Diabetic Neuropathy and Metabolic Syndrome

The categorical variables of T2DM patients’ characteristics—such as gender, smoking, drinking, chewing betel nuts, frequent exercise, and family history of DM—were described using absolute and relative frequency. Age, BMI, SBP, DBP, HbA1c, fasting glucose, triglyceride, total cholesterol, HDL-c, LDL-c, and UA were all continuous variables. For continuous variables, mean and standard deviation values, as well as absolute and relative frequencies, were used to describe the patient features. The normality tests of continuous variables—such as age, BMI, SBP, DBP, HbA1c, fasting glucose, triglyceride, total cholesterol, HDL-c, LDL-c, and UA—were performed by Kolmogorov–Smirnov test, and all variables underlying the dataset were found to be normally distributed. Chi-squared test was used in inferential statistics to examine the relationship between DN and T2DM patients with and without MetS. Additionally, logistic regression techniques were used to examine the relationships between the onset of DN and each of the potentially related variables in a univariate analysis and to build DN models. The best multivariable models were then selected utilizing the model selection approach after taking into account the significant factors in each statistic testing. When the p-value was less than 0.05, the results were considered significant and were presented as odds ratios (OR) with a 95% confidence interval (CI). IBM SPSS Statistics 24 was used to conduct the statistical analysis.