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Biomarker Research Applications in Alzheimer's Disease

dc.contributor.advisorSmith, J. Carson
dc.contributor.authorCieslak, Zofia
dc.contributor.authorAcha, Beatrice
dc.contributor.authorHemani, Danny
dc.contributor.authorKubli, Anjali
dc.contributor.authorLee, So Min
dc.contributor.authorMgboji, Rejoyce
dc.contributor.authorNallani, Madhulika C.
dc.contributor.authorPark, Michael J.
dc.contributor.authorSamson, Mahalet
dc.contributor.authorWu, Benjamin
dc.contributor.authorSmith, J. Carson
dc.date.accessioned2021-05-15T00:59:05Z
dc.date.available2021-05-15T00:59:05Z
dc.date.issued2021-05
dc.identifierhttps://doi.org/10.13016/nxn9-vko5
dc.identifier.urihttp://hdl.handle.net/1903/27058
dc.description.abstractAlzheimer’s Disease (AD) affects millions of older individuals and is a growing problem without an accessible diagnosis method, drug target for treatment, or model of the longitudinal progression of the disease. The project, led by University of Maryland Gemstone Team BRAIN, aims to determine how changes in memory, visuospatial ability, the plasma amyloid β 42/40 ratio, and the total hippocampal volume can be used to accurately predict the onset and progression of AD. Using the Alzheimer’s Disease Neuroimaging Initiative, a database that compiles data from nationwide studies, we analyze cognitive function (memory and visuospatial ability), plasma biomarkers (amyloid β 42/40 ratio), and brain imaging (hippocampal volume). Data analysis consists of using programs such as Python and JASP to analyze data from the ADNI database, and finding significant relationships between variables through statistical analysis. Our results suggest that the impact of the e4 allele on memory and visuospatial ability over time may be strong in people who show early cognitive decline, independent of age, sex and education, and that hippocampal volume loss is greater in people who carry the e4 allele independent of covariates. Furthermore, it is unclear if plasma biomarkers reflect brain pathology. Team BRAIN’s future research goals include addressing disparities in AD development among different demographic and socioeconomic groups, using our findings to work towards a novel and cost-effective approach to diagnosing and treating AD to eradicate boundaries in the access to care, applying machine learning to propose a model of prediction and longitudinal progression, and expanding the variable set to include more biomarkers.en_US
dc.language.isoen_USen_US
dc.subjectKinesiologyen_US
dc.subjectSPHLen_US
dc.subjectTeam BRAINen_US
dc.subjectGemstoneen_US
dc.subjectAlzheimer's Diseaseen_US
dc.subjectADNIen_US
dc.subjectBiomarkersen_US
dc.titleBiomarker Research Applications in Alzheimer's Diseaseen_US
dc.typePresentationen_US
dc.relation.isAvailableAtMaryland Center for Undergraduate Research
dc.relation.isAvailableAtDigital Repository at the University of Maryland
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md)


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