Association Between Elevated Brain Amyloid and Subsequent Cognitive Decline Among Cognitively Normal Persons
Open accessAbstract
Importance: Among cognitively normal individuals, elevated brain amyloid (defined by cerebrospinal fluid assays or positron emission tomography regional summaries) can be related to risk for later Alzheimer-related cognitive decline. Objective: To characterize and quantify the risk for Alzheimer-related cognitive decline among cognitively normal individuals with elevated brain amyloid. Design, Setting, and Participants: Exploratory analyses were conducted with longitudinal cognitive and biomarker data from 445 cognitively normal individuals in the United States and Canada. Participants were observed from August 23, 2005, to June 7, 2016, for a median of 3.1 years (interquartile range, 2.0-4.2 years; maximum follow-up, 10.3 years) as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Exposures: Individuals were classified at baseline as having normal (n = 243) or elevated (n = 202) brain amyloid using positron emission tomography amyloid imaging or a cerebrospinal fluid assay of amyloid β. Main Outcomes and Measures: Outcomes included scores on the Preclinical Alzheimer Cognitive Composite (PACC; a sum of 4 baseline standardized z scores, which decreases with worse performance), Mini-Mental State Examination (MMSE; 0 [worst] to 30 [best] points), Clinical Dementia Rating Sum of Boxes (CDR-Sum of Boxes; 0 [best] to 18 [worst] points), and Logical Memory Delayed Recall (0 [worst] to 25 [best] story units). Results: Among the 445 participants (243 with normal amyloid, 202 with elevated amyloid), mean (SD) age was 74.0 (5.9) years, mean education was 16.4 (2.7) years, and 52% were women. The mean score for PACC at baseline was 0.00 (2.60); for MMSE, 29.0 (1.2); for CDR-Sum of Boxes, 0.04 (0.14); and for Logical Memory Delayed Recall, 13.1 (3.3). Compared with the group with normal amyloid, those with elevated amyloid had worse mean scores at 4 years on the PACC (mean difference, 1.51 points [95% CI, 0.94-2.10]; P < .001), MMSE (mean difference, 0.56 points [95% CI, 0.32-0.80]; P < .001), and CDR-Sum of Boxes (mean difference, 0.23 points [95% CI, 0.08-0.38]; P = .002). For Logical Memory Delayed Recall, between-group score was not statistically significant at 4 years (mean difference, 0.73 story units [95% CI, -0.02 to 1.48]; P = .056). Conclusions and Relevance: Exploratory analyses of a cognitively normal cohort followed up for a median of 3.1 years suggest that elevation in baseline brain amyloid level, compared with normal brain amyloid level, was associated with higher likelihood of cognitive decline, although the findings are of uncertain clinical significance. Further research is needed to assess the clinical importance of these differences and measure longer-term associations.
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PET and CSF can identify cognitively unimpaired individuals at high risk of future cognitive decline and progression to AD dementia.
“Additionally, PET and CSF can identify cognitively unimpaired individuals at high risk of future cognitive decline and progression to AD dementia14,15.”
From Highly accurate blood test for Alzheimer's disease is similar or superior to clinical cerebrospinal fluid tests (2024) · cites this paper as [14]
The Methods and Discussion (page numbers unavailable) show that PET or CSF amyloid measures identified cognitively normal people more likely to experience subsequent cognitive decline. The measured progression outcomes were cognitive symptoms and MCI, not AD dementia; the Discussion says predictive value for the dementia stage requires further follow-up. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCognitively unimpaired individuals with abnormal amyloid biomarkers have more rapid progression of atrophy, hypometabolism, and clinical/cognitive decline than individuals without biomarker evidence of Aβ deposition.
“Numerous studies have shown that CU individuals with abnormal amyloid biomarkers have more rapid progression of atrophy, hypometabolism, and clinical/cognitive decline than individuals without biomarker evidence of Aβ deposition [13,33,80,93–99]”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [97]
The Discussion (page not available in our copy) reports greater ventricular enlargement, indicating faster atrophy, and more cognitive and clinical decline among participants with elevated amyloid. The Methods > Outcomes section says FDG-PET measured brain glucose metabolism, but the retrieved passages do not report faster progression of hypometabolism in that group. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cites
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
<b>mice</b>: Multivariate Imputation by Chained Equations in<i>R</i>
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationA mutation in APP protects against Alzheimer’s disease and age-related cognitive decline
Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., Stefansson, H., Sulem, P., Gudbjartsson, D., Maloney, J., et al. (2012) · Nature · doi:10.1038/nature11283 · Cited by 1,738 · see citing works
Natural history of mild cognitive impairment in older persons
Bennett, D. A., Wilson, R. S., Schneider, J. A., Evans, D. A., Beckett, L. A., Aggarwal, N. T., Barnes, L. L., Fox, J. H., & Bach, J. (2002) · Neurology · doi:10.1212/wnl.59.2.198 · Cited by 1,037 · see citing works
Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study
Vos, S. J., Xiong, C., Visser, P. J., Jasielec, M. S., Hassenstab, J., Grant, E. A., Cairns, N. J., Morris, J. C., Holtzman, D. M., & Fagan, A. M. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70194-7 · Cited by 564 · see citing works
Apolipoprotein E in Alzheimer's Disease: An Update
Yu, J.-T., Tan, L., & Hardy, J. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014300 · Cited by 394 · see citing works
Flagged first, then by citations
Research works printed but not linked: 23
- [1]Dubois B Albert ML Amnestic MCI or prodromal Alzheimer’s disease? Lancet Neurol 2004 3 4 246 248 15039037
- [2]Rowe CC Ellis KA Rimajova M Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging Neurobiol Aging 2010 31 8 1275 1283 20472326
- [3]Sperling RA Aisen PS Beckett LA Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease Alzheimers Dement 2011 7 3 280 292 21514248 PMC3220946
- [4]Braak H Braak E Frequency of stages of Alzheimer-related lesions in different age categories Neurobiol Aging 1997 18 4 351 357 9330961
- [5]Davies L Wolska B Hilbich C A4 amyloid protein deposition and the diagnosis of Alzheimer’s disease Neurology 1988 38 11 1688 1693 3054625
- [6]Vos SJ Xiong C Visser PJ Preclinical Alzheimer’s disease and its outcome Lancet Neurol 2013 12 10 957 965 24012374 PMC3904678
- [7]Jack CR Jr Therneau TM Wiste HJ Transition rates between amyloid and neurodegeneration biomarker states and to dementia Lancet Neurol 2016 15 1 56 64 26597325 PMC4784263
- [8]Insel PS Donohue MC Mackin RS Cognitive and functional changes associated with Aβ pathology and the progression to mild cognitive impairment Neurobiol Aging 2016 48 172 181 27710807
- [9]Sperling RA Rentz DM Johnson KA The A4 study: stopping AD before symptoms begin? Sci Transl Med 2014 6 228 228fs13 PMC4049292 24648338
- [10]Weiner MW Veitch DP Aisen PS Impact of the Alzheimer’s Disease Neuroimaging Initiative, 2004 to 2014 Alzheimers Dement 2015 11 7 865 884 26194320 PMC4659407
- [11]Landau SM Breault C Joshi AD Amyloid-β imaging with Pittsburgh compound B and florbetapir J Nucl Med 2013 54 1 70 77 23166389 PMC3747730
- [12]Folstein MF Folstein SE McHugh PR “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician J Psychiatr Res 1975 12 3 189 198 1202204
- [13]Morris JC The Clinical Dementia Rating (CDR): current version and scoring rules Neurology 1993 43 11 2412 2414 8232972
- [15]Donohue MC Sperling RA Salmon DP The preclinical Alzheimer cognitive composite JAMA Neurol 2014 71 8 961 970 24886908 PMC4439182
- [17]Dale AM Fischl B Sereno MI Cortical surface-based analysis I: segmentation and surface reconstruction Neuroimage 1999 9 2 179 194 9931268
- [18]Reitan RM Wolfson D The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation 2nd Tucson, AZ Neuropsychology Press 1993
- [20]Breslow NE Clayton DG Approximate inference in generalized linear mixed models J Am Stat Assoc 1993 88 421 9 25 10.1080/01621459.1993.10594284 · DOI
- [21]Laird NM Ware JH Random-effects models for longitudinal data Biometrics 1982 38 4 963 974 7168798
- [22]Akaike H Information Theory and an Extension of the Maximum Likelihood Principle Parzen E Tanabe K Kitagawa G Selected Papers of Hirotugu Akaike New York, NY Springer 1998 199 213
- [25]Hardy J Selkoe DJ The amyloid hypothesis of Alzheimer’s disease Science 2002 297 5580 353 356 12130773
- [26]Yu JT Tan L Hardy J Apolipoprotein E in Alzheimer’s disease Annu Rev Neurosci 2014 37 79 100 24821312
- [27]Mormino EC Betensky RA Hedden T Amyloid and APOE ε4 interact to influence short-term decline in preclinical Alzheimer disease Neurology 2014 82 20 1760 1767 24748674 PMC4035706
- [28]Kozauer N Katz R Regulatory innovation and drug development for early-stage Alzheimer’s disease N Engl J Med 2013 368 13 1169 1171 23484795
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High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis
Schindler, S. E., Bollinger, J. G., Ovod, V., Mawuenyega, K. G., Li, Y., Gordon, B. A., Holtzman, D. M., Morris, J. C., Benzinger, T. L. S., Xiong, C., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000008081 · Cited by 885 · see citing works
Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study
Hanseeuw, B. J., Betensky, R. A., Jacobs, H. I. L., Schultz, A. P., Sepulcre, J., Becker, J. A., Cosio, D. M. O., Farrell, M., Quiroz, Y. T., Mormino, E. C., et al. (2019) · JAMA Neurology · doi:10.1001/jamaneurol.2019.1424 · Cited by 865 · see citing works
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The impact of amyloid‐beta and tau on prospective cognitive decline in older individuals
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Self-citationRoberts, R. O., Aakre, J. A., Kremers, W. K., Vassilaki, M., Knopman, D. S., Mielke, M. M., Alhurani, R., Geda, Y. E., Machulda, M. M., Coloma, P., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.0629 · Cited by 223 · see citing works
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Cummings, J. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.05.006 · Cited by 140 · see citing works
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Insel, P. S., Weiner, M., Mackin, R. S., Mormino, E., Lim, Y. Y., Stomrud, E., Palmqvist, S., Masters, C. L., Maruff, P. T., Hansson, O., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000007831 · Cited by 128 · see citing works
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Self-citationTimmers, M., Streffer, J. R., Russu, A., Tominaga, Y., Shimizu, H., Shiraishi, A., Tatikola, K., Smekens, P., Börjesson-Hanson, A., Andreasen, N., et al. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-018-0415-6 · Cited by 112 · see citing works
Staffaroni, A., Elahi, F., McDermott, D., Marton, K., Karageorgiou, E., Sacco, S., Paoletti, M., Caverzasi, E., Hess, C., Rosen, H., et al. (2017) · Seminars in Neurology · doi:10.1055/s-0037-1608808 · Cited by 91 · see citing works
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“Differences, however, may be pronounced when comparing the trajectories during follow-up in the study between the groups of those that have and those that have not shown elevated levels of A β at baseline [37].”
Mattsson, N., Groot, C., Jansen, W. J., Landau, S. M., Villemagne, V. L., Engelborghs, S., Mintun, M. M., Lleo, A., Molinuevo, J. L., Jagust, W. J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.009 · Cited by 88 · see citing works
Hampel, H., Shaw, L. M., Aisen, P., Chen, C., Lleó, A., Iwatsubo, T., Iwata, A., Yamada, M., Ikeuchi, T., Jia, J., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12372 · Cited by 77 · see citing works
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Assunção, S. S., Sperling, R. A., Ritchie, C., Kerwin, D. R., Aisen, P. S., Lansdall, C., Atri, A., & Cummings, J. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-00984-y · Cited by 71 · see citing works
Self-citationFantoni, E. R., Chalkidou, A., O’ Brien, J. T., Farrar, G., & Hammers, A. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-171093 · Cited by 68 · see citing works
Palmqvist, S., Insel, P. S., Zetterberg, H., Blennow, K., Brix, B., Stomrud, E., Initiative, T. A. D. N., study, T. S. B., Mattsson, N., & Hansson, O. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.08.014 · Cited by 65 · see citing works
Amyloid PET and cognitive decline in cognitively normal individuals: the SCIENCe project
Timmers, T., Ossenkoppele, R., Verfaillie, S. C. J., van der Weijden, C. W. J., Slot, R. E. R., Wesselman, L. M. P., Windhorst, A. D., Wolters, E. E., Yaqub, M., Prins, N. D., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.02.020 · Cited by 63 · see citing works
Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults
ten Kate, M., Visser, P. J., Bakardjian, H., Barkhof, F., Sikkes, S. A. M., van der Flier, W. M., Scheltens, P., Hampel, H., Habert, M.-O., Dubois, B., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00067 · Cited by 41 · see citing works
Panza, F., Seripa, D., Lozupone, M., Solfrizzi, V., Imbimbo, B. P., Barulli, M. R., Tortelli, R., Capozzo, R., Bisceglia, P., Dimitri, A., et al. (2018) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2018.1389885 · Cited by 41 · see citing works
Alzheimer’s Disease Clinical Trials: Moving Toward Successful Prevention
Rafii, M. S., & Aisen, P. S. (2019) · CNS Drugs · doi:10.1007/s40263-018-0598-1 · Cited by 36 · see citing works
Self-citationJagust, W. J., Koeppe, R. A., Rabinovici, G. D., Villemagne, V. L., Harrison, T. M., Landau, S. M., & Initiative, T. A. D. N. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14165 · Cited by 34 · see citing works
Dang, C., Harrington, K. D., Lim, Y. Y., Ames, D., Hassenstab, J., Laws, S. M., Yassi, N., Hickey, M., Rainey-Smith, S., Robertson, J., et al. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-180507 · Cited by 31 · see citing works
Biomarker-based signature of alzheimer's disease in pre-MCI individuals
Chipi, E., Salvadori, N., Farotti, L., & Parnetti, L. (2019) · Brain Sciences · doi:10.3390/brainsci9090213 · Cited by 27 · see citing works
- Authors
- Donohue, M. C., Sperling, R. A., Petersen, R., Sun, C.-K., Weiner, M. W., Aisen, P. S., & Initiative, F. T. A. D. N.
- Affiliations
- University of Southern California (US)
- Brigham and Women's Hospital (US)
- Mayo Clinic (US)
- San Francisco VA Medical Center (US)
- Corresponding author
- Donohue, Michael C.
- ORCID
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2017-06-13
- Volume
- 317
- Issue
- 22
- Pages
- 2305
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC5736301
- OpenAlex
- W2626001339
- Cited by
- 452
- References
- 10
- Citations by year
- 2026: 33 · 2025: 58 · 2024: 59 · 2023: 42 · 2022: 43 · 2021: 45 · 2020: 54 · 2019: 50 · 2018: 57 · 2017: 10
- FWCI
- 38.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
- Alzheimer's Association (U01 AG024904, BAND-14-338179, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Weston Brain Institute
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, AG024904)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- National Institute on Aging (U01-AG-024904, W81XWH-12-2–0012, AG 024904, R01AG049750, K24AG035007, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- brain amyloid, cognitive decline, Alzheimer's disease, cognitively normal older adults, amyloid PET, cerebrospinal fluid amyloid-beta, preclinical Alzheimer's disease, risk prediction, longitudinal cognitive change, ADNI cohort, Clinical Dementia Rating, Mini-Mental State Examination, amyloid biomarkers
- MeSH terms
- Positron Emission Tomography Computed Tomography, Aged, Alzheimer Disease, Brain, Canada, Cognition, Female, Follow-Up Studies, Humans, Male, Mental Status Schedule, Mental Recall, Time Factors, United States, Biomarkers, Amyloid beta-Peptides
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 ›
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