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Shapes of the Trajectories of 5 Major Biomarkers of Alzheimer Disease
Open accessAbstract
OBJECTIVE: To characterize the shape of the trajectories of Alzheimer disease biomarkers as a function of Mini-Mental State Examination (MMSE) score. DESIGN AND SETTING: Longitudinal registries from the Mayo Clinic and the Alzheimer's Disease Neuroimaging Initiative. PATIENTS: Two different samples (n = 343 and n = 598) were created that spanned the cognitive spectrum from normal to Alzheimer disease dementia. Subgroup analyses were performed in members of both cohorts (n = 243 and n = 328) who were amyloid positive at baseline. MAIN OUTCOME MEASURES: The shape of biomarker trajectories as a function of MMSE score, adjusted for age, was modeled and described as baseline (cross-sectional) and within-subject longitudinal effects. Biomarkers evaluated were cerebrospinal fluid (CSF) Aβ42 and tau levels, amyloid and fluorodeoxyglucose positron emission tomography imaging, and structural magnetic resonance imaging. RESULTS: Baseline biomarker values generally worsened (ie, nonzero slope) with lower baseline MMSE score. Baseline hippocampal volume, amyloid positron emission tomography, and fluorodeoxyglucose positron emission tomography values plateaued (ie, nonlinear slope) with lower MMSE score in 1 or more analyses. Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE score. Nonconstant within-subject rates (deceleration) of biomarker change were found in only 1 model. CONCLUSIONS: Biomarker trajectory shapes by MMSE score were complex and were affected by interactions with age and APOE status. Nonlinearity was found in several baseline effects models. Nonconstant within-subject rates of biomarker change were found in only 1 model, likely owing to limited within-subject longitudinal follow-up. Creating reliable models that describe the full trajectories of Alzheimer disease biomarkers will require significant additional longitudinal data in individual participants.
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“So far, biomarker studies demonstrated the successive development of primary pathologies including amyloid-β and tau (as measured by biofluid and molecular PET markers) followed by alterations of medial temporal and parietal grey matter (as measured by volumetric MRI and FDG-PET) in both sporadic late-onset Alzheimer’s disease (Jack et al., 2012) and genetically-caused Alzheimer’s disease (Bateman”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Jack et al., 2012
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Self-citationHypothetical 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-citationFitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011) · Wiley Series in Probability and Statistics · doi:10.1002/9781119513469 · Cited by 4,639
Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound‐B
Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., Bergström, M., Savitcheva, I., Huang, G., Estrada, S., et al. (2004) · Annals of Neurology · doi:10.1002/ana.20009 · Cited by 4,496
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-citationCerebrospinal 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
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Research works printed but not linked: 2
- [10]Mintun MA Larossa GN Sheline YI [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease Neurology 2006 67 446 452 16894106
- [66]Sperling RA Aisen PS Beckett LA Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer Assocation Workgroup Alzheimers Dement 2011 PMC3220946 21514248
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Items 101–146 of 146
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Ibarra, R., Radanovic, M., Pais, M. V., Talib, L. L., & Forlenza, O. V. (2020) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988720944237 · Cited by 8
Change point analyses in prodromal Alzheimer’s disease
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Zhuo, J., Zhang, Y., Liu, Y., Liu, B., Zhou, X. A., Bartlett, P. F., Jiang, T., & Initiative, A. D. N. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab017 · Cited by 7
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Sanger, B., & Anderson, N. D. (2022) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbac073 · Cited by 6
Sacchi, L., Carandini, T., Fumagalli, G., Pietroboni, A. M., Contarino, V. E., Siggillino, S., Arcaro, M., Fenoglio, C., Zito, F., Marotta, G., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210593 · Cited by 6
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Dai, N., Kang, H., Jones, G. L., Fiecas, M. J. A., & Initiative, A. D. N. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11588 · Cited by 4
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Wischik, C. M., Storey, J. M. D., Wischik, D., & Harrington, C. R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802173-6.00015-0 · Cited by 3
You, Y., Ward, J., Ho, F. K. W., Lyall, D. M., & Hastie, C. E. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf350 · Cited by 2
Lopez, S., Hampel, H. J., Percio, C. D., Noce, G., Lizio, R., Teipel, S. J., Dyrba, M., González‐Escamilla, G., Bakardjian, H., Chiesa, P. A., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70438 · Cited by 2
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Mattsson, N., Grigoriou, S., & Zetterberg, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-72938-1_11 · Cited by 2
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Zhou, J., Qiu, Y., Chen, S., Liu, L., Liao, H., Chen, H., Lv, S., & Li, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.572350 · Cited by 2
Masters, C. L., Gu, B. J., Laws, S. M., Lim, Y. Y., Roberts, B. R., Villemagne, V. L., & Beyreuther, K. T. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811304-2.00003-1 · Cited by 1
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Marinescu, R. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04805 · Cited by 1
Downer, B. G. (2014) · UKnowledge (University of Kentucky)
Wharf, C., & London, E. (2014)
Advanced MRI Analysis for Computer-Aided Diagnosis of Dementia
Bron, E. E. (2016) · EUR Research Repository (Erasmus University Rotterdam)
Ramanan, V. K. (2014) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1951
Imaging Retinal Amyloid – The Virtual Brain Biopsy
Dow, C. T. (2016) · Journal of Molecular Imaging & Dynamics · doi:10.4172/2155-9937.1000125
Woodcock, J., Polhamus, D., Angersbach, B. S., Raghavan, N., Romano, G. J., Romero, K., Shaw, L. M. J., Corrigan, B., Gordon, M. F., Katz, R. G., et al. (2014)
Self-citationGenetic and lifestyle influences on memory, brain structure, and dementia
Ferencz, B. (2017) · Figshare
Mining high-level brain imaging genetic associations
Yao, X. (2018) · IUScholarWorks (Indiana University) · doi:10.7912/c2/903
Cerebrospinal Fluid Biomarkers in Alzheimer’s Disease and Frontotemporal Dementia
Mattsson, N., & Zetterberg, H. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6380-0_9
Hippocampal Subregions Volume and Texture for the Diagnosis of Mild Cognitive ImpairmentPreprint
Chu, T., Liu, Y., Zhang, Z., Zhang, G., Dong, F., Dong, J., & Lin, S. (2023) · Research Square · doi:10.21203/rs.3.rs-2182063/v2
Varghese, B. A., Gujar, S. A., Cen, S., Lei, X., Hwang, D., Barisano, G., & Sheikh‐Bahaei, N. (2024) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim62974.2024.10783512
Delmotte, K., Schaeverbeke, J. M., Poesen, K., & Vandenberghe, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14457018
Barthélemy, N. R., Bateman, R. J., Hirtz, C., Marin, P., Bécher, F., Sato, C., Gabelle, A., & Lehmann, S. (2020) · Figshare · doi:10.6084/m9.figshare.11998815
Barthélemy, N. R., Bateman, R. J., Hirtz, C., Marin, P., Bécher, F., Sato, C., Gabelle, A., & Lehmann, S. (2020) · Figshare · doi:10.6084/m9.figshare.11998812
Gao, K., 宋友军, Bao, J., Maes, M. H. J., Yao, D., Biswal, B. B., Wang, P., & Initiative, A. D. N. (2026) · medRxiv · doi:10.64898/2026.04.16.26350695
Frontal Release Signs and Future Decline in Research Participants With Intact Cognition
Bojarski, L., Jicha, G. A., Coskun, E. P., Schmitt, F. A., Eldik, L. J. V., & Abner, E. L. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.17060
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.05
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.references
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.07
Third Section: Design of a dimeric-nanobody specific for Aβ42 oligomers: the Dimeric-DesAb-O
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.06
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.03
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.04
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.02
Conclusion and Future Perspectives
Napolitano, L. (2026) · Firenze University Press eBooks · doi:10.36253/979-12-215-0993-9.08
Beyond gray matter: unveiling the critical role of white matter in Alzheimer's disease
Gao, K., Bao, J., 宋友军, Lin, C., Iturria‐Medina, Y., Biswal, B. B., & Wang, P. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111816
Metadata
- Authors
- Jack, C. R., Vemuri, P., Wiste, H. J., Weigand, S. D., Lesnick, T. G., Lowe, V., Kantarci, K., Bernstein, M. A., Senjem, M. L., Gunter, J. L., Boeve, B. F., Trojanowski, J. Q., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., Knopman, D. S., & Alzheimer's Disease Neuroimaging Initiative, F. T.
- Affiliations
- Mayo Clinic (US)
- University of Pennsylvania (US)
- University of California San Diego (US)
- University of California, San Francisco (US)
- Corresponding author
- Jack, Clifford R.
- ORCID
- Venue
- Archives of Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 2012-07-01
- Volume
- 69
- Issue
- 7
- Pages
- 856-67
- ISSN
- 0003-9942 (ISSN-L)
- PMC
- PMC3595157
- OpenAlex
- W2014117421
- Cited by
- 145
- References
- 66
- Citations by year
- 2026: 12 · 2025: 3 · 2024: 7 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 17 · 2019: 6 · 2018: 9 · 2017: 10 · 2016: 9 · 2015: 11 · 2014: 20 · 2013: 23 · 2012: 6
- FWCI
- 13.71
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Archives of Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Mayo Foundation for Medical Education and Research (AG16574)
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, R01AG11378, P50‐AG16574, U01AG06786, U01AG006786, R01AG011378, U19AG010483, 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
- Alzheimer's disease, biomarker trajectories, Mini-Mental State Examination, cerebrospinal fluid amyloid-beta 42, tau protein, amyloid PET, FDG-PET, hippocampal volume, apolipoprotein E, cognitive decline, amyloid-positive
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Amyloid, Aniline Compounds, Cognition Disorders, Cross-Sectional Studies, Female, Hippocampus, Humans, Immunoassay, Magnetic Resonance Imaging, Male, Mental Status Schedule, Peptide Fragments, Thiazoles, Cohort Studies, Biomarkers, Amyloid beta-Peptides, tau Proteins, Nonlinear Dynamics, Fluorodeoxyglucose F18, Positron-Emission Tomography, Apolipoprotein E4
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