Spss statistics software version 20

Baseline characteristics of the RA cohort and incidence of CV events were presented by appropriate descriptive statistics. COX regression analysis was performed to determine CV event risk and contributing risk factors over time. Statistical analyses were performed using IBM-SPSS statistics software version 20.0.

Statistical analyses were done with SPSS Statistics software version 20.0 (IBM Corp., Armonk, NY, USA). The Kruskal–Wallis test was used to assess the significant differences in the microbial phyla and genera between the two groups. A t-test was performed to assess the differences in pH, VFA, lactate, and metabolite levels. Statistical significance was declared at P < 0.05. The P values of the metabolomics and microbial data were corrected using a false discovery rate (FDR). The FDR-corrected P -values below 0.05 were considered statistically significant.
A partial least squares discriminant analysis (PLS-DA) was accomplished with SIMCA-P software. The PLS-DA was determined by the goodness-of-fit parameter (R2X) and the predictive ability parameter (Q2). Based on the PLS-DA results, the metabolites were plotted in accordance with their importance in the separation. Every compound had a value—the variable importance in the projection (VIP)—where VIP > 1.0 indicated that a metabolite was significantly different. The Spearman’s rank correlation coefficient was calculated with GraphPad Prism software version 6.00 (GraphPad Software, San Diego, CA, USA) to evaluate the metabolites and microbiota.

The statistical analysis of this work was performed using IBM^® SPSS Statistics software version 20.0. Fisher’s exact test was used to compare categorical variables. For normally distributed variables, we used Student’s t-test. For non-parametric variables, we used the Mann–Whitney U-test. SF36 variables and IPAQ scores were compared with Pearson’s correlation. The level of significance adopted for this research was p < 0.05. Post hoc power analysis was performed using the G*power program [36 (link)]. When analyzing the physical and mental components of the SF36 summary scores, we obtained effect sizes (d) of 1.75 and 1.37, respectively.

Pearson’s chi-square test was used to analyze deviation from Hardy-Weinberg equilibrium (HWE). We used the Open Source Epidemiologic Statistics for Public Health software, version 3.01, for the estimation of sample size and power of study. Chi-square test was used for comparing categorical variables. The association between IL-35 and demographic data was analyzed using multivariable logistic regression and adjusted for other covariates. P<0.05 was considered statistically significant. Statistical analysis was performed using the IBM SPSS Statistics software, version 20.0 (SPSS Inc., Chicago, Illinois, United States of America).