Understanding Alzheimer’s Disease Knowledge in Low-Income, Richmond, VA Community Dwelling Older Adults

Background: Different populations of individuals demonstrate varying levels of Alzheimer’s disease (AD) knowledge, as well as commonly held misconceptions about the nature of the disease and its risk factors. Older adults often demonstrate lower scores on Alzheimer’s disease knowledge scales and African American adults are often specifically not aware of their higher Alzheimer’s risk status compared to other racial groups. In addition, African American older adults are more likely to receive the fewest AD interventions. Methods: We measured the Alzheimer’s knowledge of twenty community-dwelling elders at two separate time points (baseline and 6 month follow-up) as part of a larger study on AD health coaching. Participants (n=20) were recruited from low-income communities within the Richmond, Virginia (RVA) area; the sample was 85% African American individuals (n=17), 45% female (n=9) and 55% male (n=11). Participants completed demographic measures, true/false AD knowledge measures, a relational ageism scale, and questions about their health and habits. Results: Similar to previous research, this population of older adults held common misconceptions about AD, including the ideas that mental exercise can prevent Alzheimer’s disease (20% answered correctly) and individuals with Alzheimer’s are incapable of making decisions about their care (30% answered correctly). In this sample, the majority of African American older adults were aware of the fact that they make up the population at the highest risk for developing Alzheimer’s disease (80% answered correctly). Analyses also found no significant relationship between AD knowledge and health outcomes, alcohol consumption, or education. Conclusion: AD knowledge needs to be better addressed in low-income, racially diverse older adults.https://scholarscompass.vcu.edu/gradposters/1073/thumbnail.jp

Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark

In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model

On bloodvessel branching analysis for the detection of Alzheimer's disease

Alzheimer’s Disease (AD) is increasingly prevalent in modern society and methods for its diagnosis are only just starting to emerge. Given images of brain tissue, we show how Alzheimer’s disease can be detected from the branching structures of blood vessels. This is achieved by a new approach which counts the branching points and derives measures which are suited to the analysis of small branching structures. The measures are formulated to be rotation, scale and position invariant and are deployed in tandem with more standard measures. Analysis on a database comprised of brain tissue samples from subjects who are normal, with Alzheimer’s and age matched normal has shown capability to classify correctly images of brain tissue from subjects afflicted with Alzheimer’s disease.<br/

Alzheimer\u27s Disease Today & Tomorrow

Alzheimer’s Disease (AD) has impacted me on a personal level and professional level. I witnessed my grandfather slowly slip away at the hands of AD over the course of a decade. As a psychiatric nurse, I have provided care for geriatric patients diagnosed with Alzheimer’s Disease and stuck in a cycle of acute psychiatric distress and chronic neurodegeneration. Over 100 years ago, Dr. Alois Alzheimer first described AD. (Alzheimer’s Association, 2016) In 1994, former President Ronald Reagan put AD in the spotlight when he publicly shared his diagnosis. (Alzheimer’s Association, 2016) In 2013, the CDC estimates as many as 5 million Americans suffered from AD. By 2050 a nearly three-fold increase in AD cases will impact 14 million Americans (CDC, 2015). The progress and impact of AD research can be as slow as the pathophysiologic changes in the brain of an Alzheimer’s patient. So where are we today and where will we be tomorrow, in relation to Alzheimer’s Disease

A fruitful fly forward : the role of the fly in drug discovery for neurodegeneration

AD, Alzheimer’s disease; APP, amyloid precursor protein; BBB, blood brain barrier; GFP, green fluorescent protein; HTS, high-throughput screening; HD, Huntington’s disease; LB, Lewy bodies; PD, Parkinson’s disease; PolyQ, Polyglutamine; RNAi, RNA interference; SNCA, α-synuclein gene; UAS, Upstream Activating Sequence.peer-reviewe

CDiP technology for reverse engineering of sporadic Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disease that causes cognitive impairment for which neither treatable nor preventable approaches have been confirmed. Although genetic factors are considered to contribute to sporadic AD, for the majority of AD patients, the exact causes of AD aren’t fully understood. For AD genetics, we developed cellular dissection of polygenicity (CDiP) technology to identify the smallest unit of AD, i.e., genetic factors at a cellular level. By CDiP, we found potential therapeutic targets, a rare variant for disease stratification, and polygenes to predict real-world AD by using the real-world data of AD cohort studies (Alzheimer’s Disease Neuroimaging Initiative: ADNI and Japanese Alzheimer’s Disease Neuroimaging Initiative: J-ADNI). In this review, we describe the components and results of CDiP in AD, induced pluripotent stem cell (iPSC) cohort, a cell genome-wide association study (cell GWAS), and machine learning. And finally, we discuss the future perspectives of CDiP technology for reverse engineering of sporadic AD toward AD eradication

Exploring the Use of Mindfulness with Individuals Diagnosed with Alzheimer’s Disease

Alzheimer’s disease is one of the leading causes of death in the United States. This is a progressive disease with no cure. Are there interventions available to give individuals with Alzheimer’s disease hope? One such intervention is the use of Mindfulness practices. The purpose of this research is to explore how professionals working with individuals with Alzheimer’s disease use mindfulness in their practice and what the benefits of using mindfulness may be. Using a qualitative design, three participant were interviewed who use mindfulness with individuals with Alzheimer’s disease. The data was analyze using inductive coding of the research. Four themes were identified. These included, benefits of the use of mindfulness with AD, lowering distress and stress in individuals with AD when using mindfulness practices, the stages of AD that would benefit from mindfulness, and the training and education needed on mindfulness when working with individuals with Alzheimer’s disease. The use of mindfulness practices offers hope to individuals with Alzheimer’s disease. The research on mindfulness practice is reality new and more research is needed. As our country continues to get older and more individuals develop Alzheimer’s disease more research on effective treatment options is needed

Inflammaging in the Alzheimer’s Brain and Beyond: Insights from a Transgenic Mouse Model on the Sex-specific Pathophysiology of Alzheimer’s Disease

Aging and sex are major risk factors for developing late-onset Alzheimer’s disease. Compared to men, women experience worse neuropathological burden and cognitive decline despite living longer with the disease. Similarly, male 3xTg-AD mice, developed to model Alzheimer’s disease, no longer consistently exhibit standard Alzheimer’s neuropathology yet experience higher rates of mortality - providing a unique opportunity to further elucidate this dichotomy. We hypothesized that sex differences in the biological aging process yield distinct pathological and molecular Alzheimer’s disease signatures in males and females, which could be harnessed for therapeutic and biomarker development. We aged male and female, 3xTg-AD and B6129 control mice across their respective lifespans (n=3-8 mice per sex, strain, and age group) and longitudinally assessed neuropathological hallmarks of Alzheimer’s disease, markers of hepatic inflammation, splenic mass and morphology, as well as plasma cytokine levels. We conducted RNA sequencing analysis on bulk brain tissue and examined differentially expressed genes (DEGs) between 3xTg-AD and B6129 samples and across ages in each sex. We also examined DEGs between clinical Alzheimer’s and control parahippocampal gyrus brain tissue samples from the Mount Sinai Brain Bank study in each sex. 3xTg-AD females significantly outlived 3xTg-AD males and exhibited progressive Alzheimer’s neuropathology, while 3xTg-AD males demonstrated progressive hepatic inflammation, splenomegaly, circulating inflammatory proteins, and minimal Alzheimer’s neuropathological hallmarks. Instead, 3xTg-AD males experienced an accelerated upregulation of immune-related gene expression in the brain relative to females. Our clinical investigations revealed that individuals with Alzheimer’s disease develop similar sex-specific alterations in neuronal and immune function. In diseased males of both species, we observed greater upregulation of complement-related gene expression, and lipopolysaccharide was predicted as the top upstream regulator of DEGs. Our data demonstrate that chronic inflammation and complement activation are associated with increased mortality, indicating that age-related changes in immune response contribute to sex differences in Alzheimer’s disease trajectories. We provide evidence that aging and transgene-driven disease progression trigger a widespread inflammatory response in 3xTg- AD males, which mimics the impact of lipopolysaccharide stimulation despite the absence of infection

Link between Insomnia and the Development of Alzheimer's disease?

Alzheimer’s disease (AD) is the most common type of dementia, affecting many adults over the age of 65. There is no known cure of AD. According to the Alzheimer’s Association, “every 65 seconds, someone in the United States develops Alzheimer’s disease” (2019). “Alzheimer’s causes problems with memory, thinking and behavior. Symptoms develop slowly and can become severe enough to interfere with daily tasks. Alzheimer’s disease is the sixth leading cause of death in the United States, with an estimated 5.7 million Americans of all ages are living with AD in 2018 (Alzheimer’s Association, 2019). The recommended amount of sleep per night for older adults is 7-8 hours (National Sleep Foundation, 2019). Insomnia is a common sleep disorder, with symptoms of difficulty falling asleep, staying asleep, or both, as stated by the National Institutes of Health (2019). Forty-four percent of people over the age of 65 experience symptoms of insomnia. It is suggested that insomnia increases the development of one of the major pathological agents in AD: amyloid-beta plaques. The purpose of this Evidenced Based Practice Brief is to research how insomnia compared to adequate sleep in patients sixty-five years and older increases the risk of the development of AD. By looking at the research regarding whether insomnia increases a patient’s risk of developing Alzheimer’s disease, nurses will be able to provide current evidence based practice when aiding in preventative strategies for Alzheimer’s disease

Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification