Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques
Year: 2019
Type: article
Abstract: BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. METHODS: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS: Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.
Source: Journal of Alzheimer s Disease
DOI: 10.3233/jad-191038
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging
Awards: G1012222616, G104586243, G1298807586, G1554142629, G1593117279, G1650819308, G1827704456, G1880935189, G2100670906, G2105457377, G2152643862, G2189010064, G2227922493, G2340207221, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, Predictive analytics for cognitive decline and Alzheimer's disease, G3131374986, G3146424788, G3216574877, G3553156159, G3703561073, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, Application of latent and integrative approaches to understand the spectrum of cognitive aging, G43180419, G4501223628, G4800177743, G5207404921, G5209990037, G5221124280, G528601746, G5365730499, G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5740349702, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G6948409098, G6979286903, Stress, Unconstructive Repetitive Thought and Cognitive Aging, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G8305370824, Role of impaired cognitive states & risk factors in conversion to mixed dementias, G839208290, G8852128998, G8895907669, G8897063270, G8957659042, G9000039018, G9000048310, G9000122335
OpenAlex ID: W2997795833