Spss statistics software for windows version 21

Chi-squared or Fisher’s exact test was used for categorical data. The Mann–Whitney U test was used to compare continuous variables. Overall survival was assessed by the Kaplan–Meier analysis with the log-rank test. Factors that were significant in univariate survival analysis were entered into the multivariate Cox proportional hazards model to determine the independent predictors associated with survival. The IBM SPSS Statistics for Windows software, version 21.0 (IBM Corp., Armonk, NY, USA), was used for statistical analysis. A p value < 0.05 was considered statistically significant.

Data are presented as mean±s.d. A logarithmic transformation was performed for skewed variables. To assess differences between baseline and follow-up measurements, the paired samples t-test was used. To adjust the results of analyses of 23 individual FA levels in KOBS and DPS for multiple comparisons, Bonferroni correction was used and thus P-value <0.002 was considered statistically significant. Linear regression analysis, with weight loss and serum fasting insulin as independent variables, was used to estimate their effect on serum FA composition. For the analyses of subcutaneous adipose tissue mRNA expression in KOBS, the expression levels for each gene per sample in the custom gene panel were normalized based on the total number of aligned reads of the corresponding sample and the results are shown as percentage of total transcript reads. A nominal P-value <0.05 was considered statistically significant. Statistical significance for changes in FAs between the two study groups was calculated with independent samples t-test. There was homogeneity of variances for all FAs in independent samples t-test, as assessed by Levene's test, except for n-3 FAs (P=0.0002). The IBM SPSS Statistics for Windows software, Version 21 (IBM Corp., Armonk, NY, USA), was used for statistical analyses.

The IBM SPSS Statistics for Windows software, version 21 (IBM Corp., Armonk, NY, USA), was used to enter and analyze data. Continuous variables were presented as mean ± standard deviation (SD), and frequency and percentages were used to describe categorical variables. We computed the prevalence of renal impairment and its 95% confidence intervals (CIs). In univariate analysis, we used the Chi-square test to determine the association between renal impairment and polypharmacy. To identify factors associated with renal impairment, we utilized a multivariable Poisson regression model with robust variance, and the findings were reported as adjusted prevalence ratio (aPR) with a 95% Confidence Intervals (95%CI). We chose this model because odds ratios obtained in cross-sectional studies using logistic regression may overestimate prevalence ratio when the outcome is prevalent [27 (link)]. The model included variables found to be relevant in the literature and those determined to be significant by univariate analysis. All P-values were two-sided, and P < 0.05 was considered statistically significant.

Data were analyzed using SPSS Statistics for Windows Software, version 21 (IBM Software, Armonk, NY, USA). Descriptive statistics were used to calculate the maximum, minimum, mean, and standard deviation values. The normality of the data was assessed by the Shapiro–Wilk test. Comparison of two and more than two groups was performed by the one-way analysis of variance (ANOVA) test followed by post hoc Tukey’s test. Repeated measurement analysis was also carried out for weight measurement analysis. All the p values of less than 0.05 were considered statically significant.