Statistical analysis
We estimated the effect of orlistat on serum uric acid level by pooling baseline and final mean differences and standard deviation (SD) values and 95% CI of the studies in both intervention and control groups. A random-effects model by DerSimonian and Laird method [14] was used to estimate the overall effect size. In addition, standard error of mean (SEM) was converted to SD by following the formula[15]: SD = SEM\(\times\) \(\sqrt{n}\) (n = number of participants in each group). SDs of the mean differences were calculated as follows:
\(\text{SD}_{\text{Change}}\)=\(\sqrt{{{((SD}_{\text{Baseline}})}^{2}+{{(SD}_{\text{Final}})}^{2})-(2\times R\times\text{SD}_{\text{Baseline}}\times\text{SD}_{\text{Final}})}\)assuming a correlation coefficient (R) 0.5 as it is a conservative estimate for an expected range of 0-1. Between studies heterogeneity was examined using Cochrane’s Q test (significance point at P < 0.1) and \(I^{2}\) statistic with values greater than 50% as evidence of moderate to high heterogeneity.[16]. In order to find the potential sources of heterogeneity, we performed subgroup analyses to evaluate whether results were different by intervention period (3 month and 6 month, sample size (< 100 and ≥ 100 participants), health status (metabolic syndrome, hypercholesterolemia, PCOS and NAFLD). We also performed sensitivity analysis by removed each study from the analysis. Then, potential publication bias was assessed by using Egger’s test [17] and Begg’s regression test . In case of detecting potential publication bias P values less than 0.05. Duval & Tweedie “trim and fill” approach was applied to adjust analysis for the impacts of publication bias[18]. Random-effects meta-regression analysis was conducted to evaluate the association between changes in serum uric acid and potential confounders including duration of treatment. The collected information from studies was imported into Comprehensive Meta-Analysis software version 2 (Biostat, Inc., Englewood, NJ, USA)[19]. A P value of < 0.05 was considered as statistically significant.