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: 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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Investigator: Marufjon Salokhiddinov
Project Title: Multimodal Neuroimaging and Biomarker-Based Prediction of Early Alzheimer’s Disease Progression Using Knight ADRC Longitudinal Data
Date: May 19, 2026 at 11:52 pm
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Aim 1: To quantify the relationship between structural MRI volumetric markers and Alzheimer’s disease biomarker status. This aim will evaluate whether regional brain volume measures are associated with amyloid positiv
Aim 2: To determine whether baseline MRI volumetric markers predict longitudinal cognitive decline and clinical progression.
Aim 3: To develop and validate a multimodal prediction model combining MRI, clinical, cognitive, and biomarker data for early Alzheimer’s disease risk stratification.
Aim 4: To evaluate whether MRI-based volumetric signatures differ across biomarker-defined Alzheimer’s disease stages and can detect preclinical neurodegeneration before overt cognitive impairment.
Investigator: Marta Porniece
Project Title: Exploration of CSF and plasma proteomics
Date: May 19, 2026 at 8:59 am
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Aim 1: Primary aim is to conduct a targeted reanalysis of the CSF SomaLink proteomic profiles to investigate how biological factors, specifically age, sex, and BMI, influence the protein-balance signatures identified in the original study and correlate these to plasma proteome.
Aim 2: Analyzing the individual proteomic profiles within the blood (plasma) and correlate these to age, sex, and BMI,
Aim 3: Analyzing the individual proteomic profiles within the CSF and correlate these to age, sex, and BMI,
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Investigator: Yue Huang
Project Title: CSF MMP10 as a renal-function–modified marker of cognitive vulnerability in aging and Alzheimer’s disease
Date: May 19, 2026 at 7:44 am
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Aim 1: Validate whether CSF MMP10 is associated with baseline cognitive impairment and dementia severity in Knight ADRC participants
Aim 2: Test whether CSF MMP10 predicts longitudinal cognitive decline across memory, global cognition, and CDR-based outcomes.
Aim 3: Examine whether CSF MMP10 is associated with MRI markers of vascular brain injury, including WMH burden and brain atrophy.
Aim 4: Explore whether renal dysfunction or kidney disease history modifies the association of CSF MMP10 with cognitive decline and MRI injury.
Investigator: Takahisa Kanekiyo, M.D., Ph.D
Project Title: Immune activation markers in age-related cognitive decline and Alzheimer’s disease
Date: May 14, 2026 at 9:02 am
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Aim 1: alidate CSF biomarker profiles associated with conversion from MCI to dementia, with a focus on inflammatory and AD-related markers that may improve prediction of disease progression beyond established CSF biomarkers.
Aim 2: Validate plasma biomarker signatures associated with progression from cognitively unimpaired status to mild cognitive impairment, focusing on inflammatory and Alzheimer’s disease-related biomarkers that may predict incident MCI among cognitively unimpaired individuals.
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Investigator: FU XIAOZHOU
Project Title: Construction of a Predictive Model for the Conversion from Cognitive Impairment to Alzheimer’s Disease Based on Multimodal Feature Fusion
Date: May 12, 2026 at 3:42 am
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Aim 1: Biomarker Discovery
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Investigator: Elham Ghanbarian
Project Title: Inflammation and Non-Amyloid Neurodegeneration in Aging and Alzheimer’s Disease
Date: May 8, 2026 at 11:54 am
Request ID: D2629
Aim 1: To investigate the contribution of non-amyloid neurodegenerative processes to cognitive decline and medial temporal lobe degeneration in aging
Aim 2: To investigate the contribution of systemic inflammation to cognitive decline and medial temporal lobe degeneration in aging
Aim 3: To assess the potential of non-amyloid degeneration processes and systemic inflammation to improve differentiation between Alzheimer’s disease and related pathologies such as LATE
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Investigator: Ruiwen Zhou
Project Title: Dynamic Multimodal Prediction of Alzheimer’s Disease Progression Using Longitudinal Data with Missing-Modality Generative Modeling
Date: May 6, 2026 at 11:29 am
Request ID: D2628
Aim 1: Develop a Longitudinal Multimodal Deep Learning Framework for Dynamic Prediction of Alzheimer’s Disease Progression
Aim 2: Design a Generative Missing-Modality Framework for Longitudinal Multimodal Alzheimer’s Disease Data
Aim 3: Identify Longitudinal Multimodal Biomarkers and Disease Trajectories Associated with Alzheimer’s Disease Progression
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Investigator: Shubham Chandra
Project Title: Virtual Spectral Decomposition of Multiplexed Plasma Biomarkers for Multi-Class Dementia Routing: Extending a Blood-Based Alzheimer’s Disease Signature Framework to FTD, DLB, and Vascular Dementia
Date: May 4, 2026 at 7:56 am
Request ID: D2627
Aim 1: Validate a 4-biomarker VSD framework (pT217, Aβ42/40, NfL, GFAP) developed on ADNI in the Knight ADRC cohort to assess cross-cohort generalizability of AD disease signature coupling patterns and exclusion logic against amyloid PET ground truth.
Aim 2: Identify candidate plasma proteins from the NULISAseq CNS panel that provide differential signal across AD, DLB, FTD, and PD diagnostic groups, to define new VSD channel weights and dendritic branch terminals for multi-class dementia routing.
Aim 3: Characterize biomarker coupling structure within the non-AD neurodegeneration subgroup to determine whether VSD channel activation patterns can discriminate FTD from DLB from vascular contributions using expanded protein panels.
Aim 4: Evaluate longitudinal stability of VSD-derived disease route assignments across repeated plasma draws to assess whether routing classifications track with clinical progression or remain static at baseline assignment.
Investigator: Carlos Cruchaga
Project Title: Transcriptomics and multiomic predictive models in AD and ADRD
Date: May 4, 2026 at 7:21 am
Request ID: D2626
Aim 1: 1. To benchmark the blood circRNA and proteomic models in predicting AD compared to MS tau analytes in CSF and plasma and determine on how the overlap with other biomarkers.
Aim 2: Pathways and cell type enrichment of the transcripts and proteins associated with AD phenotypes (risk, onset, progression) or endophenotypes (AD biomarker levels).
Aim 3: Integrating the MS proteomic data with the rest of the omic data to further refine the AI-derived model.
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