Low-density lipoprotein cholesterol within the normal range and nonalcoholic fatty liver disease in the non-obese Chinese population: a secondary analysis based on a cross-sectional study CURRENT

Background Evidence regarding the relationship between normal low-density lipoprotein cholesterol (LDL-c) levels and non-alcoholic fatty liver disease (NAFLD) was limited. Therefore, this dissertation seeks to investigate the relationship between LDL-c and NAFLD in the non-obese Chinese population after adjusting for other covariates. Methods The present study was a cross-sectional study. A total of 183903 non-obese participants were involved in a Wenzhou Medical Center of Wenzhou People’s Hospital from 2010 to 2014. The target independent variable and the dependent variable were LDL-c measured at baseline and NAFLD respectively. Covariates involved in this study included SEX, AGE, BMI, SBP, DBP, FPG, ALB, ALT, AST, BUN, Cr, TG, TC, HDL-c, UA. It was noted that the entire study was completed by Dan-Qin Sun et al., and uploaded the data to the DATADRYAD website. The author only used this data for secondary analysis. The average age of 183903 selected participants was 41.0 ± 14.1 years old,and about 49.6% of them was male. After adjusting potential confounders (SEX, AGE, BMI, FPG, ALB, GLB, ALT, AST, GGT, BUN, Cr, TG, TC, HDL-c, UA), non-linear relationship was detected between normal LDL-c levels and NAFLD, whose point was 1.51. The effect sizes and the confidence intervals on the left and right sides of inflection point were 0.87 (0.64, 1.18) and 1.79 (1.67, 1.92), respectively. The relationship between normal LDL-c levels and NAFLD is non-linear. Normal LDL-c levels was positively correlated with NAFLD when LDL-c was more than 1.51. Data from: Association of cholesterol within the normal range and NAFLD in the non-obese a cross-sectional and longitudinal study. follows: age, sex, body mass index (BMI), fasting plasma glucose(FPG), albumin (ALB), globulin (GLB), alanine aminotransferase (ALT), aspartate transaminase γ-glutamyltranspeptidase blood urea nitrogen creatinine triglyceride total cholesterol high-density lipoprotein cholesterol uric acid bilirubin low density lipoprotein fatty liver disease Syrian


Introduction
Non-alcoholic fatty liver disease (NAFLD) results from hepatic fat accumulation in the absence of quantities of alcohol and any secondary cause [1][2][3][4][5] . Histologically, It comprises a range of pathologic conditions including simple nonalcoholic steatosis, nonalcoholic steatohepatitis (NASH) and hepati cirrhosis [6,7] . NAFLD is a major, worldwide public health problem and its incidence among adults and adolescents has increased rapidly in recent years. Salvoza etal [8] found that the prevalence of NAFLD is 30-40% in the United States, 2-44% in Europe and 15-45% in Asia, respectively.
One meta-analysis reports that it is the relatively high prevalence of NAFLD found in the Asian population (27%) [9][10][11] . The prevalence rate of NAFLD in the general population of China varies from 24.77-43.91% in recent years [11,12] .
NAFLD is associated with an increased risk of developing to severe and decompensated liver diseases such as cirrhosis and hepatocellular carcinoma [13][14][15] . In addition, NAFLD also can increase the risk of cardiovascular disease, type 2 diabetes and chronic kidney disease [16][17][18][19] .
Obesity is a well-known risk factor for the development of NAFLD, and NAFLD is now considered to be one of the most prevalent manifestations of obesity-related metabolic syndrome (MS) [20] . However, there is growing evidence that the prevalence of NAFLD in non-obese individuals is not uncommon.
Especially in Asia, where people are less obese than in Western countries and the prevalence of NAFLD is increasing with time [21] . Studies have shown that approximately 15-21% of the Asian patients with NAFLD are non-obese.
Dyslipidemia is a comorbidity of NAFLD, which can lead to hypertriglyceridaemia, reductions in highdensity lipoprotein cholesterol (HDL-c), an increase in the size of very low-density lipoprotein and lowdensity lipoprotein cholesterol (LDL-c) [22] . In addition, more and more studies have shown that LDL-c is involved in the development of NAFLD and non-alcoholic steatohepatitis (NASH) [23][24][25] . This mechanism is still unclear, but insulin resistance and disorders of fat and sugar metabolism may explain some reasons [26][27][28] . In addition, damage to the LRP6 receptor is also responsible [29,30] .
In any case, so far, no studies have explored the curves relationship between LDL-c and NAFLD. In biomedical research, connection between exposures and outcomes may be non-linear. In that case, researchers need a more effective method to deal with non-linear relationship. In this study, we use curves to fit LDL-c and NAFLD.

Study population
Sun D-Q, Wu S-J, Liu W-Y, et al [31] completed the entire study. In order to allow to understand the entire research process more clearly, we have outlined the steps of the study here. The specific
The ultrasound criteria for the diagnosis of fatty liver were based on those suggested by the Chinese Liver Disease Association. The assessment of NAFLD described in detail in the original.

Statistical analysis
Continuous variables were expressed as mean ± standard deviation (normal distribution) or median (quartile) (skewed distribution), and categorical variables were expressed in frequency or as a percentage. The One-Way Anova (normal istribution), Kruscal Whallis H (skewed distribution) test and chi-square tests (categorical variables) were used to determine any statistical differences between the means and proportions of the groups. Univariate linear regression model was used to evaluate the associations between LDL-c and NAFLD. Both non-adjusted and multivariate adjusted models were listed in the paper. According to the recommendation of STROBE statement, we simultaneously showed the results of unadjusted, minimally adjusted analyses and those from fully adjusted analyses. Whether the covariances were adjusted determined by the following principle: when added to this model, changed the matched odds ratio by at least 10% [33] . Besides, we also used generalized additive model (GAM) to identify the non-linear relationship. If the non-linear correlation was observed, a two-piecewise linear regression model was performed to calculate the threshold effect of the LDL-c and NAFLD in terms of the smoothing plot. When the ratio between LDL-c and NAFLD appears obvious in smoothed curve, recursive method calculates automatically the inflection point, where the maximum model likelihood will be used [34] . All of the analyses were performed with the statistical software packages R (http://www. R-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). P values less than 0.05 (two-sided) were considered statistically significant.

Results
Baseline characteristics of selected participants A total of 183903 participants were sclected for the final data analysis based on the inclusion and exclusion criteria. We showed baseline characteristics of these selected participants in Table 1 according to Quartile of LDL-c. In general, the average age of the 183903 selected participants was 41.0 ± 14.1 years old, and about 49.6% of them were male. statistically significant differences were   7 We listed the results of univariate analyses in   1.59 to 1.82, P < 0.001). For the purpose of sensitivity analysis, we also handled LDL-c as Categorical variable (Quartile), and found that the same trend was observed as well (p for trend was less than 0.001). Besides,we also found the trend of the effect size in different model was equidistant. The analyses of non-linear relationship Because LDL-c was continuous variable, the analyses of non-linear relationship are necessary. In the present study (Fig. 1), we found that the relationship between LDL-c and NAFLD was non-linear (after adjusting AGE, SEX, BMI, ALT, AST, GGT, BUN, Cr, TG, HDL-c, FPG, TC, UA, GLB and ALB). By twopiecewise linear regression model, we calculated that the inflection point was 1.51. On the left side of the inflection point, the effect size, 95% CI and P value were 0.87, 0.64 to 1.18 and 0.3760, respectively. However, On the right side of the inflection point, the effect size, 95% CI and P value were 1.79, 1.67 to 1.92 and p < 0.0001, respectively (Table 4).

Discussion
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 [26] .

Conclusion
Increased normal LDL-c levels are independently associated with an elevated risk of NAFLD in nonobese individuals. The relationship between normal LDL-c levels and the risk of NAFLD is non-linear.
LDL-c is positively correlated with NAFLD when normal LDL-c levels is larger than 1.51 mmol/L. Figure 1 The non-linear relationship between LDL-c and NAFLD. A non-linear relationship between them was detected after adjusting for AGE, SEX, BMI, ALT, AST, GGT, BUN, Cr, TG, FPG, TC, UA, HDL-c, GLB and ALB

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