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

doi:10.1038/s42003-020-1079-x

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 journal › Article › peer-review

31 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 language	English
Article number	352
Journal	Communications Biology
Volume	3
Issue number	1
DOIs	https://doi.org/10.1038/s42003-020-1079-x
State	Published - Dec 1 2020

Bibliographical note

Funding Information:
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neu-roimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neu-rotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We also thank Dr. Erin Abner of University of the Kentucky for statistical support and Mr. Thomas Dolan of the University of Kentucky for graphical design. 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).

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

ASJC Scopus subject areas

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

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10.1038/s42003-020-1079-x

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title = "β-amyloid and tau drive early Alzheimer{\textquoteright}s disease decline while glucose hypometabolism drives late decline",

abstract = "Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer{\textquoteright}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{\textquoteright}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.",

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

note = "Funding Information: Data used in preparation of this article were obtained from the Alzheimer{\textquoteright}s Disease Neu-roimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer{\textquoteright}s Association; Alzheimer{\textquoteright}s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neu-rotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer{\textquoteright}s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We also thank Dr. Erin Abner of University of the Kentucky for statistical support and Mr. Thomas Dolan of the University of Kentucky for graphical design. 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). Publisher Copyright: {\textcopyright} 2020, The Author(s).",

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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

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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.

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

Abstract

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