Prism 7.0a

All data have been presented as mean ± SEM. Continuous data with two treatment groups were analyzed with a Student’s t-test. Categorical data were analyzed by Fisher’s exact test. Experiments with continuous data and only two treatments were compared using Student’s t-test. Experiments comparing two treatments over time were analyzed by two-way ANOVA with Sidak’s multiple comparisons test comparing treatments (e.g., cell lines) within the same time point. All analyses were performed in Prism 7.0a. p < 0.05 was considered statistically significant.

The continuous variables are expressed as mean ± SD and categorical variables are reported in percentages. The normality of data was test by Shapiro Wilk (S-W test). The independent-samples t test and the Mann–Whitney U test were applied for normally and non-normally distributed data, respectively. Correlations were detected by Pearson’s or Spearman’s depending on the distribution of the data. If Pearson’s correlation analysis was statistically significant, multiple linear regression analysis was performed. Multiple linear regression analyses were performed to determine the association of urinary uric acid excretion with prostaglandins. Statistical significance for all analysis was set at P < 0.05. Statistical analysis was performed with software SPSS 22.0 and Prism 7.0a.

Figures and statistics were performed using the Prism 7.0a software. All the data shown represent the mean ± SEM. Chi-Square test was performed to compare the relative abundance of the bacterial phylum/genus of the intestinal microbiota. Data that were not normally distributed according to the D’Agostino & Pearson normality test were log transformed and distribution was re-assessed. Student’s t test or ANOVA with the Newman-Keuls test were used to determine significant differences between two groups or to analyze significant differences among multiple test groups, respectively.

The continuous variables are reported in mean ± SD and categorical variables are presented in percentages. Medians with inter quartile range (IQR) are presented in case of nonparametric data. Comparisons between groups were made by Student’s t-test and one-way ANOVA. Least significance difference (LSD) test was used when the variance was homogeneous, or Tamhane’s T2 test was used. Non-normally distributed data was compared using Mann-Whitney U test. Categorical data was compared using the Chi-square test. Correlations were detected by Pearson’s or Spearman’s depending on the distribution of the data. If Pearson’s correlation analysis was statistically significant, multiple linear regression analysis was performed. Multiple linear regression analyses were performed to determine the association of urinary uric acid excretion with 24 h ambulatory blood pressure values. Statistical significance for all analyses was set at P < 0.05. Statistical analysis was performed with software SPSS 22.0, Stata 14.0, and Prism 7.0a.

The ANOVA assessed differences between means of values; a Sidak’s multiple comparisons test was used in case of multiple comparisons. These statistical analyses, along with linear regression analysis, were performed using Prism GraphPad 7.0a for Macintosh. A p-value ≤ 0.05 was considered statistically significant.