Baseline characteristics of selected participants
13,178 participants in total were selected for the present research through strict selection criteria. Table 1, grouped by TyG quartiles, shows the baseline characteristics of these selected participants. In general, the average age of those selected was 43.36 ± 8.89 years old, and 51.02% of those were male. A total of 2361 had NAFLD and the prevalence was 17.92%. No statistically significant differences were detected among different TyG groups (all p values < 0.05). Compared to that of other three groups (Q 1-3), Participants in the highest group of TyG (Q 4) had the higher values in age, BMI, WC, ALT, AST, GGT, TC, TG, HbA1C, FPG, SBP, DBP and consisted of more male, past and current smoker, NAFLD than those of the other groups. The contrary results were detectd in HDL-C, female, regular exercise, non -smoker, non-NAFLD.
The results of the univariate analysis were showed in Table 2. We performed univariate binary logistic regression and found that HDL-C and regular exercise were negatively correlated with NAFLD. To the contrary, the univariate analysis displayed that age, BMI, WC, ALT, AST, GGT, TC, TG, HbA1C, FPG, SBP, DBP, TyG, ethanol consumption, male, smoking status (past and current) were positively correlated with Nonalcoholic fatty liver.
Results of unadjusted and adjusted binary logistic regression
In this study, we used univariate and multivariate binary logistic regression to construct three models through to analyze to assess the independent effect of TyG on NAFLD. Table 3 shows the effect sizes (Odds ratio (OR)) and 95% confidence intervals. In the unadjusted model, TyG showed positive correlation with NAFLD (OR=7.79, 95% confidence interval (CI): 7.08 - 8.56, P <0.0001). In the minimum-adjusted model, the result did not change significantly after adjusting for age and gender (OR=6.18, 95% CI 5.59 - 6.83, P <0.0001). In fully adjusted model (adjusted age, gender, ALT, AST, GGT, SP, BMI, WC, regular exercise, HDL-C, TC, HbA1C, Ethanol consumption, Smoking status), the association between TyG and NAFLD could also be detected (OR=2.45, 95% CI 2.12-2.82, P <0.0001). For the stability of our results, TyG was treated as categorical variable (Quartile of TyG) and the P for trend in the fully adjusted model was agreed with the result when TyG was a continuous variable.
The results of the nonlinearity of TyG and NAFLD
In the present study, the non-linear correlation between TyG and NAFLD was analyzed by the Smooth curve and the Generalized additive model (Figure 1 and Table 4). After adjusting for age, gender, BMI, WC, ALT, AST, GGT, HDL-C, TC, TG, HbA1C, FPG, SBP, DBP, ethanol consumption, regular exercise, and smoking status, we detected that the correlation between TyG and NAFLD was non-linear. Using two-piecewise binary logistic regression and recursive algorithm, the inflection point of curve was calculated to be 8.22 (log-likelihood ratio test P 0.023). When TyG was less than 8.22, the effect size and 95% CI were 3.26, 2.44 - 4.35, respectively. When TyG was greater than or equal to 8.22, the effect size and 95% CI were 2.09, 1.72 - 2.54, respectively.
The results of subgroup analyses
The stratification variables (age, gender, ALT, AST, GGT, HDL-C, TC, HbA1c, obesity, visceral fat obesity, hypertension, smoking status, ethanol consumption, regular exercise) were used to assess the trend of effect sizes (Table 5). The tests of interaction were statistically significant for these variables including gender, GGT, Obesity, visceral fat obesity. For men, one unit increase of TyG was correlated with 3.20 times higher risk of NAFLD. For women, an increase of the TyG by one unit was associated with 2.24 times higher risk of NAFLD. The change in gender is significant (P for interaction = 0,0076). The stronger association was also detected in low GGT (3.73 with low GGT vs 2.25 with middle GGT, and vs 2.12 with high GGT ), non-obesity (2.72 with non-obesity vs 1.99 with obesity), non-visceral fat obesity (2.65 with non-visceral fat obesity vs 1.88 with visceral fat obesity).
ROC analyses for TyG, ALT, FPG, and TG to predict the risk of NAFLD
For predicting the risk of NAFLD, we performed ROC analyses by using TyG, ALT, FPG, and TG in Figure 2 and Table 6. Among the total population, the areas under the ROC curve (AUC) of TyG was 0.810（95 % CI 0.804 - 0.817）, which was worse than ALT [ 0.829 (95 % CI 0.822 - 0.835), P < 0.0001] but better than TG [ 0.799 (95 % CI 0.792 - 0.805), P < 0.0001] and FPG [ 0.715 (95 % CI 0.707 - 0.722), P < 0.0001]. Among the men, the ROC AUCs (95% CI) were 0.748 (0.737 - 0.758), 0.797 (0.788 - 0.807), 0.635 (0.624 - 0.647) and 0.739 (0.728 - 0.749) for TyG, ALT, FPG, and TG, respectively. Among the women, AUCs of ROC (95% CI) for TyG, ALT, FPG, and TG were 0.818 (0.809 - 0.828), 0.765 (0.755 - 0.776), 0.719 (0.708 - 0.730), and 0.804 (0.794 - 0.814), respectively. For men, the AUC was worse for TyG than ALT but better than TG and FPG (P <0.0001). For women, the AUC for the TyG was superior to ALT, FPG, and TG (P<0.0001).