Prediction of Alzheimer's dementia

被引:1
作者
Jessen, F. [1 ]
Dodel, R. [2 ]
机构
[1] Univ Klinikum Bonn, Klin & Poliklin Psychiat & Psychotherapie, Klin Behandlungs & Forschungszentrum Neurodegener, DZNE, Bonn, Germany
[2] Univ Marburg, Klin & Poliklin Neurol, D-35043 Marburg, Germany
来源
NERVENARZT | 2014年 / 85卷 / 10期
关键词
Alzheimer's dementia; Prediction; Mild cognitive impairment; Biomarkers; Prevention; MILD COGNITIVE IMPAIRMENT; DISEASE; MCI; BIOMARKERS;
D O I
10.1007/s00115-014-4064-0
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The prediction of Alzheimer's dementia is relevant for the development and design of prevention trials but also for individual counselling of patients. There are two key characteristics which determine the level of prediction that can be achieved. Firstly, the prevalence of Alzheimer's dementia in the respective setting is important. In low prevalence settings, such as primary care populations, it is probably impossible to achieve positive predictive values above 50 %. In high prevalence settings, such as memory clinics, the positive predictive value of Alzheimer's dementia can be much higher. The second major characteristic is the level of cognitive impairment of an individual. The predictive power for Alzheimer's dementia increases from the cognitively healthy status to the status of progressive mild cognitive impairment. Prediction can further be increased by the use of cerebral spinal fluid and brain imaging biomarkers of Alzheimer's disease. The combination of different biomarkers may increase prediction even further. The present article reviews studies and outlines the principles of prediction of Alzheimer's dementia.
引用
收藏
页码:1233 / 1237
页数:5
相关论文
共 50 条
  • [21] Nailfold Capillary Morphology in Alzheimer's Disease Dementia
    Cousins, Clara C.
    Alosco, Michael L.
    Cousins, Henry C.
    Chua, Alicia
    Steinberg, Eric G.
    Chapman, Kimberly R.
    Bing-Canar, Hanaan
    Tripodis, Yorghos
    Knepper, Paul A.
    Stern, Robert A.
    Pasquale, Louis R.
    JOURNAL OF ALZHEIMERS DISEASE, 2018, 66 (02) : 601 - 611
  • [22] The Alzheimer Myth and Biomarker Research in Dementia
    Richard, Edo
    Schmand, Ben
    Eikelenboom, Piet
    Westendorp, Rudi G.
    Van Gool, Willem A.
    JOURNAL OF ALZHEIMERS DISEASE, 2012, 31 : S203 - S209
  • [23] Multicenter Resting State Functional Connectivity in Prodromal and Dementia Stages of Alzheimer's Disease
    Teipel, Stefan J.
    Metzger, Coraline D.
    Brosseron, Frederic
    Buerger, Katharina
    Brueggen, Katharina
    Catak, Cihan
    Diesing, Dominik
    Dobisch, Laura
    Fliessbach, Klaus
    Franke, Christiana
    Heneka, Michael T.
    Kilimann, Ingo
    Kofler, Barbara
    Menne, Felix
    Peters, Oliver
    Polcher, Alexandra
    Priller, Josef
    Schneider, Anja
    Spottke, Annika
    Spruth, Eike J.
    Thelen, Manuela
    Thyrian, Rene J.
    Wagner, Michael
    Duezel, Emrah
    Jessen, Frank
    Dyrba, Martin
    JOURNAL OF ALZHEIMERS DISEASE, 2018, 64 (03) : 801 - 813
  • [24] Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease
    Johnson, Piers
    Vandewater, Luke
    Wilson, William
    Maruff, Paul
    Savage, Greg
    Graham, Petra
    Macaulay, Lance S.
    Ellis, Kathryn A.
    Szoeke, Cassandra
    Martins, Ralph N.
    Rowe, Christopher C.
    Masters, Colin L.
    Ames, David
    Zhang, Ping
    BMC BIOINFORMATICS, 2014, 15
  • [25] Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease
    Prestia, Annapaola
    Caroli, Anna
    van der Flier, Wiesje M.
    Ossenkoppele, Rik
    Van Berckel, Bart
    Barkhof, Frederik
    Teunissen, Charlotte E.
    Wall, Anders E.
    Carter, Stephen F.
    Scholl, Michael
    Choo, Il Han
    Nordberg, Agneta
    Scheltens, Philip
    Frisoni, Giovanni B.
    NEUROLOGY, 2013, 80 (11) : 1048 - 1056
  • [26] A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data
    Li, Hongming
    Habes, Mohamad
    Wolk, David A.
    Fan, Yong
    ALZHEIMERS & DEMENTIA, 2019, 15 (08) : 1059 - 1070
  • [27] Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis
    Ma, Hai Rong
    Sheng, Li Qin
    Pan, Ping Lei
    Wang, Gen Di
    Luo, Rong
    Shi, Hai Cun
    Dai, Zhen Yu
    Zhong, Jian Guo
    TRANSLATIONAL NEURODEGENERATION, 2018, 7
  • [28] Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer's disease dementia
    Arbizu, Javier
    Prieto, E.
    Martinez-Lage, P.
    Marti-Climent, J. M.
    Garcia-Granero, M.
    Lamet, I.
    Pastor, P.
    Riverol, M.
    Gomez-Isla, M. T.
    Penuelas, I.
    Richter, J. A.
    Weiner, M. W.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2013, 40 (09) : 1394 - 1405
  • [29] Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data
    Soerensen, Arnd
    Blazhenets, Ganna
    Ruecker, Gerta
    Schiller, Florian
    Meyer, Philipp Tobias
    Frings, Lars
    Weiner, Michael W.
    Aisen, Paul
    Weiner, Michael
    Petersen, Ronald
    Jack, Clifford R., Jr.
    Jagust, William
    Trojanowki, John Q.
    Toga, Arthur W.
    Beckett, Laurel
    Green, Robert C.
    Saykin, Andrew J.
    Morris, John
    Shaw, Leslie M.
    Khachaturian, Zaven
    Sorensen, Greg
    Carrillo, Maria
    Kuller, Lew
    Raichle, Marc
    Paul, Steven
    Davies, Peter
    Fillit, Howard
    Hefti, Franz
    Holtzman, David
    Mesulam, M. Marcel
    Potter, William
    Snyder, Peter
    Lilly, Eli
    Logovinsky, Veronika
    Montine, Tom
    Jimenez, Gustavo
    Donohue, Michael
    Gessert, Devon
    Harless, Kelly
    Salazar, Jennifer
    Cabrera, Yuliana
    Walter, Sarah
    Hergesheimer, Lindsey
    Harvey, Danielle
    Bernstein, Matthew
    Fox, Nick
    Thompson, Paul
    Schuff, Norbert
    DeCArli, Charles
    Borowski, Bret
    NEUROIMAGE-CLINICAL, 2019, 21
  • [30] Vascular dementia subtypes, pathophysiology, genetics, neuroimaging, biomarkers, and treatment updates along with its association with Alzheimer's dementia and diabetes mellitus
    Prajjwal, Priyadarshi
    Marsool, Mohammed Dheyaa Marsool
    Inban, Pugazhendi
    Sharma, Bhavya
    Asharaf, Shahnaz
    Aleti, Soumya
    Gadam, Srikanth
    Al Sakini, Ahmed Sermed
    Hadi, Dalia Dhia
    DM DISEASE-A-MONTH, 2023, 69 (05):