Literature DB >> 25881908

Neuropsychological Testing Predicts Cerebrospinal Fluid Amyloid-β in Mild Cognitive Impairment.

Benjamin M Kandel1, Brian B Avants2, James C Gee2, Steven E Arnold3, David A Wolk4.   

Abstract

BACKGROUND: Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear.
OBJECTIVE: To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-β (Aβ)1 - 42, in patients with MCI, as compared to neuroimaging biomarkers.
METHODS: We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status.
RESULTS: The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67 ± 0.02 and odds ratio of 2.5 ± 0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67 ± 0.03, OR 3.2 ± 1.2), followed by hippocampal volume (AUC 0.64 ± 0.02, OR 2.4 ± 0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68 ± 0.03 (OR 3.38 ± 1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC.
CONCLUSION: Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD.

Entities:  

Keywords:  Alzheimer’s disease; magnetic resonance imaging; mild cognitive impairment; positron emission tomography

Mesh:

Substances:

Year:  2015        PMID: 25881908      PMCID: PMC4699841          DOI: 10.3233/JAD-142943

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  37 in total

1.  Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer's disease?

Authors:  G Chételat; B Desgranges; V de la Sayette; F Viader; F Eustache; J-C Baron
Journal:  Neurology       Date:  2003-04-22       Impact factor: 9.910

2.  Quantitative evaluation of disease progression in a longitudinal mild cognitive impairment cohort.

Authors:  Hilkka Runtti; Jussi Mattila; Mark van Gils; Juha Koikkalainen; Hilkka Soininen; Jyrki Lötjönen
Journal:  J Alzheimers Dis       Date:  2014       Impact factor: 4.472

3.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.

Authors: 
Journal:  Neural Comput       Date:  1998-09-15       Impact factor: 2.026

4.  Cerebrospinal fluid levels of β-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia.

Authors:  Peder Buchhave; Lennart Minthon; Henrik Zetterberg; Asa K Wallin; Kaj Blennow; Oskar Hansson
Journal:  Arch Gen Psychiatry       Date:  2012-01

5.  Pragmatics of measuring recognition memory: applications to dementia and amnesia.

Authors:  J G Snodgrass; J Corwin
Journal:  J Exp Psychol Gen       Date:  1988-03

6.  APOE modifies the association between Aβ load and cognition in cognitively normal older adults.

Authors:  K Kantarci; V Lowe; S A Przybelski; S D Weigand; M L Senjem; R J Ivnik; G M Preboske; R Roberts; Y E Geda; B F Boeve; D S Knopman; R C Petersen; C R Jack
Journal:  Neurology       Date:  2011-12-21       Impact factor: 9.910

7.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.

Authors:  Leslie M Shaw; Hugo Vanderstichele; Malgorzata Knapik-Czajka; Christopher M Clark; Paul S Aisen; Ronald C Petersen; Kaj Blennow; Holly Soares; Adam Simon; Piotr Lewczuk; Robert Dean; Eric Siemers; William Potter; Virginia M-Y Lee; John Q Trojanowski
Journal:  Ann Neurol       Date:  2009-04       Impact factor: 10.422

Review 8.  Promising developments in neuropsychological approaches for the detection of preclinical Alzheimer's disease: a selective review.

Authors:  Dorene M Rentz; Mario A Parra Rodriguez; Rebecca Amariglio; Yaakov Stern; Reisa Sperling; Steven Ferris
Journal:  Alzheimers Res Ther       Date:  2013-11-21       Impact factor: 6.982

9.  Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau.

Authors:  Bradford C Dickerson; David A Wolk
Journal:  Front Aging Neurosci       Date:  2013-10-11       Impact factor: 5.750

10.  Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects.

Authors:  Michael Ewers; Matthias Brendel; Angela Rizk-Jackson; Axel Rominger; Peter Bartenstein; Norbert Schuff; Michael W Weiner
Journal:  Neuroimage Clin       Date:  2013-11-04       Impact factor: 4.881

View more
  12 in total

Review 1.  Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.

Authors:  Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; John C Morris; Ronald C Petersen; Andrew J Saykin; Leslie M Shaw; Arthur W Toga; John Q Trojanowski
Journal:  Alzheimers Dement       Date:  2017-03-22       Impact factor: 21.566

2.  Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment.

Authors:  Min Young Chun; Geon Ha Kim; Hee Kyung Park; Dong Won Yang; SangYun Kim; Seong Hye Choi; Jee Hyang Jeong
Journal:  J Clin Med       Date:  2022-06-15       Impact factor: 4.964

3.  Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data.

Authors:  Kellen K Petersen; Richard B Lipton; Ellen Grober; Christos Davatzikos; Reisa A Sperling; Ali Ezzati
Journal:  Neurology       Date:  2022-04-25       Impact factor: 11.800

4.  Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.

Authors:  Ali Ezzati; Danielle J Harvey; Christian Habeck; Ashkan Golzar; Irfan A Qureshi; Andrea R Zammit; Jinshil Hyun; Monica Truelove-Hill; Charles B Hall; Christos Davatzikos; Richard B Lipton
Journal:  J Alzheimers Dis       Date:  2020       Impact factor: 4.472

5.  Diagnostic and Prognostic Accuracy of the Cogstate Brief Battery and Auditory Verbal Learning Test in Preclinical Alzheimer's Disease and Incident Mild Cognitive Impairment: Implications for Defining Subtle Objective Cognitive Impairment.

Authors:  Nikki H Stricker; Emily S Lundt; Sabrina M Albertson; Mary M Machulda; Shehroo B Pudumjee; Walter K Kremers; Clifford R Jack; David S Knopman; Ronald C Petersen; Michelle M Mielke
Journal:  J Alzheimers Dis       Date:  2020       Impact factor: 4.472

6.  White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration.

Authors:  Benjamin M Kandel; Brian B Avants; James C Gee; Corey T McMillan; Guray Erus; Jimit Doshi; Christos Davatzikos; David A Wolk
Journal:  Alzheimers Dement (Amst)       Date:  2016-04-07

7.  Correlation between CSF biomarkers of Alzheimer's disease and global cognition in a psychogeriatric clinic cohort.

Authors:  Márcia Radanovic; Carlos A Oshiro; Thiago Q Freitas; Leda L Talib; Orestes V Forlenza
Journal:  Braz J Psychiatry       Date:  2019 Nov-Dec       Impact factor: 2.697

8.  Detecting Alzheimer's disease biomarkers with a brief tablet-based cognitive battery: sensitivity to Aβ and tau PET.

Authors:  Elena Tsoy; Amelia Strom; Leonardo Iaccarino; Sabrina J Erlhoff; Collette A Goode; Anne-Marie Rodriguez; Gil D Rabinovici; Bruce L Miller; Joel H Kramer; Katherine P Rankin; Renaud La Joie; Katherine L Possin
Journal:  Alzheimers Res Ther       Date:  2021-02-08       Impact factor: 6.982

9.  The preclinical amyloid sensitive composite to determine subtle cognitive differences in preclinical Alzheimer's disease.

Authors:  Alice Hahn; Young Ju Kim; Hee Jin Kim; Hyemin Jang; Hanna Cho; Seong Hye Choi; Byeong C Kim; Kyung Won Park; Duk L Na; Juhee Chin; Sang Won Seo
Journal:  Sci Rep       Date:  2020-08-12       Impact factor: 4.379

10.  Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms.

Authors:  Sebastian Palmqvist; Philip S Insel; Henrik Zetterberg; Kaj Blennow; Britta Brix; Erik Stomrud; Niklas Mattsson; Oskar Hansson
Journal:  Alzheimers Dement       Date:  2018-10-23       Impact factor: 21.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.