Statistical analysis
All statistical analyses were fulfilled using IBM SPSS Statistics software (Version 24) (IBM SPSS Statistics, Armonk, USA). The normality of variables was established using the Kolmogorov- Smirnov test. DII scores were categorized into quartiles. The differences in variables across the quartiles of DII were detected using One-way ANOVA. A comparison of the mean intake of nutrients with global mean intake per day was performed using one-sample t-test. To compare the categorical variables across quartiles, the Chi-squared test was applied. Linear regression analysis was used in 3 models (Model 0, linear regression analysis without adjustment; Model I, linear regression analysis with adjustment for energy intake; Model II, linear regression analysis with correction for age, energy intake, physical activity, and education) for determination of the Association between DII score as the independent variable with anthropometric variables and PSQI continuous score as the dependent variables. Moreover, the odds ratio (OR) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression in 3 different models, including Model 0: unadjusted, Model 1: adjusted for energy intake and Model 2: adjustment for age, education, physical activity, energy intake. Also, p-values for linear trends was used in logistic regression to compare the OR between different quarters (Q1to Q4). The lower quartile of DII score (Q1) was used as the reference category. A p-value of less than 0.05 was regarded to be statistically significant.