TY - JOUR
T1 - β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline
AU - Hammond, Tyler C.
AU - Xing, Xin
AU - Wang, Chris
AU - Ma, David
AU - Nho, Kwangsik
AU - Crane, Paul K.
AU - Elahi, Fanny
AU - Ziegler, David A.
AU - Liang, Gongbo
AU - Cheng, Qiang
AU - Yanckello, Lucille M.
AU - Jacobs, Nathan
AU - Lin, Ai Ling
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s42003-020-1079-x
DO - 10.1038/s42003-020-1079-x
M3 - Article
C2 - 32632135
AN - SCOPUS:85087457692
VL - 3
JO - Communications Biology
JF - Communications Biology
IS - 1
M1 - 352
ER -