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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.accessioned2022-05-09T16:00:32Z
dc.date.available2022-05-09T16:00:32Z
dc.date.issued2022-04-25
dc.date.submitted2021-12-09
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336938
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 in vitro 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 ADAMTS2, encoding a metallopeptidase, were the respective regional pathology and gene most associated with cognitive decline, while PRTN3, encoding a serine protease, was the key protective feature. ADAMTS2, but not PRTN3, was related to amyloid and tau load in the previous study while LTF 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.languageen
dc.publisherFrontiers Media S.A.
dc.subjectNeuroscience
dc.subjectAlzheimer’s disease
dc.subjectcognition
dc.subjectmachine learning
dc.subjecttranscriptomics
dc.subjectpathology
dc.titleThe Key Factors Predicting Dementia in Individuals With Alzheimer’s Disease-Type Pathology
dc.typeOther
dc.date.updated2022-05-09T16:00:32Z
prism.publicationNameFrontiers in Aging Neuroscience
prism.volume14
dc.identifier.doi10.17863/CAM.84359
dcterms.dateAccepted2022-03-23
rioxxterms.versionofrecord10.3389/fnagi.2022.831967
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1663-4365


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