Core G: University of Kentucky Alzheimer's Disease Core Center

SPECIFIC AIM 1: Collect and facilitate data sharing of fluid biomarkers and longitudinal neuroimaging from our large, baseline cognitively normal, longitudinal cohort. We will generate a core set of AD biomarkers in CSF, supplemented by an exploratory core panel of inflammatory biomarkers in CSF and blood to support intra- and extra-mural research programs. Longitudinal MRI and amyloid-PET images will be analyzed, with raw images and data provided to researchers requiring such data for their research needs. SPECIFIC AIM 2: Support biomarker initiatives for NIH-funded national projects such as the NACC, ADGC/NCRAD, ADNI, and other Alzheimer Disease Center programs. Provide complementary, comparative, and additive novel core data that will enhance existing NIH Biomarker initiatives. See letters of support from NACC, NCRAD, ADGC, ADCS, ATRI, and UPenn stressing the importance of these contributions. SPECIFIC AIM 3: Characterization of a trial-ready, biomarker-characterized, subject cohort to enhance engagement in clinical translational interventional studies in the area of preclinical and prodromal dementia disease states. This will support our initiatives facilitating clinical translational research within UK and in the broader National/International research setting that go beyond ADCS, ATRI and GAP-NET initiatives. SPECIFIC AIM 4: Enhance investigations and development of novel biomarkers for AD and AD-mimics that have been a successful central focus of research for our Neuropathology Core including primary age-related dementias (PART), cerebral age-related TDP-43 and sclerosis (CARTS), and vascular contributions to cognitive impairment and dementia (VCID). SPECIFIC AIM 5: Synergize with existing ADC cores to enrich the research projects supported by ADC resources. The biomarker core will synergize UK-ADC biomarker efforts across cores to support biomarker research. The biomarker core will also enable longitudinal risk-modeling led by the DMS Core, and allow the development of clinical algorithms that may more accurately detect underlying biological disease states rather than simply describing clinical phenotypes in conjunction with the clinical core.