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Background and Aim: Obesity causes a decrease kidney function and an increase in kidney volume. The aim of this study was to understand the relationship among kidney volume, obesity and blood pressure in Mexican-American children in South Texas. Methods: To study those effects, data was collected from 454 ultrasound done on 289 girls and 762 ultrasound done on 382 boys visiting a pediatric clinic in South Texas from 2003 to 2018. The relationship between kidney volume and obesity was analyzed. IBM SPSS is used for data analysis. Results: Children with fatty livers have a higher kidney volume when compared to children with non-fatty livers. When comparing children classified as BMI percentile (0, 50%), BMI percentile [50%, 85%), BMI percentile [85%, 95%), and BMI percentile above 95%, the kidney volume is increasing as BMI percentile increases. We also found that there is a positive relationship between the kidney volume and systolic blood pressure. Children with high systolic blood pressure (above 119 mmHg) have a larger volume when compared to children with low blood pressure (above 110 but less than or equal 119 mmHg), 110 and below mmHg. Conclusion: Obesity causes inflammation, and lipid accumulation. These effects can cause an increase in kidney volume. Kidney volume increases can be caused by hypertension. This is even severe for Mexican-American children in south Texas. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the possible factors causing fatty liver in children based on ultrasound data of children in south Texas, and to establish machine learning models of fatty liver in children to provide ideas for the prevention and treatment of fatty liver in children.Methods: The binary classification model of fatty liver problem in obese children in Texas was established under the multiple model. First, we selected important features using the CatBoost algorithm. Second, the best parameters of the algorithm were selected on the training set and the validation set by using the grid search method, and all six models were tested on the test set. The six models then were compared by area under the curve value, precision, accuracy, recall rate, and F1 score in a model evaluation. Then, two algorithms, logic regression and CatBoost, were selected to establish prediction models of fatty liver disease in children.Results: We selected body mass index, height, liver size, kidney volume, glomerular filtration rate, and liver diameter as the features used in the machine learning model. The prediction models we chose showed that children with higher body mass index at the same age tended to have a greater probability of fatty liver.Conclusions: Based on the analysis of the results of the two prediction models established by logistic regression and CatBoost, we determined that the mean probability of fatty liver in severely obese children was between 74.47% and 92.22%, 73.45% and 85.41% in obese children, and slightly higher in boys than in girls, with a mean difference of 3.00% to 3.95%. [ABSTRACT FROM AUTHOR]