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dc.contributor.authorMcCorkindale, Andrew N
dc.contributor.authorPatrick, Ellis
dc.contributor.authorDuce, James A
dc.contributor.authorGuennewig, Boris
dc.contributor.authorSutherland, Greg T
dc.date.accessioned2022-06-14T01:02:54Z
dc.date.available2022-06-14T01:02:54Z
dc.date.issued2022-04-25
dc.identifier.issn1663-4365
dc.identifier.other35557837
dc.identifier.otherPMC9085578
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338054
dc.description.abstractDementia 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.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101525824
dc.sourceessn: 1663-4365
dc.subjectPathology
dc.subjectCognition
dc.subjectAlzheimer’s disease
dc.subjectMachine Learning
dc.subjectTranscriptomics
dc.titleThe Key Factors Predicting Dementia in Individuals With Alzheimer's Disease-Type Pathology.
dc.typeArticle
dc.date.updated2022-06-14T01:02:54Z
prism.publicationNameFrontiers in aging neuroscience
prism.volume14
dc.identifier.doi10.17863/CAM.85463
rioxxterms.versionofrecord10.3389/fnagi.2022.831967
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidSutherland, Greg T [0000-0003-2493-9736]


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International