The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Longitudinal Change of Biomarkers in Cognitive Decline
Open accessAbstract
OBJECTIVE: To delineate the trajectories of Aβ42 level in cerebrospinal fluid (CSF), fludeoxyglucose F18 (FDG) uptake using positron emission tomography, and hippocampal volume using magnetic resonance imaging and their relative associations with cognitive change at different stages in aging and Alzheimer disease (AD). DESIGN: Cohort study. SETTING: The 59 study sites for the Alzheimer's Disease Neuroimaging Initiative. PARTICIPANTS: A total of 819 participants 55 to 90 years of age with normal cognition, mild cognitive impairment, and AD who were followed up during the period from 2005 to 2007. MAIN OUTCOME MEASURES: Rates of change in level of Aβ42 in CSF, FDG uptake, hippocampal volume, and the Alzheimer Disease's Assessment Scale-cognitive subscale score during up to 36 months of follow-up by diagnostic group as well as prediction of cognitive change by each biomarker. RESULTS: Reductions in the level of Aβ42 in CSF were numerically greater in participants with normal cognition than in participants with mild cognitive impairment or AD; whereas both glucose metabolic decline and hippocampal atrophy were significantly slower in participants with normal cognition than in participants with mild cognitive impairment or AD. Positive APOE4 status accelerated hippocampal atrophic changes in participants with mild cognitive impairment or AD, but did not modify rates of change in level of Aβ42 in CSF or FDG uptake. The Alzheimer Disease's Assessment Scale-cognitive subscale scores were related only to the baseline level of Aβ42 in CSF and the baseline FDG uptake in participants with normal cognition, which were about equally associated with change in FDG uptake and hippocampal volume in participants with mild cognitive impairment and best modeled by change in FDG uptake in participants with AD. CONCLUSION: Trajectories of Aβ42 level in CSF, FDG uptake, and hippocampal volume vary across different cognitive stages. The longitudinal patterns support a hypothetical sequence of AD pathology in which amyloid deposition is an early event before hypometabolism or hippocampal atrophy, suggesting that biomarker prediction for cognitive change is stage dependent.
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Cites
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
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
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181cb3e25 · Cited by 2,572
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“and early conversion to dementia in mild cognitive impairment (MCI) in previous studies.(10-13) Several longitudinal FDG-PET studies also suggested that regional hypometabolism predicted clinical progression or conversion to AD.”
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Longitudinal Modeling of Age-Related Memory Decline and the APOE ε4 Effect
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Apolipoprotein E, Dementia, and Cortical Deposition of β-Amyloid Protein
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Cited by
Jack, 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-citationAdvancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321
Biomarker Modeling of Alzheimer’s Disease
Jack, C. R., & Holtzman, D. M. (2013) · Neuron · doi:10.1016/j.neuron.2013.12.003 · Cited by 1,002
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Liu, E., et al. (2013) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,010
Jack, 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
Metadata
- Authors
- Lo, R. Y.
- Venue
- Archives of Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 2011-10-01
- Volume
- 68
- Issue
- 10
- Pages
- 1257
- ISSN
- 0003-9942 (ISSN-L)
- OpenAlex
- W1982881439
- Cited by
- 189
- References
- 12
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- green
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