Cardiovascular disease (CVD) has always been a major public health challenge and is the leading cause of death and disability worldwide, including in China (Mortality and Death, 2016). Ischemic heart disease (IHD), ischemic stroke (IS), and intracerebral hemorrhage (ICH) accounted for the majority of the deaths caused by CVD, which becomes more and more serious as the aggravation of population aging (Mortality and Death, 2016; Wang et al., 2017). In recent decades, with the concern about the burden of CVD, researchers not only focus on the modifiable risk factors, including smoking, drinking, physical activity, and diet, but also pay more and more attention to the internal biological mechanism (Virani et al., 2020; Boehme et al., 2017). Plasma lipids, especially low-density lipoprotein cholesterol (LDL-C) which may accumulate in the arterial wall, gradually form atherosclerotic plaques and block the corresponding artery, is generally considered to be associated with risks of IHD, IS, and ICH (Varbo and Nordestgaard, 2014; Arora et al., 2019; Sun et al., 2019).

Eggs are one of the richest sources of dietary cholesterol, but they also contain a wide variety of essential nutrients and bioactive compounds, such as high-quality protein, fat-soluble and B vitamins, phospholipids, and choline (Andersen, 2015; Blesso, 2015). Evidence for the association between eggs and CVD remains controversial in both observational and prospective studies based on Western populations, with positive association being reported in some studies (Zhong et al., 2019) and others finding no significant association (Shin et al., 2013; Guo et al., 2018; Drouin-Chartier et al., 2020; Dehghan et al., 2020; Rong et al., 2013). Similar controversial findings have been found in the Chinese population. An inverse association between egg consumption and CVD risk was published,^[16] whereas more (>10 eggs per week) and less (<1 per week) egg intake was found to be harmful to cardiovascular health (Xia et al., 2020). With regret, few studies have assessed the role of individual plasma cholesterol levels in such association, making the association clearer. On the other hand, Lipoproteins can be divided into large, medium and small subclasses according to particle size and include a series of constituents including cholesterol, phospholipids, triglycerides, and apolipoprotein which is difficult for conventional approaches to quantitate. Nuclear magnetic resonance (NMR) metabolomics provides a new opportunity to explore the associations between exposure, diseases and lipoprotein and other small-molecule metabolic markers in a more detailed perspective (Nagana Gowda and Raftery, 2019).

The present study selected a targeted NMR-based metabolomics platform that previous large-scale studies have widely used, (Würtz et al., 2017) covering 14 lipoproteins and their subfractions with different densities and particle sizes, as well as other fatty acids, amino acids, and ketone-body-related metabolites. We aimed to simultaneously explore the associations of self-reported egg consumption with these metabolic markers, and of these markers with CVD risk in a nested case-control study in the China Kadoorie Biobank (CKB).

Age-, sex-, and region-adjusted baseline characteristics of the 4778 participants according to whether they developed CVD are shown in Table 1. Briefly, the mean (SD) age was 47.0 (8.2) years, 50.1% were women, and 29.0% resided in urban areas. The mean (SD) frequency of egg consumption and plasma total cholesterol concentration were 2.6 (2.3) days/week and 3.5 (0.6) mmol/L, respectively. Compared with controls, participants who subsequently developed any subtype of CVD were more likely to have poor self-rated health, to prevalent obesity, diabetes, and hypertension, and to have a family history of diabetes or CVD. Among them, MI or IS cases had a lower level of education but a higher level of household income, whereas ICH cases had higher levels of SBP and DBP.

Among the 225 metabolic markers or derived traits, 24 were associated with the frequency of egg consumption at FDR <5% (Figure 1). The associations for all 225 markers are shown in Supplementary file 1a. Egg consumption was positively associated with lipoprotein particle concentrations of very large and large HDL. Similarly, within very large and large HDL, there were positive associations of total lipids, total cholesterol including its subclasses (cholesterol esters and free cholesterol), or phospholipids with egg consumption. Conversely, there was an inverse association of cholesterol esters in small VLDL with egg consumption. In addition to the absolute concentrations of lipids, the percentage of total cholesterol and cholesterol esters in large HDL were positively associated with egg consumption. Besides lipids, there were positive associations of acetate and apolipoprotein A1 with egg consumption, whereas an inverse association was observed for the ratio of apolipoprotein B to apolipoprotein A1.

Figure 1

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Figure 1—source data 1

Significant associations of egg consumption and metabolic markers, and associations of these markers with risks of CVD.

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Among 225 metabolic markers or derived traits, 104, 123, and 55 were associated with CVD and its subtypes, MI and IS, respectively, at FDR <5% (Supplementary file 1). For total cholesterol within small VLDL, each SD increment in log-transformed value was associated with increased risks of CVD (OR 1.15 [95%CI 1.07–1.24]), MI (1.25 [1.14–1.38]), and IS (1.15 [1.05–1.27]), whereas the association with total cholesterol within large HDL was in the opposite direction (0.88 [0.81–0.95] for CVD and 0.83 [0.75–0.91] for MI). However, only one metabolic marker, glucose, showed a positive association with the risk of ICH. ORs for MI, IS, and ICH associated with all 225 metabolic markers were provided in Supplementary file 1b. Among markers associated with risk of CVD, MI, or IS, 14, 15, and 3 were simultaneously associated with egg consumption. There was a clear pattern between the associations of egg consumption with metabolic markers and of these metabolic markers with disease risk; that is, metabolic markers associated with higher egg consumption tended to be associated with lower risk of CVD, MI, and IS (Pearson correlation: −0.64, –0.67, and –0.67, respectively; Figure 2 and Supplementary file 1).

Figure 2

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Figure 2—source data 1

Global comparison of SD differences of 225 log-transformed metabolic markers associated with weekly days of egg consumption and CVD risks.

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In sensitivity analyses, associations between egg consumption and metabolic markers remained essentially unchanged after further adjustment for hypertension, diabetes, and family history of diabetes or CVD (Figure 3). Similar associations were observed when restricting the analyses to participants without any metabolic markers below the limit of detection or rejected by quality control (n=4,251) and using the weekly amount of egg consumption as a continuous independent variable instead of its frequency (Figure 4). However, when BMI was added to the basic model, the associations of egg consumption with metabolites were attenuated toward the null (Figure 3).

Figure 3

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Figure 4

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Based on data of FFQ and NMR metabolomics collected from the CKB population, we simultaneously assessed the associations between self-reported egg consumption and metabolic markers, and further explored whether these markers were associated with the risk of CVD and its subtypes. The results showed that egg consumption was positively associated with apolipoprotein A1, acetate, mean particle diameter of HDL, as well as lipid profiles in very large and large HDL including lipoprotein particle diameters, total lipids, total cholesterol, cholesterol esters, free cholesterol, and phospholipids. In contrast, inverse associations were observed in cholesterol esters in small VLDL, and the ratio of apolipoprotein B to apolipoprotein A1. Moreover, most of these metabolic markers were found to be inversely associated with the risk of CVD. On the whole, 225 metabolic markers showed a pattern. That is, their association with egg consumption and CVD risk were directionally opposite. Our results explained the protective effect of egg consumption on CVD risk through metabolism in the Chinese population.

Results from previous observational studies describing the association between egg consumption and CVD risk are inconsistent. However, few studies have focused on plasma lipid metabolites between such associations so far. Some studies examined the metabonomics characteristics of plasma or feces in a wide range of food groups, including eggs, but had not shown much interest in lipid metabolites (Guertin et al., 2014; Mitry et al., 2019). Other studies combined eggs with other foods, such as ham, as a feed intervention to investigate consumers' metabolomics characteristics, but the results can not reflect the metabolite changes caused by egg consumption alone (Rådjursöga et al., 2019; Rådjursöga et al., 2017). This also gives expression to the necessity and urgency of the present study.

Even so, a number of studies and meta-analyses have explored the conventional lipids associated with egg consumption using blood biochemical analyses. Recently, a cohort study based on 39,021 overnight fasting adults in China collected egg consumption data and blood lipids biochemical analysis (Liu et al., 2020). It was found that compared with the low egg consumption group (<26.79 g/d), medium or high consumption tertiles (>26.79 g/d) had higher levels of HDL-C, which was in line with the present study. It is worth noting that the mediating analysis of that study found that BMI or waist circumference (WC) could partly explain the association between egg consumption and HDL-C. The present study also supported the hypothesis according to sensitivity analysis, in which the associations were no longer significant by additional adjustment of BMI.

Consistent with our results, three small intervention studies based on Americans or Japanese observed positive effects for HDL-C and apolipoprotein A1 (Kishimoto et al., 2016; Missimer et al., 2017; Lemos et al., 2018). In addition, a meta-analysis of 66 eligible randomized clinical trials (RCT) involving 3,185 participants found a non-linear effect for VLDL-C, showing inverse association at the egg consumption level corresponding to the present study (<1.5 eggs/day) (Khalighi Sikaroudi et al., 2020).

The debate over egg consumption centered on its effect on cholesterol metabolism. In our study, there was no significant association between egg consumption and lipoprotein in other sizes or densities except small VLDL, very large, and large HDL. To some extent, this reflected the balance between dietary cholesterol absorption and endogenous cholesterol biosynthesis, which accounts for about 25% and 75%, respectively, varying with different food consumption habits (Blesso and Fernandez, 2018; Lütjohann et al., 2018; Kim and Campbell, 2018). HDL is widely believed to be cardiovascular-friendly lipoproteins responsible for binding and esterifying cholesterol in other tissues for subsequent excretion. The present study suggests that moderate egg consumption increases large HDL levels without altering triglyceride-rich lipoproteins. This mechanism may improve cholesterol metabolic characteristics, thus observing the protective effect on cardiovascular.

Our study provides substantial evidence for the recommendation of appropriate egg consumption in current dietary guidelines in China. The Dietary Guidelines for Chinese Residents recommended that each standard adult should eat 40–50 g eggs a day without discarding the yolk (Wang et al., 2016). However, according to the China Statistical Yearbook, the average egg purchase by Chinese residents in 2019 was 10.7 kilograms, or about 29.3 grams per day, which can be a partial estimate of actual consumption. Statistics NBo, 2020 Although this number has increased over the past decade, it still falls short of dietary guidelines. Hence, more health education and health promotion strategies and policies to encourage moderate egg consumption need to be developed to improve lipid metabolite characteristics in the Chinese population, contributing to CVD prevention.

This study based on the CKB population has many strengths, including relatively large sample size, accurately identified CVD and its subtype events, collection of as many covariates as possible, and the quantification of a wide range of metabolites based on NMR platform, such as lipoproteins and their constituents with varies of size, density, and chemical structure. Besides, the present study restricted the analysis to those free of CVD history and lipid-lowering therapy to minimize the possible confounding or even causal inversion of hypercholesterolemic participants. Our study also had several limitations. First, similar to other large-scale nutritional epidemiological studies, there was an unavoidable recall bias in the FFQ used for estimating egg and other food consumption in our study. There was also measurement bias when five options were used to estimate the weekly days of egg consumption. However, the results of our study were similar to those of previous observational studies or intervention studies, and these results remained robust when the imputed weekly amount of egg consumption was used as the independent variable. Second, even if potential confounding factors such as BMI, frequency of other food consumption, history of chronic diseases, and family history were adjusted in multivariate models or sensitivity analysis, residual confounding due to uncollected or suboptimally collected factors still existed. Third, as the weekly egg consumption of the present study (less than 2 eggs per day) was relatively lower compared with other studies, it is necessary to be cautious when concluding beyond this range. Finally, given the cross-sectional nature, the associations between baseline egg consumption and baseline level of plasma metabolites did not strongly elucidate their causality. Further longitudinal studies are needed to verify the causal roles of lipid metabolites in the association between egg consumption and CVD risk.

For researchers who are interested to access the original data, the access policy and procedures are available at https://www.ckbiobank.org/site/. In brief, the China Kadoorie Biobank (CKB) is being conducted jointly by the Clinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, and Chinese Academy of Medical Sciences (CAMS) in Beijing. Requesters should be employees of a recognized academic institution, health service organization, or charitable research organization with experience in medical research. Requestors should be able to demonstrate, through their peer-reviewed publications in the area of interest, their ability to carry out the proposed study. After registration, details of the required information are provided on the CKB Data Access System. The CKB Access Team will review and respond to data requests within 6-8 weeks. In order to explain more clearly, all the variables required for the analysis of this study are as follows: 1. Background: Basic [Baseline, 1st resurvey, 2nd resurvey], Demographics [Baseline]; 2. Tea consumption: Basic [Baseline], Details [Baseline]; 3. Alcohol consumption: Basic [Baseline], Details [Baseline]; 4. Smoking: Basic [Baseline], Details [Baseline]; 5. Diet: Staple foods [Baseline, 1st resurvey, 2nd resurvey], Animal products [Baseline, 1st resurvey, 2nd resurvey], Vegetables [Baseline, 1st resurvey, 2nd resurvey], Other foods [Baseline, 1st resurvey, 2nd resurvey], Drinks [2nd resurvey]; 6. Medical history: Self-rated [Baseline], Personal [Baseline], Family [Baseline]; 7. Physical activity: Summary - MET [Baseline]; 8. Mental health: Satisfaction [Baseline]; 9. Physical exam: Height and weight [Baseline], Body composition [Baseline], Blood pressure [Baseline], Blood glucose [Baseline]; 10. Biochemistry data: Blood biomarkers (lab) [Baseline], Blood biomarkers (NMR) [Baseline], Blood biomarkers (NMR) - Chylomicrons and extremely large VLDL [Baseline], Blood biomarkers (NMR) - Very large VLDL [Baseline], Blood biomarkers (NMR) - Large VLDL [Baseline], Blood biomarkers (NMR) - Medium VLDL [Baseline], Blood biomarkers (NMR) - Small VLDL [Baseline], Blood biomarkers (NMR) - Very small VLDL [Baseline], Blood biomarkers (NMR) - IDL [Baseline], Blood biomarkers (NMR) - Large LDL [Baseline], Blood biomarkers (NMR) - Medium LDL [Baseline], Blood biomarkers (NMR) - Small LDL [Baseline], Blood biomarkers (NMR) - Very large HDL [Baseline], Blood biomarkers (NMR) - Large HDL [Baseline], Blood biomarkers (NMR) - Medium HDL [Baseline], Blood biomarkers (NMR) - Small HDL [Baseline], Blood biomarkers (NMR) - LDL [Baseline], Blood biomarkers (NMR) - HDL [Baseline], Blood biomarkers (NMR) - fatty acids [Baseline]. We uploaded Stata code that was used to analyze the data. The numbers used to generate Figures 1 and 2 were actually table S1 and S2, which were also uploaded as Excel files. We also uploaded high-resolution raw images (TIF format) of Figures 1 and 2.

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Author details

1. Lang Pan

   Department of Epidemiology and Biostatistics, Peking University, Beijing, China

   Contribution

   Visualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5231-367X

2. Lu Chen

   Department of Epidemiology and Biostatistics, Peking University, Beijing, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

3. Jun Lv

   1. Department of Epidemiology and Biostatistics, Peking University, Beijing, China
   2. Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
   3. Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China

   Contribution

   Conceptualization, Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7916-3870

4. Yuanjie Pang

   Department of Epidemiology and Biostatistics, Peking University, Beijing, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4826-8861

5. Yu Guo

   Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Pei Pei

   Chinese Academy of Medical Sciences, Beijing, China

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

7. Huaidong Du

   1. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
   2. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

8. Ling Yang

   1. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
   2. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

9. Iona Y Millwood

   1. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
   2. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

10. Robin G Walters

    1. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
    2. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

    Contribution

    Writing - review and editing

    Competing interests

    No competing interests declared

11. Yiping Chen

    1. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
    2. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

    Contribution

    Writing - review and editing

    Competing interests

    No competing interests declared

12. Weiwei Gong

    NCDs Prevention and Control Department, Zhejiang CDC, Hangzhou, China

    Contribution

    Writing - review and editing

    Competing interests

    No competing interests declared

13. Junshi Chen

    China National Center for Food Safety Risk Assessment, Beijing, China

    Contribution

    Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

14. Canqing Yu

    1. Department of Epidemiology and Biostatistics, Peking University, Beijing, China
    2. Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

    Contribution

    Data curation, Methodology, Project administration, Writing - review and editing

    For correspondence

    yucanqing@pku.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0019-0014

15. Zhengming Chen

    Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Data curation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

16. Liming Li

    1. Department of Epidemiology and Biostatistics, Peking University, Beijing, China
    2. Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

    Contribution

    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

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