Statistical analysis
CKD
REGRESSION
This study was conducted as an exploratory pilot study, with CKD patients recruited for the study whenever possible. Therefore, there was no formal calculation of sample size. Instead, the available data were used to determine the sample size. Statistical data were analyzed using SPSS (v.26; SPSS, Inc), with qualitative variables expressed as rates or percentages and continuous variables described by means ± standard deviation (SD) or median and 25th-75th interquartiles. We calculated the variables’ changes (deltas) as final value minus baseline value. For between-group comparisons, we used an independent Student’s t-test for data with a normal distribution, and a Mann-Whitney U-test for data with a non-normal distribution. For within-group comparisons, we used a paired t-test for normally distributed data, and a Wilcoxon signed-rank test for nonnormally distributed data. Qualitative data proportion or categorical variables were measured by a chi-square test or Fisher’s exact test. Due to the small number of missing value, the missing data was imputed using mean imputation. We adjusted for confounding factors using multiple linear regression, and selected the ENTER method for screening variables. Any variables with uneven baselines, clinically relevant variables with The gut microbiota alpha diversity index (chao1 index) used a t-test. Statistical significance of the beta diversity weighted and un-weighted UniFrac matrices were measured by ANOSIM. Alpha and beta diversity, as well as Principal Coordinate Analysis (PCoA), were analyzed using QIIME (v.1.8.0) (