Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2017.00046
Keywords
disease progression; memory; recognition discriminability; mild cognitive impairment; Alzheimer's disease; signal detection theory
Categories
Funding
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- BioClinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd
- Genentech, Inc.
- GE Healthcare
- Innogenetics, N.V.
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Synarc Inc.
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
Ask authors/readers for more resources
Background : Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods : A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results : 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF A beta 1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD ( chi(2) = 38.23, df = 14, p < 0.001). Conclusions : The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available