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Egg consumption and risk of coronary artery disease in the Million Veteran Program
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Original article
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Egg consumption and risk of coronary artery disease in the Million Veteran Program a, b, c, *, Yuk-Lam Ho a, Xuan-Mai T. Nguyen a, b, c, Rachel M. Quaden a, Luc Djousse David R. Gagnon a, d, J. Michael Gaziano a, b, c, Kelly Cho a, b, c a
Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA Harvard Medical School, Boston, MA, USA Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA d Boston University School of Public Health, Boston, MA, USA b c
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a r t i c l e i n f o
s u m m a r y
Article history: Received 8 September 2019 Accepted 16 December 2019
Background & aims: Limited and inconsistent data are available on the relation between egg consumption and risk of myocardial infarction (MI) and it is unclear if adiposity or type 2 diabetes modifies egg-MI relation. We tested the primary hypothesis that egg consumption is positively associated with incidence of MI among veterans. In secondary analyses, we examined potential effect modification of egg-MI relation by adiposity and type 2 diabetes. Methods: We analyzed data collected on 188,267 US veterans who were enrolled in the Million Veteran Program (MVP) from 2011 to 2018. Information on egg consumption was obtained via self-administered food frequency questionnaire and we used electronic health records to identify incident MI. Results: The mean age was 64.4 (SD ¼ 12.0) years and 9.9% of the population were female. We ascertained 10,260 new cases of non-fatal MI during an average follow up of 3.24 years (range: 0.002 to 7.49 y). Hazard ratio (95% CI) for non-fatal MI were 1.00 (ref), 0.93 (0.85e0.1.02), 0.96 (0.87e1.05), 0.98 (0.89e1.07), 1.08 (0.98e1.19), 1.11 (1.00e1.24), and 1.13 (1.00e1.28) for egg consumption of <1/month, 1 e3/month, 1/week, 2e4/week, 5e6/week, 1/d, and 2þ/d, respectively, controlling for age, sex, race, body mass index, smoking, exercise, alcohol intake, and overall dietary pattern (p non-linear trend 0.019). In secondary analyses, we observed similar results with a composite endpoint including fatal MI, coronary angioplasty and revascularization. Conclusions: Our data showed no association of infrequent consumption of eggs with non-fatal MI but a slightly elevated risk with intake of 1 or more eggs per day among US veterans. © 2019 Published by Elsevier Ltd.
Keywords: Diet Coronary artery disease Epidemiology Risk factors
1. Introduction Coronary artery disease (CAD) is still associated with a high burden in the United States, despite steady decline in CAD death rate over the past decades [1]. Previous studies have reported beneficial effects of healthy dietary patterns on the incidence of CAD [2e6], stroke [4e6] and cardiovascular risk factors including diabetes [7e9], hypertension [10,11], and dyslipidemia [12]. Consequently, adherence to healthy dietary patterns can complement pharmacological and other interventions that are currently
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* Corresponding author. Massachusetts Veterans Epidemiology Research and Information Center, Boston VA Healthcare System, 150 S Huntington Ave, Boston, MA 02130, USA. ). E-mail address: [email protected] (L. Djousse
being used to reduce financial and societal burden of CAD. While there is mounting evidence in support of protective effects of certain components of a healthy dietary pattern such as fatty fish, whole grains, and fruit and vegetables on CAD risk, data on the effects of egg consumption on CAD risk have been limited and inconsistent [13,14]. Eggs are rich in protein, minerals, vitamins, and omega-3 fatty acids with known health benefits [15] and are available worldwide at reasonable costs. Prior concerns about egg consumption and rate of cardiovascular disease have been partly derived from the cholesterol content of egg (about 200 mg of cholesterol in an average egg [16]) and choline content that can be metabolized to trimethylamine N-oxide (known to adversely affect cardiovascular risk [17e19]). However, short-term experimental studies, intervention with eggs did not raise LDL-cholesterol and in some studies [20], has been associated with increase in HDL-
https://doi.org/10.1016/j.clnu.2019.12.017 0261-5614/© 2019 Published by Elsevier Ltd.
L et al., Egg consumption and risk of coronary artery disease in the Million Veteran Program, Clinical Nutrition, Please cite this article as: Djousse https://doi.org/10.1016/j.clnu.2019.12.017
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cholesterol [21,22]. Furthermore, data have been inconsistent on the effects of egg consumption on glucose metabolism, incidence of type 2 diabetes, and other cardiovascular risk factors [23,24]. While some studies reported no association of egg consumption with CAD, endothelial function, or subclinical disease, other investigators have documented a higher risk of CAD with frequent egg consumption [25e27]. The heterogeneity across results could be partially due to inadequate adjustment for overall dietary patterns and/or limited statistical power to allow for subgroup analyses. In particular, adiposity is a risk factor for CAD and diabetes and it is possible that egg consumption may accelerate progression of atherosclerosis and exacerbate CAD event rate in diabetic or overweight/obese people. Hence, it is important to determine whether diabetes or body mass index modifies the relation of egg consumption with CAD risk. The Million Veteran Program by virtue of its large sample size and thousands of CAD events is suitable to address some of the gaps outlined above. Hence, we sought to test the hypothesis that egg consumption is associated with a higher risk of non-fatal myocardial infarction (MI) among US veterans after adjustment for major confounding factors including overall diet quality using Dietary Approach to Stop Hypertension (DASH) score. In a secondary analysis, we examined whether the relation of egg consumption with non-fatal MI risk is modified by body mass index and diabetes. 2. Method
ranking for foods that are encouraged in DASH (all but sweetened beverages and meats) [31]. For meats and sweetened beverages, where low intake is desired in DASH, we assigned a lowest value for quintile 5 and highest value for quintile 1 (example: a veteran in the first quintile of sweetened beverage was assigned a value of 5). Finally, we summed up the component scores to obtain an overall modified DASH score ranging from 7 to 35 [32]. 2.3. Outcome assessment We defined non-fatal MI using ICD9 Codes 410e411, 413e414 and ICD10 codes I20eI25 (excluding I25.2). In a secondary outcome, we also included coronary deaths (ICD10 I20eI25) and coronary angioplasty or revascularization (CPT Codes 33510e33536, 9292x, 9293x, 9294x, 92973, 92974, and 92975; ICD9 Procedure codes 36.x and 00.66). The validity of using ICD codes for the diagnosis of cardiovascular disease among veterans has been previously published [33]. 2.4. Important covariates We collected information on age, sex, race, education, body mass index, alcohol consumption, exercise, and smoking at baseline through self-reported survey. Comorbidities were derived through Veterans Health Administration (VHA) electronic medical records system, Corporate Data Warehouse (CDW), using ICD 9 and ICD10 codes.
2.1. Population 2.5. Statistical analysis Million Veteran Program (MVP) is an ongoing prospective cohort study designed to study genetic determinants of chronic diseases. All participants signed informed consent and the Veterans Affairs Central Institutional Review Board approved the study protocol. A detailed description of MVP design and methods has been published [28]. As of January 2019, 702,740 veterans have been enrolled. Among enrollees 325,790 participated in the MVP Lifestyle Survey and provided data on egg consumption, and 250,950 of those with egg consumption data had complete demographic and clinical visit data. Current analyses were restricted to 188,267 out of 250,950 participants who were free of CAD at baseline. 2.2. Assessment of egg consumption and overall diet quality We used the Willett semi-quantitative food frequency questionnaire to collect baseline information on egg consumption and other dietary factors. Reproducibility and validity of the Willett food frequency questionnaire have been previously reported [29,30]. Study participants were asked the following question: “For each food listed, please mark the column indicating how often, on average, you have used the amount specified during the past year”. Possible answers were: “Never or less than once per month”; “1e3 per month”; “once (1) a week”; “2e4 per week”; “5e6 per week”; “once (1) a day”; “2e3 per day”; “4e5 per day”; and “6 þ per day”. Food groups listed included eggs (one egg), dairy foods, fruit and vegetables, meats, sweets, baked goods, cereals, and beverages. We collapsed the last three categories of egg consumption due to limited number of events (22 MI events for 4e5 eggs per day and 31 MI events for 6þ/d). We constructed a modified DASH score based on the following 7 components: fruits, vegetables, nuts and legumes, low-fat dairy products, wholegrain, sweetened beverages, and red and processed meats [31]. Due to a lack of nutrient database in MVP at the time of these analyses, we were unable to include estimate of sodium intake in the DASH score. We created quintiles for each of the seven components and assigned quintile
We calculated person-time of follow up from food frequency assessment until the first occurrence of MI event, death [34], or last visit recorded in the CDW. We computed the incidence rate of MI within each category of egg consumption and used Cox proportional hazard models to estimate hazard ratios with 95% confidence interval of MI. Proportional hazard assumptions were tested by examining Schoenfeld residuals. We assessed confounding by building sequential models based on a priori knowledge of confounders of egg-MI relation. After adjustment for age in the initial model, the fully adjusted model controlled for age, sex, race, education, body mass index, smoking (never, former, current smokers), exercise, alcohol intake, and DASH score. In secondary analyses, we stratified by body mass index (<25 vs. 25þ kg/m2) and prevalence of diabetes (yes vs. no) because adiposity and diabetes are established CAD risk factors and their effects could be potentiated by egg consumption. We also examined the shape of the egg-MI relation by using restricted cubic splines with knots placed at 2, 3, 4 servings/week (using LGTPHCURV9 macro) [35]. Finally, we repeated main analyses using competing risk model to test the robustness of the findings. In sensitivity analysis, we excluded MI events that occurred within 1 year of follow up. All analyses were performed on SAS Enterprise Guide 7.1 and alpha level at 0.05. 3. Results We analyzed data on 188,267 veterans of whom 90.1% were men and mean age was 64.4 (SD ¼ 12.0) years. Median frequency of egg consumption was 3 eggs per week (25th and 75th percentile: 1 and 3 eggs/week, respectively). Frequent consumption of egg was associated with male gender, higher body mass index, lower DASH score, former smoking, and a higher prevalence of diabetes and hypertension (Table 1). During a mean follow up of 3.24 years (range: 0.002e7.49 years), 10,260 new cases of non-fatal MI occurred. Crude incidence rates of non-fatal MI were 16.3, 15.5, 15.9, 16.7, 19.0, 20.5, and 19.1 cases per 1000 person-years for egg
L et al., Egg consumption and risk of coronary artery disease in the Million Veteran Program, Clinical Nutrition, Please cite this article as: Djousse https://doi.org/10.1016/j.clnu.2019.12.017
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Table 1 Baseline characteristics of 188,267 participants in the Million Veteran Program by frequency of egg intake. Characteristics
Age (y) Body mass index (kg/m2) Male (%) White (%) Black (%)
Frequency of egg consumption <1/Month (n ¼ 10,911)
1e3/Month (n ¼ 30,909)
1/Week (n ¼ 42,848)
2e4/Week (n ¼ 64,510)
5e6/Week (n ¼ 19,856)
1/Day (n ¼ 12,319)
2þ/Day (n ¼ 6914)
63.5 (12) 28.1 (5.5) 86.2 78.9 13.0 2.3 97.7 20.8 (5.5) 17.1 48.5 45.2 21.0 57.4
63.6 (11.7) 28.8 (5.3) 87.7 80.5 11.9 2.5 97.5 20.2 (5.0) 19.0 49.4 53.4 22.7 59.3
64.4 (12.1) 28.8 (5.2) 90.1 84.3 8.7 2.0 98.0 21.2 (4.9) 17.1 51.9 58.9 23.3 59.1
64.9 (11.9) 29.3 (5.5) 91.2 82.0 10.2 2.2 97.8 21.7 (4.9) 17.2 53.0 59.3 25.7 62.0
64.6 (11.9) 29.7 (5.8) 91.5 80.7 10.7 2.5 97.5 21.6 (4.9) 18.3 53.4 57.5 28.2 63.4
64.0 (12.4) 29.6 (5.9) 90.3 78.4 12.1 3.1 96.9 22.0 (5.1) 18.1 53.3 53.0 29.6 63.5
63.6 (12.3) 30.3 (6.3) 92.9 80.9 9.7 3.3 96.7 21.5 (5.1) 18.5 53.4 53.8 31.8 64.8
43.0 11.5 26.7 18.7 4.5 (1.0) 2.6 (0.9) 1.3 (0.4) 1.5 (1.0)
44.3 12.9 28.5 14.4 4.6 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.1)
39.5 14.7 31.3 14.5 4.5 (1.0) 2.6 (0.8) 1.2 (0.4) 1.6 (1.1)
39.5 14.3 32.6 13.7 4.5 (1.0) 2.6 (0.8) 1.2 (0.4) 1.6 (1.1)
41.6 14.0 30.2 14.2 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.2)
40.5 13.7 28.8 17.1 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.3)
43.0 12.0 27.6 17.4 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.7 (1.2)
DASH: dietary approach to stop hypertension.
consumption of <1/month, 1e3/month, 1/week, 2e4/week, 5e6/ week, 1/day, and 2/day, respectively. Corresponding hazard ratios (95% CI) were 1.00 (ref), 0.93 (0.85e1.02), 0.96 (0.87e1.05), 0.98 (0.89e1.07), 1.08 (0.98e1.19), 1.11 (1.00e1.24), and 1.13 (1.00e1.28), respectively, controlling for age, sex, race, education, body mass index, exercise, smoking, alcohol intake, and DASH score, Table 2). Using restricted cubic spline, we found suggestive evidence of a threshold relation between egg consumption and non-fatal MI incidence (p non-linear 0.019, Fig. 1); while egg consumption of up to 4 per week was not associated with CAD risk, intake above 4 per week was associated with a higher risk of CAD in a doseeresponse manner, Fig. 1). In a sensitivity analysis using competing risk model, multivariable adjusted hazard ratios (95% CI) were 1.00 (ref), 0.93 (0.85e1.03), 0.96 (0.88e1.05), 0.98 (0.90e1.07), 1.08 (0.97e1.19), 1.11 (0.99, 1.23), and 1.13 (1.00e1.28) from the lowest to the highest
category of egg consumption. We observed similar results when fatal and non-fatal MI and coronary angioplasty and revascularization were used as composite outcome (Table 2). When stratified by body mass index and prevalent diabetes, there was no statistically significant interaction between egg consumption and body mass index (p interaction 0.53) or prevalent diabetes (p interaction 0.42) albeit slightly stronger results in overweight/obese and people with prevalent diabetes (Table 3). Additional adjustment for serum LDL-cholesterol and triglycerides yielded similar conclusion of non-linear relation of egg consumption with MI (p non-linear trend 0.017) and further adjustment for fried foods did not alter the results (p non-linear trend 0.016). Lastly, to further assess residual confounding by dietary pattern, we repeated analyses among subjects with best dietary pattern (top 2 quintiles of DASH score) and those with poor dietary pattern
Table 2 Hazard ratios (95% CI) for primary (non-fatal MI) and secondary (fatal/non-fatal MI, PCI & CABG) outcomes by frequency of egg consumption. Frequency of egg intake
Cases/Total
Crude incidence (/1000PY)
Hazard ratio (95% CI) Model 1a
Crude Primary outcome (non-fatal myocardial infarction) <1/Month 599/10,911 16.3 1e3/Month 1600/30,909 15.5 1/Week 2249/42,848 15.9 2e4/Week 3487/64,510 16.7 5e6/Week 1165/19,856 19.0 1/Day 731/12,319 20.5 2þ/Day 429/6914 19.1 p for non-linear Secondary outcome (fatal/non-fatal myocardial infarction, CABG, and PCI) <1/Month 633/10,911 17.2 1e3/Month 1701/30,909 16.5 1/Week 2378/42,848 16.8 2e4/Week 3708/64,510 17.8 5e6/Week 1236/19,856 20.2 1/Day 780/12,319 22.1 2þ/Day 461/6914 20.4 p for non-linear a b
Model 2b
1.00 0.95 0.98 1.03 1.17 1.18 1.26 0.18
(ref) (0.87, (0.90, (0.94, (1.06, (1.06, (1.12,
1.05) 1.07) 1.12) 1.29) 1.31) 1.43)
1.00 (ref) 0.95 (0.87, 0.96 (0.87, 0.99 (0.90, 1.13 (1.02, 1.16 (1.04, 1.24 (1.10, 0.008
1.04) 1.05) 1.08) 1.24) 1.29) 1.41)
1.00 (ref) 0.93 (0.85, 0.96 (0.87, 0.98 (0.89, 1.08 (0.98, 1.11 (1.00, 1.13 (1.00, 0.019
1.02) 1.05) 1.07) 1.19) 1.24) 1.28)
1.00 0.96 0.98 1.03 1.17 1.19 1.28 0.17
(ref) (0.87, (0.90, (0.95, (1.07, (1.07, (1.14,
1.05) 1.07) 1.12) 1.29) 1.32) 1.45)
1.00 (ref) 0.96 (0.87, 0.95 (0.87, 0.99 (0.91, 1.13 (1.02, 1.17 (1.05, 1.26 (1.12, 0.005
1.05) 1.04) 1.08) 1.24) 1.30) 1.42)
1.00 (ref) 0.94 (0.85, 0.96 (0.88, 0.98 (0.90, 1.08 (0.98, 1.12 (1.01, 1.15 (1.02, 0.012
1.02) 1.05) 1.07) 1.19) 1.24) 1.30)
Model 1: Adjusted for age; PCI: percutaneous coronary intervention; CABG: coronary artery bypass graft; MI: myocardial infarction. Model 2: Adjusted for age, sex, race, education, body mass index, exercise, smoking, alcohol intake, and DASH score.
L et al., Egg consumption and risk of coronary artery disease in the Million Veteran Program, Clinical Nutrition, Please cite this article as: Djousse https://doi.org/10.1016/j.clnu.2019.12.017
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Fig. 1. Restricted cubic spline depicting the relation of egg consumption with risk of non-fatal myocardial infarction among veterans.
(bottom 3 quintiles of DASH score) and found similar results in both groups (data not shown). 4. Discussion In this large prospective cohort of 188,267 US veterans, while egg consumption of up to 4 per week was not associated with the incidence of non-fatal MI, intake of 5 or more eggs per week was associated with about 10% elevated risk, especially among veterans with diabetes and those who were overweight or obese. Our primary results are consistent with findings from a metaanalysis of 9 prospective studies which reported that for each additional increase in one egg per day, the multivariable adjusted relative risk was 0.99 (95% CI: 0.85e1.15; P ¼ 0.88 for linear trend) [14]. In another meta-analysis of seven prospective studies, there was no association of egg consumption with incidence of CAD: pooled relative risk comparing high versus low egg consumption was 0.97 (95% CI: 0.88e1.07, p for heterogeneity ¼ 0.67, I2 ¼ 0.00) and no clear doseeresponse trend was apparent [13]. A prospective study of half a million Chinese reported a 12% lower risk of CAD (95% CI: 7%e16%) comparing daily intake of 0.8 egg with none [36] and that association was stronger in people with normal body mass index (p interaction BMI*egg ¼ 0.05) [36]. Our findings were
slightly stronger in subjects with body mass index 25 kg/m2 or prevalent diabetes and we observed no evidence for a statistically significant interaction between body mass index or diabetes and egg consumption on the risk of MI. Unfortunately, the China Kadoorie Biobank study [36] did not data on egg intake of 1 eggs per day for comparison with our results. In contrast, analyses of prospective data from 514 Western Australian Aborigines (256 women, 258 men) aged 15e88 years showed a high risk of fatal and non-fatal MI [HR: 2.59 (95% CI: 1.11e6.04) when comparing >2 eggs/week with none in a model controlling for age, sex, total cholesterol, mean arterial pressure, and waist girth] [37]. A major limitation of this study is a lack of adjustment for overall dietary pattern as potential positive confounder. In fact, Burke et al. [37] noted that egg consumption was associated with smoking, heavy drinking, adding salt to prepared foods, consumption of processed meats, and not trimming fat from meat; these factors could have led to an overestimation of the effect size associated with egg consumption and CAD. In a pooled analysis of six US prospective cohorts, consumption of each additional half egg was associated with an 8% higher risk of CAD (95% CI: 3%e14%) in a model adjusting for age, sex, race/ethnicity, and education; however, further control for dietary patterns attenuated the association [HR: 1.04 (95% CI: 0.99e1.10) in multivariable adjusted model plus alternate healthy eating index 2000; HR: 1.05 (95% CI: 0.99e1.11) for multivariable model plus additional adjustment for dietary approach to stop hypertension; and HR: 1.05 (95% CI: 0.99e1.10) for multivariable model with additional adjustment for alternate Mediterranean diet score [27]. Taken together, these results underscore the importance of adequate adjustment for overall dietary patterns when evaluating the relation of eggs with MI and other outcomes as single foods are seldom consumed alone. Egg consumption appears to be clustered with intake of 1) other foods associated with a higher risk of MI (i.e., red meats, saturated fat, and salt intake [38,39]) and/or 2) behaviors known to adversely influence coronary disease risk including smoking [38e40]. In our study, adjustment for red meat along with other food groups did not alter the conclusion. To further assess the possibility of residual confounding by unhealthy food items consumed with eggs, we repeated main analyses with subjects within the top 2 quintiles of DASH (most likely healthy eaters) versus bottom 3 quintiles and observed similar results in both groups. Our secondary analyses showed a slightly elevated risk of nonfatal MI in people with diabetes or body mass index of 25þ kg/ m2 consuming 5 or more eggs per week (despite no statistically significant interaction between diabetes/adiposity and egg intake). This is in contrast with findings from male physicians [40] and from the NHANES [41] which reported no association of egg consumption with incidence of MI and death from MI or stroke, respectively, among subjects with prevalent diabetes. In the PREDIMED study, egg consumption was not associated with cardiovascular disease
Table 3 Hazard ratios (95% CI) for non-fatal MI by frequency of egg consumption stratified by body mass index and prevalent diabetes. Frequency of egg intake
BMI <25 kg/m2
BMI 25 kg/m2
Diabetic
<1/Month 1e3/Month 1/Week 2e4/Week 5e6/Week 1/Day 2þ/Day
1.00 0.85 0.92 0.88 0.98 1.08 1.05
1.00 0.95 0.97 1.00 1.11 1.12 1.16
1.00 0.94 0.93 0.95 1.09 1.16 1.20
(ref) (0.71, (0.77, (0.74, (0.79, (0.87, (0.79,
1.03) 1.10) 1.05) 1.21) 1.35) 1.40)
(ref) (0.86, 1.06) (0.88, 1.08) (0.91, 1.11) (0.99, 1.24) (0.99, 1.27) (1.01e1.33)
(ref) (0.80, (0.79, (0.82, (0.93, (0.97, (0.98,
Q7 Non-diabetic 1.10) 1.09) 1.11) 1.29) 1.39) 1.45)
1.00 0.92 0.95 0.96 1.03 1.01 1.02
(ref) (0.82, (0.85, (0.86, (0.91, (0.89, (0.86,
1.03) 1.07) 1.07) 1.17) 1.16) 1.20)
*Adjusted for age, sex race, education, body mass index, exercise, smoking, alcohol intake, and dietary approach to stop hypertension score. BMI: body mass index; MI: myocardial infarction.
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among people with or without type 2 diabetes [42]. In contrast, higher egg consumption was associated with a higher risk of MI in diabetic subjects in the Health Professionals Follow-up study [HR: 2.02 (95% CI: 1.05e3.87) comparing 1 egg/d with <1 egg/week] and in nurses with prevalent diabetes [HR: 1.49 (95% CI: 0.88e2.52), p trend 0.008 comparing 1 egg/d vs. <1 egg/week) [25]. Additional studies are needed to further elucidate the role of frequent egg consumption on MI risk in people with diabetes or those who are overweight or obese. Limitations of the current study include a single assessment of egg consumption which limits our ability to account for change in dietary habits over time as potential source of bias. Self-reported information on diet could have been inaccurate and biased the results towards no association if non-differential. Although most veterans are likely to receive their care at VA hospitals, we cannot exclude incomplete ascertainment of MI cases in current analyses. We did not have adequate number of fatal MI across categories of egg intake to examine the relation of egg intake with fatal MI separately. Lastly, we were not able to assess the influence of fried, boiled, or scrambled eggs on incidence of MI due to a lack of such information in MVP. Strengths of our study include a large sample size with adequate number of incident MI for sub-analyses; prospective design; adjustment for dietary patterns using DASH score; robustness of results in various sensitivity analyses; and the availability of data on several relevant confounders. In conclusion, our data suggest no association of infrequent egg consumption with non-fatal MI but a slightly elevated risk of nonfatal MI with consumption of 5 or more eggs per week, especially among veterans with diabetes or overweight/obesity. Statement of authorship Study design and drafting the manuscript: Luc Djousse. Statistical analyses: Yuk-Lam Ho & Rachel Quaden. Data acquisition: Michael Gaziano, Kelly Cho, David Gagnon. Critical review of the manuscript and data interpretation; All authors. Final approval for submission: All authors. Sources of funding Q2 Q3
This research was funded by the Department of Veterans Affairs Office of Research and Development, Million Veteran Program Grant # MVP001 and VA Merit Award I01 BX003340-01. Conflict of interest
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Dr. Djousse has received in the past an investigator-initiated grant from the American Egg Board. None of the co-authors has disclosures to declare. Acknowledgments This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration. This publication does not represent the views of the Department of Veterans Affairs or the U.S. government. We are indebted to the MVP participants and staff. We thank Ms. Petra Schubert, MS, for her contribution in preparing data sets for current analyses. References
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Associations of dietary cholesterol or egg consumption with incident cardiovascular disease and mortality. J Am Med Assoc 2019;321:1081e95. [28] Gaziano JM, Concato J, Brophy M, et al. Million Veteran Program: a megabiobank to study genetic influences on health and disease. J Clin Epidemiol 2016;70:214e23. [29] Stein AD, Shea S, Basch CE, Contento IR, Zybert P. Consistency of the Willett semiquantitative food frequency questionnaire and 24-hour dietary recalls in estimating nutrient intakes of preschool children. Am J Epidemiol 1992;135: 667e77.
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[30] Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol 1992;135:1114e26. [31] Bertoia ML, Triche EW, Michaud DS, et al. Mediterranean and Dietary Approaches to Stop Hypertension dietary patterns and risk of sudden cardiac death in postmenopausal women. Am J Clin Nutr 2014;99: 344e51. [32] Djousse L, Ho YL, Nguyen XT, et al. DASH score and subsequent risk of coronary artery disease: the findings from Million Veteran Program. J Am Heart Assoc 2018;7. [33] Floyd JS, Blondon M, Moore KP, Boyko EJ, Smith NL. Validation of methods for assessing cardiovascular disease using electronic health data in a cohort of Veterans with diabetes. Pharmacoepidemiol Drug Saf 2016;25:467e71. [34] Center of Excellence for Suicide Prevention. Joint Department of Veterans Affairs (VA) and Department of Defense (DoD) Suicide Data Repository e National Death Index (NDI). http://vaww.virec.research.va.gov/Mortality/ Overview.htm; Extract 2019 Mar 14.
[35] Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med 1989;8:551e61. [36] Qin C, Lv J, Guo Y, et al. Associations of egg consumption with cardiovascular disease in a cohort study of 0.5 million Chinese adults. Heart 2018. [37] Burke V, Zhao Y, Lee AH, et al. Health-related behaviours as predictors of mortality and morbidity in Australian Aborigines. Prev Med 2007;44:135e42. [38] Djousse L, Gaziano JM, Buring JE, Lee IM. Egg consumption and risk of type 2 diabetes in men and women. Diabetes Care 2009;32:295e300. [39] Djousse L, Kamineni A, Nelson TL, et al. Egg consumption and risk of type 2 diabetes in older adults. Am J Clin Nutr 2010;92:422e7. [40] Djousse L, Gaziano JM. Egg consumption in relation to cardiovascular disease and mortality: the Physicians' Health Study. Am J Clin Nutr 2008;87:964e9. [41] Scrafford CG, Tran NL, Barraj LM, Mink PJ. Egg consumption and CHD and stroke mortality: a prospective study of US adults. Public Health Nutr 2011;14:261e70. [42] Diez-Espino J, Basterra-Gortari FJ, Salas-Salvado J, et al. Egg consumption and cardiovascular disease according to diabetic status: the PREDIMED study. Clin Nutr 2017;36:1015e21.
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Original article
Q10 Q9
Egg consumption and risk of coronary artery disease in the Million Veteran Program a, b, c, *, Yuk-Lam Ho a, Xuan-Mai T. Nguyen a, b, c, Rachel M. Quaden a, Luc Djousse David R. Gagnon a, d, J. Michael Gaziano a, b, c, Kelly Cho a, b, c a
Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA Harvard Medical School, Boston, MA, USA Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA d Boston University School of Public Health, Boston, MA, USA b c
Q1
a r t i c l e i n f o
s u m m a r y
Article history: Received 8 September 2019 Accepted 16 December 2019
Background & aims: Limited and inconsistent data are available on the relation between egg consumption and risk of myocardial infarction (MI) and it is unclear if adiposity or type 2 diabetes modifies egg-MI relation. We tested the primary hypothesis that egg consumption is positively associated with incidence of MI among veterans. In secondary analyses, we examined potential effect modification of egg-MI relation by adiposity and type 2 diabetes. Methods: We analyzed data collected on 188,267 US veterans who were enrolled in the Million Veteran Program (MVP) from 2011 to 2018. Information on egg consumption was obtained via self-administered food frequency questionnaire and we used electronic health records to identify incident MI. Results: The mean age was 64.4 (SD ¼ 12.0) years and 9.9% of the population were female. We ascertained 10,260 new cases of non-fatal MI during an average follow up of 3.24 years (range: 0.002 to 7.49 y). Hazard ratio (95% CI) for non-fatal MI were 1.00 (ref), 0.93 (0.85e0.1.02), 0.96 (0.87e1.05), 0.98 (0.89e1.07), 1.08 (0.98e1.19), 1.11 (1.00e1.24), and 1.13 (1.00e1.28) for egg consumption of <1/month, 1 e3/month, 1/week, 2e4/week, 5e6/week, 1/d, and 2þ/d, respectively, controlling for age, sex, race, body mass index, smoking, exercise, alcohol intake, and overall dietary pattern (p non-linear trend 0.019). In secondary analyses, we observed similar results with a composite endpoint including fatal MI, coronary angioplasty and revascularization. Conclusions: Our data showed no association of infrequent consumption of eggs with non-fatal MI but a slightly elevated risk with intake of 1 or more eggs per day among US veterans. © 2019 Published by Elsevier Ltd.
Keywords: Diet Coronary artery disease Epidemiology Risk factors
1. Introduction Coronary artery disease (CAD) is still associated with a high burden in the United States, despite steady decline in CAD death rate over the past decades [1]. Previous studies have reported beneficial effects of healthy dietary patterns on the incidence of CAD [2e6], stroke [4e6] and cardiovascular risk factors including diabetes [7e9], hypertension [10,11], and dyslipidemia [12]. Consequently, adherence to healthy dietary patterns can complement pharmacological and other interventions that are currently
Q8
* Corresponding author. Massachusetts Veterans Epidemiology Research and Information Center, Boston VA Healthcare System, 150 S Huntington Ave, Boston, MA 02130, USA. ). E-mail address: [email protected] (L. Djousse
being used to reduce financial and societal burden of CAD. While there is mounting evidence in support of protective effects of certain components of a healthy dietary pattern such as fatty fish, whole grains, and fruit and vegetables on CAD risk, data on the effects of egg consumption on CAD risk have been limited and inconsistent [13,14]. Eggs are rich in protein, minerals, vitamins, and omega-3 fatty acids with known health benefits [15] and are available worldwide at reasonable costs. Prior concerns about egg consumption and rate of cardiovascular disease have been partly derived from the cholesterol content of egg (about 200 mg of cholesterol in an average egg [16]) and choline content that can be metabolized to trimethylamine N-oxide (known to adversely affect cardiovascular risk [17e19]). However, short-term experimental studies, intervention with eggs did not raise LDL-cholesterol and in some studies [20], has been associated with increase in HDL-
https://doi.org/10.1016/j.clnu.2019.12.017 0261-5614/© 2019 Published by Elsevier Ltd.
L et al., Egg consumption and risk of coronary artery disease in the Million Veteran Program, Clinical Nutrition, Please cite this article as: Djousse https://doi.org/10.1016/j.clnu.2019.12.017
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cholesterol [21,22]. Furthermore, data have been inconsistent on the effects of egg consumption on glucose metabolism, incidence of type 2 diabetes, and other cardiovascular risk factors [23,24]. While some studies reported no association of egg consumption with CAD, endothelial function, or subclinical disease, other investigators have documented a higher risk of CAD with frequent egg consumption [25e27]. The heterogeneity across results could be partially due to inadequate adjustment for overall dietary patterns and/or limited statistical power to allow for subgroup analyses. In particular, adiposity is a risk factor for CAD and diabetes and it is possible that egg consumption may accelerate progression of atherosclerosis and exacerbate CAD event rate in diabetic or overweight/obese people. Hence, it is important to determine whether diabetes or body mass index modifies the relation of egg consumption with CAD risk. The Million Veteran Program by virtue of its large sample size and thousands of CAD events is suitable to address some of the gaps outlined above. Hence, we sought to test the hypothesis that egg consumption is associated with a higher risk of non-fatal myocardial infarction (MI) among US veterans after adjustment for major confounding factors including overall diet quality using Dietary Approach to Stop Hypertension (DASH) score. In a secondary analysis, we examined whether the relation of egg consumption with non-fatal MI risk is modified by body mass index and diabetes. 2. Method
ranking for foods that are encouraged in DASH (all but sweetened beverages and meats) [31]. For meats and sweetened beverages, where low intake is desired in DASH, we assigned a lowest value for quintile 5 and highest value for quintile 1 (example: a veteran in the first quintile of sweetened beverage was assigned a value of 5). Finally, we summed up the component scores to obtain an overall modified DASH score ranging from 7 to 35 [32]. 2.3. Outcome assessment We defined non-fatal MI using ICD9 Codes 410e411, 413e414 and ICD10 codes I20eI25 (excluding I25.2). In a secondary outcome, we also included coronary deaths (ICD10 I20eI25) and coronary angioplasty or revascularization (CPT Codes 33510e33536, 9292x, 9293x, 9294x, 92973, 92974, and 92975; ICD9 Procedure codes 36.x and 00.66). The validity of using ICD codes for the diagnosis of cardiovascular disease among veterans has been previously published [33]. 2.4. Important covariates We collected information on age, sex, race, education, body mass index, alcohol consumption, exercise, and smoking at baseline through self-reported survey. Comorbidities were derived through Veterans Health Administration (VHA) electronic medical records system, Corporate Data Warehouse (CDW), using ICD 9 and ICD10 codes.
2.1. Population 2.5. Statistical analysis Million Veteran Program (MVP) is an ongoing prospective cohort study designed to study genetic determinants of chronic diseases. All participants signed informed consent and the Veterans Affairs Central Institutional Review Board approved the study protocol. A detailed description of MVP design and methods has been published [28]. As of January 2019, 702,740 veterans have been enrolled. Among enrollees 325,790 participated in the MVP Lifestyle Survey and provided data on egg consumption, and 250,950 of those with egg consumption data had complete demographic and clinical visit data. Current analyses were restricted to 188,267 out of 250,950 participants who were free of CAD at baseline. 2.2. Assessment of egg consumption and overall diet quality We used the Willett semi-quantitative food frequency questionnaire to collect baseline information on egg consumption and other dietary factors. Reproducibility and validity of the Willett food frequency questionnaire have been previously reported [29,30]. Study participants were asked the following question: “For each food listed, please mark the column indicating how often, on average, you have used the amount specified during the past year”. Possible answers were: “Never or less than once per month”; “1e3 per month”; “once (1) a week”; “2e4 per week”; “5e6 per week”; “once (1) a day”; “2e3 per day”; “4e5 per day”; and “6 þ per day”. Food groups listed included eggs (one egg), dairy foods, fruit and vegetables, meats, sweets, baked goods, cereals, and beverages. We collapsed the last three categories of egg consumption due to limited number of events (22 MI events for 4e5 eggs per day and 31 MI events for 6þ/d). We constructed a modified DASH score based on the following 7 components: fruits, vegetables, nuts and legumes, low-fat dairy products, wholegrain, sweetened beverages, and red and processed meats [31]. Due to a lack of nutrient database in MVP at the time of these analyses, we were unable to include estimate of sodium intake in the DASH score. We created quintiles for each of the seven components and assigned quintile
We calculated person-time of follow up from food frequency assessment until the first occurrence of MI event, death [34], or last visit recorded in the CDW. We computed the incidence rate of MI within each category of egg consumption and used Cox proportional hazard models to estimate hazard ratios with 95% confidence interval of MI. Proportional hazard assumptions were tested by examining Schoenfeld residuals. We assessed confounding by building sequential models based on a priori knowledge of confounders of egg-MI relation. After adjustment for age in the initial model, the fully adjusted model controlled for age, sex, race, education, body mass index, smoking (never, former, current smokers), exercise, alcohol intake, and DASH score. In secondary analyses, we stratified by body mass index (<25 vs. 25þ kg/m2) and prevalence of diabetes (yes vs. no) because adiposity and diabetes are established CAD risk factors and their effects could be potentiated by egg consumption. We also examined the shape of the egg-MI relation by using restricted cubic splines with knots placed at 2, 3, 4 servings/week (using LGTPHCURV9 macro) [35]. Finally, we repeated main analyses using competing risk model to test the robustness of the findings. In sensitivity analysis, we excluded MI events that occurred within 1 year of follow up. All analyses were performed on SAS Enterprise Guide 7.1 and alpha level at 0.05. 3. Results We analyzed data on 188,267 veterans of whom 90.1% were men and mean age was 64.4 (SD ¼ 12.0) years. Median frequency of egg consumption was 3 eggs per week (25th and 75th percentile: 1 and 3 eggs/week, respectively). Frequent consumption of egg was associated with male gender, higher body mass index, lower DASH score, former smoking, and a higher prevalence of diabetes and hypertension (Table 1). During a mean follow up of 3.24 years (range: 0.002e7.49 years), 10,260 new cases of non-fatal MI occurred. Crude incidence rates of non-fatal MI were 16.3, 15.5, 15.9, 16.7, 19.0, 20.5, and 19.1 cases per 1000 person-years for egg
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Table 1 Baseline characteristics of 188,267 participants in the Million Veteran Program by frequency of egg intake. Characteristics
Age (y) Body mass index (kg/m2) Male (%) White (%) Black (%)
Frequency of egg consumption <1/Month (n ¼ 10,911)
1e3/Month (n ¼ 30,909)
1/Week (n ¼ 42,848)
2e4/Week (n ¼ 64,510)
5e6/Week (n ¼ 19,856)
1/Day (n ¼ 12,319)
2þ/Day (n ¼ 6914)
63.5 (12) 28.1 (5.5) 86.2 78.9 13.0 2.3 97.7 20.8 (5.5) 17.1 48.5 45.2 21.0 57.4
63.6 (11.7) 28.8 (5.3) 87.7 80.5 11.9 2.5 97.5 20.2 (5.0) 19.0 49.4 53.4 22.7 59.3
64.4 (12.1) 28.8 (5.2) 90.1 84.3 8.7 2.0 98.0 21.2 (4.9) 17.1 51.9 58.9 23.3 59.1
64.9 (11.9) 29.3 (5.5) 91.2 82.0 10.2 2.2 97.8 21.7 (4.9) 17.2 53.0 59.3 25.7 62.0
64.6 (11.9) 29.7 (5.8) 91.5 80.7 10.7 2.5 97.5 21.6 (4.9) 18.3 53.4 57.5 28.2 63.4
64.0 (12.4) 29.6 (5.9) 90.3 78.4 12.1 3.1 96.9 22.0 (5.1) 18.1 53.3 53.0 29.6 63.5
63.6 (12.3) 30.3 (6.3) 92.9 80.9 9.7 3.3 96.7 21.5 (5.1) 18.5 53.4 53.8 31.8 64.8
43.0 11.5 26.7 18.7 4.5 (1.0) 2.6 (0.9) 1.3 (0.4) 1.5 (1.0)
44.3 12.9 28.5 14.4 4.6 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.1)
39.5 14.7 31.3 14.5 4.5 (1.0) 2.6 (0.8) 1.2 (0.4) 1.6 (1.1)
39.5 14.3 32.6 13.7 4.5 (1.0) 2.6 (0.8) 1.2 (0.4) 1.6 (1.1)
41.6 14.0 30.2 14.2 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.2)
40.5 13.7 28.8 17.1 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.6 (1.3)
43.0 12.0 27.6 17.4 4.5 (1.0) 2.6 (0.9) 1.2 (0.4) 1.7 (1.2)
DASH: dietary approach to stop hypertension.
consumption of <1/month, 1e3/month, 1/week, 2e4/week, 5e6/ week, 1/day, and 2/day, respectively. Corresponding hazard ratios (95% CI) were 1.00 (ref), 0.93 (0.85e1.02), 0.96 (0.87e1.05), 0.98 (0.89e1.07), 1.08 (0.98e1.19), 1.11 (1.00e1.24), and 1.13 (1.00e1.28), respectively, controlling for age, sex, race, education, body mass index, exercise, smoking, alcohol intake, and DASH score, Table 2). Using restricted cubic spline, we found suggestive evidence of a threshold relation between egg consumption and non-fatal MI incidence (p non-linear 0.019, Fig. 1); while egg consumption of up to 4 per week was not associated with CAD risk, intake above 4 per week was associated with a higher risk of CAD in a doseeresponse manner, Fig. 1). In a sensitivity analysis using competing risk model, multivariable adjusted hazard ratios (95% CI) were 1.00 (ref), 0.93 (0.85e1.03), 0.96 (0.88e1.05), 0.98 (0.90e1.07), 1.08 (0.97e1.19), 1.11 (0.99, 1.23), and 1.13 (1.00e1.28) from the lowest to the highest
category of egg consumption. We observed similar results when fatal and non-fatal MI and coronary angioplasty and revascularization were used as composite outcome (Table 2). When stratified by body mass index and prevalent diabetes, there was no statistically significant interaction between egg consumption and body mass index (p interaction 0.53) or prevalent diabetes (p interaction 0.42) albeit slightly stronger results in overweight/obese and people with prevalent diabetes (Table 3). Additional adjustment for serum LDL-cholesterol and triglycerides yielded similar conclusion of non-linear relation of egg consumption with MI (p non-linear trend 0.017) and further adjustment for fried foods did not alter the results (p non-linear trend 0.016). Lastly, to further assess residual confounding by dietary pattern, we repeated analyses among subjects with best dietary pattern (top 2 quintiles of DASH score) and those with poor dietary pattern
Table 2 Hazard ratios (95% CI) for primary (non-fatal MI) and secondary (fatal/non-fatal MI, PCI & CABG) outcomes by frequency of egg consumption. Frequency of egg intake
Cases/Total
Crude incidence (/1000PY)
Hazard ratio (95% CI) Model 1a
Crude Primary outcome (non-fatal myocardial infarction) <1/Month 599/10,911 16.3 1e3/Month 1600/30,909 15.5 1/Week 2249/42,848 15.9 2e4/Week 3487/64,510 16.7 5e6/Week 1165/19,856 19.0 1/Day 731/12,319 20.5 2þ/Day 429/6914 19.1 p for non-linear Secondary outcome (fatal/non-fatal myocardial infarction, CABG, and PCI) <1/Month 633/10,911 17.2 1e3/Month 1701/30,909 16.5 1/Week 2378/42,848 16.8 2e4/Week 3708/64,510 17.8 5e6/Week 1236/19,856 20.2 1/Day 780/12,319 22.1 2þ/Day 461/6914 20.4 p for non-linear a b
Model 2b
1.00 0.95 0.98 1.03 1.17 1.18 1.26 0.18
(ref) (0.87, (0.90, (0.94, (1.06, (1.06, (1.12,
1.05) 1.07) 1.12) 1.29) 1.31) 1.43)
1.00 (ref) 0.95 (0.87, 0.96 (0.87, 0.99 (0.90, 1.13 (1.02, 1.16 (1.04, 1.24 (1.10, 0.008
1.04) 1.05) 1.08) 1.24) 1.29) 1.41)
1.00 (ref) 0.93 (0.85, 0.96 (0.87, 0.98 (0.89, 1.08 (0.98, 1.11 (1.00, 1.13 (1.00, 0.019
1.02) 1.05) 1.07) 1.19) 1.24) 1.28)
1.00 0.96 0.98 1.03 1.17 1.19 1.28 0.17
(ref) (0.87, (0.90, (0.95, (1.07, (1.07, (1.14,
1.05) 1.07) 1.12) 1.29) 1.32) 1.45)
1.00 (ref) 0.96 (0.87, 0.95 (0.87, 0.99 (0.91, 1.13 (1.02, 1.17 (1.05, 1.26 (1.12, 0.005
1.05) 1.04) 1.08) 1.24) 1.30) 1.42)
1.00 (ref) 0.94 (0.85, 0.96 (0.88, 0.98 (0.90, 1.08 (0.98, 1.12 (1.01, 1.15 (1.02, 0.012
1.02) 1.05) 1.07) 1.19) 1.24) 1.30)
Model 1: Adjusted for age; PCI: percutaneous coronary intervention; CABG: coronary artery bypass graft; MI: myocardial infarction. Model 2: Adjusted for age, sex, race, education, body mass index, exercise, smoking, alcohol intake, and DASH score.
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Fig. 1. Restricted cubic spline depicting the relation of egg consumption with risk of non-fatal myocardial infarction among veterans.
(bottom 3 quintiles of DASH score) and found similar results in both groups (data not shown). 4. Discussion In this large prospective cohort of 188,267 US veterans, while egg consumption of up to 4 per week was not associated with the incidence of non-fatal MI, intake of 5 or more eggs per week was associated with about 10% elevated risk, especially among veterans with diabetes and those who were overweight or obese. Our primary results are consistent with findings from a metaanalysis of 9 prospective studies which reported that for each additional increase in one egg per day, the multivariable adjusted relative risk was 0.99 (95% CI: 0.85e1.15; P ¼ 0.88 for linear trend) [14]. In another meta-analysis of seven prospective studies, there was no association of egg consumption with incidence of CAD: pooled relative risk comparing high versus low egg consumption was 0.97 (95% CI: 0.88e1.07, p for heterogeneity ¼ 0.67, I2 ¼ 0.00) and no clear doseeresponse trend was apparent [13]. A prospective study of half a million Chinese reported a 12% lower risk of CAD (95% CI: 7%e16%) comparing daily intake of 0.8 egg with none [36] and that association was stronger in people with normal body mass index (p interaction BMI*egg ¼ 0.05) [36]. Our findings were
slightly stronger in subjects with body mass index 25 kg/m2 or prevalent diabetes and we observed no evidence for a statistically significant interaction between body mass index or diabetes and egg consumption on the risk of MI. Unfortunately, the China Kadoorie Biobank study [36] did not data on egg intake of 1 eggs per day for comparison with our results. In contrast, analyses of prospective data from 514 Western Australian Aborigines (256 women, 258 men) aged 15e88 years showed a high risk of fatal and non-fatal MI [HR: 2.59 (95% CI: 1.11e6.04) when comparing >2 eggs/week with none in a model controlling for age, sex, total cholesterol, mean arterial pressure, and waist girth] [37]. A major limitation of this study is a lack of adjustment for overall dietary pattern as potential positive confounder. In fact, Burke et al. [37] noted that egg consumption was associated with smoking, heavy drinking, adding salt to prepared foods, consumption of processed meats, and not trimming fat from meat; these factors could have led to an overestimation of the effect size associated with egg consumption and CAD. In a pooled analysis of six US prospective cohorts, consumption of each additional half egg was associated with an 8% higher risk of CAD (95% CI: 3%e14%) in a model adjusting for age, sex, race/ethnicity, and education; however, further control for dietary patterns attenuated the association [HR: 1.04 (95% CI: 0.99e1.10) in multivariable adjusted model plus alternate healthy eating index 2000; HR: 1.05 (95% CI: 0.99e1.11) for multivariable model plus additional adjustment for dietary approach to stop hypertension; and HR: 1.05 (95% CI: 0.99e1.10) for multivariable model with additional adjustment for alternate Mediterranean diet score [27]. Taken together, these results underscore the importance of adequate adjustment for overall dietary patterns when evaluating the relation of eggs with MI and other outcomes as single foods are seldom consumed alone. Egg consumption appears to be clustered with intake of 1) other foods associated with a higher risk of MI (i.e., red meats, saturated fat, and salt intake [38,39]) and/or 2) behaviors known to adversely influence coronary disease risk including smoking [38e40]. In our study, adjustment for red meat along with other food groups did not alter the conclusion. To further assess the possibility of residual confounding by unhealthy food items consumed with eggs, we repeated main analyses with subjects within the top 2 quintiles of DASH (most likely healthy eaters) versus bottom 3 quintiles and observed similar results in both groups. Our secondary analyses showed a slightly elevated risk of nonfatal MI in people with diabetes or body mass index of 25þ kg/ m2 consuming 5 or more eggs per week (despite no statistically significant interaction between diabetes/adiposity and egg intake). This is in contrast with findings from male physicians [40] and from the NHANES [41] which reported no association of egg consumption with incidence of MI and death from MI or stroke, respectively, among subjects with prevalent diabetes. In the PREDIMED study, egg consumption was not associated with cardiovascular disease
Table 3 Hazard ratios (95% CI) for non-fatal MI by frequency of egg consumption stratified by body mass index and prevalent diabetes. Frequency of egg intake
BMI <25 kg/m2
BMI 25 kg/m2
Diabetic
<1/Month 1e3/Month 1/Week 2e4/Week 5e6/Week 1/Day 2þ/Day
1.00 0.85 0.92 0.88 0.98 1.08 1.05
1.00 0.95 0.97 1.00 1.11 1.12 1.16
1.00 0.94 0.93 0.95 1.09 1.16 1.20
(ref) (0.71, (0.77, (0.74, (0.79, (0.87, (0.79,
1.03) 1.10) 1.05) 1.21) 1.35) 1.40)
(ref) (0.86, 1.06) (0.88, 1.08) (0.91, 1.11) (0.99, 1.24) (0.99, 1.27) (1.01e1.33)
(ref) (0.80, (0.79, (0.82, (0.93, (0.97, (0.98,
Q7 Non-diabetic 1.10) 1.09) 1.11) 1.29) 1.39) 1.45)
1.00 0.92 0.95 0.96 1.03 1.01 1.02
(ref) (0.82, (0.85, (0.86, (0.91, (0.89, (0.86,
1.03) 1.07) 1.07) 1.17) 1.16) 1.20)
*Adjusted for age, sex race, education, body mass index, exercise, smoking, alcohol intake, and dietary approach to stop hypertension score. BMI: body mass index; MI: myocardial infarction.
L et al., Egg consumption and risk of coronary artery disease in the Million Veteran Program, Clinical Nutrition, Please cite this article as: Djousse https://doi.org/10.1016/j.clnu.2019.12.017
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among people with or without type 2 diabetes [42]. In contrast, higher egg consumption was associated with a higher risk of MI in diabetic subjects in the Health Professionals Follow-up study [HR: 2.02 (95% CI: 1.05e3.87) comparing 1 egg/d with <1 egg/week] and in nurses with prevalent diabetes [HR: 1.49 (95% CI: 0.88e2.52), p trend 0.008 comparing 1 egg/d vs. <1 egg/week) [25]. Additional studies are needed to further elucidate the role of frequent egg consumption on MI risk in people with diabetes or those who are overweight or obese. Limitations of the current study include a single assessment of egg consumption which limits our ability to account for change in dietary habits over time as potential source of bias. Self-reported information on diet could have been inaccurate and biased the results towards no association if non-differential. Although most veterans are likely to receive their care at VA hospitals, we cannot exclude incomplete ascertainment of MI cases in current analyses. We did not have adequate number of fatal MI across categories of egg intake to examine the relation of egg intake with fatal MI separately. Lastly, we were not able to assess the influence of fried, boiled, or scrambled eggs on incidence of MI due to a lack of such information in MVP. Strengths of our study include a large sample size with adequate number of incident MI for sub-analyses; prospective design; adjustment for dietary patterns using DASH score; robustness of results in various sensitivity analyses; and the availability of data on several relevant confounders. In conclusion, our data suggest no association of infrequent egg consumption with non-fatal MI but a slightly elevated risk of nonfatal MI with consumption of 5 or more eggs per week, especially among veterans with diabetes or overweight/obesity. Statement of authorship Study design and drafting the manuscript: Luc Djousse. Statistical analyses: Yuk-Lam Ho & Rachel Quaden. Data acquisition: Michael Gaziano, Kelly Cho, David Gagnon. Critical review of the manuscript and data interpretation; All authors. Final approval for submission: All authors. Sources of funding Q2 Q3
This research was funded by the Department of Veterans Affairs Office of Research and Development, Million Veteran Program Grant # MVP001 and VA Merit Award I01 BX003340-01. Conflict of interest
Q4
Dr. Djousse has received in the past an investigator-initiated grant from the American Egg Board. None of the co-authors has disclosures to declare. Acknowledgments This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration. This publication does not represent the views of the Department of Veterans Affairs or the U.S. government. We are indebted to the MVP participants and staff. We thank Ms. Petra Schubert, MS, for her contribution in preparing data sets for current analyses. References
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