Search Existing Data Requests

The Knight ADRC has supported many investigators at Washington University and at other institutions over the years. We wish to avoid the situation where two investigators study the same research question to avoid duplication of effort and potential conflict. To determine if your topic has already been studied with our resources, please search our database. If you find that your topic or a related topic has been submitted, you may wish to contact the investigator to inquire about their findings to determine how you might proceed. You may wish to collaborate or modify your request to avoid overlap. The results below reflect requests made since online requests have been accepted. As such, not all fields will have data as certain information, such as aims, were not collected until recently. If an entry has been assigned an ID number (e.g. T1004), the full request has been submitted and is either approved, disapproved or in process. If an entry has no ID number, then it represents a submission that has not yet been reviewed. Search terms are applied across an entire requests application including variables not displayed below. A more specific, detailed search may yield better results depending upon your needs.


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Investigator: Julie Wisch

Project Title: Trip Chaining Behavior in Preclinical AD

Date: August 15, 2023 at 3:49 pm

Request ID: D2325

Aim 1: To describe the trip chaining behavior older adult driverss living in the greater St. Louis, MO metroplex and neighboring Illinois

Aim 2: To identify key transition points in the process of aging/the development of preclinical AD when trip chaining behavior shifts.

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Investigator: Jeremy Strain

Project Title: Blood Brain Barrier Association with WMH Frequency and Expansion

Date: August 14, 2023 at 2:13 pm

Request ID: D2324

Aim 1: 1) Does BBB integrity associate with regional WMH burden and or regional expansion 1a) across the disease spectrum and/or 1b) in preclinical individuals.

Aim 2: 2) Does BBB integrity associate with regional WMH density, as quantified by DTI, 2a) across the disease spectrum and/or 2b) in preclinical individuals

Aim 3: 3) Can WMH and metrics of BBB predict 3a) amyloid positivity and/or 3b) cognitive outcome.

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Investigator: Junie Saint Clair

Project Title: Examining the Predictive Value of Synaptic Dysfunction and Neuronal Injury Measures on Imaging Markers of Disease Presentation and Progression in Alzheimer’s Disease

Date: July 31, 2023 at 6:29 pm

Request ID: D2323

Aim 1: Evaluate association between rates of longitudinal change in CSF levels of Ng, SNAP-25, VILIP-1 and imaging brain changes and cognition in a DIAD cohort.

Aim 2: Evaluate association between rates of longitudinal change in CSF levels of Ng, SNAP-25, VILIP-1 and imaging brain changes and cognition in aged adults LOAD cohort.

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Investigator: Jeremy Strain

Project Title: Phosphorylation Tau Profile of Aging and AD with TBI

Date: July 20, 2023 at 10:49 am

Request ID: D2322

Aim 1: Do Individuals with a history of TBI have elevated phosphorylation at tau site T217 compared to individuals without a history of TBI?

Aim 2: Do individuals with a history of TBI have a similar or different spatial relationship with magnetic resonance imaging mettrics of neuroddegeneration compared to AD?

Aim 3: Does phosphorylated tau in individuals with a history of TBI associate with cognition?

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Investigator: Gregory Wu

Project Title: MOG in Alzheimer’s Disease

Date: July 19, 2023 at 6:35 pm

Request ID: D2321

Aim 1: Determine whether circulating MOG protein is detectable in AD

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Investigator: Manuel Dietrich

Project Title: Validation meta-analysis to identify novel biomarkers for early diagnosis of Alzheimer’s

Date: June 28, 2023 at 8:44 am

Request ID: D2320

Aim 1: Validate and correlate internal bulk RNA-seq findings from human brains of AD patients with SOMAScan proteomics data from tissue, CSF, and plasma.

Aim 2: Compare and replicate internal bulk RNA-seq findings from human brains of AD patients with SOMAScan proteomics data from tissue, CSF, and plasma.

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Investigator: Beau Ances

Project Title: Predicting Cognitive Outcomes with Brain Age Gap

Date: June 26, 2023 at 2:28 pm

Request ID: D2319

Aim 1: Test whether Brain Age Gap correlates cross-sectionally with measures of cognition

Aim 2: Test whether Brain Age Gap predicts longitudinal cognitive decline

Aim 3: Identify sources of mediation between the relationship between Brain Age Gap and cognition

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Investigator: Nicole S McKay

Project Title: Prevalence of AD-pathology in older adults of the Knight Alzheimer Disease Research Center (ADRC)

Date: June 15, 2023 at 4:43 pm

Request ID: D2318

Aim 1: This proposal extends upon prior work examining the frequencies of imaging-derived -amyloidosis and neurodegeneration, among older adults with normal cognitive function.

Aim 2: Replicate the above-mentioned Jack et al. study using data from the Knight ADRC cohort to determine the extent to which our frequencies of imaging-derived -amyloidosis and neurodegeneration in non-demented participants, aligns with their prior work.

Aim 3: Extend upon prior work by also determining frequencies of commonly used biofluid metrics of AD pathology

Aim 4: Further these findings by considering tauopathy measured using tau-PET


Investigator: Chengjie Xiong

Project Title: AD biomarkers and cancer

Date: June 15, 2023 at 12:01 pm

Request ID: D2317

Aim 1: To assess how cancer may be associated with changes in AD biomarkers

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Investigator: Kellen Petersen

Project Title: Disease Progression Modelling of Alzheimer’s Disease Using In Vivo Biomarkers, Neuropsychological Assessments, and Neuropathological Data

Date: May 16, 2023 at 10:07 am

Request ID: D2316

Aim 1: To implement machine learning and event-based disease progression models using cross-sectional data to determine disease trajectories and temporal subtypes

Aim 2: To apply dynamical disease progression models to longitudinal data to assess rates of disease projection and determine individual variability

Aim 3: To demonstrate the feasibility of a novel application of machine learning and event-based disease progression models to neuropathological data

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