The Key Factors Predicting Dementia in Individuals With Alzheimer's Disease-Type Pathology.
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Publication Date
2022-04-25Journal Title
Frontiers in aging neuroscience
ISSN
1663-4365
Volume
14
Language
eng
Type
Article
This Version
VoR
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McCorkindale, A. N., Patrick, E., Duce, J. A., Guennewig, B., & Sutherland, G. T. (2022). The Key Factors Predicting Dementia in Individuals With Alzheimer's Disease-Type Pathology.. Frontiers in aging neuroscience, 14 https://doi.org/10.3389/fnagi.2022.831967
Abstract
Dementia affects millions of individuals worldwide, yet there are no effective treatments. Alzheimer's disease, the most common form of dementia, is characterized by amyloid and tau pathology with amyloid accumulation thought to precipitate tau pathology, neurodegeneration, and dementia. The Religious Orders Study and Memory and Aging Project (ROSMAP) cohort is a unique resource with quantitative pathology from multiple brain regions, RNA sequencing, and longitudinal cognitive data. Our previous work applying machine learning to the RNA sequencing data identified lactoferrin (LTF) as the gene most predictive of amyloid accumulation with a potential amyloidogenic mechanism identified <i>in vitro</i> and with cell-culture models. In the present study, we examined which pathologies and genes were related to cognitive status (dementia, mild impairment, and no cognitive impairment) and rate of cognitive decline. Tau load in the anterior cingulate and <i>ADAMTS2</i>, encoding a metallopeptidase, were the respective regional pathology and gene most associated with cognitive decline, while <i>PRTN3</i>, encoding a serine protease, was the key protective feature. <i>ADAMTS2</i>, but not <i>PRTN3</i>, was related to amyloid and tau load in the previous study while <i>LTF</i> was not related to cognitive decline here. These findings confirm a general relationship between tau pathology and dementia, show the specific importance of tau pathology in the anterior cingulate cortex and identify ADAMTS2 as a potential target for slowing cognitive decline.
Keywords
Pathology, Cognition, Alzheimer’s disease, Machine Learning, Transcriptomics
Identifiers
35557837, PMC9085578
External DOI: https://doi.org/10.3389/fnagi.2022.831967
This record's URL: https://www.repository.cam.ac.uk/handle/1810/338054
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