Unknown

Dataset Information

0

?-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline.


ABSTRACT: Clinical trials focusing on therapeutic candidates that modify ?-amyloid (A?) have repeatedly failed to treat Alzheimer's disease (AD), suggesting that A? may not be the optimal target for treating AD. The evaluation of A?, 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 A? 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 A? and tau as targets in early AD and glucose metabolism as a target in later AD.

SUBMITTER: Hammond TC 

PROVIDER: S-EPMC7338410 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Hammond Tyler C TC   Xing Xin X   Wang Chris C   Ma David D   Nho Kwangsik K   Crane Paul K PK   Elahi Fanny F   Ziegler David A DA   Liang Gongbo G   Cheng Qiang Q   Yanckello Lucille M LM   Jacobs Nathan N   Lin Ai-Ling AL  

Communications biology 20200706 1


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 t  ...[more]

Similar Datasets

| S-EPMC5262471 | biostudies-literature
| S-EPMC3786871 | biostudies-literature
| S-EPMC10200306 | biostudies-literature
| S-EPMC6935717 | biostudies-literature
| S-EPMC5915512 | biostudies-literature
| S-EPMC5831864 | biostudies-literature
| S-EPMC7193377 | biostudies-literature
| S-EPMC7650820 | biostudies-literature
| S-EPMC8800231 | biostudies-literature
| S-EPMC5106509 | biostudies-literature