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Taking the Next Steps in the Diagnosis of Alzheimer's Disease: The Use of Biomarkers

Published online by Cambridge University Press:  07 November 2014

Steven T. DeKosky
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Alzheimer's disease (AD) is a progressive disorder in which neurodegeneration begins decades before clinical symptoms appear. Detecting AD during this preclinical phase presents both the enormous challenge of identifying at-risk patients prior to symptom onset and the potential reward of treating patients early enough to prevent or slow disease progression. Given that a 5-year delay in the onset of the clinical manifestations of AD could result in almost a 50% reduction in disease prevalence, early detection of AD is a major focus of clinical research. Several objective, measurable indicators of preclinical and clinical characteristics of AD are currently available or in development. These biomarkers are promising because they promote identification of individuals at risk for AD onset and disease progression; diagnostic accuracy and treatment during the early stages of AD; and the development of disease-modifying therapies that may potentially slow or prevent disease progression during the preclinical phase of AD.

Type
Expert Review Supplement
Information
CNS Spectrums , Volume 13 , Issue S3: Next Steps in Alzheimer's Disease: Improvements in Diagnosis and Treatment , March 2008 , pp. 7 - 10
Copyright
Copyright © Cambridge University Press 2008

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References

1.Blennow, K, de Leon, MJ, Zetterberg, H. Alzheimer's disease. Lancet. 2006;368(9533):387–403.Google Scholar
2.DeKosky, ST. Pathology and pathways of Alzheimer's disease with an update on new developments in treatment. J Am Geriatr Soc. 2003;51(suppl):S314–S320.Google Scholar
3.DeKosky, ST, Marek, K. Looking backward to move forward: early detection of neurodegenerative disorders. Science. 2003;302(5646):830–834.CrossRefGoogle ScholarPubMed
4.Brookmeyer, R, Gray, S. Kawas, C. Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset. Am J Public Health. 1998;88(9):1337–1342.Google Scholar
5.Petersen, RC, Smith, GE, Waring, SC, Ivnik, RJ, Tangalos, EG, Kokmen, E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56(3):303–308.Google Scholar
6.Hardy, J, Selkoe, DJ. The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science. 2002;297(5580):353–356.Google Scholar
7.Thal, DR, Rüb, U, Orantes, M, Braak, H. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58(12):1791–1800.Google Scholar
8.Braak, H, Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–259.Google Scholar
9.Chan, D, Fox, NC, Jenkins, R, Scahill, RI, Crum, WR, Rossor, MN. Rates of global and regional cerebral atrophy in AD and frontotemporal dementia. Neurology. 2001;57(10):1756–1763.Google Scholar
10.Fox, NC, Schott, JM. Imaging cerebral atrophy: normal ageing to Alzheimer's disease. Lancet. 2004;363(9406):392–394.Google Scholar
11.Jack, CR Jr, Shiung, MM, Weigand, SD, et al.Brain atrophy rates predict subseguent clinical conversion in normal elderly and amnestic MCI. Neurology. 2005;65(8):1227–1231.Google Scholar
12.Resnick, SM, Pham, DL, Kraut, MA, Zonderman, AB, Davatzikos, C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci. 2003;23(8):3295–3301.Google Scholar
13.Chan, D, Janssen, JC, Whitwell, JL, et al.Change in rates of cerebral atrophy over time in early-onset Alzheimer's disease: longitudinal MRI study. Lancet. 2003;362(9390):1121–1122.Google Scholar
14.Silbert, LC, Quinn, JF, Moore, MM, et al.Changes in premorbid brain volume predict Alzheimer's disease pathology. Neurology. 2003;61(4):487–492.Google Scholar
15.Ridha, BH, Barnes, J, Bartlett, JW, et al.Tracking atrophy progression in familial Alzheimer's disease: a serial MRI study. Lancet Neurol. 2006;5(10):828–834.Google Scholar
16.de Leon, MJ, George, AE, Stylopoulos, LA, Smith, G, Miller, DC. Early marker for Alzheimer's disease: the atrophic hippocampus. Lancet. 1989;2(8664):672–673.Google Scholar
17.Rusinek, H, De Santi, S, Frid, D, et al.Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology. 2003;229(3):691–696.Google Scholar
18.Fox, NC, Warrington, EK, Freeborough, PA, et al.Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study. Brain. 1996;119(pt. 6):2001–2007.CrossRefGoogle ScholarPubMed
19.Jack, CR Jr, Petersen, RC, Xu, YC, et al.Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52(7):1397–1403.CrossRefGoogle ScholarPubMed
20.Silverman, DH, Small, GW, Chang, CY, et al.Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA. 2001;286(17):2120–2127.CrossRefGoogle ScholarPubMed
21.De Santi, S, de Leon, MJ, Rusinek, H, et al.Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging. 2001;22(4):529–539.Google Scholar
22.de Leon, MJ, Convit, A, Wolf, OT, et al.Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A. 2001;98(19):10966–10971.Google Scholar
23.Arnáiz, E, Jelic, V, Almkvist, O, et al.Impaired cerebral glucose metabolism and cognitive functioning predict deterioration in mild cognitive impairment. Neuroreport. 2001;12(4):851–855.Google Scholar
24.Ray, S, Britschgi, M, Herbert, C, et al.Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med. 2007;13(11):1359–1362.Google Scholar
25.Sharma, P, Lindahl, T, Jensen, M, et al. Detection of Alzheimer's disease based on gene expression patterns in peripheral blood cells. Paper presented at: 12th International Congress of the International Psychogeriatric Association; September 22, 2005: Stockholm, Sweden.Google Scholar
26.Mehta, PD, Pirttila, T, Patrick, BA, Barshatzsky, M, Mehta, SP. Amyloid beta protein 1-40 and 1-42 levels in matched cerebrospinal fluid and plasma from patients with Alzheimer disease. Neurosci Lett. 2001;304(1–2):102–106.Google Scholar
27.Mayeux, R, Honig, LS, Tang, MX, et al.Plasma Aβ40, and Aβ42 and Alzheimer's disease: relation to age, mortality, and risk. Neurology. 2003;61(9):1185–1190.Google Scholar
28.van Oijen, M, Hofman, A, Soares, HD, Koudstaal, PJ, Preteler, MM. Plasma Aβ (1-40) and Aβ (1-42) and the risk of dementia: a prospective case-cohort study. Lancet Neurol. 2006;5(8):655–660.Google Scholar
29.Burczynski, ME, Dorner, AJ. Transcriptional profiling of peripheral blood cells in clinical pharmacogenomic studies. Pharmacogenomics. 2006;7(2):187–202.CrossRefGoogle ScholarPubMed
30.Moore, DF, Li, H, Jeffries, N, et al.Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation. 2005;111(2):212–221.Google Scholar
31.Tang, Y, Lu, A, Ran, R, et al.Human blood genomics: distinct profiles for gender, age and neurofibromatosis type 1. Brain Res Mol Brain Res. 2004;132(2):155–167.Google Scholar
32.Achiron, A, Gurevich, M, Friedman, N, Kaminski, N, Mandel, M. Blood transcriptional signatures of multiple sclerosis: unigue gene expression of disease activity. Ann Neurol. 2004;55(3):410–417.Google Scholar
33.Maes, OC, Xu, S, Yu, B, Chertkow, HM, Wang, E, Schipper, HM. Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. Neurobiol Aging. 2007;28(12):1795–1809.Google Scholar
34.Lönneborg, A, Booij, B, Jensen, M, et al. Accurate and early detection of Alzheimer's disease using a gene expression signature in blood. Paper presented at: 2nd Alzheimer's Association International Conference on Prevention of Dementia; June 10, 2007; Washington, DC.Google Scholar
35.Blennow, K, Hampel, H. CSF markers for incipient Alzheimer's disease. Lancet Neurol. 2003;2(10):605–613.Google Scholar
36.Sunderland, T, Linker, G, Mirza, N, et al.Decreased β-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease. JAMA. 2003;289(16):2094–2103.Google Scholar
37.Fagan, AM, Roe, CM, Xiong, C, Mintun, MA, Morris, JC, Holtzman, DM. Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol. 2007;64(3):343–349.Google Scholar
38.Hansson, O, Zetterberg, H, Buchhave, P, Londos, E, Blennow, K, Minthon, L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 2006;5(3):228–234.CrossRefGoogle ScholarPubMed
39.Buerger, K, Teipel, SJ, Zinkowski, R, et al.CSF tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects. Neurology. 2002;59(4):627–629.Google Scholar
40.Small, GW, Kepe, V, Ercoli, LM, et al.PET of brain amyloid and tau in mild cognitive impairment. N Engl J Med. 2006;355(25):2652–2663.Google Scholar
41.Klunk, WE, Engler, H, Nordberg, A, et al.Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–319.Google Scholar
42.Bacskai, BJ, Frosch, MP, Freeman, SH, et al.Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report. Arch Neurol. 2007;64(3):431–434.Google Scholar
43.Ikonomovic, MD, Klunk, WE, Abrahamson, EE, et al.Post-mortem correlates of in vivo PIB-PET amyloid imaging in a typical case of Alzheimer's disease. Brain. 2007. In press.Google Scholar
44.Rowe, CC, Ng, S, Ackermann, U, et al.Imaging beta-amyloid burden in aging and dementia. Neurology. 2007;68(20):1718–1725.Google Scholar
45.Edison, P, Archer, HA, Hinz, R, et al.Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology. 2007;68(7):501–508.Google Scholar
46.Rabinovici, GD, Furst, AJ, O'Neil, JP, et al.11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology. 2007;68(15):1205–1212.Google Scholar
47.Fagan, AM, Mintun, MA, Mach, RH, et al.Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans. Ann Neurol. 2006;59(3):512–519.Google Scholar