https://www.selleckchem.com/products/kn-62.html Background Risk of hyperuricemia (HU) has been shown to be strongly associated with dietary factors. However, there is scarce evidence on prediction models incorporating dietary factors to estimate the risk of HU. Objective The aim of this study was to develop a prediction model to predict the risk of HU in Chinese adults based on dietary information. Design Our study was based on a cross-sectional survey, which recruited 1,488 community residents aged 18 to 60 years in Beijing from October 2010 to January 2011. The eligible participants were randomly divided into a training set (n 1 = 992) and a validation set (n 2 = 496) in the ratio of 21. We developed the prediction model in three stages. We first used a logistic regression model (LRM) based on the training set to select a set of dietary risk factors which were related to the risk of HU. Artificial neural network (ANN) was then used to construct the prediction model using the training set. Finally, we used receiver operating characteristic (ROC) curve ana in our study is successful and valuable. Conclusions This study suggests that the ANN model could be used to predict the risk of HU in Chinese adults. Further prospective studies are needed to improve the accuracy and to generalize the use of model. © 2020 Jie Zeng et al.INTRODUCTION Electronic cigarettes (e-cigarettes) have rapidly become the most commonly used tobacco product among youth in the United States. Exposure to advertising, peer use, and household use, increases the risk of current e-cigarette use; however, the influence of these factors may be dynamic across adolescence. The aim of this study is to examine the age-varying associations between e-cigarette use and peer use, household use, and exposure to e-cigarette commercials among alternative high school students in Southern California. METHODS Using data previously collected for a tobacco marketing study, we examine the age-varying associations of c