The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
Mild cognitive impairment due to Alzheimer disease in the community
Open accessAbstract
Objective: The newly proposed National Institute on Aging–Alzheimer's Association (NIA‐AA) criteria for mild cognitive impairment (MCI) due to Alzheimer disease (AD) suggest a combination of clinical features and biomarker measures, but their performance in the community is not known. Methods: The Mayo Clinic Study of Aging (MCSA) is a population‐based longitudinal study of nondemented subjects in Olmsted County, Minnesota. A sample of 154 MCI subjects from the MCSA was compared to a sample of 58 amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI‐1) to assess the applicability of the criteria in both settings and to assess their outcomes. Results: Fourteen percent of MCSA and 16% of ADNI‐1 of subjects were biomarker negative. In addition, 14% of MCSA and 12% of ADNI‐1 subjects had evidence for amyloid deposition only, whereas 43% of MCSA and 55% of ADNI‐1 subjects had evidence for amyloid deposition plus neurodegeneration (magnetic resonance imaging atrophy, fluorodeoxyglucose positron emission tomography hypometabolism, or both). However, a considerable number of subjects had biomarkers inconsistent with the proposed AD model; for example, 29% of MCSA subjects and 17% of ADNI‐1 subjects had evidence for neurodegeneration without amyloid deposition. These subjects may not be on an AD pathway. Neurodegeneration appears to be a key factor in predicting progression relative to amyloid deposition alone. Interpretation: The NIA‐AA criteria apply to most MCI subjects in both the community and clinical trials settings; however, a sizeable proportion of subjects had conflicting biomarkers, which may be very important and need to be explored. Ann Neurol 2013;74:199–208
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The rate of cognitive decline is significantly greater for cognitively impaired and CU individuals who have abnormalities in both an amyloid biomarker and a second biomarker type (which could be CSF T-tau or P-tau, atrophy, or hypometabolism) in comparison to individuals who have neither or only one of these biomarker abnormalities [30–35,39,40,42–45].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [39]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the combination of an abnormal MRI, CSF T-tau, or FDG PET study with an abnormal amyloid biomarker provides much more powerful prediction of future cognitive decline [30–35,39,40,42–45] than an abnormal amyloid study alone.”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [39]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“studies assessing biomarkers for Alzheimer's disease with PET scans have reported that about half of people with MCI have Alzheimer's-related brain changes.230,231”
From 2023 Alzheimer's disease facts and figures (2023) · cites this paper as [230]
The cited source says what the claim attributes to it.
In the citing paper
“Meanwhile, studies assessing biomarkers for Alzheimer's disease with PET scans have reported that about half of people with MCI have Alzheimer's‐related brain changes.”
From 2024 Alzheimer's disease facts and figures (2024) · cites this paper as [246]
In the citing paper
“MCI with negative biomarkers versus 1 or more AD pathology biomarkers (ie, AD pattern in CSF, high amyloid load in amyloid PET) or biomarkers of neurodegeneration (eg, AD pattern on structural MRI or fluorodeoxyglucose [FDG]-PET; high CSF tau and phospho-tau levels) has a 2 to 3 relative risk of progression to dementia (8% vs 17%-22% per year 33 ).”
From The Alzheimer’s Disease Clinical Spectrum (2019) · cites this paper as [33]
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
Diagnostic and Statistical Manual of Mental Disorders
(2012) · Springer eBooks · doi:10.1007/springerreference_121117 · Cited by 20,772
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
Self-citationAlbert, 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
Self-citationThe Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
Reitan, R. M. (1958) · Perceptual and Motor Skills · doi:10.2466/pms.1958.8.3.271 · Cited by 8,167
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864
Self-citationMeasuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,995
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806
Self-citationJack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396
Self-citationKaplan, E., Goodglass, H., Weıntraub, S., & Segal, O. (2001) · Medical Entomology and Zoology · Cited by 4,378
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-citationWechsler Adult Intelligence Scale-III
Ryan, J. J., & Lopez, S. J. (2001) · Springer eBooks · doi:10.1007/978-1-4615-1185-4_2 · Cited by 3,680
L’examen Clinique en Psychologie
Rubé, P. (1959) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1959.13.4.989 · Cited by 3,299
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,643
Self-citationReynolds, C. R., & Powel, J. (1988) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/3.4.397 · Cited by 2,123
Cerebrospinal 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-citationMild Cognitive Impairment: Ten Years Later
Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., & Jack, C. R. (2009) · Archives of Neurology · doi:10.1001/archneurol.2009.266 · Cited by 1,447
Self-citationPetersen, R. C. (2011) · New England Journal of Medicine · doi:10.1056/nejmcp0910237 · Cited by 1,289
Self-citationCSF Biomarkers and Incipient Alzheimer Disease in Patients With Mild Cognitive Impairment
Mattsson, N. (2009) · JAMA · doi:10.1001/jama.2009.1064 · Cited by 1,176
Synthesis and Evaluation of 11C-Labeled 6-Substituted 2-Arylbenzothiazoles as Amyloid Imaging Agents
Mathis, C. A., Wang, Y., Holt, D. P., Huang, G.-F., Debnath, M. L., & Klunk, W. E. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030026b · Cited by 976
Jack, C. R., Lowe, V. J., Senjem, M. L., Weigand, S. D., Kemp, B. J., Shiung, M. M., Knopman, D. S., Boeve, B. F., Klunk, W. E., Mathis, C. A., et al. (2008) · Brain · doi:10.1093/brain/awm336 · Cited by 958
Self-citationRoberts, R. O., Geda, Y. E., Knopman, D. S., Cha, R. H., Pankratz, V. S., Boeve, B. F., Ivnik, R. J., Tangalos, E. G., Petersen, R. C., & Rocca, W. A. (2008) · Neuroepidemiology · doi:10.1159/000115751 · Cited by 797
Self-citationRocca, W., Yawn, B. P., Sauver, J. S. S., Grossardt, B., & Melton, L. (2012) · Mayo Clinic proceedings · doi:10.1016/j.mayocp.2012.08.012 · Cited by 775
Self-citationHippocampal and entorhinal atrophy in mild cognitive impairment
Devanand, D. P., Pradhaban, G., Liu, X., Khandji, A., Santi, S. D., Segal, S., Rusinek, H., Pelton, G. H., Honig, L. S., Mayeux, R. P., et al. (2007) · Neurology · doi:10.1212/01.wnl.0000256697.20968.d7 · Cited by 749
Sauver, J. L. S., Grossardt, B. R., Leibson, C. L., Yawn, B. P., Melton, L. J., & Rocca, W. A. (2012) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2011.11.009 · Cited by 652
Self-citationSauver, J. L. S., Grossardt, B. R., Yawn, B. P., Melton, L. J., & Rocca, W. A. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq482 · Cited by 650
Self-citationComparing predictors of conversion and decline in mild cognitive impairment
Landau, S. M., Harvey, D., Madison, C. M., Reiman, E. M., Foster, N. L., Aisen, P. S., Petersen, R. C., Shaw, L. M., Trojanowski, J. Q., Jack, C. R., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181e8e8b8 · Cited by 616
Self-citationJack, C. R., Knopman, D. S., Weigand, S. D., Wiste, H. J., Vemuri, P., Lowe, V., Kantarci, K., Gunter, J. L., Senjem, M. L., Ivnik, R. J., et al. (2012) · Annals of Neurology · doi:10.1002/ana.22628 · Cited by 602
Self-citationIvnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G., & Petersen, R. C. (1996) · The Clinical Neuropsychologist · doi:10.1080/13854049608406689 · Cited by 547
Self-citationLopresti, B. J., Klunk, W. E., Mathis, C. A., Hoge, J. A., Ziolko, S. K., Lu, X., MELTZER, C. C., Schimmel, K., Tsopelas, N. D., DeKosky, S. T., et al. (2005) · PubMed · Cited by 536
Jack, C. R., Wiste, H. J., Vemuri, P., Weigand, S. D., Senjem, M. L., Zeng, G., Bernstein, M. A., Gunter, J. L., Pankratz, V. S., Aisen, P. S., et al. (2010) · Brain · doi:10.1093/brain/awq277 · Cited by 531
Self-citationNeuropathologic Substrate of Mild Cognitive Impairment
Markesbery, W. R., Schmitt, F. A., Kryscio, R. J., Davis, D. G., Smith, C. D., & Wekstein, D. R. (2006) · Archives of Neurology · doi:10.1001/archneur.63.1.38 · Cited by 460
Relationships between biomarkers in aging and dementia
Jagust, W. J., Landau, S. M., Shaw, L. M., Trojanowski, J. Q., Koeppe, R. A., Reiman, E. M., Foster, N. L., Petersen, R. C., Weiner, M. W., Price, J. C., et al. (2009) · Neurology · doi:10.1212/wnl.0b013e3181bc010c · Cited by 456
Self-citationMayo's older americans normative studies: WAIS-R norms for ages 56 to 97
Ivnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G., Petersen, R. C., Kokmen, E., & Kurland, L. T. (1992) · The Clinical Neuropsychologist · doi:10.1080/13854049208401877 · Cited by 446
Self-citationMRI and CSF biomarkers in normal, MCI, and AD subjects
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009) · Neurology · doi:10.1212/wnl.0b013e3181af79fb · Cited by 406
Self-citationNeuropathologic Outcome of Mild Cognitive Impairment Following Progression to Clinical Dementia
Jicha, G. A., Parisi, J. E., Dickson, D. W., Johnson, K., Cha, R., Ivnik, R. J., Tangalos, E. G., Boeve, B. F., Knopman, D. S., Braak, H., et al. (2006) · Archives of Neurology · doi:10.1001/archneur.63.5.674 · Cited by 399
Self-citationMRI correlates of neurofibrillary tangle pathology at autopsy: A voxel-based morphometry study
Whitwell, J. L., Josephs, K. A., Murray, M. E., Kantarci, K., Przybelski, S. A., Weigand, S. D., Vemuri, P., Senjem, M. L., Parisi, J. E., Knopman, D. S., et al. (2008) · Neurology · doi:10.1212/01.wnl.0000324924.91351.7d · Cited by 376
Self-citationAssociation of Lifetime Cognitive Engagement and Low β-Amyloid Deposition
Marks, S. M. (2012) · Archives of Neurology · doi:10.1001/archneurol.2011.2748 · Cited by 359
Mayo's older Americans normative studies: updated AVLT norms for ages 56 to 97
Ivnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G., Petersen, R. C., Kokmen, E., & Kurland, L. T. (1992) · Clinical Neuropsychologist · doi:10.1080/13854049208401880 · Cited by 310
Self-citationShort-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease
Knopman, D. S., Jack, C. R., Wiste, H. J., Weigand, S. D., Vemuri, P., Lowe, V. J., Kantarci, K., Gunter, J. L., Senjem, M. L., Ivnik, R. J., et al. (2012) · Neurology · doi:10.1212/wnl.0b013e3182563bbe · Cited by 252
Self-citationLeung, K. K., Barnes, J., Ridgway, G. R., Bartlett, J. W., Clarkson, M. J., Macdonald, K. E., Schuff, N. W., Fox, N. C., & Sebastien Ourselin, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.018 · Cited by 244
Evidence for Ordering of Alzheimer Disease Biomarkers
Jack, C. R. (2011) · Archives of Neurology · doi:10.1001/archneurol.2011.183 · Cited by 225
Self-citationMRI‐Based Hippocampal Volume Measurements in Epilepsy
Jack, C. R. (1994) · Epilepsia · doi:10.1111/j.1528-1157.1994.tb05986.x · Cited by 224
Self-citationPredicting MCI outcome with clinically available MRI and CSF biomarkers
Heister, D. S., Brewer, J. B., Magda, S., Blennow, K., McEvoy, L. K., & Initiative, A. D. N. (2011) · Neurology · doi:10.1212/wnl.0b013e3182343314 · Cited by 206
Comparison of 18F-FDG and PiB PET in Cognitive Impairment
Lowe, V. J., Kemp, B. J., Jack, C. R., Senjem, M. L., Weigand, S. D., Shiung, M. S., Smith, G. E., Knopman, D. S., Boeve, B. F., Mullan, B. P., et al. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.058529 · Cited by 196
Self-citationVemuri, P., Whitwell, J. L., Kantarci, K., Josephs, K. A., Parisi, J. E., Shiung, M. S., Knopman, D. S., Boeve, B. F., Petersen, R. C., Dickson, D. W., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.012 · Cited by 172
Self-citationCognition and beta-amyloid in preclinical Alzheimer's disease: Data from the AIBL study
Pike, K. E., Ellis, K. A., Villemagne, V. L., Good, N. M., Chételat, G., Ames, D. J., Szoeke, C. E. I., Laws, S. M., Verdile, G., Martins, R. N., et al. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.04.012 · Cited by 161
Automated MRI measures predict progression to Alzheimer's disease
Desikan, R. S., Cabral, H., Settecase, F., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Salat, D. H., & Fischl, B. R. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.023 · Cited by 112
Self-citationMethodological considerations for measuring rates of brain atrophy
Gunter, J. L., Shiung, M. M., Manduca, A., & Jack, C. R. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10325 · Cited by 88
Self-citationAmyloid imaging in the prediction of Alzheimer-type dementia in subjects with amnestic MCI
Visser, P. J., & Knopman, D. S. (2009) · Neurology · doi:10.1212/wnl.0b013e3181b61b5d · Cited by 14
Self-citation
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
Self-citation2023 Alzheimer's disease facts and figures
(2023) · Alzheimer's & Dementia · doi:10.1002/alz.13016 · Cited by 3,212
Knopman, D. S., Amieva, H., Petersen, R. C., Chételat, G., Holtzman, D. M., Hyman, B. T., Nixon, R. A., & Jones, D. T. (2021) · Nature Reviews Disease Primers · doi:10.1038/s41572-021-00269-y · Cited by 2,606
Self-citation2024 Alzheimer's disease facts and figures
(2024) · Alzheimer's & Dementia · doi:10.1002/alz.13809 · Cited by 2,536
2022 Alzheimer's disease facts and figures
Anonymous (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12638 · Cited by 2,469
Metadata
- Authors
- Petersen, R. C., Aisen, P., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Knopman, D. S., Mielke, M., Pankratz, V. S., Roberts, R., Rocca, W. A., Weigand, S., Weiner, M., Wiste, H., & Jack, C. R.
- Affiliations
- Mayo Clinic (US)
- University of California San Diego (US)
- University of California, San Francisco (US)
- Corresponding author
- Petersen, Ronald C.
- ORCID
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2013-08
- Volume
- 74
- Issue
- 2
- Pages
- 199-208
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- PMC
- PMC3804562
- OpenAlex
- W1559001474
- Cited by
- 265
- References
- 51
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 15 · 2023: 13 · 2022: 16 · 2021: 21 · 2020: 18 · 2019: 31 · 2018: 25 · 2017: 26 · 2016: 28 · 2015: 25 · 2014: 18 · 2013: 7
- FWCI
- 18.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annals of Neurology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (5u01ag024904-10)
- Canadian Institutes of Health Research
- National Institute on Aging (U01-AG-024904, R01AG034676, P30AG010129, U01AG006786, K01AG030514, R01AG011378, R01AG041851, P50AG016574)
- 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 › Oncology › Cancer-related cognitive impairment studies
- Keywords
- mild cognitive impairment, Alzheimer's disease, NIA-AA criteria, amyloid deposition, neurodegeneration, biomarkers, amyloid PET, FDG-PET hypometabolism, brain atrophy, conversion to dementia, Mayo Clinic Study of Aging, non-demented elderly
- MeSH terms
- Aged, Aged, 80 and over, Aging, Alzheimer Disease, Amyloid, Female, Humans, Male, Neuropsychological Tests, Random Allocation, Residence Characteristics, Biomarkers, 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
