ABSTRACT
Background
The potential cardiovascular impact of dietary cholesterol intake has been actively debated for decades.
Objectives
We aimed to evaluate associations of dietary cholesterol and egg intakes with incident cardiovascular disease (CVD) and all-cause and cause-specific mortality.
Methods
We included 96,831 US postmenopausal women aged 50–79 y without known CVD or cancer during baseline enrollment (1993–1998) of the Women's Health Initiative. Dietary information was collected using a validated FFQ. Incident CVD [i.e., ischemic heart disease (IHD) and stroke] and all-cause and cause-specific mortality were ascertained and adjudicated through February 2018.
Results
A total of 9808 incident CVD cases and 19,508 all-cause deaths occurred during a median follow-up of 17.8 y and 18.9 y, respectively. After multivariable adjustment for traditional risk factors and key dietary nutrients including dietary saturated fat, there were modest associations of dietary cholesterol intake with incident CVD (HRQ5versusQ1: 1.12; 95% CI: 1.03, 1.21; P-trend < 0.001) and all-cause mortality (HRQ5versusQ1: 1.09; 95% CI: 1.02, 1.15; P-trend < 0.001). Significant positive associations were also observed between dietary cholesterol and incident IHD (P-trend = 0.007), incident ischemic stroke (P-trend = 0.002), and CVD mortality (P-trend = 0.002), whereas there was an inverse association for incident hemorrhagic stroke (P-trend = 0.037) and no association for mortality from cancer, Alzheimer disease/dementia, respiratory diseases, or other causes (P-trend > 0.05). Higher egg consumption was also associated with modestly higher risk of incident CVD (P-trend = 0.004) and all-cause mortality (P-trend < 0.001), with HRs of 1.14 (95% CI: 1.04, 1.25) and 1.14 (95% CI: 1.07, 1.22), respectively, when comparing ≥1 egg/d with <1 egg/wk.
Conclusions
Both higher dietary cholesterol intake and higher egg consumption appeared to be associated with modestly elevated risk of incident CVD and all-cause mortality in US postmenopausal women.
Keywords: cardiovascular disease, cholesterol, diet, eggs, postmenopausal women
Introduction
The potential cardiovascular impact of dietary cholesterol intake has been actively debated for decades. Accumulative evidence from randomized dietary intervention trials has suggested that increasing intake of dietary cholesterol may increase serum LDL cholesterol (1), an established risk factor for atherosclerotic cardiovascular disease (CVD) (2). However, such increases in LDL cholesterol are typically modest and may be accomplished by increases in HDL cholesterol without meaningful changes in the ratio of LDL to HDL cholesterol (3). Thus, the clinical relevance of possible changes in lipid profile–associated dietary cholesterol remains unclear. Findings from large cohort studies of dietary cholesterol and long-term risk of CVD have been limited and heterogeneous (4).
Eggs are a relatively inexpensive source of high-quality protein and other nutrients, but also a major contributor to dietary cholesterol. The most up-to-date meta-analyses of prospective cohort studies have found no association between egg consumption and risk of total CVD (5), but instead suggested a possible inverse association between egg consumption and risk of stroke (6). Given the limited evidence supportive of a detrimental association between dietary cholesterol or egg consumption and risk of CVD, the 2015–2020 Dietary Guidelines for Americans no longer listed dietary cholesterol as a “nutrient of concern” (7). Nevertheless, the Guidelines still advised that individuals should eat dietary cholesterol as little as possible while building a healthy eating pattern.
In this context, recent findings from a pooled analysis of 6 US cohorts triggered a call for reconsideration of dietary cholesterol restriction (8, 9). In this analysis including 29,615 US participants (9), it was reported that higher dietary cholesterol intake and higher egg consumption were both associated with modestly elevated risk of CVD and all-cause mortality in a linear dose-response manner. A clearer understanding of any excess disease risk associated with dietary cholesterol intake or egg consumption is highly relevant for dietary recommendations and has significant public health implications. Therefore, we examined associations of dietary cholesterol and egg consumption with incident CVD and all-cause and cause-specific mortality in the Women's Health Initiative (WHI) (10), a large prospective study of US postmenopausal women.
Methods
Study design and population
Details of the WHI design and study population have been reported elsewhere (10). Briefly, between 1993 and 1998, 161,808 women aged 50–79 y were recruited at 40 clinical centers throughout the United States. Participants were either enrolled in the WHI Observational Study (OS) or in ≥1 of the WHI Clinical Trials (CT) that evaluated the health effects of hormone therapy (2 trials), low-fat dietary modification, and calcium and vitamin D supplementation. At the end of the initial WHI study in 2005, the first (2005–2010) and the second (2010–2020) WHI Extension Studies continued follow-up of all women who consented. The study was approved by the institutional review boards of all participating institutions, and all participants provided written informed consent.
Dietary assessment
Information on dietary intake at baseline was collected using a self-administered FFQ adapted from the Block FFQ (11, 12). Questions were added to make the WHI FFQ more sensitive to dietary fat intake. This instrument included 122 composite and single food line items for participants to denote their habitual diets over the past 3 mo. The WHI-FFQ nutrient database was derived from the University of Minnesota Nutrition Coordinating Center food and nutrient database (13), which provides information for >140 nutrients and compounds including dietary cholesterol. Previous validations against 4-d food records and 4 24-h dietary recalls showed that reliable dietary estimates were recorded by the FFQ (11). For example, energy-adjusted Pearson partial correlation coefficients between estimates by the FFQ and those by dietary recalls/food records were 0.49 for dietary cholesterol, 0.58 for total fat, 0.56 for saturated fat, and 0.65 for dietary fiber, with a mean of 0.49 across all assessed major nutrients (11). Using the MyPyramid Equivalents Database, version 2.0 (14), dietary data in units of MyPyramid equivalents were also constructed by translating frequency of food consumption into standardized food quantities. For example, 1 MyPyramid equivalent of egg (including both full egg and egg as ingredients) is an amount equal to 1 oz. equivalent of cooked lean meat, or a large whole egg (14).
Outcome ascertainment
The primary outcomes of interest were all-cause mortality and composite CVD including ischemic heart disease (IHD) and stroke. IHD included possible or definite coronary death, nonfatal myocardial infarction, or coronary revascularization. Stroke included ischemic or hemorrhagic stroke or death due to a cerebrovascular event. Participants were followed up (through 28 February, 2018) semiannually in the WHI CT and annually in the OS using in-person, mailed, or telephone questionnaires to collect information on clinical outcomes. Deaths were ascertained by reviewing death certificates, medical records, or autopsy reports, or by linkage to the National Death Index. Adjudications of CVD and death outcomes have been described in detail previously (15).
Assessment of covariates
Information on demographic characteristics, reproductive and medical histories, exogenous hormone use, family history, smoking, and alcohol drinking was collected at baseline via self-report. Blood pressure including systolic blood pressure (SBP) and diastolic blood pressure (DBP) and anthropometric measures such as weight, height, and waist circumference were measured by trained staff using standard procedures (16). BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Information on previous diagnosis and treatment of hypertension, hyperlipidemia, or diabetes by a physician was collected via questionnaire. Participants were also instructed to bring prescription medication containers during the baseline screening interview. Hypertension was defined as SBP/DBP ≥ 140/90 mm Hg or a self-reported physician's diagnosis or use of antihypertensive mediations. Dyslipidemia was defined as self-reported hyperlipidemia or recorded statin use. Physical activity was measured using the WHI physical activity questionnaire and the data were summarized in metabolic equivalent (MET)-h/wk (17).
Statistical analysis
Our current analysis considered only participants within the WHO OS or control arms of the WHI CT because all interventions in the WHI CT may lead to subsequent changes in dietary habits and/or blood lipids (18, 19). The following exclusions were applied: 1) presence of major CVD, heart failure, or cancer at baseline (n = 18,666); 2) missing dietary data (n = 137), or extreme values for dietary energy (>5000 or <600 kcal/d; n = 3189), dietary cholesterol (>1000 mg/d; n = 178), or eggs (>3 eggs/d; n = 69); and 3) participants missing follow-up (n = 465), or cases of CVD or death that occurred within the first year of follow-up (n = 656). After these exclusions, 96,831 participants remained in the analyses (Supplemental Figure 1).
Owing to the substantial correlation between dietary cholesterol and energy intake (r = 0.72), energy-adjusted dietary cholesterol (mg · 1000 kcal−1 · d−1) was calculated and used in the analyses (20). We created 5 convenient intake categories for eggs based on weekly frequency of consumption (<1, 1 to <2, 2 to <3, 3 to <7, and ≥7 eggs/wk). Baseline characteristics of participants were described by quintile of dietary cholesterol or categories of egg intake. Pearson partial correlation coefficients between dietary cholesterol and egg intakes and intakes of 9 other major food groups and 7 nutrients were calculated, with adjustment for age, region, race/ethnicity, study group, and total energy intake.
We used Cox proportional hazards models to estimate HRs and 95% CIs of CVD or mortality associated with dietary cholesterol or egg intake. Person-time of follow-up was counted from date of enrollment through date of diagnosis of CVD (for incident CVD), date of death, date of not consenting to the Extension Studies, or date of the end of follow-up, whichever came first. Four Cox models were constructed. Model 1 was adjusted for demographic factors (i.e., age, region, and race/ethnicity), study group, and socioeconomic status (i.e., education, family income, and health insurance). Model 2 was further adjusted for lifestyle behaviors (i.e., smoking status, pack-years of smoking, alcohol consumption, reason for quitting smoking or drinking, recreational physical activity, and frequency of using fat to deep fry/pan fry/sauté), medication use [i.e., aspirin use, use of nonsteroidal anti-inflammatory drugs (NSAIDs), and hormone use], self-rated health status, and total energy intake. Model 3 was further adjusted for diabetes, antihypertensive drug use, SBP, DBP, dyslipidemia, and waist circumference. We used waist circumference as an indicator of adiposity because it has been shown to be more predictive of mortality than BMI in the WHI (21) and many other population studies (22). The full model (model 4) further included 7 key nutrients for the analysis of dietary cholesterol or 9 major food groups for the analysis of egg consumption.
We used restricted cubic splines to examine potential nonlinear relations between dietary cholesterol or egg intake and risk of CVD and all-cause mortality, with 3 knots at the 10th, 50th, and 90th percentiles of the intake distribution. We stratified analyses and tested interactions between dietary cholesterol (per 100 mg · 1000 kcal−1 · d−1) or egg intake (per 0.5 eggs/d) and various demographic, dietary, and lifestyle factors and medical histories on incident CVD or all-cause mortality. For cause-specific mortality, we repeated the analyses by excluding other deaths that were not the outcome of interest because death events may have occurred competingly with each other (9). All statistical analyses were performed using Stata version 15.1 (StataCorp).
Results
Participant characteristics
Median dietary cholesterol intake was 120.9 (IQR: 94.3–153.7) mg · 1000 kcal−1 · d−1 and median egg intake was 1.3 (IQR: 0.6–2.6) eggs/wk. Baseline participant characteristics by quintiles of dietary cholesterol intake or categories of egg intake are reported in Table 1 and Supplemental Table 1, respectively. Participants with higher dietary cholesterol intake were less likely to be white, had lower levels of education and family income, and a lower rate of health insurance coverage. Higher dietary cholesterol intake was also associated with current smoking, lower levels of physical activity and diet-quality score, and higher BMI, waist circumference, and total energy intake. Furthermore, participants with higher dietary cholesterol intake were more likely to have diabetes and hypertension and to use NSAIDs, but were less likely to have dyslipidemia or to use aspirin or hormones. Distributions of baseline participant characteristics according to categories of egg intake were similar to those according to quintiles of dietary cholesterol (Supplemental Table 1).
TABLE 1.
Baseline participant characteristics according to quintile of dietary cholesterol intake in the Women's Health Initiative1
| Dietary cholesterol intake | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | |
| Median (range), mg · 1000 kcal−1 · d−1 | 73.1 (3.4–88.1) | 100.0 (88.2–110.4) | 120.9 (110.5–132.2) | 145.4 (132.3–163.4) | 193.2 (163.5–722.3) |
| Participants, n | 19,366 | 19,366 | 19,366 | 19,366 | 19,367 |
| Age, y | 63.3 ± 7.4 | 63.1 ± 7.2 | 62.9 ± 7.2 | 62.8 ± 7.2 | 63.0 ± 7.1 |
| Race/ethnicity, % | |||||
| White | 86.0 | 85.8 | 85.2 | 82.3 | 78.9 |
| Black/African American | 6.6 | 6.6 | 7.2 | 8.3 | 10.9 |
| Hispanic/Latino | 3.0 | 3.2 | 3.4 | 4.0 | 5.0 |
| Other/unknown | 4.4 | 4.3 | 4.2 | 4.9 | 5.2 |
| Education ≥ college degree, % | 48.6 | 45.0 | 41.3 | 40.2 | 36.0 |
| Family income ≥$50,000/y, % | 46.2 | 45.3 | 43.7 | 42.6 | 37.5 |
| Any health insurance, % | 97.0 | 97.1 | 96.5 | 96.0 | 94.2 |
| Current smoker, % | 3.9 | 5.1 | 6.0 | 7.2 | 9.6 |
| Moderate drinker (0.5–1.0 drink/d), % | 8.8 | 9.9 | 10.3 | 10.1 | 9.0 |
| Recreational PA, MET-h/wk | 16.6 ± 15.7 | 13.7 ± 13.8 | 12.5 ± 13.3 | 11.9 ± 13.1 | 11.0 ± 13.0 |
| Diabetes, % | 3.0 | 3.6 | 4.2 | 5.4 | 7.8 |
| Hypertension, % | 34.0 | 35.7 | 37.0 | 38.7 | 40.6 |
| Dyslipidemia, % | 16.8 | 13.9 | 13.0 | 11.7 | 10.6 |
| Aspirin use, % | 23.5 | 22.0 | 21.9 | 20.6 | 19.3 |
| NSAID use, % | 16.2 | 19.1 | 19.2 | 19.8 | 19.8 |
| Current hormone use, % | 45.7 | 47.2 | 46.2 | 45.5 | 42.9 |
| BMI, kg/m2 | 25.7 ± 5.1 | 26.8 ± 5.3 | 27.5 ± 5.6 | 28.3 ± 6.0 | 29.1 ± 60.4 |
| Waist circumference, cm | 81.1 ± 12.2 | 83.6 ± 12.6 | 85.3 ± 13.1 | 87.1 ± 13.8 | 89.2 ± 14.6 |
| Very good/excellent health (self-rated), % | 67.7 | 64.1 | 61.3 | 58.4 | 54.0 |
| Fry/sauté foods ≥3/wk, % | 11.0 | 14.5 | 17.9 | 20.0 | 21.6 |
| Total energy intake, kcal/d | 1495 ± 542 | 1597 ± 586 | 1657 ± 628 | 1691 ± 660 | 1620 ± 654 |
| Diet quality score (AHEI-2010) | 56.8 ± 10.4 | 53.8 ± 10.2 | 52.1 ± 10.2 | 50.9 ± 10.1 | 50.3 ± 10.1 |
| Food groups (daily) | |||||
| Eggs, oz equivalents2 | 0.08 ± 0.08 | 0.15 ± 0.11 | 0.23 ± 0.16 | 0.35 ± 0.23 | 0.71 ± 0.40 |
| Fruit and vegetables,2 cup equivalents | 3.09 ± 1.48 | 2.74 ± 1.32 | 2.60 ± 1.27 | 2.48 ± 1.22 | 2.26 ± 1.19 |
| Whole grain,2 oz. equivalents | 1.55 ± 1.20 | 1.31 ± 1.00 | 1.21 ± 0.94 | 1.09 ± 0.88 | 0.95 ± 0.81 |
| Refined grain,2 oz. equivalents | 3.66 ± 2.23 | 3.82 ± 2.15 | 3.86 ± 2.11 | 3.87 ± 2.14 | 3.54 ± 2.03 |
| Potatoes,2 cup equivalents | 0.23 ± 0.20 | 0.26 ± 0.21 | 0.28 ± 0.22 | 0.29 ± 0.23 | 0.27 ± 0.23 |
| Fish and shellfish,2 oz. equivalents | 0.46 ± 0.45 | 0.57 ± 0.51 | 0.64 ± 0.57 | 0.69 ± 0.62 | 0.72 ± 0.73 |
| Red meat,2 oz. equivalents | 0.67 ± 0.60 | 1.20 ± 0.90 | 1.57 ± 1.15 | 1.89 ± 1.41 | 2.00 ± 1.64 |
| Processed meat,2 oz. equivalents | 0.21 ± 0.31 | 0.35 ± 0.42 | 0.44 ± 0.50 | 0.50 ± 0.55 | 0.51 ± 0.60 |
| Organ meat,2 oz. equivalents | 0.01 ± 0.03 | 0.01 ± 0.05 | 0.02 ± 0.07 | 0.04 ± 0.10 | 0.08 ± 0.19 |
| Coffee and tea,3 cups | 2.01 ± 1.72 | 2.12 ± 1.71 | 2.17 ± 1.76 | 2.18 ± 1.76 | 2.13 ± 1.80 |
| Soft drinks and fruit juices,3 glasses | 0.90 ± 1.02 | 0.85 ± 0.91 | 0.81 ± 0.85 | 0.78 ± 0.81 | 0.71 ± 0.76 |
| Nutrients (daily) | |||||
| Dietary fiber, g/1000 kcal | 13.2 ± 4.2 | 11.0 ± 3.3 | 10.1 ± 3.1 | 9.4 ± 2.9 | 8.8 ± 3.0 |
| Saturated fat, % energy | 7.5 ± 2.4 | 9.6 ± 2.5 | 10.9 ± 2.8 | 11.8 ± 3.0 | 12.6 ± 3.3 |
| Polyunsaturated fat, % energy | 5.8 ± 2.2 | 6.4 ± 2.1 | 6.7 ± 2.1 | 6.9 ± 2.0 | 7.2 ± 2.1 |
| Monounsaturated fat, % energy | 9.4 ± 3.5 | 11.2 ± 3.2 | 12.2 ± 3.1 | 13.0 ± 3.0 | 13.9 ± 3.1 |
| Trans fat, % energy | 1.8 ± 1.2 | 2.1 ± 1.1 | 2.3 ± 1.1 | 2.4 ± 1.0 | 2.4 ± 1.0 |
| Animal protein, % energy | 9.1 ± 3.2 | 10.9 ± 2.8 | 11.9 ± 2.8 | 12.7 ± 3.0 | 13.7 ± 3.5 |
| Sodium, mg/1000 kcal | 1650 ± 335 | 1668 ± 293 | 1679 ± 281 | 1693 ± 274 | 1707 ± 280 |
n = 96,831. AHEI, Alternate Healthy Eating Index; MET, metabolic equivalent; NSAID, nonsteroidal anti-inflammatory drug; oz., ounce; PA, physical activity; Q, quintile.
Definitions and determination of what counts as 1 equivalent of food groups can be found at: https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/mped/mped2_doc.pdf.
One cup of coffee or tea was 8 fluid oz. (or 236.6 mL), 1 glass of fruit juice was 6 fluid oz. (or 177.4 mL), and 1 glass of soft drink was 12 fluid oz. (or 354.9 mL).
Supplemental Figure 2 presents Pearson partial correlation coefficients between intakes of dietary cholesterol and eggs and intakes of 9 other major food groups and 7 nutrients. Correlation between dietary cholesterol and egg consumption was substantial (r = 0.80). Dietary cholesterol was moderately and positively correlated with intakes of red and processed meat (r = 0.35), organ meat (r = 0.25), saturated fat (r = 0.48), animal protein (r = 0.46), and monounsaturated fat (r = 0.40), and was inversely correlated with intakes of fruit and vegetables (r = −0.22), whole grains (r = −0.22), and dietary fiber (r = −0.36).
Dietary cholesterol and incident CVD and mortality
During a median follow-up of 17.8 y, 9808 incident cases of CVD were identified, including 7091 IHD, 2430 ischemic stroke, and 535 hemorrhagic stroke cases. As Table 2 shows, after full adjustment for potential confounders including key dietary nutrients, higher dietary cholesterol intake was associated with modestly higher risk of CVD (HRQ5versusQ1: 1.12; 95% CI: 1.03, 1.21; P-trend < 0.001). Higher dietary cholesterol intake was also associated with higher risk of IHD ( P-trend = 0.007) and ischemic stroke (P-trend = 0.002) in the fully adjusted model. Conversely, there was a significant inverse association between dietary cholesterol and risk of hemorrhagic stroke (P-trend = 0.037) (Table 2). When cases of hemorrhagic stroke were censored, HRs across quintiles of dietary cholesterol were 1.00, 1.04, 1.07, 1.12, and 1.17 (95% CI: 1.07, 1.27) for incident atherosclerotic CVD (i.e., IHD and ischemic stroke) ( P-trend < 0.001).
TABLE 2.
Association between dietary cholesterol intake and incident CVD in the WHI1
| Dietary cholesterol intake | |||||||
|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | P-trend | Per 100 mg · 1000 kcal−1 · d−1 | |
| Median (range), mg · 1000 kcal−1 · d−1 | 73.1 (3.4–88.1) | 100.0 (88.2–110.4) | 120.9 (110.5–132.2) | 145.4 (132.3–163.4) | 193.2 (163.5–722.3) | ||
| Participants, n | 19,366 | 19,366 | 19,366 | 19,366 | 19,367 | ||
| All CVD | |||||||
| Cases, n | 1769 | 1884 | 1925 | 2061 | 2169 | ||
| Model 1, HR (95% CI) | Reference | 1.05 (0.98, 1.12) | 1.07 (1.01, 1.15) | 1.17 (1.10, 1.25) | 1.23 (1.16, 1.32) | <0.001 | 1.16 (1.12, 1.20) |
| Model 2, HR (95% CI) | Reference | 1.03 (0.96, 1.10) | 1.05 (0.98, 1.12) | 1.12 (1.05, 1.20) | 1.15 (1.08, 1.23) | <0.001 | 1.11 (1.07, 1.15) |
| Model 3, HR (95% CI) | Reference | 1.03 (0.96, 1.09) | 1.02 (0.96, 1.09) | 1.07 (1.00, 1.15) | 1.08 (1.02, 1.16) | 0.004 | 1.07 (1.03, 1.11) |
| Model 4, HR (95% CI) | Reference | 1.03 (0.96, 1.10) | 1.04 (0.96, 1.12) | 1.10 (1.02, 1.19) | 1.12 (1.03, 1.21) | <0.001 | 1.09 (1.04, 1.15) |
| IHD | |||||||
| Cases, n | 1277 | 1352 | 1364 | 1511 | 1587 | ||
| Model 1, HR (95% CI) | Reference | 1.04 (0.96, 1.12) | 1.05 (0.97, 1.13) | 1.18 (1.09, 1.27) | 1.24 (1.15, 1.34) | <0.001 | 1.17 (1.12, 1.22) |
| Model 2, HR (95% CI) | Reference | 1.02 (0.95, 1.10) | 1.02 (0.94, 1.10) | 1.13 (1.05, 1.23) | 1.16 (1.07, 1.25) | <0.001 | 1.12 (1.07, 1.17) |
| Model 3, HR (95% CI) | Reference | 1.01 (0.93, 1.09) | 1.00 (0.92, 1.08) | 1.08 (1.00, 1.17) | 1.09 (1.01, 1.17) | 0.008 | 1.07 (1.02, 1.12) |
| Model 4, HR (95% CI) | Reference | 1.01 (0.93, 1.10) | 1.01 (0.93, 1.10) | 1.10 (1.01, 1.21) | 1.11 (1.01, 1.23) | 0.007 | 1.09 (1.03, 1.16) |
| Ischemic stroke | |||||||
| Cases, n | 414 | 460 | 521 | 471 | 564 | ||
| Model 1, HR (95% CI) | Reference | 1.10 (0.96, 1.25) | 1.25 (1.10, 1.43) | 1.15 (1.01, 1.31) | 1.38 (1.21, 1.57) | <0.001 | 1.20 (1.12, 1.29) |
| Model 2, HR (95% CI) | Reference | 1.08 (0.94, 1.23) | 1.22 (1.07, 1.39) | 1.10 (0.97, 1.26) | 1.30 (1.14, 1.48) | <0.001 | 1.16 (1.07, 1.25) |
| Model 3, HR (95% CI) | Reference | 1.06 (0.93, 1.21) | 1.19 (1.04, 1.36) | 1.05 (0.92, 1.21) | 1.22 (1.07, 1.39) | 0.011 | 1.11 (1.03, 1.20) |
| Model 4, HR (95% CI) | Reference | 1.10 (0.95, 1.26) | 1.26 (1.09, 1.46) | 1.13 (0.97, 1.33) | 1.34 (1.14, 1.59) | 0.002 | 1.18 (1.07, 1.30) |
| Hemorrhagic stroke | |||||||
| Cases, n | 115 | 116 | 106 | 105 | 94 | ||
| Model 1, HR (95% CI) | Reference | 0.99 (0.77, 1.29) | 0.90 (0.69, 1.18) | 0.92 (0.70, 1.20) | 0.82 (0.62, 1.09) | 0.14 | 0.91 (0.76, 1.08) |
| Model 2, HR (95% CI) | Reference | 0.98 (0.76, 1.27) | 0.89 (0.68, 1.16) | 0.89 (0.68, 1.17) | 0.77 (0.58, 1.02) | 0.051 | 0.87 (0.73, 1.03) |
| Model 3, HR (95% CI) | Reference | 0.98 (0.76, 1.27) | 0.88 (0.67, 1.16) | 0.88 (0.67, 1.16) | 0.76 (0.57, 1.01) | 0.042 | 0.86 (0.72, 1.03) |
| Model 4, HR (95% CI) | Reference | 0.94 (0.71, 1.24) | 0.83 (0.61, 1.12) | 0.81 (0.59, 1.13) | 0.69 (0.49, 0.99) | 0.037 | 0.83 (0.66, 1.04) |
n = 96,831. Model 1: age (y), region (Northeast, South, Midwest, West), race/ethnicity (white, black/African-American, Hispanic/Latino, other), study group (WHI Observational Study or control of clinical trials), education (at most high school, some college, college or above), annual family income (<$20,000, $20,000 to <$50,000, $50,000 to <$75,000, ≥$75,000), and health insurance (none, prepaid private, other). Model 2: model 1 + smoking status (never, former, current), pack-years of smoking, alcohol consumption (0, <0.5, 0.5 to <1, ≥1 drink/d), quitting smoking/drinking owing to health problems (yes, no), recreational physical activity (metabolic equivalent-h/wk), total energy intake (kcal/d), using fat to deep fry/pan fry/sauté (<1, 1–2, 3–4, 5–6, ≥7 times/wk), aspirin use (yes, no), use of nonsteroidal anti-inflammatory drugs (yes, no), hormone use [never, former, current (<5, 5 to <10, 10 to <15, ≥15 y)], and self-rated health status (very good/excellent, good, fair/poor). Model 3: model 2 + waist circumference (cm), diabetes (yes, no), systolic and diastolic blood pressure (mm Hg), antihypertensive drug use (yes, no), and dyslipidemia (yes, no). Model 4: model 3 + energy-adjusted dietary nutrients (dietary fiber, saturated fat, polyunsaturated fat, monounsaturated fat, trans fat, animal protein, sodium). CVD, cardiovascular disease; IHD, ischemic heart disease; Q, quintile; WHI, Women's Health Initiative.
In total, 19,508 all-cause deaths were identified during a median 18.9 y of follow-up, including 5589 CVD deaths, 6228 cancer deaths, 1319 deaths from Alzheimer disease (AD)/dementia, 1448 deaths from respiratory diseases, and 4924 deaths from other causes. As Table 3 shows, higher dietary cholesterol intake was associated with modestly higher risk of all-cause mortality after the full adjustment (HRQ5versusQ1: 1.09; 95% CI: 1.02, 1.15; P-trend < 0.001). A significant positive association was also observed between dietary cholesterol and CVD mortality (P-trend = 0.002). After multivariable adjustment without adjusting for other dietary nutrients (model 3), dietary cholesterol was not associated with AD/dementia mortality, but was significantly associated with higher risk of cancer, respiratory, and other mortality. However, all these significant associations for non-CVD mortality were no longer significant after the full adjustment (all P-trend values > 0.05) (Table 3), with the association for respiratory mortality being most apparently attenuated. Therefore, we performed post hoc analyses with adjustment for dietary nutrients individually, and found that adjustment for saturated fat led to the greatest attenuation of the associations for non-CVD mortality (Figure 1).
TABLE 3.
Association between dietary cholesterol intake and mortality in the WHI1
| Dietary cholesterol intake | |||||||
|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | P-trend | Per 100 mg · 1000 kcal−1 · d−1 | |
| Median (range), mg · 1000 kcal−1 · d−1 | 73.1 (3.4–88.1) | 100.0 (88.2–110.4) | 120.9 (110.5–132.2) | 145.4 (132.3–163.4) | 193.2 (163.5–722.3) | ||
| Participants, n | 19,366 | 19,366 | 19,366 | 19,366 | 19,367 | ||
| All-cause mortality | |||||||
| Cases, n | 3649 | 3807 | 3806 | 4015 | 4231 | ||
| Model 1, HR (95% CI) | Reference | 1.05 (1.00, 1.10) | 1.09 (1.04, 1.14) | 1.21 (1.16, 1.27) | 1.34 (1.28, 1.40) | <0.001 | 1.23 (1.20, 1.26) |
| Model 2, HR (95% CI) | Reference | 1.03 (0.99, 1.08) | 1.07 (1.02, 1.12) | 1.16 (1.11, 1.22) | 1.23 (1.17, 1.29) | <0.001 | 1.16 (1.13, 1.19) |
| Model 3, HR (95% CI) | Reference | 1.01 (0.97, 1.06) | 1.04 (0.99, 1.09) | 1.10 (1.05, 1.16) | 1.15 (1.10, 1.21) | <0.001 | 1.12 (1.09, 1.15) |
| Model 4, HR (95% CI) | Reference | 0.99 (0.94, 1.03) | 1.00 (0.95, 1.05) | 1.05 (1.00, 1.11) | 1.09 (1.02, 1.15) | <0.001 | 1.09 (1.05, 1.13) |
| CVD mortality | |||||||
| Cases, n | 1009 | 1112 | 1053 | 1148 | 1267 | ||
| Model 1, HR (95% CI) | Reference | 1.11 (1.02, 1.21) | 1.11 (1.01, 1.21) | 1.27 (1.16, 1.38) | 1.45 (1.33, 1.57) | <0.001 | 1.29 (1.23, 1.36) |
| Model 2, HR (95% CI) | Reference | 1.10 (1.01, 1.20) | 1.09 (0.99, 1.18) | 1.23 (1.12, 1.34) | 1.34 (1.23, 1.46) | <0.001 | 1.23 (1.17, 1.29) |
| Model 3, HR (95% CI) | Reference | 1.07 (0.98, 1.17) | 1.04 (0.96, 1.14) | 1.14 (1.05, 1.25) | 1.23 (1.13, 1.34) | <0.001 | 1.17 (1.11, 1.23) |
| Model 4, HR (95% CI) | Reference | 1.05 (0.96, 1.15) | 1.02 (0.93, 1.12) | 1.11 (1.00, 1.23) | 1.19 (1.06, 1.33) | 0.002 | 1.17 (1.09, 1.24) |
| Cancer mortality | |||||||
| Cases, n | 1181 | 1175 | 1230 | 1302 | 1340 | ||
| Model 1, HR (95% CI) | Reference | 0.99 (0.91, 1.07) | 1.06 (0.97, 1.14) | 1.15 (1.06, 1.25) | 1.23 (1.14, 1.33) | <0.001 | 1.16 (1.10, 1.22) |
| Model 2, HR (95% CI) | Reference | 0.98 (0.90, 1.06) | 1.04 (0.96, 1.13) | 1.11 (1.03, 1.21) | 1.14 (1.05, 1.21) | <0.001 | 1.10 (1.05, 1.16) |
| Model 3, HR (95% CI) | Reference | 0.97 (0.89, 1.05) | 1.01 (0.93, 1.10) | 1.06 (0.98, 1.15) | 1.08 (0.99, 1.17) | 0.010 | 1.06 (1.01, 1.11) |
| Model 4, HR (95% CI) | Reference | 0.94 (0.86, 1.03) | 0.98 (0.90, 1.08) | 1.03 (0.93, 1.13) | 1.03 (0.93, 1.14) | 0.16 | 1.04 (0.97, 1.10) |
| AD/dementia mortality | |||||||
| Cases, n | 285 | 298 | 278 | 244 | 214 | ||
| Model 1, HR (95% CI) | Reference | 1.06 (0.90, 1.24) | 1.06 (0.90, 1.25) | 1.01 (0.85, 1.21) | 0.98 (0.82, 1.17) | 0.70 | 0.91 (0.81, 1.02) |
| Model 2, HR (95% CI) | Reference | 1.05 (0.89, 1.24) | 1.05 (0.89, 1.24) | 0.99 (0.83, 1.18) | 0.95 (0.79, 1.14) | 0.47 | 0.89 (0.79, 1.00) |
| Model 3, HR (95% CI) | Reference | 1.05 (0.89, 1.24) | 1.06 (0.90, 1.26) | 1.01 (0.85, 1.20) | 0.97 (0.81, 1.16) | 0.63 | 0.90 (0.80, 1.01) |
| Model 4, HR (95% CI) | Reference | 1.03 (0.87, 1.23) | 1.03 (0.85, 1.24) | 0.97 (0.78, 1.19) | 0.93 (0.74, 1.18) | 0.41 | 0.84 (0.72, 0.99) |
| Respiratory mortality | |||||||
| Cases, n | 236 | 250 | 268 | 336 | 358 | ||
| Model 1, HR (95% CI) | Reference | 1.05 (0.88, 1.25) | 1.17 (0.98, 1.40) | 1.54 (1.30, 1.82) | 1.71 (1.45, 2.02) | <0.001 | 1.50 (1.37, 1.63) |
| Model 2, HR (95% CI) | Reference | 1.00 (0.83, 1.19) | 1.10 (0.92, 1.31) | 1.39 (1.18, 1.65) | 1.40 (1.18, 1.65) | <0.001 | 1.32 (1.21, 1.45) |
| Model 3, HR (95% CI) | Reference | 0.99 (0.83, 1.18) | 1.09 (0.91, 1.30) | 1.38 (1.16, 1.63) | 1.37 (1.15, 1.63) | <0.001 | 1.31 (1.19, 1.43) |
| Model 4, HR (95% CI) | Reference | 0.91 (0.76, 1.10) | 0.96 (0.79, 1.16) | 1.16 (0.95, 1.42) | 1.13 (0.91, 1.40) | 0.057 | 1.17 (1.04, 1.33) |
| Other mortality | |||||||
| Cases, n | 938 | 972 | 977 | 985 | 1052 | ||
| Model 1, HR (95% CI) | Reference | 1.06 (0.97, 1.16) | 1.12 (1.02, 1.22) | 1.21 (1.11, 1.33) | 1.38 (1.27, 1.51) | <0.001 | 1.27 (1.20, 1.33) |
| Model 2, HR (95% CI) | Reference | 1.04 (0.95, 1.14) | 1.09 (0.99, 1.19) | 1.16 (1.06, 1.27) | 1.27 (1.16, 1.39) | <0.001 | 1.20 (1.14, 1.26) |
| Model 3, HR (95% CI) | Reference | 1.01 (0.93, 1.11) | 1.06 (0.96, 1.16) | 1.09 (1.00, 1.20) | 1.18 (1.07, 1.29) | <0.001 | 1.14 (1.08, 1.20) |
| Model 4, HR (95% CI) | Reference | 0.98 (0.89, 1.07) | 1.00 (0.90, 1.11) | 1.02 (0.92, 1.13) | 1.09 (0.97, 1.23) | 0.10 | 1.10 (1.03, 1.18) |
n = 96,831. Model 1: age (y), region (Northeast, South, Midwest, West), race/ethnicity (white, black/African-American, Hispanic/Latino, other), study group (WHI Observational Study or control of clinical trials), education (at most high school, some college, college or above), annual family income (<$20,000, $20,000 to <$50,000, $50,000 to <$75,000, ≥$75,000), and health insurance (none, prepaid private, other). Model 2: model 1 + smoking status (never, former, current), pack-years of smoking, alcohol consumption (0, <0.5, 0.5 to <1, ≥1 drink/d), quitting smoking/drinking owing to health problems (yes, no), recreational physical activity (metabolic equivalent-h/wk), total energy intake (kcal/d), using fat to deep fry/pan fry/sauté (<1, 1–2, 3–4, 5–6, ≥7 times/wk), aspirin use (yes, no), use of nonsteroidal anti-inflammatory drugs (yes, no), hormone use [never, former, current (<5, 5 to <10, 10 to <15, ≥15 y)], and self-rated health status (very good/excellent, good, fair/poor). Model 3: model 2 + waist circumference (cm), diabetes (yes, no), systolic and diastolic blood pressure (mm Hg), antihypertensive drug use (yes, no), and dyslipidemia (yes, no). Model 4: model 3 + energy-adjusted dietary nutrients (dietary fiber, saturated fat, polyunsaturated fat, monounsaturated fat, trans fat, animal protein, sodium). AD, Alzheimer disease; CVD, cardiovascular disease; Q, quintile; WHI, Women's Health Initiative.
FIGURE 1.
Associations of dietary cholesterol with all-cause and cause-specific mortality in the WHI (n = 96,831). In addition to multivariable adjustment (model 3), results were further adjusted for other dietary nutrients individually or concordantly. Covariates in model 3 included age (y), region (Northeast, South, Midwest, West), race/ethnicity (white, black/African-American, Hispanic/Latino, other), study group (WHI Observational Study or control of clinical trials), education (at most high school, some college, college or above), annual family income (<$20,000, $20,000 to <$50,000, $50,000 to <$75,000, ≥$75,000), health insurance (none, prepaid private, other), smoking status (never, former, current), pack-years of smoking, alcohol consumption (0, <0.5, 0.5 to <1, ≥1 drink/d), quitting smoking/drinking owing to health problems (yes, no), recreational physical activity (metabolic equivalent-h/wk), total energy intake (kcal/d), using fat to deep fry/pan fry/sauté (<1, 1–2, 3–4, 5–6, ≥7 times/wk), aspirin use (yes, no), use of nonsteroidal anti-inflammatory drugs (yes, no), hormone use [never, former, current (<5, 5 to <10, 10 to <15, ≥15 y)], self-rated health status (very good/excellent, good, fair/poor), waist circumference (cm), diabetes (yes, no), systolic and diastolic blood pressure (mm Hg), antihypertensive drug use (yes, no), and dyslipidemia (yes, no). CVD, cardiovascular disease; TFA, trans fatty acid; WHI, Women's Health Initiative.
Egg consumption and incident CVD and mortality
After multivariable adjustment and further adjustment for major food groups including red meat and processed meat, higher egg consumption was associated with modestly higher risk of total CVD, with an HR of 1.14 (95% CI: 1.04, 1.25) when comparing egg intake of daily or above with <1 egg/wk (P-trend = 0.004). Higher egg consumption was also associated with higher risk of IHD and ischemic stroke but not associated with risk of hemorrhagic stroke (Table 4). For mortality outcomes, higher egg consumption was also associated with higher risk of all-cause mortality (≥7 compared with <1 egg/wk, HR: 1.14; 95% CI: 1.07, 1.22; P-trend < 0.001), and mortality from CVD, respiratory diseases, and other causes rather than cancer or AD/dementia (Table 5).
TABLE 4.
Association between egg consumption and incident CVD in the WHI1
| Intake categories, eggs/wk | |||||||
|---|---|---|---|---|---|---|---|
| <1 | 1 to <2 | 2 to <3 | 3 to <7 | ≥7 | P-trend | Per 0.5 eggs/d | |
| Participants, n | 34,503 | 23,525 | 16,217 | 16,690 | 5896 | ||
| All CVD | |||||||
| Cases, n | 3337 | 2283 | 1649 | 1851 | 688 | ||
| Model 1, HR (95% CI) | Reference | 0.99 (0.94, 1.05) | 1.03 (0.97, 1.09) | 1.12 (1.06, 1.18) | 1.28 (1.18, 1.39) | <0.001 | 1.11 (1.08, 1.15) |
| Model 2, HR (95% CI) | Reference | 0.99 (0.94, 1.04) | 1.03 (0.97, 1.10) | 1.09 (1.03, 1.16) | 1.20 (1.10, 1.30) | <0.001 | 1.08 (1.05, 1.12) |
| Model 3, HR (95% CI) | Reference | 0.98 (0.92, 1.03) | 1.00 (0.94, 1.07) | 1.06 (0.99, 1.12) | 1.15 (1.05, 1.25) | 0.003 | 1.06 (1.03, 1.10) |
| Model 4, HR (95% CI) | Reference | 0.97 (0.92, 1.03) | 1.00 (0.94, 1.07) | 1.05 (0.99, 1.12) | 1.14 (1.04, 1.25) | 0.004 | 1.06 (1.03, 1.10) |
| IHD | |||||||
| Cases, n | 2412 | 1668 | 1160 | 1357 | 494 | ||
| Model 1, HR (95% CI) | Reference | 1.00 (0.94, 1.06) | 0.99 (0.93, 1.07) | 1.13 (1.05, 1.20) | 1.25 (1.14, 1.38) | <0.001 | 1.11 (1.07, 1.15) |
| Model 2, HR (95% CI) | Reference | 1.00 (0.94, 1.06) | 1.00 (0.93, 1.08) | 1.10 (1.02, 1.18) | 1.17 (1.05, 1.29) | 0.001 | 1.07 (1.03, 1.12) |
| Model 3, HR (95% CI) | Reference | 0.98 (0.92, 1.05) | 0.98 (0.91, 1.05) | 1.06 (0.99, 1.14) | 1.12 (1.01, 1.24) | 0.028 | 1.05 (1.01, 1.10) |
| Model 4, HR (95% CI) | Reference | 0.98 (0.92, 1.05) | 0.97 (0.91, 1.05) | 1.06 (0.99, 1.14) | 1.11 (1.00, 1.23) | 0.040 | 1.05 (1.01, 1.09) |
| Ischemic stroke | |||||||
| Cases, n | 793 | 555 | 439 | 450 | 193 | ||
| Model 1, HR (95% CI) | Reference | 1.03 (0.92, 1.15) | 1.16 (1.03, 1.31) | 1.16 (1.03, 1.31) | 1.54 (1.31, 1.80) | <0.001 | 1.17 (1.10, 1.24) |
| Model 2, HR (95% CI) | Reference | 1.02 (0.92, 1.14) | 1.16 (1.03, 1.31) | 1.13 (1.01, 1.28) | 1.46 (1.24, 1.73) | <0.001 | 1.15 (1.08, 1.22) |
| Model 3, HR (95% CI) | Reference | 1.01 (0.90, 1.12) | 1.13 (1.00, 1.27) | 1.09 (0.97, 1.24) | 1.40 (1.19, 1.66) | 0.001 | 1.13 (1.06, 1.20) |
| Model 4, HR (95% CI) | Reference | 1.00 (0.90, 1.12) | 1.13 (1.00, 1.27) | 1.09 (0.97, 1.24) | 1.40 (1.18, 1.66) | 0.001 | 1.13 (1.06, 1.20) |
| Hemorrhagic stroke | |||||||
| Cases, n | 203 | 127 | 86 | 87 | 32 | ||
| Model 1, HR (95% CI) | Reference | 0.92 (0.74, 1.15) | 0.89 (0.69, 1.14) | 0.88 (0.68, 1.13) | 0.98 (0.68, 1.43) | 0.40 | 0.94 (0.82, 1.09) |
| Model 2, HR (95% CI) | Reference | 0.93 (0.74, 1.16) | 0.90 (0.70, 1.17) | 0.89 (0.68, 1.15) | 0.99 (0.67, 1.47) | 0.47 | 0.95 (0.82, 1.11) |
| Model 3, HR (95% CI) | Reference | 0.92 (0.73, 1.15) | 0.89 (0.68, 1.15) | 0.87 (0.67, 1.14) | 0.98 (0.67, 1.14) | 0.41 | 0.95 (0.81, 1.10) |
| Model 4, HR (95% CI) | Reference | 0.93 (0.74, 1.16) | 0.90 (0.69, 1.16) | 0.89 (0.68, 1.15) | 0.98 (0.66, 1.46) | 0.46 | 0.95 (0.81, 1.11) |
n = 96,831. Model 1: age (y), region (Northeast, South, Midwest, West), race/ethnicity (white, black/African-American, Hispanic/Latino, other), study group (WHI Observational Study or control of clinical trials), education (at most high school, some college, college or above), annual family income (<$20,000, $20,000 to <$50,000, $50,000 to <$75,000, ≥$75,000), and health insurance (none, prepaid private, other). Model 2: model 1 + smoking status (never, former, current), pack-years of smoking, alcohol consumption (0, <0.5, 0.5 to <1, ≥1 drink/d), quitting smoking/drinking owing to health problems (yes, no), recreational physical activity (metabolic equivalent-h/wk), total energy intake (kcal/d), using fat to deep fry/pan fry/sauté (<1, 1–2, 3–4, 5–6, ≥7 times/wk), aspirin use (yes, no), use of nonsteroidal anti-inflammatory drugs (yes, no), hormone use [never, former, current (<5, 5 to <10, 10 to <15, ≥15 y)], and self-rated health status (very good/excellent, good, fair/poor). Model 3: model 2 + waist circumference (cm), diabetes (yes, no), systolic and diastolic blood pressure (mm Hg), antihypertensive drug use (yes, no), and dyslipidemia (yes, no). Model 4: model 3 + food groups (fruit/vegetables, red meat, processed meat, organ meat, fish/shellfish, whole grain, refined grain, potatoes, soft drinks/fruit juices, coffee/tea). CVD, cardiovascular disease; IHD, ischemic heart disease; WHI, Women's Health Initiative.
TABLE 5.
Association between egg consumption and mortality in the WHI1
| Intake categories, eggs/wk | |||||||
|---|---|---|---|---|---|---|---|
| <1 | 1 to <2 | 2 to <3 | 3 to <7 | ≥7 | P-trend | Per 0.5 eggs/d | |
| Participants, n | 34,503 | 23,525 | 16,217 | 16,690 | 5896 | ||
| All-cause mortality | |||||||
| Cases, n | 6619 | 4747 | 3300 | 3578 | 1264 | ||
| Model 1, HR (95% CI) | Reference | 1.06 (1.02, 1.10) | 1.06 (1.01, 1.10) | 1.16 (1.11, 1.21) | 1.32 (1.25, 1.41) | <0.001 | 1.13 (1.10, 1.15) |
| Model 2, HR (95% CI) | Reference | 1.06 (1.02, 1.10) | 1.07 (1.02, 1.11) | 1.13 (1.09, 1.18) | 1.23 (1.16, 1.31) | <0.001 | 1.10 (1.07, 1.12) |
| Model 3, HR (95% CI) | Reference | 1.04 (1.00, 1.08) | 1.04 (0.99, 1.08) | 1.09 (1.04, 1.14) | 1.17 (1.10, 1.25) | <0.001 | 1.07 (1.05, 1.10) |
| Model 4, HR (95% CI) | Reference | 1.03 (1.00, 1.07) | 1.03 (0.98, 1.07) | 1.08 (1.03, 1.12) | 1.14 (1.07, 1.22) | <0.001 | 1.06 (1.03, 1.09) |
| CVD mortality | |||||||
| Cases, n | 1878 | 1330 | 939 | 1054 | 388 | ||
| Model 1, HR (95% CI) | Reference | 1.05 (0.98, 1.13) | 1.06 (0.98, 1.15) | 1.21 (1.11, 1.30) | 1.45 (1.30, 1.62) | <0.001 | 1.18 (1.13, 1.23) |
| Model 2, HR (95% CI) | Reference | 1.06 (0.99, 1.14) | 1.08 (1.00, 1.17) | 1.18 (1.09, 1.28) | 1.35 (1.20, 1.51) | <0.001 | 1.14 (1.10, 1.20) |
| Model 3, HR (95% CI) | Reference | 1.03 (0.96, 1.11) | 1.04 (0.96, 1.12) | 1.12 (1.04, 1.21) | 1.27 (1.13, 1.42) | <0.001 | 1.11 (1.06, 1.16) |
| Model 4, HR (95% CI) | Reference | 1.03 (0.96, 1.11) | 1.03 (0.95, 1.12) | 1.11 (1.02, 1.20) | 1.23 (1.10, 1.39) | 0.001 | 1.10 (1.05, 1.15) |
| Cancer mortality | |||||||
| Cases, n | 2126 | 1517 | 1054 | 1138 | 393 | ||
| Model 1, HR (95% CI) | Reference | 1.04 (0.97, 1.11) | 1.04 (0.96, 1.12) | 1.12 (1.04, 1.20) | 1.19 (1.07, 1.32) | <0.001 | 1.10 (1.04, 1.12) |
| Model 2, HR (95% CI) | Reference | 1.03 (0.96, 1.10) | 1.04 (0.97, 1.12) | 1.09 (1.01, 1.18) | 1.11 (0.99, 1.25) | 0.010 | 1.05 (1.01, 1.10) |
| Model 3, HR (95% CI) | Reference | 1.01 (0.95, 1.09) | 1.02 (0.95, 1.10) | 1.06 (0.98, 1.14) | 1.06 (0.95, 1.19) | 0.14 | 1.03 (0.99, 1.07) |
| Model 4, HR (95% CI) | Reference | 1.01 (0.94, 1.08) | 1.01 (0.94, 1.09) | 1.05 (0.97, 1.13) | 1.04 (0.93, 1.17) | 0.26 | 1.02 (0.98, 1.07) |
| AD/dementia mortality | |||||||
| Cases, n | 501 | 356 | 206 | 193 | 63 | ||
| Model 1, HR (95% CI) | Reference | 1.06 (0.92, 1.21) | 0.88 (0.74, 1.03) | 0.87 (0.74, 1.03) | 1.00 (0.77, 1.30) | 0.093 | 0.94 (0.85, 1.03) |
| Model 2, HR (95% CI) | Reference | 1.05 (0.91, 1.20) | 0.87 (0.73, 1.02) | 0.85 (0.71, 1.01) | 0.94 (0.72, 1.24) | 0.055 | 0.91 (0.82, 1.01) |
| Model 3, HR (95% CI) | Reference | 1.05 (0.91, 1.21) | 0.87 (0.74, 1.03) | 0.86 (0.72, 1.02) | 0.95 (0.72, 1.25) | 0.062 | 0.92 (0.83, 1.02) |
| Model 4, HR (95% CI) | Reference | 1.05 (0.91, 1.20) | 0.87 (0.73, 1.02) | 0.85 (0.72, 1.02) | 0.93 (0.71, 1.23) | 0.054 | 0.91 (0.82, 1.01) |
| Respiratory mortality | |||||||
| Cases, n | 433 | 364 | 247 | 290 | 114 | ||
| Model 1, HR (95% CI) | Reference | 1.22 (1.06, 1.40) | 1.17 (1.00, 1.37) | 1.39 (1.20, 1.62) | 1.77 (1.44, 2.18) | <0.001 | 1.25 (1.16, 1.35) |
| Model 2, HR (95% CI) | Reference | 1.20 (1.05, 1.39) | 1.20 (1.02, 1.41) | 1.33 (1.14, 1.56) | 1.57 (1.26, 1.96) | <0.001 | 1.19 (1.10, 1.29) |
| Model 3, HR (95% CI) | Reference | 1.19 (1.03, 1.37) | 1.18 (1.00, 1.38) | 1.30 (1.11, 1.52) | 1.54 (1.24, 1.92) | <0.001 | 1.18 (1.09, 1.28) |
| Model 4, HR (95% CI) | Reference | 1.17 (1.01, 1.35) | 1.15 (0.98, 1.35) | 1.27 (1.08, 1.48) | 1.46 (1.17, 1.82) | <0.001 | 1.15 (1.06, 1.25) |
| Other mortality | |||||||
| Cases, n | 1681 | 1180 | 854 | 903 | 306 | ||
| Model 1, HR (95% CI) | Reference | 1.06 (0.98, 1.14) | 1.11 (1.02, 1.20) | 1.20 (1.11, 1.30) | 1.35 (1.19, 1.52) | <0.001 | 1.15 (1.10, 1.20) |
| Model 2, HR (95% CI) | Reference | 1.06 (0.98, 1.14) | 1.12 (1.03, 1.22) | 1.17 (1.08, 1.28) | 1.27 (1.11, 1.44) | <0.001 | 1.12 (1.07, 1.18) |
| Model 3, HR (95% CI) | Reference | 1.04 (0.96, 1.12) | 1.08 (1.00, 1.18) | 1.12 (1.03, 1.22) | 1.20 (1.05, 1.36) | 0.001 | 1.09 (1.04, 1.15) |
| Model 4, HR (95% CI) | Reference | 1.03 (0.95, 1.11) | 1.07 (0.99, 1.17) | 1.11 (1.02, 1.21) | 1.16 (1.02, 1.32) | 0.004 | 1.08 (1.03, 1.13) |
n = 96,831. Model 1: age (y), region (Northeast, South, Midwest, West), race/ethnicity (white, black/African-American, Hispanic/Latino, other), study group (WHI Observational Study or control of clinical trials), education (at most high school, some college, college or above), annual family income (<$20,000, $20,000 to <$50,000, $50,000 to <$75,000, ≥$75,000), and health insurance (none, prepaid private, other). Model 2: model 1 + smoking status (never, former, current), pack-years of smoking, alcohol consumption (0, <0.5, 0.5 to <1, ≥1 drink/d), quitting smoking/drinking owing to health problems (yes, no), recreational physical activity (metabolic equivalent-h/wk), total energy intake (kcal/d), using fat to deep fry/pan fry/sauté (<1, 1–2, 3–4, 5–6, ≥7 times/wk), aspirin use (yes, no), use of nonsteroidal anti-inflammatory drugs (yes, no), hormone use [never, former, current (<5, 5 to <10, 10 to <15, ≥15 y)], and self-rated health status (very good/excellent, good, fair/poor). Model 3: model 2 + waist circumference (cm), diabetes (yes, no), systolic and diastolic blood pressure (mm Hg), antihypertensive drug use (yes, no), and dyslipidemia (yes, no). Model 4: model 3 + food groups (fruit/vegetables, red meat, processed meat, organ meat, fish/shellfish, whole grain, refined grain, potatoes, soft drinks/fruit juices, coffee/tea). AD, Alzheimer disease; CVD, cardiovascular disease; WHI, Women's Health Initiative.
Nonlinear, stratified, and sensitivity analyses
There was no evidence for nonlinear associations of dietary cholesterol or egg intake with incident CVD or all-cause mortality (Supplemental Figures 3and4). Because of the relatively fewer participants within the high-intake ranges, we excluded participants in the top 5% of dietary cholesterol or egg intake, and still observed the linear positive associations (Supplemental Figures 5and6) (all P values for nonlinearity > 0.10).
In the analyses stratified by various demographic and lifestyle factors and medical histories, both dietary cholesterol and egg intakes were broadly associated with higher risk of CVD and all-cause mortality after the full adjustment, although the magnitudes of the associations were typically modest (Supplemental Figures 7 and 8). Stronger associations between dietary cholesterol or egg intake and incident CVD and/or all-cause mortality were observed in certain groups of the participants, such as participants having younger age, obesity, or diabetes, as compared with the associations in the corresponding opposite groups. However, only the interaction between egg intake and diabetes status on risk of all-cause mortality was marginally significant (P-interaction = 0.001) when considering multiple testing correction (at P < 0.001). The associations of dietary cholesterol or egg intake with cause-specific mortality were similar after excluding other causes of deaths that may have occurred competingly with the outcome of interest (Supplemental Tables 2 and 3).
Discussion
In this prospective study of US postmenopausal women, higher dietary cholesterol intake was found to be associated with modestly higher risk of incident CVD and all-cause mortality after multivariable adjustment for traditional risk factors and key dietary nutrients. Significant positive associations were also observed between dietary cholesterol and incident IHD, incident ischemic stroke, and CVD mortality, whereas there was an inverse association for incident hemorrhagic stroke and no association for mortality from cancer, AD/dementia, respiratory diseases, or other causes. The patterns of the associations of egg consumption with incident CVD and mortality were generally comparable with those for dietary cholesterol.
A systematic review and meta-analysis (4) has evaluated 17 prospective studies that examined the association of dietary cholesterol with risk of CVD. Results of the meta-analysis suggested a marginally nonsignificant positive association of dietary cholesterol with risk of ischemic stroke and no association with risk of hemorrhagic stroke; no meta-analysis was performed for fatal and/or nonfatal IHD owing to substantial variations in methodological quality and population characteristics between the evaluated studies (4). More recent results from a pooled analysis of 6 US cohorts (9) suggested modest positive associations of dietary cholesterol with incident CVD and all-cause mortality, similar to findings of the current study. In a nationwide cohort of Chinese individuals (23), however, intake of dietary cholesterol from eggs was inversely associated with all-cause mortality and the intake from nonegg sources was associated with higher risk. There are scant data concerning the association of dietary cholesterol with non-CVD mortality, especially mortality from cancer, AD/dementia, or respiratory diseases.
Our study and the pooled analysis (9) both found modest positive associations of egg consumption with incident CVD and all-cause mortality. However, in a recent meta-analysis (5) that combined 28 prospective studies including the aforementioned pooled analysis, egg consumption was not associated with risk of CVD, which was consistent with more recent findings from an international cohort of individuals from 50 countries (24). In another meta-analysis (6), daily or greater egg consumption, as compared with consumption of <1/wk, was associated with marginally significantly higher risk of all-cause mortality.
Potential explanations for the study-specific differences in the associations of dietary cholesterol and egg consumption with risk of CVD/mortality remain unclear. Differences in methodologic and population characteristics across these studies may have partially contributed to the differences in the observed associations, such as the inclusion of individuals with varying health status, assessment of dietary cholesterol and outcomes using different instruments, use of variable definitions for CVD [e.g., with or without including hemorrhagic stroke (4)], as well as different contributions of individual food groups to total dietary cholesterol. For example, among Chinese individuals aged ≥60 y, eggs contributed to 57.7% of dietary cholesterol during 2010–2012 (25), whereas the corresponding data among US adults in the same age range were ∼28% during 2013–2014 (26).
The potential impact of residual confounding on the associations examined in our study merits discussion. It is notable that dietary cholesterol (or egg consumption) has been shown to be associated with disease risk factors differentially in terms of strength and/or direction across populations. For example, in a large Chinese cohort in which egg consumption was inversely associated with risk of CVD (27), participants with higher egg consumption tended to have higher levels of education and family income and lower prevalence of hypertension, whereas the correlations between egg consumption and BMI and lifestyle factors appeared relatively weak. Conversely, dietary cholesterol was apparently associated with these risk factors in risk-increasing directions in our study [and also in the pooled analysis (9)], such as lower socioeconomic status and diet quality, poorer nutrient profile, excess body weight, and unhealthy lifestyle behaviors. As expected, most examined associations in our study were attenuated moderately after these covariates were added to the models. It is unclear to what extent residual confounding may have biased the associations examined in our and other previous studies. In the current study, for example, the multivariable-adjusted associations between dietary cholesterol and all-cause mortality and some cause-specific mortality were apparently attenuated after further adjustment for saturated fat alone. This observation may highlight the importance of accurate measurement and adequate adjustment for key dietary nutrients, especially saturated fat which shares food sources with dietary cholesterol, although the potential impact of over-adjustment on these fully adjusted associations cannot be completely excluded.
We found that dietary cholesterol was positively and inversely associated with risk of ischemic and hemorrhagic stroke, respectively. Higher serum total and LDL cholesterol are both associated with higher risk of ischemic stroke, whereas lower concentrations have been found to be associated with higher risk of intracerebral hemorrhage (28). Thus, there might be plausible mechanisms underlying the differences in the associations of dietary cholesterol with stroke subtypes. However, it is also possible that the differences in the associations were driven by residual confounding factors that are differentially associated with stroke subtypes.
The WHI study is notable for its specifically designed and validated FFQ that was sensitive to dietary habits, especially dietary fat intake, in this ethnically diverse sample of postmenopausal women. Additional strengths of our study include its large sample size, prospective design, long-term follow-up, careful adjustment for multiple potential confounders including key dietary nutrients, and the adjudication of outcome events. In addition to the aforementioned problems of residual confounding, there are other limitations to our study. Information on dietary intake was collected using an FFQ that asked about participants’ usual intake during the 3 mo before the baseline recruitment, such that the FFQ may not well capture the average intake for those whose diets were not stable. However, validation results for the WHI FFQ have been found to be highly comparable with those for FFQs used in other cohort studies of US older women (11), including the Nurses’ Health Study (29) in which the FFQ was repeatedly used to ask about participants’ diets over the year before the dietary assessment. Further, our findings were derived in postmenopausal women and are yet to be assessed in men and in younger populations.
In a broad sample of US postmenopausal women, higher dietary cholesterol intake and higher egg consumption were both associated with modestly higher risk of incident CVD and all-cause mortality. Our findings indicate that limiting dietary cholesterol intake while building a healthy eating pattern might be beneficial for human health. However, given the limited and inconsistent epidemiologic evidence concerning the potential cardiovascular impact of dietary cholesterol, additional prospective studies conducted in other populations with different demo-socioeconomic and lifestyle backgrounds are still needed for more definite conclusions.
Supplementary Material
ACKNOWLEDGEMENTS
A full list of all the investigators who have contributed to Women's Health Initiative science appears at: https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf. The following is a short list of WHI investigators: Program Office: (National Heart, Lung, and Blood Institute, Bethesda, MD) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller. Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg. Investigators and Academic Centers: (Brigham and Women's Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Jennifer Robinson; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (University of Nevada, Reno, NV) Robert Brunner. Women's Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Mark Espeland.
The authors’ responsibilities were as follows—G-CC and QQ: designed the research; G-CC and L-HC: developed the analytical plan; G-CC: performed the statistical analyses, prepared the tables and figures, and had primary responsibility for writing the manuscript; SW-S and QQ: directed the study; QQ: is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis; and all authors: contributed to the interpretation of data, critically reviewed and revised the manuscript, and read and approved the final manuscript. The authors report no conflicts of interest.
Notes
Supported in part by National Heart, Lung, and Blood Institute (NHLBI) grants K01HL129892, R01HL060712, and R01HL140976 (to QQ); National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK119268 and R01DK120870 (to QQ); and New York Regional Center for Diabetes Translation Research grant P30 DK111022. The Women's Health Initiative program is funded by the NHLBI, NIH, US Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C.
Supplemental Tables 1–3 and Supplemental Figures 1–8 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.
G-CC and L-HC contributed equally to this work.
Abbreviations used: AD, Alzheimer disease; CT, clinical trials; CVD, cardiovascular disease; DBP, diastolic blood pressure; IHD, ischemic heart disease; NSAID, nonsteroidal anti-inflammatory drug; OS, observational study; SBP, systolic blood pressure; WHI, Women's Health Initiative.
Contributor Information
Guo-Chong Chen, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Li-Hua Chen, Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China.
Yasmin Mossavar-Rahmani, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Victor Kamensky, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Aladdin H Shadyab, Department of Family Medicine and Public Health, University of California, San Diego School of Medicine, La Jolla, CA, USA.
Bernhard Haring, Department of Medicine I, University of Würzburg, Würzburg, Germany.
Robert A Wild, Clinical Epidemiology and Obstetrics and Gynecology, Oklahoma University Health Sciences Center, Oklahoma City, OK, USA.
Brian Silver, UMass Memorial Medical Center, Worcester, MA, USA.
Lewis H Kuller, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Yangbo Sun, Department of Epidemiology, College of Public Health, University of Iowa, Iowa, IA, USA.
Nazmus Saquib, College of Medicine, Sulaiman AlRajhi University, Al Bukayriah, Saudi Arabia.
Barbara Howard, MedStar Health Research Institute Georgetown University, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA.
Linda G Snetselaar, Department of Epidemiology, College of Public Health, University of Iowa, Iowa, IA, USA.
Marian L Neuhouser, Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Matthew A Allison, Department of Family Medicine and Public Health, University of California, San Diego School of Medicine, La Jolla, CA, USA.
Linda Van Horn, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
JoAnn E Manson, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sylvia Wassertheil-Smoller, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Qibin Qi, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Data Availability
Data described in the article, code book, and analytic code will be made available upon request pending application and approval.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data described in the article, code book, and analytic code will be made available upon request pending application and approval.