Ethics statement

The current study complied with the Declaration of Helsinki and the protocol was approved by the Ethics Review Committee of Xi’an Central Hospital. Informed written consent was obtained from each participant prior to participation.

Study setting

Xi’an is the provincial capital of Shaanxi Province in northwest China. Between March 2013 and December 2017, health examinees were recruited to assess cardiovascular disease and its potential risk factors in Xi’an. Adult participants were asked to volunteer through telephone call, on-site invitation or mailed letters in one health examination center.

A total of 10,780 individual were recruited. We further excluded those with nutrition-related disease, including diabetes, stroke or hypertension (n = 1908). We made these exclusion to minimize the prevalence-incidence bias and the effect of reverse causality led by potential confounders such as lifestyle factors [12]. We further excluded subjects with missing information of physical examination, left food frequency questionnaire total blank or missing main food intake and individuals with energy intake < 500 or > 5000 kcal/day (n = 514). Ultimately, 8358 participants including 3677 male and 4681 female remained in the final analysis.

Assessment of cholesterol intake

Dietary information was collected by a 92-item semi-quantitative food frequency questionnaire (FFQ), with nine intake frequency categories, including “almost never” to “ ≥ 3 times/day”. Our FFQ is established and revised based on the validated Xi’an FFQ in China [13]. In validation study, the deattenuated correlation coefficients for nutrients estimated by the FFQ and 3-d 24 dietary recalls were from 0.35 to 0.85 in men, and slightly lower in men [13]. Meanwhile, the correlation coefficients between the two FFQs in reproducibility study ranged from 0.41 to 0.68 in men and 0.36 to 0.66 in women.

During the FFQ investigation, participants were e-mailed or asked to recall the average portion and frequency of each food consumed during the last 12 months. Main nutrients intake, including carbohydrate, protein, fat, sodium and cholesterol intake were calculated according to Chinese Food Composition Table. When we explored the association between cholesterol intake and dyslipidemia, nutrients intake was caloric-adjusted to 1980 kcal/day (the mean intake of participants) by residual method [14].

Covariates assessment

A standard self-administered questionnaire was used to collect socioeconomics and demographics information (age, marital status, education, occupation, income, transportation and so on), history of disease, several lifestyles (smoking and physical activity) and diet. The trained public health professionals charged for explaining the definition of all items.

Evidence from the large-scale epidemiological studies indicated rational nutrients intake or better nutrition status might be associated with higher socioeconomic position, healthier lifestyle factors and there might be multi-collinearity cross different nutrients [15, 16]. As a consequence, adjusting for them in multivariate regression models would further decrease potential residual confounding.

It is reported that many nutrients are significantly correlated, such as protein, fat, and dietary cholesterol intake. To avoid multicollinearity of nutrients in regression analyses, we used principal component analysis based on all potential nutrients (saturated fatty acid, polyunsaturated fatty acid, monounsaturated fatty acids, carbohydrate, animal protein, plant protein, fiber, calcium and potassium). The first two principal components explained 73.6%. The first component exhibited the factor loadings of saturated fatty acid, polyunsaturated fatty acid, monounsaturated fatty acids, and animal protein more than 0.4. The correlation between the second component and other nutrients ranged from 0.32 to 0.90. And we took the two principal components as potential nutrition confounders in regression models.

Definition of outcomes

The participants were asked to stay fast for ≥ 8 h before the medical examination. Serum lipids were analyzed by automatic biochemical analyzer. The diagnosis criteria was based on the According to the Chinese adult dyslipidemia prevention guide (2016 edition) [17], which defined dyslipidemia as one or more of the following 4 indicators, hypercholesterolemia (TC ≥ 6.20 mmol/L); hypertriglyc-eridemia (TG ≥ 2.30 mmol/L); low levels of HDL-C (HDL-C < 1.04 mmol/L); high levels of LDL-C (LDL-C ≥ 4.14 mmol/L).

Statistical analysis

In current analysis, we created a cross product of sex and cholesterol intake to assess the possible interaction. This variable was significantly associated with dyslipidemia in regression model (Wald χ2 = 13.985, P < 0.001). Therefore, gender-specific analysis was conducted to better understand the relationship. Demographic and lifestyle information were summarized by percentage for categorical variables across quartiles of dietary cholesterol intake. Group difference was tested by analysis of variance or chi-square test. Correlation analysis was performed to describe the association between dietary cholesterol and main food and nutrients. Multivariable logistic regression models were used to estimate odds ratios (ORs) of dyslipidemia and their 95% confidence intervals (CIs), with the lowest intake group (the first quartile, Q1) as reference. Model 1 adjusted for energy, age, education and income level. Model 2 adjusted for the variables in model 1 plus physical activity level, alcohol intake, smoke status and BMI. Model 3 adjusted for the variables in model 2 plus two nutrients principal components. The linear trend across quartiles was assessed by using the median value of each quartile as a single continuous variable and entering into the regression models [18]. Restricted cubic spline (RCS) with three knots (25th, 50th, 75th percentiles) was modeled to explore the potential non-linear association, with the median value as the reference. Multivariable linear regression was conducted to evaluate the influence of cholesterol intake by increment of one S.D. (50 mg/day) on serum lipids. In exploratory analysis, we also examined the association between main food source and dyslipidemia in regression models. We also repeated regression models when analytical sample merely includes those participants who join in the survey the first time. Two-sided P < 0.05 was considered as statistically significant. The RCS analysis was performed in STATA 12.0, other were performed with SPSS version 18.0 (SPSS Inc, Chicago, USA).

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