The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Blood-Based Protein Biomarkers for Diagnosis of Alzheimer Disease
Open accessAbstract
OBJECTIVE: To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). DESIGN: Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. SETTING: General community-based, prospective, longitudinal study of aging. PARTICIPANTS: A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. RESULTS: A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. CONCLUSIONS: This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.
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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
Cortisol levels during human aging predict hippocampal atrophy and memory deficits
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CSF 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
Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins
Ray, S., Britschgi, M., Herbert, C., Takeda-Uchimura, Y., Boxer, A., Blennow, K., Friedman, L. F., Galasko, D. R., Jutel, M., Karydas, A., et al. (2007) · Nature Medicine · doi:10.1038/nm1653 · Cited by 1,048
Plasma and Cerebrospinal Fluid Levels of Amyloid β Proteins 1-40 and 1-42 in Alzheimer Disease
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Decreased cerebrospinal fluid Aβ 42 correlates with brain atrophy in cognitively normal elderly
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Standardization of measurement of β-amyloid(1-42)in cerebrospinal fluid and plasma
Vanderstichele, H., Kerschaver, E. V., Hesse, C., Davidsson, P., Buyse, M.-A., Andreasen, N., Minthon, L., Wallin, A., Blennow, K., & Vanmechelen, E. (2000) · Amyloid · doi:10.3109/13506120009146438 · Cited by 301
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“28 Recently, McAuley et al 29 showed, from an in silico model, a potential 30% to 40% decrease in hippocampal activity with long-term elevated levels of cortisol.”
Plasma apolipoprotein E and Alzheimer disease risk: The AIBL study of aging
Gupta, V. B., Laws, S. M., Villemagne, V. L., Ames, D., Bush, A. I., Ellis, K. A., Lui, J. K., Masters, C., Rowe, C. C., Szoeke, C., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318211c352 · Cited by 149
Cited by
Preclinical Alzheimer’s Disease: Definition, Natural History, and Diagnostic Criteria
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.02.002 · Cited by 1,925
Cattaneo, A., Cattane, N., Galluzzi, S., Provasi, S., Lopizzo, N., Festari, C., Ferrari, C., Guerra, U. P., Paghera, B., Muscio, C., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.08.019 · Cited by 1,382
Plasma phospholipids identify antecedent memory impairment in older adults
Mapstone, M., Cheema, A. K., Fiandaca, M. S., Zhong, X., Mhyre, T. R., MacArthur, L. H., Hall, W. J., Fisher, S. G., Peterson, D. R., Haley, J. M., et al. (2014) · Nature Medicine · doi:10.1038/nm.3466 · Cited by 1,043
A systemic view of Alzheimer disease — insights from amyloid-β metabolism beyond the brain
Wang, J., Gu, B. J., Masters, C. L., & Wang, Y.-J. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.111 · Cited by 808
Multimodal deep learning models for early detection of Alzheimer’s disease stage
Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-74399-w · Cited by 732
Metadata
- Authors
- Doecke, J. D.
- Venue
- Archives of Neurology
- Publisher
- American Medical Association (AMA)
- Published
- 2012-10-01
- Volume
- 69
- Issue
- 10
- Pages
- 1318
- ISSN
- 0003-9942 (ISSN-L)
- OpenAlex
- W2096321911
- Cited by
- 421
- References
- 9
- FWCI
- 24.80
- Type
- journal article
- Language
- en
- Open access
- green
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