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: 徐佳蕊
Project Title: 基于公共血液蛋白组数据库和机器学习的阿尔兹海默病早期血液标志物筛选及临床验证
Date: July 13, 2026 at 10:32 am
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Aim 1: 通过机器学习筛选出阿尔兹海默病早期血液标志物最佳组合
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Investigator: Junping Wang
Project Title: Structural Brain Correlates and Subtypes of Discordance Between Neurobiological Aging and Cognitive Decline in Alzheimer’s Disease
Date: July 10, 2026 at 7:03 pm
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Aim 1: To identify structural brain changes associated with discordance between neurobiological aging and cognitive decline, define data-driven subtypes, and characterize their distinct clinical, imaging, and proteomic profiles in Alzheimer’s disease.
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Investigator: yingxiang huang
Project Title: Cell Type Aging Model
Date: July 10, 2026 at 2:45 pm
Request ID: D2645
Aim 1: Alamar has identified candidate biomarker signatures in our own targeted proteomic (NULISA) cohorts. We will use The Knight-ADRC proteomics as a large, harmonized, independent dataset to test these signatures — evaluating whether their constituent proteins associate with disease outcomes
Aim 2: We will also test and compare additional candidate signatures directly within The Knight-ADRC proteomics — alternative panels, published signatures, and cell-type aging signals — benchmarking them head-to-head against our own on the same cohorts.
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Investigator: Annie J Lee
Project Title: Identifying Aging Subtypes through Machine Learning Integration of Longitudinal Clinical and High-Dimensional Omics Data
Date: July 2, 2026 at 12:27 pm
Request ID: D2644
Aim 1: External validation of TPClust in an independent longitudinal Alzheimer’s disease cohort
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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
Request ID: D2643
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
Request ID: D2642
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
Request ID: D2641
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
Request ID: D2640
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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