Establishing Biological Plausibility for Cognitive Frailty

Cognitive frailty is considered a potentially reversible age-related condition characterized by the simultaneous presence of both physical frailty and cognitive decline. The concept of cognitive frailty existing in older adults is indisputable, although the mechanisms and the directional relationship behind the dynamic association remain unexplained. Mechanisms have been suggested, often linking cognitive frailty to cognitive impairment or as a component of frailty but without an understanding of the biological bases for these associations we cannot not move forward with intervention trials. This dissertation examines the biological mechanisms for cognitive frailty. The study is the first to use a large number of protein and genetic markers identified by a systematic review to define the underlying pathology for cognitive frailty. We use an innovative Boosted trees machine learning technique for developing a population based predictive model. Xgboost is based in boosted trees and provides more efficient and accurate predictive modeling with large datasets and a rapid / robust framework for feature selection. Statistical modeling is used to design, test, and validate an accurate method for and identifying and classifying the features that predict individuals with cognitive frailty. The tree boosting model is used for the evaluation of multiple variables simultaneously and provides a high predictive value with low bias. The results presented within this dissertation create a foundation of understanding for a new aging condition and encourage translational research focused on the detection and prevention of cognitive frailty

Pharmacogenomics and SSRIs Appropriateness in Older Community Dwelling African Americans

Background: Depressive and anxiety disorders are among the most common illnesses experienced by older adults (age \u3e 60). The selective serotonin reuptake inhibitors (SSRIs) are preferred class of antidepressants for these disorders due to their high efficacy and safety profiles among older adults. However, SSRIs are metabolized by highly polymorphic cytochrome P450 enzymes, specifically CYP2D6 and CYP2C19. This can lead to variable dose-response outcomes, especially among older African American population. Objective: Analyze the frequency of CYP2D6 and CYP2C19 polymorphisms in African American older adults who are taking SSRIs and identify potential inappropriate use of SSRIs in these older adults using the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Methods: Participants (age \u3e 60) were enrolled into Translational Approaches to Personalized Health (TAPH) study. DNA samples were collected via Ora-gene saliva kits and the DNA was analyzed using the PGx Express Chip on the QuantStudio 12K Flex system. After quality control was performed, we focused on the genotypes of 12 participants who were prescribed SSRIs. Results: Only 2 participants had normal activity levels of both CYP2D6 and CYP2C19, while the rest had at least one variant allele that resulted in decreased or increased enzyme activity level. After matching the participants’ enzyme activity levels with the major metabolic pathway of their agent of SSRIs, 8 out of 12 participants are at risk of experiencing sub- or supra-therapeutic effects of SSRIs. 2 participants, especially, are at increased risk of serious adverse effect of citalopram-induced prolonged QT interval, which is more prevalent in older adults.https://scholarscompass.vcu.edu/gradposters/1148/thumbnail.jp

Evaluating the harmonisation potential of diverse cohort datasets

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

HGEN 612 - Data Science 2

All things HGEN 61

Factors Affecting Quality of Life in Patients Receiving Autologous Hematopoietic Stem Cell Transplantation.

BACKGROUND: Hematopoietic stem cell transplant (HSCT) is a potentially curative treatment for hematologic malignancies, with 22 000 HSCTs performed annually in the United States. However, decreased quality of life (QoL) is a frequent and concerning state reported by HSCT recipients. OBJECTIVES: We sought to determine if measurements of frailty and cognitive impairment were associated with fatigue and QoL in adult HSCT recipients after autologous HSCT. METHODS: Using a longitudinal study design, 32 participants 18 years or older receiving autologous HSCT were recruited from a bone marrow transplant clinic. Each participant completed 2 visits: pre-HSCT and post-HSCT. At each visit, participants completed assessment tools to measure frailty, cognitive impairment, fatigue, and QoL (assesses physical, social/family, emotional, functional, and transplant-related well-being). RESULTS: Participants with increased fatigue scores reported decreased QoL pre- and post-HSCT. Participants with increased frailty showed decreased functional well-being before HSCT and showed correlations with decreased physical, social, and transplant-related well-being post-HSCT. As expected, fatigued participants also showed increased frailty post-HSCT. Participants showed significant changes in physical well-being and fatigue between pre-HSCT and post-HSCT visits. CONCLUSION: Data analyses from this pilot study show significant correlations between subsets of QoL with fatigue and frailty in autologous HSCT participants pre- and post-HSCT. IMPLICATIONS FOR PRACTICE: Understanding the impact of frailty on fatigue and QoL in HSCT recipients is critical to assist nurses in initiating educational and behavioral interventions to help mitigate the effects of HSCT