Spss for windows program version 25

We conducted an exploratory regression analysis to identify a plausible relation between actual total intake of insoluble fibre after one year and the absolute one-year change in capillary 2-h glucose levels. We chose this outcome as it was the primary and strongest responding outcome in core publication of OptiFiT [13 (link)]. The best fitting regression curve was identified based on visual inspection of the scatter plot and level of significance. We restricted our analysis to the first year of intervention in order to limit the bias due to diabetes-related drop-outs, most of which occurred after 12 months. For our cut-off analysis, we decided against using pre-defined fibre cut-offs from current dietary recommendations, as these are mainly founded on cohort data. Instead, we compared tertiles of actual fibre intake, for which normal distribution was determined by using the Kolmogorov–Smirnov test. Given the frequent absence of normal distribution, we consistently used Kruskal–Wallis and post hoc Mann–Whitney tests for cross-sectional comparisons and Wilcoxon tests for longitudinal comparisons to assure uniform data presentation. All data are shown as means ± standard deviation. The results were considered significantly different if p < 0.05. All statistical analyses were performed using SPSS for Windows program version 25.0 (SPSS Inc, Chicago, IL, USA).

We used the Kolmogorov–Smirnov test in order to determine the normal distribution of our data. Given the frequent absence of normal distribution, we decided to conduct non-parametric tests (Mann–Whitney tests) to compare major metabolic parameters at baseline and indicators of lifestyle compliance between the responders and non-responders for both intervention arms separately. For correlations, the non-parametric Spearman approach was chosen. Comparisons between post-hoc defined risk groups were made by ANCOVA (adjusted for sex, age, baseline levels), following the original publication of TULIP [31 (link)]. In addition, in analogy to the TULIP analysis, a multivariate logistic regression model was used to identify binary factors predicting non-response [31 (link)]. We included age, BMI, waist circumference, fasting and 2 h glucose, liver fat (fatty liver index), insulin secretion (HOMA-beta) and insulin sensitivity (Cederholm index) as continuous variables, and sex and the presence of insulin-resistant fatty liver (Cederholm index below the median and FLI > 60) as independent categorial variables.
All data were presented as means ± standard deviation. The results were considered significantly different if p < 0.05. All statistical analyses were performed using SPSS for Windows program version 25.0 (SPSS Inc., Chicago, IL, USA).

Statistical analyses were performed using SPSS for windows program version 25.0 (SPSS Inc., Chicago, IL, USA). Differences in categorical variables between cases and controls were analyzed by the x² or the Fisher's exact test where appropriate. Shapiro-Wilk test was used to check for the normality of distribution of folate, vitamin B12, and homocysteine in both cases and controls. Folate, vitamin B12, and homocysteine were not normally distributed and remained non-normal even after log transformation, and hence that non-parametric analysis was performed. Differences between medians were analyzed by Mann–Whitney U test. Logistic regression was used to determine the association of folate, vitamin B12, and homocysteine with NTD-affected pregnancy. Statistical significance was set at p < 0.05.