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: Azhar Hadi
Project Title: Multimodal Deep Learning for Disease Classification
Date: October 4, 2026 at 10:49 am
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Aim 1: Develop and evaluate a multimodal deep learning model that combines structural MRI with clinical and cognitive measures to classify cognitively normal, mild cognitive impairment, and Alzheimer’s disease stages, using patient-level data splits to prevent leakage.
Aim 2: Test whether the model keeps its performance when amyloid or tau PET data are missing, using learned missing-modality handling (null tokens and modality dropout), since PET is available only for a subset of participants.
Aim 3: Predict amyloid and tau PET positivity in cognitively normal and mildly impaired participants from MRI and clinical data, to assess whether low-cost measures can support pre-screening for preclinical Alzheimer’s disease.
Aim 4: Evaluate cross-cohort generalization by testing models trained on external cohorts (ADNI) on the Knight ADRC data, after reconciling participant identifiers to avoid overlap with other publicly released Knight ADRC datasets.
Investigator: Michael Belloy
Project Title: Integrating Sex- and APOE ε4-Stratified Genetics and Multi-Omics to Identify Therapeutic Targets for Alzheimer’s Disease
Date: September 30, 2026 at 3:23 pm
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Aim 1: Expand sex- and APOE ε4-stratified GWAS to identify AD risk loci and genes.
Aim 2: Characterize sex- and APOE ε4-dependent molecular changes and integrate them with stratified genetic findings.
Aim 3: Integrate complementary genetic and molecular evidence to prioritize subgroup-informed therapeutic targets and repurposable drugs.
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Investigator: Michael Belloy
Project Title: Decoding the Menopausal Molecular Transition to Identify Female-Specific Therapeutic Targets for Alzheimer’s Disease
Date: September 30, 2026 at 3:19 pm
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Aim 1: Define plasma and CSF proteomic and metabolomic signatures of menopause and genetic liability for altered sex hormone levels.
Aim 2: Determine which menopause- and hormone-associated molecular signatures contribute to AD.
Aim 3: Integrate menopause and sex hormone molecular signatures with female-biased AD genetics to nominate therapeutic targets.
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Investigator: Michael Belloy
Project Title: Sex Differences and Menopause-Related Proteomic Signatures in Biomarker and Cognitive Responses to Lecanemab
Date: September 30, 2026 at 3:08 pm
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Aim 1: Characterize sex differences in longitudinal NULISAseq biomarker responses to lecanemab and their relationships with cognition.
Aim 2: Evaluate a menopause-related proteomic score in relation to biomarker and cognitive responses to lecanemab.
Aim 3: Determine whether cognitive trajectories during lecanemab treatment differ by sex and the interaction between sex and APOE ε4.
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Investigator: Nikolaos Scarmeas
Project Title: External Validation of an ADNI-Derived CSF Proteomic Signature for Amyloid Pathology in Non-Demented Individuals
Date: September 29, 2026 at 2:54 am
Request ID: D2666
Aim 1: Externally validate a prespecified ADNI-derived CSF proteomic signature for amyloid pathology in independent, non-demented participants using SomaScan 7K data and temporally matched amyloid biomarker assessments.
Aim 2: Evaluate the discriminative performance of the signature using ROC AUC with 95% confidence intervals, without repeating feature selection or re-estimating the original model coefficients.
Aim 3: Explore the consistency of signature performance across source cohorts (Knight ADRC and PD), clinical subgroups, and amyloid assessment methods, where sample sizes permit.
Aim 4: Explore the prognostic value of the ADNI-derived CSF proteomic signature for incident all-cause and AD dementia using longitudinal follow-up data, subject to sufficient event counts and follow-up duration.
Investigator: Yushan Wang
Project Title: Prediction and Generation of Alzheimer’s Disease Brain Structural Changes Using Longitudinal MRI Data
Date: September 28, 2026 at 10:40 pm
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Aim 1: To develop a deep learning framework (Latent Diffusion Models) for predicting future brain MRI structural changes in Alzheimer’s Disease based on baseline scans.
Aim 2: To generate high-fidelity longitudinal MRI sequences that simulate the dynamic progression of Alzheimer’s Disease (from CN to MCI to AD stages).
Aim 3: To validate the generated MRI images against real follow-up scans using quantitative metrics (e.g., FID, SSIM) and clinical assessments.
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Investigator: Lu Fei
Project Title: CSF NPY as an Aging- and Alzheimer’s Disease Progression-Associated Neuropeptide Biomarker
Date: September 28, 2026 at 3:19 am
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Aim 1: To determine whether CSF NPY protein abundance is associated with normal aging and clinical stage across the Alzheimer’s disease continuum.
Aim 2: To evaluate associations between CSF NPY levels and cognitive performance, longitudinal cognitive decline, and clinical progression.
Aim 3: To examine whether CSF NPY abundance is associated with AD-related pathology, including amyloid and tau biomarkers, and neurodegeneration-related imaging measures.
Aim 4: To integrate CSF NPY proteomic associations with available genetic/pQTL evidence to assess whether NPY is a genetically regulated biomarker linked to aging and AD-related phenotypes.
Investigator: Hongzhe Li, PhD (University of Pennsylvania)
Project Title: Cell-type-resolved glial reactivity as a longitudinal predictor of cognitive deterioration in Alzheimer’s disease
Date: September 19, 2026 at 12:39 am
Request ID: D2665
Aim 1: Test whether longitudinal CSF sTREM2 (microglia) and GFAP/YKL-40 (astrocyte) trajectories improve prediction of cognitive decline and clinical progression beyond core AD biomarkers, using joint longitudinal-survival models.
Aim 2: Test whether the prognostic contribution of astrocyte and microglial markers differs by disease stage (cognitively normal, MCI, dementia) via stage-by-marker interactions in pre-specified nested models.
Aim 3: Externally validate an ADNI-developed prediction model in Knight ADRC (leave-one-cohort-out), reporting time-dependent AUC, Brier score and calibration, with linkage to NACC UDS for harmonised outcomes.
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Investigator: Chihiro Sato
Project Title: Linking Tau Aggregation to MTBR-tau via Omics Pathways
Date: September 17, 2026 at 10:58 am
Request ID: D2664
Aim 1: Identify omics-derived pathways linking tau PET to fluid (e)MTBR-tau in Alzheimer’s disease (AD)
Aim 2: Test whether combining fluid MTBR-tau with omics-derived pathways improves prediction of tau PET and cognitive function in AD
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Investigator: Xiao Zhang
Project Title: Cell-Type–Associated Proteomic Signatures of Brain Aging and Alzheimer’s Disease in Cerebrospinal Fluid
Date: September 14, 2026 at 8:25 am
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Aim 1: Characterize age-related CSF protein trajectories and their enrichment for neuronal, glial, and vascular cell types in cognitively unimpaired individuals.
Aim 2: Develop and validate cell-type–associated CSF proteomic age models using cognitively unimpaired reference participants, and assess their reproducibility across cohorts.
Aim 3: Evaluate associations of proteomic age deviations with cognitive impairment, longitudinal cognitive decline, Alzheimer’s disease biomarkers, and APOE genotype.
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