Cadmium biomarker levels are associated with both cigarette smoking and cardiovascular disease. In this cross-sectional survey, we explore whether the association between cadmium and cardiovascular disease differs between cigarette smoking states. A cross-sectional analysis using the National Health and Nutrition Examination Survey in 2003 to 2012 was performed accounting for the nationally representative complex sampling design. All participants 45 to 79 years old with blood and urinary cadmium levels were included (n = 12,511). We explored the inter-relationships of blood and urine cadmium levels with cigarette smoking and a composite cardiovascular outcome that included self-reported myocardial infarction or stroke or both. We used multivariable logistic regressing models to further adjust for age, income, gender, hypercholesterolemia, body mass index, diabetes, smoking intensity, and time period of smoking cessation. Of the 12,511 participants, 1,330 (8.5%) had previous myocardial infarction or stroke or both. The crude prevalence ratio (PR) comparing those in the lowest tertile of blood cadmium with those in the highest tertile for the composite outcome was 1.73 (95% confidence interval [CI] 1.49 to 2.01). After adjustment for age, gender, income, self-reported diabetes, self-reported hypercholesterolemia, body mass index, and smoking status, the PR was 1.54 (95% CI 1.30 to 1.84). The adjusted PRs for each smoking subgroup were 1.54 (95% CI 1.09 to 2.18) for never-smokers, 1.57 (95% CI 1.11 to 2.23) for current smokers, and 1.31 (95% CI 0.96 to 1.78) for former smokers. These descriptive data from a nationally representative sample suggest that cadmium is related to cardiovascular outcomes even after adjustment for smoking status.
Cigarette smoking is a well-established risk factor for cardiovascular disease and a cause of premature death. One hypothesis to explain this association is that toxic exposure to chemical constituents in tobacco smoke cause persistent inflammatory changes at the level of the endothelial cell. Although the components of tobacco smoke responsible for acute cardiovascular toxicity have been elucidated, those responsible for chronic cardiovascular toxicity are unknown. Cadmium (Cd) has been hypothesized to have cardiovascular and noncardiovascular acute and chronic toxicities. In the general population, cigarette smoking is thought to be the most significant source of Cd exposure, whereas for nonsmokers vegetable consumption is a common source of exposure. After Cd is absorbed, it distributes throughout the body, including the medial layers of the arterial vasculature, where it is estimated to have a biologic half-life of 15 to 30 years. Cadmium exposure leads to deleterious changes in the vascular endothelium including inflammation, impaired nitric oxide production, decreased endothelial cell migration, endothelial cell death, and atherosclerosis. In human studies, elevated blood cadmium levels have consistently been associated with atherosclerotic disease and plaque vulnerability in smokers but less consistently with never-smokers. Cd exposure in humans in epidemiologic studies has been associated with adverse cardiovascular outcomes including myocardial infarction (MI), stroke, and heart failure. In 2 prospective studies, subjects with elevated creatinine-adjusted urinary Cd had an increased risk of MI (80%) and peripheral arterial disease (50%). Because elevated Cd levels and cigarette smoking are both associated with cardiovascular disease, any relation of Cd with heart disease must be considered within the context of cigarette smoke dosimetry. In this cross-sectional survey, we explore whether the association between Cd and cardiovascular disease differs between cigarette smoking states: never, current, and former smokers.
Methods
The National Health and Nutrition Examination Survey (NHANES) is an on-going cross-sectional survey of the civilian, noninstitutionalized US population. NHANES surveys are conducted biannually using stratified, multistage cluster probability sampling resulting in a representative US sample. NHANES consists of 3 parts: a health interview survey, a health examination survey, and a nutrition survey.
In our analyses, we pooled the cross-sectional data from 5 NHANES cycles from the years 2003 to 2012 to arrive at a sample of 50,912 subjects; 12,547 subjects met the entry criteria of being between the ages of 45 and 79 years and having a blood cadmium measurement. Our final sample consisted of 12,511 subjects who had complete data for smoking status and outcome measures. We choose the age group of 45 to 79 from a previously peer-reviewed published study that used the NHANES database to explore the cadmium-MI relation. From our sample of 12,511 subjects, we further analyzed a subgroup of 4,311 subjects who also had a urinary cadmium measurement.
Health interview data included self-reported information on age, gender, socioeconomic status, medical history, education, height, weight, hypertension, diabetes, hypercholesterolemia, and smoking history. Weight, height, and blood pressure were measured during the physical examination. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.
Smoking status was self-reported based on the response to the following questions: ‘‘Have you smoked at least 100 cigarettes in your life?’’ and “Do you now smoke cigarettes?” Respondents were then classified as never-smoker (<100 cigarettes smoked lifetime), former smoker (>100 cigarettes smoked lifetime and not currently smoking), or current smoker (>100 cigarettes smoked lifetime and currently smoking).
Smoking characteristics were further categorized for current and former smokers. The duration (years) of smoking and period of smoking cessation (years) were defined using responses to the survey questions “How old were you when you first started to smoke cigarettes fairly regularly?” and “How old were you when you last smoked cigarettes fairly regularly?” The number of cigarettes smoked per day (smoking intensity) was ascertained on the response to the survey question “During the past 30 days, on the days you smoked, about how many cigarettes did you smoke per day?” Serum cotinine levels were assessed to confirm self-reported smoking status, using a cut-off value of 3 ng/ml to identify current smokers. Less than 1% of self-reported never-smokers had cotinine levels that exceeded 3 ng/dL.
In NHANES self-report data, participants who responded yes to the questions “Has a doctor ever told you that you had a heart attack?” or “Has a doctor ever told you that you had a stroke?” were coded as having a history of an MI or stroke, respectively. We then created a dichotomous single composite outcome measure: previous episode of MI, stroke, or both (outcome positive) and no previous episode of MI or stroke (outcome negative).
Specimen collection and processing instructions and NHANES quality assurance and quality control protocols for serum cotinine, blood and urinary cadmium, and urinary creatinine are discussed in detail in the NHANES Laboratory/Medical Technologists Procedures Manual . In brief, the NHANES quality assurance and quality control protocols met the 1988 Clinical Laboratory Improvement Act mandates, and the protocol was consistent in all study cycles (2003 to 2012).
Blood Cd levels were measured in most participants, whereas urinary cadmium levels were measured in a weighted subsample of participants. Blood Cd was measured using whole blood from vacationer tubes containing EDTA. Both biomarker specimens were processed, stored, and shipped to the Division of Laboratory Sciences, National Center for Environmental Health, and Centers for Disease Control and Prevention for analysis. Levels were determined using inductively coupled plasma mass spectrometry. This multielement analytical technique is based on quadrupole inductively coupled plasma mass spectroscopy technology. The lower limits of detection for urinary and serum Cd were 0.14 and 0.03 μg/L, respectively. The interassay coefficients of variation ranged from 4.1% to 4.8% for urinary Cd and from 2.9% to 13.7% for blood Cd.
Serum cotinine was measured by an isotope dilution-high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. Urinary creatinine was measured using the Jaffe rate method (kinetic alkaline picrate).
Statistical analyses were performed using SAS software (v9.3; SAS Institute, Inc., Cary, North Carolina) and SUDAAN software (v11; RTI International, Research Triangle Park, North Carolina). Survey sample weights were used in all analyses to produce estimates representative of the US population. We examined the distribution of blood and urine Cd concentrations and other variables of interest using sample weights. We divided urinary Cd measures by urinary creatinine such that urinary Cd was expressed per gram of creatinine. Because of a skewed distribution, blood and urinary Cd variables were log transformed. Blood and urinary creatinine-adjusted Cd levels were divided into equal tertiles. We imputed all subjects with undetectable levels as the undetectable cut-off point divided by the square root of 2.
Demographic and medical history variables were presented as weighted percentages and means. Several continuous variables were coded into categorical variables using prespecified cut-off values. Specifically, cut-off points for education and household income were chosen from previous studies using the NHANES database.
We examined the prevalence of MI, stroke, and the composite outcome measure (MI, stroke, or both); we also calculated these outcome measures stratified by various categories of smoking status and cadmium levels. We used logistic regression models to quantitate crude and adjusted prevalence ratios (PR) and their 95% confidence intervals (CIs) between the highest and lowest tertiles of blood and urinary Cd for MI, stroke, and the composite outcome measure. Confounding was defined a priori as a 10% meaningful change in the beta coefficient of the variable (i.e., if the effect of a variable changed the beta coefficient by >10% when added to the model, it was retained in the final logistic regression model). In adjusted models, we included relevant demographic and smoking characteristics, and several health-related variables based on statistical and biologic criteria.
Four final logistic regression models were fitted for each cadmium biomarker. All 4 models controlled for 6 variables (age, gender, income, BMI, diabetes mellitus, and self-reported hypercholesterolemia). Model B (never-smokers only) did not adjust for other variables. Model A (entire study sample) included smoking status. Model C (current smokers only) included smoking intensity. Model D (former smokers only) included smoking cessation period; smoking intensity in model C (current smokers only) and smoking cessation period in model D (former smokers only).
Results
The median and interquartile range of blood cadmium was 0.27, 0.10, and 0.49 μg/L, respectively. The range extended from undetectable to 10.8 μg/L. About 28% of the participants had undetectable blood cadmium concentrations and were assigned a value of 0.1 μg/L (undetectable cut-off point divided by the square root of 2). An additional 9.4% of values were missing. The median and interquartile range of urinary cadmium was 0.18, 0.08, and 0.38 μg/L, respectively. The range extended from 0.03 to 14.9 μg/L. About 12% of the participants had undetectable urinary cadmium concentrations and were assigned a value of 0.026 μg/L (undetectable cut-off point divided by the square root of 2). An additional 2.2% of values were missing (data not shown).
Of the 12,511 participants, 1,330 had a positive composite outcome (MI, stroke, or both) measure. These participants were older, more likely to be male, be current smokers or former smokers, have hypercholesterolemia, hypertension, diabetes, annual household income <$20,000, and less than a high school education than those participants who did not report a previous MI or stroke ( Table 1 ). Compared with never-smokers, current smokers were younger, more likely to be male, have annual household income of <$20,000, and have less than a high school education ( Supplementary Table 1 ).
| Variable | Outcome | P value | |||
|---|---|---|---|---|---|
| Positive (N=1,330) | 95% CI | Negative (N= 11,181) | 95% CI | ||
| Mean | |||||
| Age (years) | 63.8 | (63.1, 64.5) | 57.7 | (57.4, 58.0) | <.0001 |
| BMI (kg/m 2 ) | 30.6 | (30.1, 31.0) | 29.1 | (28.9, 29.3) | <.0001 |
| Percentage | |||||
| Male | 57.4% | (53.9, 60.9) | 47.0% | (45.9, 48.0) | <.0001 |
| Less than HS Education | 30.3% | (26.3, 34.4) | 17.2% | (15.6, 18.8) | <.0001 |
| Income less than $20K/year | 28.2% | (24.7, 31.7) | 13.5% | (12.2, 14.7) | <.0001 |
| Mexican | 4.2% | (2.7, 5.7) | 5.2% | (3.9, 6.5) | |
| Other Hispanic | 2.5% | (1.5, 3.5) | 3.8% | (2.8, 4.7) | |
| Non Hispanic White | 74.0% | (70.4, 77.6) | 75.6% | (72.6, 78.6) | |
| Non Hispanic Black | 12.5% | (10.2, 14.7) | 9.9% | (8.3, 11.5) | |
| Other | 6.9% | (4.9, 8.8) | 5.6% | (4.6, 6.5) | |
| Hypertension | 72.5% | (69.4, 75.5) | 41.5% | (40.0, 43.0) | <.0001 |
| Hypercholesterolemia † | 64.2% | (60.5, 67.9) | 48.4% | (46.9, 49.9) | <.0001 |
| Diabetes mellitus | 30.6% | (27.4, 33.8) | 11.8% | (11.0, 12.7) | <.0001 |
| Smoking Status | <.0001 | ||||
| Never | 34.1% | (30.4, 37.7) | 49.6% | (48.3, 50.9) | |
| Current | 26.5% | (23.4, 29.6) | 18.8% | (17.6, 20.0) | |
| Former | 39.4% | (35.9, 42.9) | 31.6% | (30.3, 32.9) | |
∗ Composite outcome: Positive: Myocardial infarction (MI), or Stroke, or both; Negative: neither MI nor stroke.
The weighted prevalence of our composite outcome measure was 8.5%, 5.3% of who had a previous MI, and 4.0% of who had a previous stroke. Never-smokers were less likely than former or current smokers to report a previous MI or stroke. Among current smokers, 7.1% reported a previous MI and 6.1% reported a previous stroke ( Table 2 ).
| Blood cadmium | Urine cadmium | |||||||
|---|---|---|---|---|---|---|---|---|
| All | Never smokers | Current smokers | Former smokers | All | Never smokers | Current smokers | Former smokers | |
| Prevalence ∗ (95% CI) | Prevalence ∗ (95% CI) | Prevalence ∗ (95% CI) | Prevalence ∗ (95% CI) | Prevalence ∗ (95% CI) | Prevalence ∗ (95% CI) | |||
| Composite † | ||||||||
| ALL | 8.5 (7.8- 9.2) | 6.0 (5.3- 6.7) | 11.6 (9.9- 13.3) | 10.4 (9.1- 11.7) | 7.9 (6.9- 8.9) | 5.5 (4.4-6.6) | 9.9 (7.5- 12.3) | 10.3 (8.0- 12.5) |
| Tertile 1 | 6.6 (5.7- 7.5) | 4.9 (3.7- 6.0) | 9.6 (6.8- 12.3) | 7.8 (5.9- 9.7) | 4.7 (3.4- 6.1) | 3.3 (2.0- 4.7) | 6.6 (6.8- 12.3) | 5.4 (2.3- 8.5) |
| Tertile 2 | 7.8 (6.7- 8.9) | 5.9 (4.6- 7.1) | 10.4 (7.6- 13.1) | 9.3 (7.8- 10.8) | 8.6 (6.8-10.3) | 7.1 (5.3- 8.9) | 9.5 (7.6- 13.1) | 9.7 (6.1- 13.3) |
| Tertile 3 | 11.5 (10.4- 12.5) | 7.7 (6.4- 8.9) | 14.6 (12- 17.3) | 14.4 (12- 16.9) | 9.8 (7.7-12.0) | 6.4 (3.8- 9.1) | 12.7 (12- 17.3) | 13.6 (9.1- 18) |
| Myocardial Infarction | ||||||||
| ALL | 5.3 (4.8- 5.8) | 3.1 (2.5- 3.6) | 7.1 (5.8- 8.3) | 7.6 (6.5- 8.7) | 5.1 (4.1- 6.0) | 2.7 (1.8- 3.5) | 6.5 (4.4- 8.6) | 7.7 (5.6- 9.8) |
| Tertile 1 | 4.1 (3.4- 4.7) | 2.6 (1.8- 3.3) | 5.3 (3.3- 7.3) | 5.7 (4.1- 7.4) | 3.0 (1.8-4.3) | 1.6 (0.6- 2.7) | 4.1 (0.8- 7.5) | 4.1 (1.3- 6.8) |
| Tertile 2 | 4.7 (3.9- 5.6) | 2.7 (1.9- 3.5) | 6.4 (4.2- 8.6) | 6.8 (5.5- 8.2) | 5.6 (4.0-7.1) | 4.0 (2.4- 5.6) | 6.1 (2.2- 10.0) | 7.3 (3.8- 10.8) |
| Tertile 3 | 7.4 (6.6- 8.2) | 4.2 (3.2- 5.3) | 9.2 (7- 11.4) | 10.4 (8.4- 12.4) | 6.5 (4.4-8.6) | 3.4 (1.0- 5.9) | 8.6 (5.2- 12) | 9.9 (5.6- 14.2) |
| Stroke | ||||||||
| ALL | 4.0 (3.6- 4.5) | 3.4 (2.8- 4.0) | 6.1 (5.0- 7.2) | 3.7 (3.1- 4.4) | 3.5 (3.0- 4.1) | 3.1 (2.2- 4.0) | 4.3 (3.0- 5.6) | 3.7 (2.7-4.6) |
| Tertile 1 | 2.9 (2.3- 3.5) | 2.6 (1.7- 3.4) | 5.1 (3.3- 7.0) | 2.3 (1.4- 3.2) | 1.9 (1.1- 2.7) | 1.9 (0.8- 3.0) | 2.9 (0.2- 5.6) | 1.5 (0.4- 2.6) |
| Tertile 2 | 3.9 (3.2- 4.6) | 3.7 (2.5- 4.8) | 5.1 (3.6- 6.7) | 3.6 (2.6- 4.6) | 3.7 (2.7- 4.7) | 3.8 (2.5- 5.0) | 4.2 (1.7- 6.6) | 3.1 (1.5- 4.7) |
| Tertile 3 | 5.4 (4.6- 6.3) | 4.2 (3.3- 5.2) | 7.8 (5.6- 9.9) | 5.5 (4.3- 6.8) | 4.6 (3.3-5.8) | 3.2 (1.2- 5.2) | 6.2 (3.1- 9.2) | 6.0 (3.8- 8.3) |
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