Cardiovascular disease risk in middle-aged ultra-endurance athletes

2018 Fall.Includes bibliographical references.Background: It is widely accepted that aerobic exercise has the ability to reduce cardiovascular disease (CVD) risk. However, recent studies suggest that volumes of exercise that greatly exceed physical activity guidelines may be damaging to the heart. Currently, it is unclear if individuals who train for ultra-endurance races are at an elevated risk of developing CVD compared to those that perform lower amounts of physical activity. Purpose: To use traditional and novel measures of CVD risk to determine if individuals that train for ultra-endurance races have a greater CVD risk compared to participants that engage in recreational physical activity. Methods: We studied two groups of healthy, middle-aged adults (40-65 y); Control (CON, n=18) subjects included individuals who were meeting current physical activity guidelines and the athletes (ATH, n=25) had been training for ultra-endurance events for 10 years. We used cardiac computed tomography (CT) to calculate coronary artery calcium scores (CACS) and magnetic resonance imaging (MRI) to assess for myocardial fibrosis (MF). Vascular function was evaluated using carotid-femoral pulse wave velocity (cfPWA) and flow-mediated dilation (FMD). 10-Year coronary heart disease (CHD) risk was also determined using a risk score calculator. Results: CACS > 0 was observed in 2 CON and 8 ATH; however, the presence of CAC was not significantly different between groups (P>0.05). Additionally, no participants in CON or ATH had MF. CON had higher cfPWV compared to ATH (6.9±0.2 vs 6.2±0.2 m/s, P0.05). Furthermore, there were no group differences in CHD risk (CON; 1.6±0.3 vs ATH; 2.4±0.6 %, P>0.05). Conclusion: ATH training for ultra-endurance races are not at a greater risk of experiencing a cardiac event than individuals that meeting current physical activity guidelines

Novel modulators of blood pressure with age: a physiological and bioinformatics-based approach

2021 Fall.Includes bibliographical references.Systolic blood pressure (SBP) increases with age and is a significant risk factor cardio- and cerebrovascular diseases. While the causes of high blood pressure (hypertension) have been extensively studied, the causes of the age-related rise in blood pressure independent of chronic disease remain unclear. Thus, the identification of novel mechanisms underlying age-related high blood pressure may lead to new strategies to reduce chronic disease risk in older adults. Therefore, the goal of this dissertation was to use both physiological and bioinformatics-based approaches to better elucidate contributors to elevated blood pressure in healthy older adults. The main findings are that 1) inhibition of Rho-kinase (an enzyme that participates in numerous cellular/regulatory pathways) lowers systemic blood pressure in healthy older adults concomitant with reduced vascular resistance but not improved endothelial function, 2) genes expression patterns in peripheral white blood cells differ in healthy older adults with elevated SBP compared to those with normal SBP and transcriptomic (RNA) changes relate to vascular and immune function, and 3) circulating chemokines and whole blood immune-related transcripts track with elevated SBP in healthy older adults. Taken together, this work shows that Rho-kinase, circulating RNA transcripts, and circulating chemokines may be novel therapeutic targets and/or biomarkers of elevated blood pressure in healthy older adults with untreated hypertension

Involuntary Breathing Movement Pattern Recognition and Classification via Force-Based Sensors

The study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in supine positions on force plates. We segmented their data into no-movement (NM) phases, i.e., the easy phase (EP) and IBM phase (comprising several events or sub-phases of IBM). Acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five–six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggles and were labeled as classes for classification using support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had low computational costs and high robustness of 96–99% accuracy

Use of NMR logging to obtain estimates of hydraulic conductivity in the High Plains aquifer, Nebraska, USA

Hydraulic conductivity (K) is one of the most important parameters of interest in groundwater applications because it quantifies the ease with which water can flow through an aquifer material. Hydraulic conductivity is typically measured by conducting aquifer tests or wellbore flow (WBF) logging. Of interest in our research is the use of proton nuclear magnetic resonance (NMR) logging to obtain information about water-filled porosity and pore space geometry, the combination of which can be used to estimate K. In this study, we acquired a suite of advanced geophysical logs, aquifer tests, WBF logs, and sidewall cores at the field site in Lexington, Nebraska, which is underlain by the High Plains aquifer. We first used two empirical equations developed for petroleum applications to predict K from NMR logging data: the Schlumberger Doll Research equation (KSDR) and the Timur-Coates equation (KT-C), with the standard empirical constants determined for consolidated materials. We upscaled our NMR-derived K estimates to the scale of the WBF-logging K(KWBF-logging) estimates for comparison. All the upscaled KT-C estimates were within an order of magnitude of KWBF-logging and all of the upscaled KSDR estimates were within 2 orders of magnitude of KWBF-logging. We optimized the fit between the upscaled NMR-derived K and KWBF-logging estimates to determine a set of site-specific empirical constants for the unconsolidated materials at our field site. We conclude that reliable estimates of K can be obtained from NMR logging data, thus providing an alternate method for obtaining estimates of K at high levels of vertical resolution