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: Anita Nikolova Penkova
Project Title: Alzheimer’s Disease Theraputic Target Discovery
Date: June 16, 2026 at 1:44 am
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Aim 1: Define reproducible molecular disease-state programs in AD Identify biologically meaningful AD patient programs/subtypes before downstream modeling. The goal is not just clustering, but a defensible disease-state landscape that captures stable basins, transition zones, and patient-level uncertainty
Aim 2: Build subtype-specific disease networks that capture active AD biology For each molecular program, construct protein co-expression networks that reflect disease biology within that patient group, then overlay known protein-protein interactions from STRING. The goal is to move beyond generic PPI ma
Aim 3: Identify and explain subtype-specific therapeutic target candidates using GNNs Train graph neural networks separately within each subtype/program to identify proteins whose abundance and network context best distinguish disease biology. The key output is not just prediction accuracy. The real goal
Aim 4: Prioritize genetically supported, druggable, and externally replicable targets Validate GNN-prioritized proteins using causal and translational evidence. This includes: Mendelian randomization with pQTLs and AD GWAS; colocalization to reduce LD-confounding risk; reverse MR to check directionality
Investigator: Yuepeng Deng
Project Title: External validation of a paired CSF–plasma protein balance signature for predicting MCI-to-AD conversion in the Knight ADRC cohort
Date: June 15, 2026 at 10:58 pm
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Aim 1: Validate whether a predefined six-protein CSF–plasma AD ratio score predicts conversion from MCI to AD dementia in an independent Knight ADRC cohort.
Aim 2: Test whether the ratio score improves clinical risk stratification beyond age, sex, education, APOE ε4, baseline MMSE and baseline CDR-SB.
Aim 3: Evaluate whether a non-APP/MAPT component of the ratio signature retains prognostic association after adjustment for amyloid and tau biomarkers
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Investigator: Antonio del Sol Mesa
Project Title: Cell-type-Informed Multi-Omic Characterization of CSF in aging and NeuroDegeneration
Date: June 12, 2026 at 4:40 am
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Aim 1: Itentify cell-enriched signal in CSF proteomics.
Aim 2: Identify aging dynamics of cell-type-specific processes from CSF samples.
Aim 3: Identify determine disease-associated deviations from the cell-specific dynamics.
Aim 4: Assess progression of the deviations in longitudinal data and their potential for predicting disease onset.
Investigator: Chao Tang
Project Title: Apathy and Alzheimer’s Disease: Associations with Clinical Severity, Cognitive Decline, and Alzheimer’s Disease Biomarkers
Date: June 5, 2026 at 10:02 pm
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Aim 1: Determine whether apathy is associated with Alzheimer’s disease diagnosis and clinical severity by comparing apathy prevalence and severity across cognitively normal, MCI, and AD dementia participants, adjusting for demographics and depressive symptoms.
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Investigator: Sheretta Butler-Barnes
Project Title: Pathways of Stress and Support: How Social Determinants Influence Dementia Outcomes in Black Americans
Date: June 4, 2026 at 9:50 pm
Request ID: D2637
Aim 1: To identify SDOH (i.e., access to transportation, financial security, healthcare experiences, & discrimination) and the association with dementia severity.
Aim 2: Test whether social connectedness (i.e., lower reports of social isolation, activity, & community safety) moderates the association between SDOH (i.e., access to transportation, financial security, healthcare experiences, & discrimination) and dementia severity.
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Investigator: Maurizio Giorelli
Project Title: Development and validation of the Alzheimer Dynamic Instability Score (ADIS): a biological markers and risk factors approach to detect critical transitions in Alzheimer’s diseases
Date: June 3, 2026 at 1:30 pm
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Aim 1: To develop the Alzheimer Dynamic Instability Score (ADIS) as a composite measure of resilience loss and dynamic instability. We will construct ADIS by integrating longitudinal measures of variability, deterioration velocity, acceleration of decline, cross-domain coupling, and biomarker burden
Aim 2: We will evaluate whether ADIS progressively increases from cognitively normal (CN) individuals to mild cognitive impairment (MCI) and Alzheimer’s disease dementia (AD), supporting its validity as a marker of disease-related instability.
Aim 3: Using longitudinal data, we will test whether elevated ADIS values predict conversion from MCI to AD dementia and accelerated cognitive decline over time.
Aim 4: We will compare the predictive performance of ADIS against established biomarkers (pTau217, NfL, GFAP, hippocampal volume, MMSE, CDR-SB) and determine whether incorporation of ADIS into machine-learning models improves risk stratification and early detection of tipping-point–like transitions.
Investigator: Xinyuan Bi
Project Title: Multi-modal diagnosis of Alzheimer’s disease
Date: May 28, 2026 at 2:25 am
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Aim 1: Screening the biomarkers and identify the potential causal link among biomarkers
Aim 2: Establish a diagnostic model for Alzheimer’s disease using deep learning
Aim 3: Establish a multi-modal assay for Alzheimer’s disease-related biomarkers
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Investigator: Aneesh Asokan
Project Title: Validation of Plasma Biomarkers for Aging and Neurodegeneration in Asian Indian Populations
Date: May 26, 2026 at 5:23 am
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Aim 1: Identify plasma protein biomarkers associated with aging and neurodegenerative phenotypes in Asian Indian populations the NULISAseq platform.
Aim 2: Replicate and validate biomarker candidates in European/North American cohorts (including ADRC) across South Asian cohorts via cross-cohort proteomics comparison.
Aim 3: Perform pathway enrichment analyses to characterize biological mechanisms underlying differential biomarker expression across populations.
Aim 4: To characterize the population-level distribution of established neurodegenerative biomarkers including p-Tau217, NfL, Aβ42, GFAP, and APOE4, in an Indian cohort and benchmark these profiles against globally reported reference cohorts.
Investigator: Shipeng Xiong
Project Title: A Machine Learning Workflow for Screening Alzheimer’s Disease Based on Plasma p-tau217/Aβ1–42
Date: May 21, 2026 at 5:52 pm
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Aim 1: To reduce the interval of indeterminate results, by developing a workflow for AD risk stratification of participants with cognitive impairment.
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Investigator: Annie Lee
Project Title: Dynamic Multi-Omics Modeling of Heterogeneous Clinico-Neuropathological Progression in Alzheimer’s Disease
Date: May 20, 2026 at 9:46 pm
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Aim 1: Identify dynamic preclinical clinico-neuropathological subgroups that define heterogeneous trajectories preceding dementia onset. We request Clinical data (e.g., longitudinal cognition, diagnosis), Fluid Biomarker data (e.g., longitudinal CSF and blood), and available multi-omics data.
Aim 2: Identify post-diagnosis progression subgroups associated with heterogeneous clinical progression and survival. We request Clinical data (e.g., longitudinal functional outcomes), Fluid Biomarker data, and Genetics data (e.g., available multi-omics data).
Aim 3: Identify molecular mechanisms underlying heterogeneous disease trajectories across progression subgroups.
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