Stata 12

MT-PCR data were processed in Excel 16.13.1 (Microsoft, USA), Stata 12.1 (STATA Corp, USA), visualized using GraphPad Prism 7.0d (GraphPad Software Inc., USA) and stool samples deemed infection positive or negative as per MT Analysis Software output (AusDiagnostics Pty. Ltd., Sydney). Kappa statistics (interrater reliability), sensitivity, specificity and correlation with epidemiological variables were calculated to evaluate performance of the MT-PCR method compared to the gold standard KKTS using Stata 12.1 (STATA Corp, USA). Impacts of STH infections defined as stunting and wasting were calculated as height-for-weight, height-for-age and weight-for-age z-scores compared to the 2006 WHO child growth standard [32 ]. Anthropometric z-score calculations were performed in Stata 12.1 (STATA Corp, USA) using the “zscores06” command [33 ].

All of the sequenced data were compared between individuals in groups (A vs. B and C vs. D) by an optimized version of Phred software to ABI 3700, Phred version (0.020425.c). Hardy-Weinberg equilibrium (HWE) was tested using exact significance as implemented in STATA 12.1. Testing of genotypes HWE in all subjects with normal resilience (group A and C) were determined and the threshold for significant deviation from HWE was set as 0.01. Single nucleotide polymorphisms that were fulfilling HWE were included in further analyses. Minor allele frequencies were measured using STATA 12.1. The normality of residuals was checked graphically with STATA 12.1. Linkage disequilibrium (LD) statistics D’ and r2 in paired SNPs were calculated using Pairwise LD in PLINK (r2 ≥0.8, D’ = 1). For statistical analysis, all descriptive data were expressed as mean ± Standard Deviation. Differences in means between groups were considered significant if p<0.05. Chi-square test used for the detection of group differences in allele frequency and independent t-test. One-way ANOVA was used for the comparison of genetic variants with demographic and psychological data between groups. Multiple-comparison analysis correction was conducted by the Bonferroni correction test.

All the meta-analyses were performed on Review Manager 5.3 and STATA12.0 with a significance level of P <0.05. To calculate the effect size for each study, the summary standard mean difference (SMD) and 95% confidence interval were applied to evaluate the serum BDNF values between T2DM and healthy control (HC), T2DM with or without cognitive impairment. Pooled SMD and corresponding 95% confidence interval were calculated using the inverse variances method. Heterogeneity was estimated using the Cochran Q (P) and the inconsistency index where a P value less than 0.05 and I² value greater than 50% indicated the presence of significant heterogeneity across the enrolled studies. If notable heterogeneity was observed, a random-effect model was applied and subgroup analyses were used to determine factors that contributed to the heterogeneity and to explore how those factors influenced the results. Subgroup analysis was stratified by the BDNF measuring instruments brand (China or USA; same brand in China or USA), ethnicity (Asian or European), and population [adults or the aged (years≥60)]. In addition, sensitivity analysis was performed to evaluate the reliability of included studies using STATA 12.0. The Egger’s test and the Begg’s test were applied to evaluate potential publication bias using STATA 12.0.

Statistical analysis was performed using STATA 12.0 software. First, we transformed ORs, RRs, or HRs and their CIs to their natural logarithms and SEs. We directly considered HR as RR7 (link)19 (link) and computed the combined RRs and 95% CIs from the estimates reported in each study20 (link)21 (link). Heterogeneity was quantified using I² values and chi-square test22 (link); when both I² ≤ 50% and p > 1.0 indicated no or acceptable heterogeneity23 (link), we used the fixed-effects model; otherwise, we used the random-effects model. In addition, we performed subgroup analyses on the basis of stratified ORs, RRs, and HRs, given that these pooled may result in the overestimation of OR variance24 (link). We also conducted a dose-response meta-analysis using STATA 12.0 software with restricted cubic spline function by the method of Orini25 (link) for those studies reported sufficient data, including RRs, serving size, and the sample size in each categories. Furthermore, we performed subgroups analyses on the basis of the study design, and type of cancer, adjustment, and definition of reference group. Publication bias was assessed by visual inspection of the funnel plots26 (link).

Review Manager 5.3 and Stata 12.0 software were used to analyze the data. Forest plots were generated to analyze the ORs and 95%CIs. Heterogeneity among studies was assessed by Q and I² tests. An I² value of 0% indicates no observed heterogeneity, whereas, 25% indicates low, 50% indicates moderate and 75% indicates high heterogeneity[17 (link)]. A random effects model was utilized when the heterogeneity is high, otherwise, the fixed effects model was applied. Sensitivity analysis and subgroup analysis were conducted to find the potential source of heterogeneity. Publication bias was qualitatively assessed by funnel plot generation which was conducted using Revman 5.3, and quantitatively evaluated by Egger weighted regression test and Begg rank correlation test, which were calculated using Stata 12.0 software. A P value ≤0.05 was regarded as statistically significant.