Might E-cadherin promoter polymorphisms of rs16260 and rs5030625 associate with the risk of nephrolithiasis?

Prediction model of artificial neural network for the risk of hyperuricemia incorporating dietary risk factors in a Chinese adult study

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 (n₁ = 992) and a validation set (n₂ = 496) in the ratio of 2:1. 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 analysis to assess the accuracy of the prediction model using training and validation sets.

Results

In the training set, the mean age of participants with and without HU was 39.3 (standard deviation [SD]: 9.65) and 38.2 (SD: 9.38) years, respectively. Patients with HU consisted of 101 males (77.7%) and 29 females (22.3%). The LRM found that food frequency (vegetables [odds ratio (OR) = 0.73], meat [0.72], eggs [0.80], plant oil [0.78], tea [0.51], eating habits (breakfast [OR = 1.28]), and the salty cooking style (OR = 1.33) were associated with risk of HU. In the ANN analysis, we selected a three-layer back propagation neural network (BPNN) model with 14, 3, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The areas under the ROC of the training and validation sets were 0.827 and 0.814, respectively. HU would occur when the incidence probability is greater than 0.128. The indicators of accuracy, sensitivity, specificity, and Yuden Index suggested that the ANN model 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.

E-cadherin gene polymorphisms and susceptibility to urolithiasis in Iraqi children

AIM

This case/control study aimed to assess the impact of two single nucleotide polymorphisms (SNPs) in the promoter region of CDH1 gene (-160C>A and -347G>GA) on urinary stone formation in a sample of Iraqi children.

METHODS

Forty-seven children with urolithiasis and 50 age- and gender-matched healthy controls were included in the study. DNA was isolated from peripheral blood and direct sequencing was used for genotyping.

RESULTS

The homozygous genotype of the SNP CHD1 -160C>A was more frequent in control than cases (18% vs. 6.38%) with significant difference (OR = 0.229, 95%CI = 0.056-0.943, P = 0.041). Furthermore, cases showed significantly less frequency of the mutant allele (allele A) of this SNP (OR = 0.403, 95%C = 0.210-0.776, P = 0.007).

CONCLUSION

These results strongly indicate a protective role of allele A of the SNP CHD1 -160C>A against urinary calculi formation in children.