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.

This feasibility study explored the use of an evolutionary mismatch narrative in nutritional education intervention aiming to reduce ultra-processed foods in the diets of veterans with type 2 diabetes and improve diabetic outcomes. Ultra-processed foods are foods that are primarily manufactured through industrial processes. These foods are high in calories but low in nutritional content. Diets high in these foods have been linked to increased health risks. One of the major health risks is type 2 diabetes. Type 2 diabetes is a chronic disease that is developed when cells become unable to properly utilize insulin. Over time this may lead to additional health conditions such as nerve damage, cardiovascular disease, and renal disease. Evolutionary mismatch narrative nutritional intervention offers a different approach to nutritional education to help reduce ultra-processed foods in diets. This study was a randomized controlled feasibility study at the Phoenix VA. Eleven participants were enrolled and randomly selected to be given either an evolutionary mismatch narrative education intervention or general nutritional education about ultra-processed foods. 24-hour diet recalls and blood chemistry were collected and analyzed. Blood chemistry provided diabetes related measurements which included glucose, HbA1c, insulin, HOMA-IR, and C-reactive protein. Statistically significant findings in this study included percentage of ultra-processed foods decreasing for both control and experimental groups from week 0 to week 4 (p=0.014), and C-reactive protein levels between the control and experimental groups (p=0.042). However, baseline C-reactive protein concentrations were lower in the experimental group such that normalizing for group differences at baseline revealed no significant difference in C-reactive protein change between interventions (p = 1.000). There were no other statistically significant values regarding diabetes related measurements. The results from this study suggest that nutritional education in general may help decrease ultra-processed food consumption.