Matching Items (81)
Description
Background: The purpose of this study was to assess the efficacy of a lifestyle intervention on cardiorespiratory fitness in Latino youth with obesity and prediabetes. <br/>Methods: Participants (n=50) in this study were taken from a larger randomized controlled trial (n=180, BMI ≥ 95th percentile). Youth participated in a 6-month lifestyle intervention that included physical activity (60 minutes, 3x/week) and nutrition and wellness classes (60 minutes, 1x/week) delivered to families at the Lincoln Family YMCA in Downtown Phoenix. The primary outcome was cardiorespiratory fitness measured at baseline and post-intervention.<br/>Results: The mean BMI for the sample was 33.17 ± 4.54 kg/m2, which put the participants in the 98.4th percentile. At baseline, the mean VO2max was 2737.02 ± 488.89 mL/min. The mean relative VO2max was 30.65 ± 3.87 mL/kg/min. VO2max values significantly increased from baseline to post-intervention (2737.022 ± 483.977 mL/min vs 2932.654 ± 96.062 mL/min, p<0.001). <br/>Conclusion: Culturally-grounded, family-focused lifestyle interventions are a promising approach for improving cardiorespiratory fitness in high-risk youth at risk for diabetes.
ContributorsEstrada, Lourdes Alexa (Author) / Shaibi, Gabriel (Thesis director) / Peña, Armando (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Obesity in Hispanic youth has reached alarmingly high levels, increasing the risk of type 2 diabetes, hyperlipidemia, hypertension, and cardiovascular disease. In Mexican American children ages 6-11 years, 41.7% are overweight and obese, 24.7% are obese and 19.6% have a Body Mass Index (BMI) greater than the 97th percentile. While personal, behavioral, and environmental factors contribute to these high rates, emerging literature suggests acculturation, self-efficacy and social support are key influences. The one-group, pre- and post-test, quasi-experimental design used a community-based participatory research (CBPR) method to test the feasibility, acceptability, and preliminary efficacy of the 8-week intervention. Social Cognitive Theory (SCT) was used to guide the design. Measurements included an analysis of recruitment, retention, participant satisfaction, observation of intervention sessions, paired t-tests, effect sizes, and bivariate correlations between study variables (acculturation, nutrition and physical activity [PA] knowledge, attitude and behaviors, perceived confidence and social support) and outcome variables (BMI z-score, waist circumference and BP percentile) Findings showed the SSLN program was feasible and acceptable. Participants (n = 16) reported that the curriculum was fun and they learned about nutrition and PA. The retention rate was 94%. The preliminary effects on adolescent nutrition and PA behaviors showed mixed results with small-to-medium effect sizes for nutrition knowledge and attitude, PA and sedentary behavior. Correlation analysis among acculturation and study variables was not significant. Positive associations were found between perceived confidence in eating and nutrition attitude (r = .61, p < .05) and nutrition behavior (r = .62, p < .05), perceived confidence in exercise and nutrition behavior (r = .66, p < .05), social support from family for exercise and PA behavior (r = .67, p < .01) and social support from friends for exercise and PA behavior (r = .56, p < .05). These findings suggest a culturally specific healthy eating and activity program for adolescents was feasible and acceptable and warrants further investigation, since it may fill a gap in existing obesity programs designed for Hispanic youth. The positive correlations suggest further testing of the theoretical model.
ContributorsStevens, Carol (Author) / Gance-Cleveland, Bonnie (Thesis advisor) / Komnenich, Pauline (Committee member) / Shaibi, Gabriel (Committee member) / Arcoleo, Kimberly (Committee member) / Arizona State University (Publisher)
Created2011
Description
Cardiovascular disease (CVD) is the number one cause of death in the United States and type 2 diabetes (T2D) and obesity lead to cardiovascular disease. Obese adults are more susceptible to CVD compared to their non-obese counterparts. Exercise training leads to large reductions in the risk of CVD and T2D. Recent evidence suggests high-intensity interval training (HIT) may yield similar or superior benefits in a shorter amount of time compared to traditional continuous exercise training. The purpose of this study was to compare the effects of HIT to continuous (CONT) exercise training for the improvement of endothelial function, glucose control, and visceral adipose tissue. Seventeen obese men (N=9) and women (N=8) were randomized to eight weeks of either HIT (N=9, age=34 years, BMI=37.6 kg/m2) or CONT (N=8, age=34 years, BMI=34.6 kg/m2) exercise 3 days/week for 8 weeks. Endothelial function was assessed via flow-mediated dilation (FMD), glucose control was assessed via continuous glucose monitoring (CGM), and visceral adipose tissue and body composition was measured with an iDXA. Incremental exercise testing was performed at baseline, 4 weeks, and 8 weeks. There were no changes in weight, fat mass, or visceral adipose tissue measured by the iDXA, but there was a significant reduction in body fat that did not differ by group (46±6.3 to 45.4±6.6%, P=0.025). HIT led to a significantly greater improvement in FMD compared to CONT exercise (HIT: 5.1 to 9.0%; CONT: 5.0 to 2.6%, P=0.006). Average 24-hour glucose was not improved over the whole group and there were no group x time interactions for CGM data (HIT: 103.9 to 98.2 mg/dl; CONT: 99.9 to 100.2 mg/dl, P>0.05). When statistical analysis included only the subjects who started with an average glucose at baseline > 100 mg/dl, there was a significant improvement in glucose control overall, but no group x time interaction (107.8 to 94.2 mg/dl, P=0.027). Eight weeks of HIT led to superior improvements in endothelial function and similar improvements in glucose control in obese subjects at risk for T2D and CVD. HIT was shown to have comparable or superior health benefits in this obese sample with a 36% lower total exercise time commitment.
ContributorsSawyer, Brandon J (Author) / Gaesser, Glenn A (Thesis advisor) / Shaibi, Gabriel (Committee member) / Lee, Chong (Committee member) / Swan, Pamela (Committee member) / Buman, Matthew (Committee member) / Arizona State University (Publisher)
Created2013
Description
Purpose: The purpose of this study was to examine the acute effects of two novel intermittent exercise prescriptions on glucose regulation and ambulatory blood pressure. Methods: Ten subjects (5 men and 5 women, ages 31.5 ± 5.42 yr, height 170.38 ± 9.69 cm and weight 88.59 ± 18.91 kg) participated in this four-treatment crossover trial. All subjects participated in four trials, each taking place over three days. On the evening of the first day, subjects were fitted with a continuous glucose monitor (CGM). On the second day, subjects were fitted with an ambulatory blood pressure monitor (ABP) and underwent one of the following four conditions in a randomized order: 1) 30-min: 30 minutes of continuous exercise at 60 - 70% VO2peak; 2) Mod 2-min: twenty-one 2-min bouts of walking at 3 mph performed once every 20 minutes; 3) HI 2-min: eight 2-min bouts of walking at maximal incline performed once every hour; 4) Control: a no exercise control condition. On the morning of the third day, the CGM and ABP devices were removed. All meals were standardized during the study visits. Linear mixed models were used to compare mean differences in glucose and blood pressure regulation between the four trials. Results: Glucose concentrations were significantly lower following the 30-min (91.1 ± 14.9 mg/dl), Mod 2-min (93.7 ± 19.8 mg/dl) and HI 2-min (96.1 ± 16.4 mg/dl) trials as compared to the Control (101.1 ± 20 mg/dl) (P < 0.001 for all three comparisons). The 30-min trial was superior to the Mod 2-min, which was superior to the HI 2-min trial in lowering blood glucose levels (P < 0.001 and P = 0.003 respectively). Only the 30-min trial was effective in lowering systolic ABP (124 ± 12 mmHg) as compared to the Control trial (127 ± 14 mmHg; P < 0.001) for up to 11 hours post exercise. Conclusion: Performing frequent short (i.e., 2 minutes) bouts of moderate or high intensity exercise may be a viable alternative to traditional continuous exercise in improving glucose regulation. However, 2-min bouts of exercise are not effective in reducing ambulatory blood pressure in healthy adults.
ContributorsBhammar, Dharini Mukeshkumar (Author) / Gaesser, Glenn A (Thesis advisor) / Shaibi, Gabriel (Committee member) / Buman, Matthew (Committee member) / Swan, Pamela (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Background: Evidence about the purported hypoglycemic and hypolipidemic effects of nopales (prickly pear cactus pads) is limited. Objective: To evaluate the efficacy of nopales for improving cardiometabolic risk factors and oxidative stress, compared to control, in adults with hypercholesterolemia. Design: In a randomized crossover trial, participants were assigned to a 2-wk intervention with 2 cups/day of nopales or cucumbers (control), with a 2 to 3-wk washout period. The study included 16 adults (5 male; 46±14 y; BMI = 31.4±5.7 kg/m2) with moderate hypercholesterolemia (low density lipoprotein cholesterol [LDL-c] = 137±21 mg/dL), but otherwise healthy. Main outcomes measured included: dietary intake (energy, macronutrients and micronutrients), cardiometabolic risk markers (total cholesterol, LDL-c, high density lipoprotein cholesterol [HDL-c], triglycerides, cholesterol distribution in LDL and HDL subfractions, glucose, insulin, homeostasis model assessment, and C-reactive protein), and oxidative stress markers (vitamin C, total antioxidant capacity, oxidized LDL, and LDL susceptibility to oxidation). Effects of treatment, time, or interactions were assessed using repeated measures ANOVA. Results: There was no significant treatment-by-time effect for any dietary composition data, lipid profile, cardiometabolic outcomes, or oxidative stress markers. A significant time effect was observed for energy, which was decreased in both treatments (cucumber, -8.3%; nopales, -10.1%; pTime=0.026) mostly due to lower mono and polyunsaturated fatty acids intake (pTime=0.023 and pTime=0.003, respectively). Both treatments significantly increased triglyceride concentrations (cucumber, 14.8%; nopales, 15.2%; pTime=0.020). Despite the lack of significant treatment-by-time effects, great individual response variability was observed for all outcomes. After the cucumber and nopales phases, a decrease in LDL-c was observed in 44% and 63% of the participants respectively. On average LDL-c was decreased by 2.0 mg/dL (-1.4%) after the cucumber phase and 3.9 mg/dL (-2.9%) after the nopales phase (pTime=0.176). Pro-atherogenic changes in HDL subfractions were observed in both interventions over time, by decreasing the proportion of HDL-c in large HDL (cucumber, -5.1%; nopales, -5.9%; pTime=0.021) and increasing the proportion in small HDL (cucumber, 4.1%; nopales, 7.9%; pTime=0.002). Conclusions: These data do not support the purported benefits of nopales at doses of 2 cups/day for 2-wk on markers of lipoprotein profile, cardiometabolic risk, and oxidative stress in hypercholesterolemic adults.
ContributorsPereira Pignotti, Giselle Adriana (Author) / Vega-Lopez, Sonia (Thesis advisor) / Gaesser, Glenn (Committee member) / Keller, Colleen (Committee member) / Shaibi, Gabriel (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012