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: Yuguang Wang
Project Title: Oral–Brain Axis in Alzheimer’s Disease: Mechanisms, Biomarker Discovery, and Translational Potential
Date: June 30, 2026 at 10:41 am
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Aim 1: Construct a longitudinal analytic framework integrating oral, clinical, cognitive, and biomarker data to investigate the oral–brain axis in Alzheimer’s disease.
Aim 2: Examine mechanistic links between oral microbial dysbiosis, host biological responses, and brain-related outcomes, including cognition, neurodegenerative biomarkers, and imaging phenotypes.
Aim 3: Discover and prioritize oral–brain biomarkers associated with Alzheimer’s disease onset and progression, with particular emphasis on markers that are measurable, reproducible, and biologically interpretable.
Aim 4: Assess the translational value of these biomarkers by building predictive and explanatory models for early detection, progression monitoring, and future clinically actionable applications.
Investigator: Gwang-Woo Jeong
Project Title: Women-focused morphometry of amygdalar and hippocampal subfields in Alzheimer’s disease
Date: June 25, 2026 at 7:41 am
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Aim 1: To identify sex-specific volumetric signatures of amygdalar and hippocampal subfields that discriminate AD from cognitively normal women using FreeSurfer segmentation across multi-site cohorts.
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Investigator: Hosun Lee
Project Title: Plasma Proteomic Aging Models for a GLP-1 Obesity Clinical Trial
Date: June 24, 2026 at 9:52 pm
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Aim 1: Build a CellAge-style proteomic aging clock using Knight-ADRC SomaScan data.
Aim 2: Calculate cellular age gaps and check clinical relevance.
Aim 3: Set up a reference workflow for our clinical trial.
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Investigator: Sunghwan Kim
Project Title: Direct antemortem plasma → autopsy validation of a blood-biomarker staging model of Alzheimer’s disease, and the co-pathology identity of a neurodegeneration-first plasma subtype.
Date: June 24, 2026 at 8:36 am
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Aim 1: Direct plasma → neuropathology. Antemortem plasma p-tau217 (C2N mass spec) and Aβ42/40, GFAP, NfL, plus model-derived stage, vs Braak, Thal/CERAD, ADNC, quantitative plaque/tangle burden. H: p-tau217 strongest plasma correlate of ADNC; stage monotonic with ADNC.
Aim 2: Co-pathology basis of the NfL-early subtype. Subtype assignment vs TDP-43/LATE, Lewy body disease, hippocampal sclerosis, cerebrovascular pathology, adjusted for ADNC/age/sex/PMI. H: NfL-early subtype enriched for LATE/Lewy at relatively low ADNC.
Aim 3: Specificity & prognosis. p-tau217/Aβ42-40 track AD pathology, not co-pathology independent of ADNC; baseline stage/subtype relate to antemortem CDR/cognition trajectory.
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Investigator: Hengguan Huang
Project Title: Pathway-Constrained Gut-Brain Modeling of Preclinical Alzheimer’s Disease
Date: June 22, 2026 at 12:48 am
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Aim 1: Identify matched Knight ADRC clinical, cognitive, biomarker, MRl/PET, and CSF summaries forparticipants with existing preclinical AD gut microbiome profiles to create a de-identified gut-brainanalytic dataset.
Aim 2: Model associations between microbial taxa/functions, amyloid/tau status, cognition, CSF biomarkers,MRI/PET summaries, and timing covariates to characterize early gut-brain signatures in AD.
Aim 3: Develop and validate pathway-constrained statistical/ML models that map microbiome-derivedpathway signals to brain and biomarker outcomes while accounting for age, sex, APOE, medications,and comorbidities.
Aim 4: Assess model robustness and uncertainty, identify interpretable gut-metabolite-brain pathways, andgenerate hypotheses for future prospective validation.
Investigator: Hengguan Huang
Project Title: PathwayDecisionSets: Mechanism-Grounded Action Learning for Early AD Screening
Date: June 22, 2026 at 12:09 am
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Aim 1: Develop a mechanism-grounded framework that outputs calibrated decision sets, enabling safe and interpretable clinical action selection for early AD screening.
Aim 2: Implement a risk-controlled evaluation protocol that prioritizes decision quality and resource-conscious triage over traditional, forced single-point diagnostic predictions.
Aim 3: Validate the model’s reliability in high-stakes clinical scenarios by utilizing a selective “review trigger” mechanism that intelligently requests human intervention when evidence is conflicting or ambiguous
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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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