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.

Document Type


Publication Date


Notes/Citation Information

Published in Communications Biology, v. 3, issue 1, article no. 352.

© The Author(s) 2020

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Digital Object Identifier (DOI)


Funding Information

This research was supported by NIH grants R01AG054459 and RF1AG062480 to A.-L.L., R03AG063250, R03AG054936, and R01 LM012535 to K.N., ADNI Psychometrics R01AG029672 to P.K.C., training grant T32DK007778 to L.M.Y., and NSF CAREER award (IIS #1553116) to N.J. This study is also a part of the collaboration from the Friday Harbor Psychometrics Conference sponsored by R13AG030995 (PI: Mungas).

Related Content

The datasets analyzed during the current study are available in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository, http://adni.loni.usc.edu/.

The code used in analysis for the current study are available in a GitHub control repository, https://github.com/linbrainlab/machinelearning.git.

42003_2020_1079_MOESM1_ESM.pdf (673 kB)
Supplementary Information

42003_2020_1079_MOESM2_ESM.pdf (80 kB)
Reporting Summary

42003_2020_1079_MOESM3_ESM.pdf (425 kB)
Peer Review File