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

Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai Ling Lin

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

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.

Original languageEnglish
Article number352
JournalCommunications Biology
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences

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