Browsing by Subject "Preclinical"

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    Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease
    (Elsevier, 2020) Dincer, Aylin; Gordon, Brian A.; Hari-Raj, Amrita; Keefe, Sarah J.; Flores, Shaney; McKay, Nicole S.; Paulick, Angela M.; Shady Lewis, Kristine E.; Feldman, Rebecca L.; Hornbeck, Russ C.; Allegri, Ricardo; Ances, Beau M.; Berman, Sarah B.; Brickman, Adam M.; Brooks, William S.; Cash, David M.; Chhatwal, Jasmeer P.; Farlow, Martin R.; la Fougère, Christian; Fox, Nick C.; Fulham, Michael J.; Jack, Clifford R., Jr.; Joseph-Mathurin, Nelly; Karch, Celeste M.; Lee, Athene; Levin, Johannes; Masters, Colin L.; McDade, Eric M.; Oh, Hwamee; Perrin, Richard J.; Raji, Cyrus; Salloway, Stephen P.; Schofield, Peter R.; Su, Yi; Villemagne, Victor L.; Wang, Qing; Weiner, Michael W.; Xiong, Chengjie; Yakushev, Igor; Morris, John C.; Bateman, Randall J.; Benzinger, Tammie L.S.; Neurology, School of Medicine
    Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.
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    Implementation of Subjective Cognitive Decline criteria in research studies
    (Elsevier, 2017-03) Molinuevo, José L; Rabin, Laura A.; Amariglio, Rebecca; Buckley, Rachel; Dubois, Bruno; Ellis, Kathryn A.; Ewers, Michael; Hampel, Harald; Klöppel, Stefan; Rami, Lorena; Reisberg, Barry; Saykin, Andrew J.; Sikkes, Sietske; Smart, Colette M.; Snitz, Beth E.; Sperling, Reisa; van der Flier, Wiesje M.; Wagner, Michael; Jessen, Frank; Radiology and Imaging Sciences, School of Medicine
    INTRODUCTION Subjective Cognitive Decline (SCD) manifesting prior to clinical impairment could serve as a target population for early intervention trials in Alzheimer’s disease (AD). A working group, the Subjective Cognitive Decline Initiative (SCD-I), published SCD research criteria in the context of preclinical AD. To successfully apply them, a number of issues regarding assessment and implementation of SCD needed to be addressed. METHODS Members of the SCD-I met to identify and agree upon topics relevant to SCD criteria operationalization in research settings. Initial ideas and recommendations were discussed with other SCD-I working group members and modified accordingly. RESULTS Topics included SCD inclusion and exclusion criteria, together with the informant’s role in defining SCD presence and the impact of demographic factors. DISCUSSION Recommendations for the operationalization of SCD in differing research settings, with the aim of harmonization of SCD measurement across studies are proposed, to enhance comparability and generalizability across studies.
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    In vivo validation of late-onset Alzheimer's disease genetic risk factors
    (bioRxiv, 2023-12-24) Sasner, Michael; Preuss, Christoph; Pandey, Ravi S.; Uyar, Asli; Garceau, Dylan; Kotredes, Kevin P.; Williams, Harriet; Oblak, Adrian L.; Lin, Peter Bor-Chian; Perkins, Bridget; Soni, Disha; Ingraham, Cindy; Lee-Gosselin, Audrey; Lamb, Bruce T.; Howell, Gareth R.; Carter, Gregory W.; Radiology and Imaging Sciences, School of Medicine
    Introduction: Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action. Methods: Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE4 and Trem2*R47H. Potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts. Results: We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes. Discussion: These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics.
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    Model organism development and evaluation for late‐onset Alzheimer's disease: MODEL‐AD
    (Wiley, 2020-11-23) Oblak, Adrian L.; Forner, Stefania; Territo, Paul R.; Sasner, Michael; Carter, Gregory W.; Howell, Gareth R.; Sukoff-Rizzo, Stacey J.; Logsdon, Benjamin A.; Mangravite, Lara M.; Mortazavi, Ali; Baglietto-Vargas, David; Green, Kim N.; MacGregor, Grant R.; Wood, Marcelo A.; Tenner, Andrea J.; LaFerla, Frank M.; Lamb, Bruce T.; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a major cause of dementia, disability, and death in the elderly. Despite recent advances in our understanding of the basic biological mechanisms underlying AD, we do not know how to prevent it, nor do we have an approved disease‐modifying intervention. Both are essential to slow or stop the growth in dementia prevalence. While our current animal models of AD have provided novel insights into AD disease mechanisms, thus far, they have not been successfully used to predict the effectiveness of therapies that have moved into AD clinical trials. The Model Organism Development and Evaluation for Late‐onset Alzheimer's Disease (MODEL‐AD; www.model-ad.org) Consortium was established to maximize human datasets to identify putative variants, genes, and biomarkers for AD; to generate, characterize, and validate the next generation of mouse models of AD; and to develop a preclinical testing pipeline. MODEL‐AD is a collaboration among Indiana University (IU); The Jackson Laboratory (JAX); University of Pittsburgh School of Medicine (Pitt); Sage BioNetworks (Sage); and the University of California, Irvine (UCI) that will generate new AD modeling processes and pipelines, data resources, research results, standardized protocols, and models that will be shared through JAX's and Sage's proven dissemination pipelines with the National Institute on Aging–supported AD Centers, academic and medical research centers, research institutions, and the pharmaceutical industry worldwide.