Sampson Sahl (bladetaste80)

Introduction Residence in a disadvantaged neighborhood associates with adverse health exposures and outcomes, and may increase risk for cognitive impairment and dementia. Utilization of a publicly available, geocoded disadvantage metric could facilitate efficient integration of social determinants of health into models of cognitive aging. Methods Using the validated Area Deprivation Index and two cognitive aging cohorts, we quantified Census block-level poverty, education, housing, and employment characteristics for the neighborhoods of 2119 older adults. We assessed relationships between neighborhood disadvantage and cognitive performance in domains sensitive to age-related change. Results Participants in the most disadvantaged neighborhoods (n = 156) were younger, more often female, and less often college-educated or white than those in less disadvantaged neighborhoods (n = 1963). Disadvantaged neighborhood residence associated with poorer performance on tests of executive function, verbal learning, and memory. Discussion This geospatial metric of neighborhood disadvantage may be valuable for exploring socially rooted risk mechanisms, and prioritizing high-risk communities for research recruitment and intervention.Introduction Preclinical testing in animal models is a critical component of the drug discovery and development process. While hundreds of interventions have demonstrated preclinical efficacy for ameliorating cognitive impairments in animal models, none have confirmed efficacy in Alzheimer's disease (AD) clinical trials. Critically this lack of translation to the clinic points in part to issues with the animal models, the preclinical assays used, and lack of scientific rigor and reproducibility during execution. In an effort to improve this translation, the Preclinical Testing Core (PTC) of the Model Organism Development and Evaluation for Late-onset AD (MODEL-AD) consortium has established a rigorous screening strategy with go/no-go decision points that permits unbiased assessments of therapeutic agents. Methods An initial screen evaluates drug stability, formulation, and pharmacokinetics (PK) to confirm appreciable brain exposure in the disease model at the pathologically relevant ages, followed by pharmacoe PTC pipeline is a National Institute on Aging (NIA)-supported resource accessible to the research community for investigators to nominate compounds for testing (https//stopadportal.synapse.org/), and these resources will ultimately enable better translational studies to be conducted.Background We sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease (AD). Method Using EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR-based machine learning model for predicting AD onset within 10 years. Results Applied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63-0.77). Important predictive factors included cardiovascular factors and laboratory blood testing. Conclusion Routinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.Introduction It has been reported that environmental factors such as hypoxia could contribute to the pathogenesis of Alzheimer's disease (AD). Therapeutics like hyperbaric oxygen treatment, which improves tissue oxygen supply and ameliorates hypoxic conditions in the brain, may be an alternative therapy for AD and amnestic mild cognitive impairment (aMCI). The present work aims to investiga