Previous studies showed that LDL-c was associated with NAFLD, but the relationship between the risk factor LDL-c and NAFLD was not fully described. We conducted a PubMed search and three scientific papers were retrieved from the database as of the end of March 2020. All of these studies showed that LDL-c was associated with NAFLD[24, 31, 32]. Sun D-Q, Wu S-J,Liu W-Y, et al. found that LDL-c was associated with NAFLD in the non-obese Chinese population in a cross-sectional and longitudinal study[31, 32]. In their studies, the multivariable logistic regression models were used to calculate the OR of LDL-c on NAFLD. After adjusting potential confounders (SEX, AGE, BMI, DBP, SBP, ALB, ALT, AST, BUN, Cr, FPG, HDL-c, TC, TG and UA), the OR gradually increased in Q1 to Q4 of LDL-c quartile, and the P for trend was less than 0.05. This suggests that the connection between LDL-c and NAFLD is non-linear. However, none of them discussed the non-linear connection between LDL-c and NAFLD. To our knowledge, this is the first study to investigate the non-linear relationship between LDL-c and NAFLD.
In the present study, we used GLM and GAM models to elucidate the relationship between LDL-c and NAFLD among participants. As is shown in the fully adjusted model, LDL-c was associated with NAFLD. When we handled LDL-c as a categorical variable, the same trend was observed. However, the results obtained from GAM and two-piecewise linear regression model showed that the relationship between LDL-c and NAFLD was non-linear, and the correlations between LDL-c and NAFLD were different on the left and right sides of the inflection point (LDL-c = 1.51). LDL-c, as assessed at baseline, was not statistically significant on the left side of the inflection point, but LDL-c was positively associated with NAFLD on the right of the inflection point.
Our study has a number of strengths
(1) we not only use the generalized linear model to evaluate the linear relationship between LDL-c and NAFLD, but also use the generalized additive model to clarify the nonlinear relationship. GAM has obvious advantages in dealing with non-linear relations and it can handle the non-parametric smoothing and will fit a regression spline to the data. The use of GAM will help us to better discover the real relationships between exposure and outcome. we detect this nonlinear relationship in our study after adjusting confounding factors which were not founded by previous study. (2) this study is an observational study including unavoidable potential confounding, so we used strict statistical adjustment to minimize residual confounding.
There are some limitations in our study
(1) this study is a analytical cross-sectional study and therefore only provides weak evidence between exposure and outcome, and it is difficult to distinguish the cause and effect. (2) because the study population contains only Chinese, it may be not generalisable to other biographic ethic groups. (3) the study is lack of anthropometric parameters regarding central obesity, lifestyle and dietary factors. In addition,we cannot determine the severity of NAFLD diagnosed by ultrasonographic. Due to the limitation of the original data, we cannot observe the correlation between insulin resistance and NAFLD, although insulin resistance may be closely associated with NAFLD in non-obese individuals.