Core B: Clinical Core Core Leader: David A. Wolk, M.D.; Co-Core Leader: Jason Karlawish, M.D. Project Summary/Abstract The mission of the Penn ADCC Clinical Core is to provide patient data (cognitive, neuroimaging, biofluid, genetic, autopsy) to support the thematic goals of the ADCC and the broader AD research community. A major challenge to developing effective therapeutic interventions for disease modification or symptomatic treatment is the growing understanding that AD is a heterogeneous condition. At least two features characterize this heterogeneity: mixed and multiple pathologies and differential involvement of brain regions or networks. The Penn ADCC views this heterogeneity, as well as the potential shared mechanisms of different proteinopathies and the realization that the antecedents of AD and related disorders occur in preclinical and prodromal stages, as opportunities to enhance precision in prognostication, disease monitoring, and targeting of underlying pathophysiology. As such, the major themes of our ADCC in this renewal are to increase the understanding of (1) the interplay of AD and related disorders pathology in the clinical spectrum of AD, (2) the factors which result in clinical and network level heterogeneity in AD, and (3) the relationship of these phenomena to models of transmissibility. The result will be to increase insight into different AD phenotypes and disease mechanisms through the spectrum of preclinical AD through symptomatic stages. These goals are related to and dependent on our strong tradition of biomarker studies which continue to be a focus for both Core B and the overall ADCC, including development and refinement of these measures and investigation into approaches for their implementation and disclosure in clinical settings. The Clinical Core is highly integrated with the other Penn ADCC Cores and it will work towards achieving the following aims to advance the scientific mission of the Center: (1) To identify and longitudinally evaluate individuals across the continuum of AD and ?normal? cognitive aging, gathering clinical, neuroimaging, and biosample data with an emphasis on heterogeneity in clinical expression, further enhanced by inclusion of the FTLD Module. (2) To collaborate with the Outreach and Recruitment Core to facilitate participation of individuals, with an emphasis on African Americans, in Clinical Core research activities, which will add diversity critical to understanding the influence of comorbid risk factors and genetics on disease expression. (3) To foster integration of Core B activities with the other ADCC Cores, including with the Research Education Component (Core F) to train the next generation of investigators in AD research and with the Data Management, Biostatistics & Bioinformatics Core (Core C) and the Neuropathology, Genetics & Biomarker Core (Core D) to collect and manage clinical data, biomarker studies, and biological samples in a manner that facilitates local, national, and international collaborative studies and sample sharing among NIA-funded ADCs and other qualified investigators through the Administrative Core (Core A). Accomplishment of these goals will catalyze achievement of our broader mission to improve diagnosis and treatment of AD.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Center Core Grants (P30)
Project #
5P30AG010124-29
Application #
9753093
Study Section
Special Emphasis Panel (ZAG1)
Project Start
Project End
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
29
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Type
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169
Kirson, Noam Y; Scott Andrews, J; Desai, Urvi et al. (2018) Patient Characteristics and Outcomes Associated with Receiving an Earlier Versus Later Diagnosis of Probable Alzheimer's Disease. J Alzheimers Dis 61:295-307
Barnes, Josephine; Bartlett, Jonathan W; Wolk, David A et al. (2018) Disease Course Varies According to Age and Symptom Length in Alzheimer's Disease. J Alzheimers Dis 64:631-642
Zee, Jarcy; Xie, Sharon X (2018) The Kaplan-Meier Method for Estimating and Comparing Proportions in a Randomized Controlled Trial with Dropouts. Biostat Epidemiol 2:23-33
Kovacs, Gabor G; Kwong, Linda K; Grossman, Murray et al. (2018) Tauopathy with hippocampal 4-repeat tau immunoreactive spherical inclusions: a report of three cases. Brain Pathol 28:274-283
Stites, Shana D; Milne, Richard; Karlawish, Jason (2018) Advances in Alzheimer's imaging are changing the experience of Alzheimer's disease. Alzheimers Dement (Amst) 10:285-300
Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161
Stites, Shana D; Harkins, Kristin; Rubright, Jonathan D et al. (2018) Relationships Between Cognitive Complaints and Quality of Life in Older Adults With Mild Cognitive Impairment, Mild Alzheimer Disease Dementia, and Normal Cognition. Alzheimer Dis Assoc Disord 32:276-283
Hansson, Oskar; Seibyl, John; Stomrud, Erik et al. (2018) CSF biomarkers of Alzheimer's disease concord with amyloid-? PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement 14:1470-1481
Agogo, George O; Ramsey, Christine M; Gnjidic, Danijela et al. (2018) Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout. Int Psychogeriatr 30:1477-1487

Showing the most recent 10 out of 720 publications