2.4. Statistical Analysis
All statistical analyses were conducted using R 4.0.4 or SPSS 24.0. Descriptive statistics were calculated using mean ± standard deviation for continuous variables, Comprehensive changes in lipid profiles in response to different interventions were captured using the fuzzy c-means clustering algorithm (R package “Mfuzz”), followed by a paired Student’s test to assess differences between t1 and t2 in each treatment group. Data were auto-scaled prior to analysis. Specific attention was paid to lipids that were not altered in the Con group but showed pronounced changes between t1 and t2 in the VD, EX, or EX + VD groups (To identify lipids with similar physiological and molecular characteristics that were altered by EX, VD, or EX + VD intervention, a co-expression network of the plasma lipids was constructed using weighted gene correlation network analysis (R package “WGCNA”). A signed hybrid network and power of 8 were used for the scale-free topology criterion (model fitting index RPlasma lipids and/or measured clinical indices at baseline were subjected to a random forest algorithm incorporated into a repeated double cross-validation framework with unbiased variable selection (R package “MUVR”) to identify optimal panels of markers that could discriminate responders (i.e., participants who benefitted from the interventions and showed within-individual differences in glycemic control indicators before and after intervention) from non-responders (i.e., participants who underwent interventions but did not exhibit improved glycemic control indicators). Prediction accuracy was determined by misclassification rate % and area under the receiver operating characteristic curve (AROC, 95% CI).