Spss regression

The genotypes for the six IrisPlex SNPs (rs12913832, rs1800407, rs12896399, rs16891982, rs1393350, rs12203592), gender and eye colours were retrieved and subjected to association testing which involved the pooled training and testing set from the previous study [11 (link)] (the total number of 1020 individuals) and prediction modeling using neural networks and logistic regression approaches. Analyses were performed with IBM SPSS Statistics v. 23 (SPSS Inc., Chicago, IL, USA). The bundle includes IBM SPSS Regression and IBM SPSS Neural Networks modules.

All data were presented as the mean ± standard deviation. For univariate analysis, we used Student's t-test and χ-square test for categorical data. For multivariate logistic regression analysis after controlling simultaneously for potential confounders, we selected independent variables which were significantly higher in AIT group compared with Diet group for univariate analysis, such as postprandial PG at 1-hr and 2-hr in 75 g OGTT and number of abnormal values in 75 g OGTT. We also used the forward selection and backward elimination methods. Logistic regression analyses with each independent variable to explore risk factors contributing to AIT were also performed. We performed receiver-operating characteristic (ROC) curve to identify clinical factors to predict the requirement for AIT. We determined a cut-off value by the point on the ROC curve closest to the upper left corner. P values of less than 0.05 were considered statistically significant. The data were analyzed with IBM SPSS Statistics Ver. 22.0 and IBM SPSS Regression (IBM).