B., & Garibotto, V. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1168470 · Cited by 4
Biomarkers for Alzheimer's Disease – An Overview
Tyagi, U., Tripathy, D. B., Gupta, A., Bisht, K., & Nain, A. (2024) · Macromolecular Symposia · doi:10.1002/masy.202200218 · Cited by 3
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction
Kim, Y., Choi, G., & Hwang, S. J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_50 · Cited by 3
Alzheimer’s Disease: Epidemiology, Etiology, Risk Factors, and Future Predictions
Jojo, G. M., Johnson, J., Kuppusamy, G., & Karri, V. V. S. R. (2024) · Springer eBooks · doi:10.1007/978-981-96-0259-9_3 · Cited by 3
Amyloid Imaging in Dementia and Neurodegenerative Disease
Ponisio, M. R., Iranpour, P., & Benzinger, T. L. S. (2021) · Springer eBooks · doi:10.1007/978-3-030-82367-2_11 · Cited by 4
Imaging-Based Biomarkers in Neurological Diseases-A Critical ReviewPreprint
Kale, M. B., Wankhede, N. L., Taksande, B. G., Upaganlawar, A., Umekar, M. J., Koppula, S., & Kopalli, S. R. (2023) · Preprints.org · doi:10.20944/preprints202309.0536.v1 · Cited by 2
Identifiability-Guided Assessment of Digital Twins in Alzheimer’s Disease Clinical Research and CarePreprint
Jiang, J., Petrella, J. R., Hao, W., & Initiative, T. A. D. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.17.670697 · Cited by 3

Authors: Cummings, J.
Affiliations: Cleveland Clinic (US)
Corresponding author: Cummings, Jeffrey
ORCID: Cummings, Jeffrey
Venue: Alzheimer's & Dementia
Publisher: Wiley
Published: 2018-06-21
Volume: 15
Issue: 1
Pages: 172-178
ISSN: 1552-5260 (ISSN-L)
1552-5279
DOI: 10.1016/j.jalz.2018.05.006
PMC: PMC6417790
OpenAlex: W2809499549
Cited by: 140
References: 54
Citations by year: 2026: 13 · 2025: 12 · 2024: 25 · 2023: 18 · 2022: 12 · 2021: 21 · 2020: 21 · 2019: 18
FWCI: 11.72
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Alzheimer s & Dementia (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Digital Scholarship - UNLV (University of Nevada Reno) (submittedVersion, cc-by-nc-nd)
Funders: National Institutes of Health
National Institute of General Medical Sciences (P20GM109025)
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: Alzheimer's disease
MeSH terms: Alzheimer Disease, Clinical Trials as Topic, Humans, United States, Biomarkers, National Institute on Aging (U.S.)

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

The National Institute on Aging—Alzheimer's Association Framework on Alzheimer's disease: Application to clinical trials

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks