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Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD)

PI
Saykin, Andrew
DAR ID
11323
Initial Approval Date
November 25, 2024
Current DAR Status
Active
PI Institution
Indiana University School of Medicine
PI Institution Country
United States
PI Institution Type
Non-Profit
This U19 aims to bridge these knowledge gaps for discovery and validation of Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD) in multi-ethnic populations. CLEAR-AD U19 is based on the premise that AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood. The specific aims of CLEAR-AD are: 1) To discover CLPMS of the complex and heterogeneous AD pathophysiology and its co-pathologies. 2) To identify longitudinal CLPMS that detect and predict dynamic neuroimaging, fluid biomarker, and clinical changes across AD spectrum. 3) To characterize differences and similarities in CLPMS profiles across NHW, African American (AA) and Latino American (LA) participants to uncover biomarker patterns in multi-ethnic groups. 4) To make these vast resources available to the scientific community to amplify and accelerate its impact. In this U19, we will leverage NIH-funded ADNI, MCSA and ADRC cohorts of >3,700 multi-ethnic participants to generate >20,000 multi-omics measures (Omics Core) that will be processed and integrated with >48,000 harmonized AD cognitive, neuroimaging and fluid endophenotypes (Analytic Core). Using these data, we will identify brain region and cell-type specific CLPMS, which reflect biological subtypes of AD and disease stage (Project 1). We will discover longitudinal changes in CLPMS that predict cognitive and A/T/N/V progression (Project 2). We will define longitudinal cognitive and A/T/N/V changes and CLPMS in URP that are either conserved with NHW or population-specific (Project 3). This U19 will a) Identify the next generation of AD biomarkers with mechanistic insights; b) Establish a precision medicine approach for rigorous multi-omics biomarker discovery and validation in AD; c) Discover molecules that can serve as biomarkers and therapeutic targets; d) Enhance biomarker research in trial-ready multi-ethnic populations; and e) Generate and share a vast and harmonized resource of endophenotype and multi-omics data in NIH-funded cohorts.
There is a clear and immediate need for the discovery of peripheral molecular signatures linked to central disease processes, core and co-pathologies in Alzheimer’s Disease (AD), that will serve as precision medicine blood-based biomarkers for diagnostic, prognostic, theragnostic and therapeutic purposes. AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood, i.e. centrally-linked peripheral molecular signatures (CLPMS). This U19 will leverage deeply phenotyped, longitudinal NIH-funded multi-ethnic cohorts and cross-disciplinary expertise for multi-omics data generation and its integration with harmonized AD endophenotypes, will share these data and utilize them in integrated U19 projects to discover CLPMS that will serve as the next generation of AD biomarkers.

Reported Publications (16)

Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.

  1. A DAT1 gene and APOE ε4 interaction is associated with apathy and structural brain changes in mild cognitive impairment and Alzheimer's disease. Malik R, et al. Journal of Alzheimer's disease : JAD. 2025. PMID: 39610284
  2. Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer's disease. Xie L, et al. Scientific reports. 2025. PMID: 40825989
  3. Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction. Venkatesh R, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025. PMID: 41231230
  4. Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's Disease risk prediction. Venkatesh R, et al. medRxiv : the preprint server for health sciences. 2025. PMID: 40502591
  5. LD-informed deep learning for Alzheimer's gene loci detection using WGS data. Jo T, et al. Alzheimer's & dementia (New York, N. Y.). 2025. PMID: 39822590
  6. Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer's disease. Liu S, et al. Journal of translational medicine. 2025. PMID: 40653482
  7. Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease. Liu S, et al. medRxiv : the preprint server for health sciences. 2025. PMID: 39830273
  8. Uncertainty-aware genomic classification of Alzheimer's disease: a transformer-based ensemble approach with Monte Carlo dropout. Jo T, et al. Briefings in bioinformatics. 2025. PMID: 41212591
  9. CYP1B1-RMDN2 Alzheimer's disease endophenotype locus identified for cerebral tau PET. Nho K, et al. Nature communications. 2024. PMID: 39304655
  10. Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation. Pugalenthi PV, et al. BioData mining. 2024. PMID: 39538253
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  1. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries. García-Marín LM, et al. medRxiv : the preprint server for health sciences. 2024. PMID: 39371125
  2. GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia. Shade LMP, et al. Nature genetics. 2024. PMID: 39379761
  3. Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease. He B, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. PMID: 39285750
  4. LD-informed deep learning for Alzheimer's gene loci detection using WGS data. Jo T, et al. medRxiv : the preprint server for health sciences. 2024. PMID: 39371140
  5. Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes. Rosewood TJ, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. PMID: 39440837
  6. Transcriptional risk scores in Alzheimer's disease: From pathology to cognition. Pyun JM, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. PMID: 37563770