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- All Subjects: Health Sciences
- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Vega-Lopez, Sonia
- Resource Type: Text
Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Background. Effects of lifestyle interventions on early biomarkers of oxidative stress and CVD risk in youth with prediabetes are unknown. Objective. To evaluate the effects of a lifestyle intervention to prevent type 2 diabetes among obese prediabetic Latino adolescents on oxidized lipoproteins. Design: In a quasi-experimental design, 35 adolescents (51.4% male, age 15.5(1.0) y, body mass index (BMI) percentile 98.5(1.2), and glucose 2 hours after an oral glucose tolerance test-OGTT 141.2(12.2) mg/dL) participated in a 12-week intervention that included weekly exercise (three 60 min-sessions) and nutrition education (one 60 min-session). Outcomes measured at baseline and post-intervention were: fasting oxidized LDL and oxidized HDL (oxLDL and oxHDL) as oxidative stress variables; dietary intake of fresh fruit and vegetable (F&V) and fitness (VO2max) as behavioral variables; weight, BMI, body fat, and waist circumference as anthropometric variables; fasting glucose and insulin, 2hour glucose and insulin after an OGTT, insulin resistance (HOMA-IR), and lipid panel (triglycerides, total cholesterol, VLDL-c, LDL-c, HDL-c, and Non-HDL) as cardiometabolic variables. Results. Comparing baseline to post-intervention, significant decreases in oxLDL concentration were shown (51.0(14.0) and 48.7(12.8) U/L, p=0.022); however, the intervention did not decrease oxHDL (395.2(94.6) and 416.1(98.4) ng/mL, p=0.944). F&V dietary intake (116.4(97.0) and 165.8(91.0) g/d, p=0.025) and VO2max (29.7(5.0) and 31.6(4.7) ml*kg-1*min-1, p<0.001) significantly increased. Within-subjects correlations between changes in F&V intake and oxidized lipoproteins, adjusted for VO2max changes, were non-significant (R=-0.15, p=0.52 for oxLDL; R=0.22, p=0.25 for oxHDL). Anthropometric variables were significantly reduced (weight -1.3% p=0.042; BMI -2.2% and BMI percentile -0.4%, p=0.001; body fat -6.6% and waist circumference -1.8%, p=0.025). Cardiometabolic variables significantly improved, including reductions in glucose 2hour (-19.3% p<0.001), fasting insulin (-12.9% p=0.008), insulin 2hour (-53.5% p<0.001), and HOMA-IR (-12.5% p=0.015), with 23 participants (66%) that reverted toward a normal glucose tolerance status. Most lipid panel significantly changed (triglycerides -10.2% p=0.032; total cholesterol -5.4% p=0.002; VLDL-c -10.4% p=0.029; HDL-c -3.2% p=0.022; and Non-HDL -5.5% p=0.0007). Conclusion. The intervention resulted in differential effects on oxidized lipoproteins and significant improvements in behavioral, anthropometric and cardiometabolic variables, reducing the high metabolic risk of obese prediabetic kids.
ContributorsRenteria Mexia, Ana Maria (Author) / Shaibi, Gabriel Q (Thesis advisor) / Vega-Lopez, Sonia (Committee member) / Swan, Pamela D (Committee member) / Olson, Micah L (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Background: Hispanic women are at high risk for Type 2 Diabetes (T2D), in part due to their high prevalence of obesity, which may influence the development of insulin resistance and disease onset. Unhealthy eating contributes to T2D risk. Dietary patterns are the combination of total foods and beverages among individual’s over time, but there is limited information regarding its role on T2D risk factors among Hispanic women. Objective: To identify a posteriori dietary patterns and their associations with diabetes risk factors (age, BMI, abdominal obesity, elevated fasting blood glucose, and hemoglobin A1c) among overweight/obese Hispanic women. Design: Cross-sectional dietary data were collected among 191 women with or at risk for T2D using the Southwestern Food Frequency Questionnaire capturing the prior three months of intake. Dietary patterns were derived using exploratory factor analysis. Regression scores were used to explore associations between dietary patterns and diabetes risk factors. Results: The patterns derived were: 1) “sugar and fat-laden”, with high loads of sweets, drinks, pastries, and fats; 2) “plant foods and fish”, with high loads of vegetables, fruits, fish, and beans; 3) “soups and starchy dishes”, with high loads of soups, starchy foods, and mixed dishes; 4) “meats and snacks”, with high loads of red meat, salty snacks, and condiments; 5) “beans and grains”, with high loads of beans and seeds, whole-wheat and refined grain foods, fish, and alcohol; and 6) “eggs and dairy”, with high loads of eggs, dairy, and fats. The “sugar and fat-laden” and “meats and snacks” patterns were negatively associated with age (r= -0.230, p= 0.001 and r= -0.298, p<0.001, respectively). Scores for “plant foods and fish” were associated with fasting blood glucose (r= 0.152, p= 0.037). There were no other statistically significant relationships between the dietary patterns and risk factors for T2D. Conclusions: A variety of patterns with healthy and unhealthy traits among Hispanic women were observed. Being younger may play an important role in adhering to a dietary pattern rich in sugary and high-fat foods and highlights the importance of assessing dietary patterns among young women to early identify dietary traits detrimental for their health.
ContributorsArias-Gastelum, Mayra (Author) / Vega-Lopez, Sonia (Thesis advisor) / Der Ananian, Cheryl (Committee member) / Whisner, Corrie (Committee member) / Bruening, Meg (Committee member) / Hooker, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Objective: Parents play a critical role in their child's diets, yet there is lack of research in
the US comparing parental perception of their child’s diet with quantitatively assessed diet quality. We examined the association between parent perception of their child’s overall diet and the child’s diet quality, as measured by frequency of consumption of key food categories.
Methods: Secondary analysis was conducted using data from two independent cross- sectional panels of surveys with parents of a 3-18 year old child. Data collection took place in 2009-2010 and 2014, the random sample was drawn from low-income cities. Well-established survey questions assessed parental perception of their child’s diet and frequency of consumption of fruits, vegetables, sugar-sweetened beverages (SSB), fast food and unhealthy snacks. Diet quality scores were calculated for each child, with higher scores reflective of healthier diets (max score= 40). Ordered logistic regressions examined associations between parental perception and consumption of food categories. Multinomial logistic regressions examined associations between levels of concordance in parent perception and diet scores by demographic sub-groups.
Results: Almost half of children were non-Hispanic black (46%) and 40% were Hispanic. Overall 52% of parents strongly agreed, 33% somewhat agreed, 10% somewhat disagreed, and 4% strongly disagreed that their child eats a healthy diet. The mean diet quality score for the sample was 20.58 ± 6.7. Children from our sample with the unhealthiest diet had a mean frequency of fruit intake = 0.8 times/day and SSBs = 2.2 times/day. Children with the healthiest diet had a mean consumption of fruit=1.7/day and
SSBs= 0.4/day. Parental perception of their child’s diet was significantly higher when their child consumed more fruit (p<0.001) and vegetables (p<0.001) and lower when their child consumed more fast food (p<0.001), SSBs (p=0.01) and unhealthy snacks (p=0.02). Over half of parents overestimated the healthfulness of their child’s diet (61%). Parent, child and household demographics did not moderate this association.
Conclusions: Although parental perceptions that their child eats healthy are associated when their child eats more healthy foods and less unhealthy foods, parents’ perceptions still do not align with their child’s diet.
the US comparing parental perception of their child’s diet with quantitatively assessed diet quality. We examined the association between parent perception of their child’s overall diet and the child’s diet quality, as measured by frequency of consumption of key food categories.
Methods: Secondary analysis was conducted using data from two independent cross- sectional panels of surveys with parents of a 3-18 year old child. Data collection took place in 2009-2010 and 2014, the random sample was drawn from low-income cities. Well-established survey questions assessed parental perception of their child’s diet and frequency of consumption of fruits, vegetables, sugar-sweetened beverages (SSB), fast food and unhealthy snacks. Diet quality scores were calculated for each child, with higher scores reflective of healthier diets (max score= 40). Ordered logistic regressions examined associations between parental perception and consumption of food categories. Multinomial logistic regressions examined associations between levels of concordance in parent perception and diet scores by demographic sub-groups.
Results: Almost half of children were non-Hispanic black (46%) and 40% were Hispanic. Overall 52% of parents strongly agreed, 33% somewhat agreed, 10% somewhat disagreed, and 4% strongly disagreed that their child eats a healthy diet. The mean diet quality score for the sample was 20.58 ± 6.7. Children from our sample with the unhealthiest diet had a mean frequency of fruit intake = 0.8 times/day and SSBs = 2.2 times/day. Children with the healthiest diet had a mean consumption of fruit=1.7/day and
SSBs= 0.4/day. Parental perception of their child’s diet was significantly higher when their child consumed more fruit (p<0.001) and vegetables (p<0.001) and lower when their child consumed more fast food (p<0.001), SSBs (p=0.01) and unhealthy snacks (p=0.02). Over half of parents overestimated the healthfulness of their child’s diet (61%). Parent, child and household demographics did not moderate this association.
Conclusions: Although parental perceptions that their child eats healthy are associated when their child eats more healthy foods and less unhealthy foods, parents’ perceptions still do not align with their child’s diet.
ContributorsEliason, Jessica (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / DeWeese, Robin (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2019
Description
Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
ContributorsYoon, Hyunsoo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland S. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Background: Despite the reported improvements in glucose regulation associated with flaxseeds (Linum usitatissimum) few clinical trials have been conducted in diabetic participants. Objective: To evaluate the efficacy of ground flaxseed consumption at attenuating hyperglycemia, dyslipidemia, inflammation, and oxidative stress as compared to a control in adults with non-insulin dependent type 2 diabetes (T2D). Design: In a randomized parallel arm controlled efficacy trial, participants were asked to consume either 28 g/d ground flaxseed or the fiber-matched control (9 g/d ground psyllium husk) for 8 weeks. The study included 17 adults (9 male, 8 females; 46±14 y; BMI: 31.4±5.7 kg/m2) with a diagnosis of T2D ≥ 6 months. Main outcomes measured included: glycemic control (HbA1c, fasting plasma glucose, fasting serum insulin, and HOMA-IR), lipid profile (total cholesterol, LDL-C, HDL-C, total triglycerides, and calculated VLDL-C), markers of inflammation and oxidative stress (TNF-alpha, TBARS, and NOx), and dietary intake (energy, total fat, total fiber, sodium). Absolute net change for measured variables (week 8 values minus baseline values) were compared using Mann-Whitney U non-parametric tests, significance was determined at p ≤ 0.05. Results: There were no significant changes between groups from baseline to week 8 in any outcome measure of nutrient intake, body composition, glucose control, or lipid concentrations. There was a modest decrease in TNF-alpha in the flaxseed group as compared to the control (p = 0.06) as well as a mild decrease in TBARS in the flaxseed as compared to the control group (p = 0.083), though neither were significant. Conclusions: The current study did not detect a measurable association between 28 g/d flaxseed consumption for 8 weeks in T2D participants and improvements in glycemic control or lipid profiles. There was a modest, albeit insignificant, decrease in markers of inflammation and oxidative stress in the flaxseed group as compared to the control, which warrants further study.
ContributorsRicklefs, Kristin (Author) / Sweazea, Karen L (Thesis advisor) / Johnston, Carol S (Committee member) / Gaesser, Glenn (Committee member) / Vega-Lopez, Sonia (Committee member) / Gonzales, Rayna (Committee member) / Arizona State University (Publisher)
Created2015
Description
In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation.
In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.
In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.
In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
ContributorsRupp, Shawn Michael (Author) / Wilson Sayres, Melissa A (Thesis advisor) / Kusumi, Kenro (Committee member) / DeNardo, Dale (Committee member) / Arizona State University (Publisher)
Created2016
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is broadly accepted that physical activity provides substantial health benefits. Despite strong evidence, approximately 60% to 95% of US adults are insufficiently active to obtain these health benefits. This dissertation explored five projects that examined the measurement properties and methodology for a variety of physical activity assessment methods. Project one identified validity evidence for the new MyWellness Key accelerometer in sixteen adults. The MyWellness Key demonstrated acceptable validity evidence when compared to a criterion accelerometer during graded treadmill walking and in free-living settings. This supports the use of the MyWellness Key accelerometer to measure physical activity. Project two evaluated validity (study 1) and test-retest reliability evidence (study 2) of the Global Physical Activity Questionnaire (GPAQ) in a two part study. The GPAQ was compared to direct and indirect criterion measures including object and subjective physical activity instruments. These data provided preliminary validity and reliability evidence for the GPAQ that support its use to assess physical activity. Project three investigated the optimal h.d-1 of accelerometer wear time needed to assess daily physical activity. Using a semi-simulation approach, data from 124 participants were used to compare 10-13 h.d-1 to the criterion 14 h.d-1. This study suggested that a minimum accelerometer wear time of 13 h.d-1 is needed to provide a valid measure of daily physical activity. Project four evaluated validity and reliability evidence of a novel method (Movement and Activity in Physical Space [MAPS] score) that combines accelerometer and GPS data to assess person-environment interactions. Seventy-five healthy adults wore an accelerometer and GPS receiver for three days to provide MAPS scores. This study provided evidence for use of a MAPS score for future research and clinical use. Project five used accelerometer data from 1,000 participants from the 2005-2006 National Health and Nutrition Examination Study. A semi-simulation approach was used to assess the effect of accelerometer wear time (10-14 h.d-1) on physical activity data. These data showed wearing for 12 h.d-1 or less may underestimate time spent in various intensities of physical activity.
ContributorsHerrmann, Stephen (Author) / Ainsworth, Barbara (Thesis advisor) / Gaesser, Glenn (Committee member) / Der Ananian, Cheryl (Committee member) / Kang, Minsoo (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Latino youth are disproportionately impacted by obesity, prediabetes and type 2 diabetes (T2D). Pediatric obesity is characterized by abnormal increases in pro-inflammatory markers, including interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and monocyte chemoattractant protein-1 (MCP-1) and reductions in anti-inflammatory markers, high molecular weight adiponectin (HMW Adpn) and interleukin-10 (IL-10). Interleukin-1 receptor antagonist (IL-1ra) is an anti-inflammatory that is positively associated with obesity. IL-6, TNF-α, MCP-1 and IL-1ra have been associated with reduced insulin sensitivity and β-cell dysfunction, two central pathophysiologic mediators of glucose intolerance, while HMW Adpn and IL-10 have been associated with increased insulin sensitivity and β-cell function. The United States Diabetes Prevention Program (DPP) supported lifestyle intervention as the cornerstone approach for preventing T2D among adults with prediabetes, yet no studies to date have assessed the efficacy of an adapted DPP among Latino youth with prediabetes. In this dissertation, three studies were conducted. The first cross-sectional study among Latino youth with prediabetes and obesity (n=65) demonstrated that MCP-1 (β=-0.001, p=0.027; β=0.03, p=0.033), HMW Adpn (β=0.2, p<0.001; β=-2.2, p=0.018), and IL-1ra (β=-0.03, p=0.006; β=0.09, p=0.009) significantly predicted insulin sensitivity (measured by whole body insulin sensitivity index, WBISI) and glucose tolerance (measured by 2-hr glucose concentrations from an oral glucose tolerance test), respectively. Only HMW Adpn significantly predicted β-cell function, measured by oral disposition index, or oDI (β=0.6, p<0.001). The second study was a randomized control trial that demonstrated the efficacy of lifestyle intervention (INT, n=79) for improving oDI among Latino youth with prediabetes and obesity, compared to a usual care control (UCC, n=38) group. No differences were found for changes in WBISI (Δ0.1, p=0.899) or 2-hr glucose (Δ-7.2, p=0.260) between groups. The third study was a secondary analysis (INT n=46, UCC n=29) that demonstrated no significant effects on IL-6, TNF-α, MCP-1, HMW Adpn, IL-10, or IL-1ra (all interactions, p>0.05).
ContributorsPena, Armando (Author) / Shaibi, Gabriel Q. (Thesis advisor) / Vega-Lopez, Sonia (Committee member) / Sears, Dorothy D (Committee member) / Ayers, Stephanie L (Committee member) / Olson, Micah L (Committee member) / Arizona State University (Publisher)
Created2022