β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline

• Published in: Communications biology Vol. 3; no. 1; p. 352
• Main Authors: Hammond, Tyler C., Xing, Xin, Wang, Chris, Ma, David, Nho, Kwangsik, Crane, Paul K., Elahi, Fanny, Ziegler, David A., Liang, Gongbo, Cheng, Qiang, Yanckello, Lucille M., Jacobs, Nathan, Lin, Ai-Ling
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.07.2020; Nature Publishing Group
• Subjects: 59/57; 59/78; 631/378/116/2396; 692/53/2423; Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Amyloid beta-Peptides - metabolism; Biomarkers; Biomarkers - metabolism; Biomedical and Life Sciences; Brain - diagnostic imaging; Brain - metabolism; Brain - pathology; Clinical trials; Dementia; Dementia disorders; Disease Progression; Female; Glucose; Glucose - metabolism; Humans; Learning algorithms; Life Sciences; Machine learning; Magnetic Resonance Imaging; Male; Medical imaging; Mental Status and Dementia Tests; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Positron-Emission Tomography; Tau protein; tau Proteins - metabolism; β-Amyloid
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-020-1079-x

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD. Here the authors analyze the Alzheimer’s Disease Neuroimaging Initiative dataset using random forest machine learning methods and determine that Aβ and tau biomarkers are better predictors of early dementia status, while glucose hypometabolism is a better predictor of later dementia status. These results suggest the need for stage-oriented Alzheimer’s disease treatments.