Prädiktive Diagnostik der Alzheimer-Demenz

Ayda Rostamzadeh; Frank Jessen

doi:10.1055/a-0981-4529

PSYCH up2date, Table of Contents

PSYCH up2date 2020; 14(03): 203-215
DOI: 10.1055/a-0981-4529

Organische psychische Störungen

Prädiktive Diagnostik der Alzheimer-Demenz

Ayda Rostamzadeh

,

Frank Jessen

Subject Editor: Wissenschaftlich verantwortlich gemäß Zertifizierungsbestimmungen für diesen Beitrag ist Dr. med. Ayda Rostamzadeh, Köln.

Abstract

All articles of this category

Mit modernen Früherkennungsuntersuchungen lässt sich die Alzheimer-Krankheit (Alzheimer’s Disease, AD) lange vor Symptombeginn der Alzheimer-Demenz nachweisen. Patienten fragen immer häufiger nach entsprechenden diagnostischen Verfahren. Der Einsatz von Biomarkern für die Früherkennung der AD ist somit zunehmend klinisch bedeutsam. Dieser Artikel gibt einen Überblick über die derzeitigen prädiktiven Verfahren der Alzheimer-Demenz.

Full Text

References

Literatur
1 Bickel H. Die Häufigkeit von Demenzerkrankungen. Berlin: Dtsch Alzheimer Gesellschaft; 2016
2 Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 1991; 82: 239-259
3 Blennow K, de Leon MJ, Zetterberg H. Alzheimer’s disease. Lancet 2006; 368: 387-403
4 Jack CR, Knopman DS, Jagust WJ. et al. Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013; 12: 207-216
5 Jessen F, Amariglio RE, Van Boxtel M. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s Dement 2014; 10: 844-852
6 Wolfsgruber S, Polcher A, Koppara A. et al. Cerebrospinal Fluid Biomarkers and Clinical Progression in Patients with Subjective Cognitive Decline and Mild Cognitive Impairment. J Alzheimer’s Dis 2017; 58: 939-950
7 Van Maurik IS, Slot RER, Verfaillie SCJ. et al. Personalized risk for clinical progression in cognitively normal subjects – The ABIDE project. Alzheimer’s Res Ther 2019; 11: 33
8 Molinuevo JL, Rabin LA, Amariglio R. et al. Implementation of subjective cognitive decline criteria in research studies. Alzheimer’s Dement 2017; 13: 296-311
9 Petersen RC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol 2004; 62: 1160-1163; discussion 1167
10 Hu C, Yu D, Sun X. et al. The prevalence and progression of mild cognitive impairment among clinic and community populations: a systematic review and meta-analysis. Int Psychogeriatrics 2017; 29: 1595-1608
11 Petersen RC, Caracciolo B, Brayne C. et al. Mild cognitive impairment: A concept in evolution. J Intern Med 2014; 275: 214-228
12 Petersen RC, Lopez O, Armstrong MJ. et al. Practice guideline update summary: Mild cognitive impairment. Neurology 2018; 90: 126-135
13 Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia – Meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 2009; 119: 252-265
14 Albert MS, DeKosky ST, Dickson D. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on. Alzheimer’s Dement 2011; 7: 270-279
15 Handels RLH, Vos SJB, Kramberger MG. et al. Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers. Alzheimer’s Dement 2017; 13: 903-912
16 Vos SJB, Verhey F, Fröhlich L. et al. Prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage. Brain 2015; 138: 1327-1338
17 Deuschl G, Maier W. S3-Leitlinie Demenzen. Dtsch Gesellschaft für Neurol 2016; 128
18 McKhann GM, Knopman DS, Chertkow H. et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 2011; 7: 263-269
19 Jack CR, Bennett DA, Blennow K. et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016; 87: 539-547
20 Jack CR, Bennett DA, Blennow K. et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement 2018; 14: 535-562
21 Lewczuk P, Riederer P, O’Bryant SE. et al. Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry. World J Biol Psychiatry 2017; 1-85
22 Lewczuk P, Matzen A, Blennow K. et al. Cerebrospinal Fluid Aβ42/40 Corresponds Better than Aβ42 to Amyloid PET in Alzheimer’s Disease. J Alzheimer’s Dis 2016; 55: 813-822
23 Landau SM, Thomas BA, Thurfjell L. et al. Amyloid PET imaging in Alzheimer’s disease: A comparison of three radiotracers. Eur J Nucl Med Mol Imaging 2014; 41: 1398-1407
24 Fagan AM, Mintun MA, Mach RH. et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta;42 in humans. Ann Neurol 2006; 59: 512-519
25 Pike KE, Ellis KA, Villemagne VL. et al. Cognition and beta-amyloid in preclinical Alzheimer’s disease: Data from the AIBL study. Neuropsychologia 2011; 49: 2384-2390
26 Vemuri P, Wiste HJ, Weigand SD. et al. MRI and CSF biomarkers in normal, MCI, and AD subjects: Diagnostic discrimination and cognitive correlations. Neurology 2009; 73: 287-293
27 Chhatwal JP, Schultz AP, Marshall GA. et al. Temporal T807 binding correlates with CSF tau and phospho-tau in normal elderly. Neurology 2016; 87: 920-926
28 Gordon BA, Friedrichsen K, Brier M. et al. The relationship between cerebrospinal fluid markers of Alzheimer pathology and positron emission tomography tau imaging. Brain 2016; 139: 2249-2260
29 Smith R, Wibom M, Pawlik D. et al. Correlation of in Vivo [18F]Flortaucipir with Postmortem Alzheimer Disease Tau Pathology. JAMA Neurol 2018; 76: 310-317
30 Johnson KA, Schultz A, Betensky RA. et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol 2016; 79: 110-119
31 Glodzik-Sobanska L, Rusinek H, Mosconi L. et al. The role of quantitative structural imaging in the early diagnosis of Alzheimer’s disease. Neuroimaging Clin N Am 2005; 15: 803-826
32 Scheltens P, Launer LJ, Barkhof F. et al. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: Interobserver reliability. J Neurol 1995; 24: 557-560
33 Mosconi L, Tsui WH, De Santi S. et al. Reduced hippocampal metabolism in MCI and AD: Automated FDG-PET image analysis. Neurology 2005; 64: 1860-1867
34 Beach TG, Monsell SE, Phillips LE. et al. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J Neuropathol Exp Neurol 2012; 71: 266-273
35 Knopman DS, Dekosky ST, Cummings JL. et al. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001; 56: 1143-1153
36 Ossenkoppele R, Jansen WJ, Rabinovici GD. et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA 2015; 313: 1939-1949
37 Knopman DS, Petersen RC. Mild Cognitive Impairment and Mild Dementia: A Clinical Perspective. Mayo Cin Proc 2014; 89: 1452-1459
38 Herukka S-K, Simonsen AH, Andreasen N. et al. Recommendations for CSF AD biomarkers in the diagnostic evaluation of MCI. Alzheimer’s Dement 2017; 13: 285-295
39 Shaw LM, Arias J, Blennow K. et al. Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer’s disease. Alzheimer’s Dement 2018; 14: 1505-1521
40 Johnson KA, Minoshima S, Bohnen NI. et al. Update on appropriate use criteria for amyloid PET imaging: Dementia experts, mild cognitive impairment, and education. Alzheimer’s Dement 2013; 9: e106-e109
41 Schneider JA, Arvanitakis Z, Leurgans SE. et al. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann Neurol 2009; 66: 200-208