The spatial distribution of BUN reference values of Chinese healthy adults: a cross-section study.

The blood urea nitrogen (BUN) is generally regarded as a significant serum marker in estimating renal function. This study aims to explore the geographical distribution of BUN reference values of Chinese healthy adults, and provide a scientific basis for determining BUN reference values of Chinese healthy adults of different regions according to local conditions. A total of 25,568 BUN reference values of healthy adults from 241 Chinese cities were collected in this study, and 17 geographical indices were selected as explanatory variables. The correlation analysis was used to examine the significance between BUN reference value and geographical factors, then five significant indices were extracted to build two predictive models, including principal component analysis (PCA) and support vector regression (SVR) model, then the optimal model was selected by model test to predict BUN reference values of the whole China, finally the distribution map was produced. The results show that BUN reference value of Chinese healthy adult was characteristically associated with latitude, altitude, annual mean temperature, annual mean relative humidity, and annual precipitation. The model test shows, compared with SVR model, the PCA model possesses superior simulative and predictive ability. The distribution map shows that the BUN reference values of Chinese healthy adult are lower in the east and higher in the west. These results indicate that the BUN reference value is significantly affected by geographical environment, and the BUN reference values of different regions could be seen clearly on distribution map.

The effect of geographical indices on left ventricular structure in healthy Han Chinese population.

The left ventricular posterior wall thickness (LVPWT) and interventricular septum thickness (IVST) are generally regarded as the functional parts of the left ventricular (LV) structure. This paper aims to examine the effects of geographical indices on healthy Han adults' LV structural indices and to offer a scientific basis for developing a unified standard for the reference values of adults' LV structural indices in China. Fifteen terrain, climate, and soil indices were examined as geographical explanatory variables. Statistical analysis was performed using correlation analysis. Moreover, a back propagation neural network (BPNN) and a support vector regression (SVR) were applied to developing models to predict the values of two indices. After the prediction models were built, distribution maps were produced. The results show that LV structural indices are characteristically associated with latitude, longitude, altitude, average temperature, average wind velocity, topsoil sand fraction, topsoil silt fraction, topsoil organic carbon, and topsoil sodicity. The model test analyses show the BPNN model possesses better simulative and predictive ability in comparison with the SVR model. The distribution maps of the LV structural indices show that, in China, the values are higher in the west and lower in the east. These results demonstrate that the reference values of the adults' LV structural indices will be different affected by different geographical environment. The reference values of LV structural indices in one region can be calculated by setting up a BPNN, which showed better applicability in this study. The distribution of the reference values of the LV structural indices can be seen clearly on the geographical distribution map.