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- All Subjects: Diabetes
- Creators: Sweazea, Karen
- Creators: College of Health Solutions
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
Chronic diseases place a financial burden on the United States and claim the lives of nearly 2 million Americans every year. Among the chronic diseases that plague American people, type 2 diabetes is particularly prevalent and injurious. Thus, action is warranted to improve prevention and management of this disease. Nutrition plays a significant role in prevention and management of type 2 diabetes and other chronic diseases. Registered dietitians, as nutrition experts, are qualified to use medical nutrition therapy as a method of prevention and treatment for chronic diseases using a nutritional approach. However, there is no consensus as to which eating pattern is the most efficacious. The aim of this review of research was to examine how plant-based eating patterns impact chronic disease conditions, with an emphasis on type 2 diabetes mellitus, as compared to omnivorous eating patterns. A literature search was conducted through the ASU Library, PubMed, and CINAHL using terms related to plant-based diets and chronic diseases, such as type 2 diabetes. The results revealed that a plant-based eating pattern may be beneficial in the prevention and treatment of certain chronic diseases, such as type 2 diabetes. Specifically, adults who have type 2 diabetes and consume a plant-based diet may exhibit enhanced glycemic control as evidenced by less insulin resistance, increased incretin and insulin secretion, greater insulin sensitivity, and improved HbA1c levels. There is sufficient evidence for registered dietitians to recommend a plant-based approach to patients with type 2 diabetes who would like to achieve enhanced glycemic control.
Objective: There were three main objectives of the study. One objective was to elucidate potential new relationships via linear regression. Another objective was to determine which factors were indicative of Type 2 DM in the population. Finally, the last objective was to compare the incidence of Type 2 DM in the dataset to trends seen elsewhere.
Methods: The dataset was uploaded from an open source site with citation onto Python. The dataset, created in 1990, was composed of 768 female patients across 9 different attributes (Number of Pregnancies, Plasma Glucose Levels, Systolic Blood Pressure, Triceps Skin Thickness, Insulin Levels, BMI, Diabetes Pedigree Function, Age and Diabetes Presence (0 or 1)). The dataset was then cleaned using mean or median imputation. Post cleaning, linear regression was done to assess the relationships between certain factors in the population and assessed via the probability statistic for significance, with the exclusion of the Diabetes Pedigree Function and Diabetes Presence. Reverse stepwise logistic regression was used to determine the most pertinent factors for Type 2 DM via the Akaike Information Criterion and through the statistical significance in the model. Finally, data from the Center of Disease Control (CDC) Diabetes Surveillance was assessed for relationships with Female DM Percenatge in Pinal County through Obesity or through Physical Inactivity via simple logistic regression for statistical significance.
Results: The majority of the relationships found were statistically significant with each other. The most pertinent factors of Type 2 DM in the dataset were the number of pregnancies, the plasma glucose levels as well as the Blood Pressure. Via the USDS Data from the CDC, the relationships between Female DM Percentage and the obesity and inactivity percentages were statistically significant.
Conclusion: The trends found in the study matched the trends found in the literature. Per the results, recommendations for better diabetes control include more medical education as well as better blood sugar monitoring.With more analysis, there can be more done for checking other factors such as genetic factors and epidemiological analysis. In conclusion, the study accomplished its main objectives.
within cells that result in changes in the metabolism of carbohydrates and immune functions.
Ingesting glucocorticoids has also been linked to insulin resistance, a main feature of Type 2
diabetes. Experiments including polymerase chain reaction, western blotting, and glycogen
synthase analysis were conducted to determine if exposure to higher doses of dexamethasone, a
glucocorticoid, induces insulin resistance in cultured rat skeletal muscle via interaction with
thioredoxin-interacting protein (TXNIP). Treatment with dexamethasone was shown to cause
mild increases in TXNIP while a definitive increase or decrease in insulin signaling was unable
to be determined.