Matching Items (23)
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Background: Sugars form advanced glycation end products (AGEs) throughnatural metabolism and interactions with proteins, lipids, and nucleic acids, which accumulate in tissues and have been implicated in the etiology of chronic diseases. Due to the increased consumption of fructose and its high ability to form AGEs, a further understanding of

Background: Sugars form advanced glycation end products (AGEs) throughnatural metabolism and interactions with proteins, lipids, and nucleic acids, which accumulate in tissues and have been implicated in the etiology of chronic diseases. Due to the increased consumption of fructose and its high ability to form AGEs, a further understanding of this association is important to clarify the role of sugars in disease. The objective was to explore the association between usual fructose intake and serum levels of AGEs, as measured by carboxymethyl-lysine (CML) and methylglyoxal derivative (MG-H1), in healthy adults. Methods: This is a secondary analysis of a 15-d controlled feeding study (n=100) with participants consuming their usual diet conducted in the Phoenix metropolitan area. To assess participants’ usual diet, they were asked to complete two 7-d food diaries, which were then used to create custom 15-d menu plans administered during the feeding period. Forty participants were selected based on their 15-d mean total fructose intake for this analysis [top and bottom 20% of the sample distribution (median, IQR); high fructose (HF) n= 20, 72.6 (66.1-90.4) g/day, low fructose (LF) n= 20, 28.8 (22.7-32.2) g/day. Fasting serum collected five weeks after the feeding period were analyzed for CML and MG-H1, two well-established AGEs, using ELISA kits. A database of 549 common foods with known CML amounts was used to calculate exogenous CML intake based on daily food intake data. A general linear model was fitted to investigate the difference in serum CML and MG-H1 between LF and HF groups while adjusting for age, gender, BMI, and exogenous CML intake. Results: Participants in the HF group had significantly higher serum CML and lower MG-H1 levels compared to participants in the LF group (p=0.013 and p=0.002, respectively). This difference remained statistically significant after adjusting for covariates. Conclusions: The findings suggest that endogenous CML formation may be an explanation for the significantly higher serum CML levels in the HF compared to the LF group. This is significant in further understanding mechanisms of fructose intake and disease etiology and could have implications for at-risk populations consuming a high fructose diet.
ContributorsWeigand, Bethany (Author) / Tasevska, Natasha (Thesis advisor) / Sweazea, Karen (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2021
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Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with

Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with MS, multivariate statistics and advanced machine learning algorithms provide myriad opportunities for bioinformatics insights beyond simple univariate data comparisons. In this dissertation, the application of MS-based metabolomics is introduced with an emphasis on biomarker discovery for human disease detection. To advance disease diagnosis using MS-based metabolomics, numerous statistical techniques have been implemented in this research including principal component analysis, factor analysis, partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA, random forest, receiver operating characteristic analysis, as well as functional pathway/enzyme enrichment analyses. These approaches are highly useful for improving classification sensitivity and specificity related to disease-induced biological variation and can help identify useful biomarkers and potential therapeutic targets. It is also shown that MS-based metabolomics can distinguish between clinical and prodromal disease as well as similar diseases with related symptoms, which may assist in clinical staging and differential diagnosis, respectively. Additionally, MS-based metabolomics is shown to be promising for the early and accurate detection of diseases, thereby improving patient outcomes, and advancing clinical care. Herein, the application of MS methods and chemometric statistics to the diagnosis of breast cancer, coccidioidomycosis (Valley fever), and senile dementia (Alzheimer's disease) are presented and discussed. In addition to presenting original research, previous efforts in biomarker discovery will be synthesized and appraised. A Comment will be offered regarding the state of the science, specifically addressing the inefficient model of repetitive biomarker discovery and the need for increased translational efforts necessary to consolidate metabolomics findings and formalize purported metabolic markers as laboratory developed tests. Various factors impeding the translational throughput of metabolomics findings will be carefully considered with respect to study design, statistical analysis, and regulation of biomedical diagnostics. Importantly, this dissertation will offer critical insights to advance metabolomics from a scientific field to a practical one including targeted detection, enhanced quantitation, and direct-to-consumer considerations.
ContributorsJasbi, Paniz (Author) / Johnston, Carol S (Thesis advisor) / Gu, Haiwei (Thesis advisor) / Lake, Douglas F (Committee member) / Sweazea, Karen (Committee member) / Tasevska, Natasha (Committee member) / Arizona State University (Publisher)
Created2022
Description

In March 2020, the COVID-19 pandemic triggered a sudden and severe economic downturn. Between February and May 2020, the number of unemployed individuals rose by more than 14 million, resulting in an unprecedented increase in the unemployment rate, which went from 3.8% in February to 14.4% in April. Even though unemployment

In March 2020, the COVID-19 pandemic triggered a sudden and severe economic downturn. Between February and May 2020, the number of unemployed individuals rose by more than 14 million, resulting in an unprecedented increase in the unemployment rate, which went from 3.8% in February to 14.4% in April. Even though unemployment has declined in recent months, with some individuals returning to work, the rate is still much higher than it was one year ago (7.9% in September 2020 vs. 3.5% in September 2019). Further, as of September 2020, there are 19.4 million persons unable to work due to the pandemic, as well as 6.3 million persons working only part time even though they would prefer to work more.

Created2020-11
Description

The coronavirus (COVID-19) pandemic has affected employment and food security globally and in the United States. To understand the impacts of COVID-19 on food security in Arizona, a representative survey of Arizona households was launched online from July 1 to August 10, 2020. This brief provides an overview of changes

The coronavirus (COVID-19) pandemic has affected employment and food security globally and in the United States. To understand the impacts of COVID-19 on food security in Arizona, a representative survey of Arizona households was launched online from July 1 to August 10, 2020. This brief provides an overview of changes in food security rate, perceived worries and challenges about food security, as well as behavioral changes and strategies adopted since the pandemic. Additional briefs from the Arizona survey covering topics on economic consequences, food access, and participations in food assistance programs during the pandemic are also available.

ContributorsAcciai, Francesco (Author) / Yellow Horse, Aggie J. (Author) / Martinelli, Sarah (Author) / Josephson, Anna (Author) / Evans, Tom P. (Author) / Ohri-Vachaspati, Punam (Author)
Created2020-11
Description

The coronavirus (COVID-19) pandemic led to disruptions in the food supply and high rates of unemployment and under-employment, both in Arizona and nationally. These emergencies required food assistance programs to adapt quickly and in unprecedented ways by relaxing eligibility criteria, improvising on delivery modalities, and increasing benefits. To examine food assistance program

The coronavirus (COVID-19) pandemic led to disruptions in the food supply and high rates of unemployment and under-employment, both in Arizona and nationally. These emergencies required food assistance programs to adapt quickly and in unprecedented ways by relaxing eligibility criteria, improvising on delivery modalities, and increasing benefits. To examine food assistance program participation during the pandemic, we collected data from a representative sample of 620 Arizona households. The sample was drawn from across Arizona in July-August 2020 using an online survey. This brief provides the summary for participation in key food assistance programs, namely, the Supplementary Nutrition Assistance Program (SNAP), the Special Supplemental Program for Women Infants and Children (WIC), School Food Programs, and the emergency food assistance provided through food pantries.

ContributorsMartinelli, Sarah (Author) / Acciai, Francesco (Author) / Yellow Horse, Aggie J. (Author) / Josephson, Anna (Author) / Ohri-Vachaspati, Punam (Author)
Created2020-11
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With more than 19 million confirmed COVID-19 cases across the United States1 and over 500,000 in Arizona as of December 2020, the ongoing pandemic has had devastating impacts on local, national, and global economies. Prior to the pandemic (February 2020), based on U.S. Bureau of Labor Statistics data, the unemployment rate

With more than 19 million confirmed COVID-19 cases across the United States1 and over 500,000 in Arizona as of December 2020, the ongoing pandemic has had devastating impacts on local, national, and global economies. Prior to the pandemic (February 2020), based on U.S. Bureau of Labor Statistics data, the unemployment rate in Arizona was 6.5%, compared to 4.9% at the national level.3 Since the beginning of the COVID-19 pandemic (March 2020), the United States has experienced striking increases in the unemployment rate, reaching 13.2% in April. Similarly, in Arizona, the unemployment rate jumped to over 13.5% in April. The unemployment rates have since declined both nationally and in Arizona but remain higher compared to February 2020. In November 2020 (the most recent data available), the national unemployment rate was 6.7%, while in Arizona the rate was 7.8%—the 10th highest unemployment rate among all U.S. states.

Created2020-12
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As of May 2022, there have been more than 80 million confirmed cases of COVID-19 across the United States, and over two million cases in Arizona. The pandemic has had a devastating impact on local, national, and global economies. This brief features the findings from data collected from a survey

As of May 2022, there have been more than 80 million confirmed cases of COVID-19 across the United States, and over two million cases in Arizona. The pandemic has had a devastating impact on local, national, and global economies. This brief features the findings from data collected from a survey administered to Arizona residents in April of 2021, as well as national statistics, to understand some of the economic consequences of COVID-19 and its impacts on Arizona households.

Created2022-06-01
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The onset of the COVID-19 pandemic in March 2020 and the resulting closures of schools, businesses, and restaurants led to a massive economic disruption in Arizona. The unemployment rate at its peak reached 14.2% (April 2020) - a level even higher than during the great recession of 2008. High unemployment

The onset of the COVID-19 pandemic in March 2020 and the resulting closures of schools, businesses, and restaurants led to a massive economic disruption in Arizona. The unemployment rate at its peak reached 14.2% (April 2020) - a level even higher than during the great recession of 2008. High unemployment rates, coupled with a breakdown of local and national food supply chains, led to a remarkable increase in food insecurity rates among Arizona households. More than a year later, as vaccines became widely available and restrictions were lifted, schools and business began to reopen, and most activities slowly returned to pre-pandemic standards. The effects of the pandemic on food insecurity and food-related behaviors, however, might have long-lasting effects. This brief describes levels of food insecurity, food assistance program participation, job disruption, and food related behaviors among 814 households in Arizona, in the 12 months preceding the pandemic (March 2019 – March 2020) and approximately one year after the onset of the COVID-19 pandemic ( January 2021 –April 2021). Data collection took place between April and May 2021.

Created2021-08
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Urinary sucrose and fructose has been suggested as a predictive biomarker of total sugars intake based on research involving UK adults. The purpose of this study was to determine the association between total sugars consumption and 24-hour urinary sucrose and fructose (24uSF) in US adult population and to investigate the

Urinary sucrose and fructose has been suggested as a predictive biomarker of total sugars intake based on research involving UK adults. The purpose of this study was to determine the association between total sugars consumption and 24-hour urinary sucrose and fructose (24uSF) in US adult population and to investigate the effect of physical activity on this association. Fifty seven free-living healthy subjects 20 to 68 years old, participated in a 15-day highly controlled feeding study, consuming their habitual diet, provided by the research metabolic kitchen. Dietary sugars were estimated using Nutrition Data System for Research (NDSR). Subjects collected eight 24-hour urine samples measured for urinary sucrose and fructose. Physical activity was assessed daily using a validated 15-day log that inquired about 38 physical activities across six domains; home activities, transportation, occupation, conditioning, sports and leisure. The mean total sugars intake and added sugars intake of the sample was 112.2 (33.1) g/day and 65.8 (29.0) g/day (9.7%EI), respectively. Significant moderate positive correlation was found between 15-d mean total sugars intake and 8-day mean 24uSF (r = 0.56, p < 0.001). Similarly, added sugars were moderately correlated with 24uSF (r = 0.56, p < 0.001), while no correlation was found between naturally-occurring sugars and 24uSF (r = 0.070, p < 0.001). In a linear multiple regression, total and added sugars each explained 30% of variability in 24uSF (Adjusted R2, p value; total sugars: 0.297, 0.001; added sugars: 0.301, p < 0.001). Physical activity had no effect on the association between dietary and urinary sugars in neither the correlation nor the linear regression analysis. 24uSF can be used as a biomarker for total and added sugars consumption in US adults, although its predictability was weaker compared to findings involving UK adults. No evidence was found showing that physical activity levels affect the association between 24uSF and total sugars intake in US adults. More detailed investigation through future feeding studies including subjects with wide range of sugars intake and of different ethnic/racial backgrounds are needed to better understand the characteristics of the biomarker and its uses.
ContributorsMohan, Chitra (Author) / Tasevska, Natasha (Thesis advisor) / Ainsworth, Barbara (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2019
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Background: Higher intake of carbohydrates in the evening and later eating times has been associated with higher total energy intake (TEI)1-3 and higher risk of being overweight or obese.1,4 Though existing evidence indicates a link between added sugars intake and increased body mass index (BMI), the effect of daily patterns

Background: Higher intake of carbohydrates in the evening and later eating times has been associated with higher total energy intake (TEI)1-3 and higher risk of being overweight or obese.1,4 Though existing evidence indicates a link between added sugars intake and increased body mass index (BMI), the effect of daily patterns of added sugars intake on TEI and BMI is unknown. Research on added sugars has relied on self-report dietary assessments with limited days of dietary data, resulting in unreliable estimates. The purpose of this thesis was to describe patterns of added sugars consumption, and to investigate the relationship between dietary sugars, eating patterns, TEI, and BMI using 15-days of dietary data from a feeding study. Methods: 40 participants age 18 to 70 years completed a 15-d highly controlled feeding study which imitated their normal diet, while recording meal times. Meals and snacks were coded based on participant identified, time-of-day, and meal content specific criteria. All consumed foods and beverages were carefully weighed and entered into the Nutrition Data System for Research (NDSR) for analysis. Pearson correlation, independent t-test, one-way repeated measures analysis of variance (ANOVA) with post hoc tests, and multiple linear regressions were used to investigate the association between patterns of added sugars and energy intake, as well as eating frequency (EF), with TEI and BMI. Results: 15-d median added sugars intake was 9.7% of total calories. The highest contribution to added sugars intake (% of g/d) came from snacks (44%) in women and from afternoon (39%) consumption in men. The highest contribution to TEI came from dinner (30%) and afternoon (34%) consumption in women, and from lunch (31%) or dinner (30%) and afternoon (35%) consumption in men. Total eating occasion (EO) frequency had a negative association with TEI (r = -0.31) and no association with % energy from added sugars. In multivariate regression models, besides sex, % energy from beverages only (Adjusted R2 = 0.41) and % added sugars from dinner (Adjusted R2 = 0.39) were significant predictors of TEI, while none of the variables were associated with BMI. Conclusion: Changing one’s pattern of eating, (EF and % energy from beverages only and % added sugars from dinner), may reduce TEI, potentially reducing BMI.
ContributorsGunnerson, Hannah Marie (Author) / Tasevska, Natasha (Thesis advisor) / Johnston, Carol (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Arizona State University (Publisher)
Created2019