Abstract
Background:
Low to moderate acute cadmium exposure has been associated with increased risk of chronic diseases, such as cardiovascular and kidney disease. Little is known about the association between urinary cadmium levels—an indicator of longer-term exposure—and metabolic syndrome (MetS).
Methods:
We analysed data from 3982 participants aged 20-<80 years of the National Health and Nutrition Examination Survey 2001–2014. Urinary cadmium levels were measured and adjusted for creatinine using spot urine samples. Cadmium levels were evaluated in quintiles (Q). MetS was defined by National Cholesterol Education Program’s Adult Treatment Panel III report criteria. Prevalence odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression accounting for complex survey design, while adjusting for potential confounders and stratifying by sex and smoking status.
Results:
In the overall study population, there was a marginal inverse association between urinary cadmium and MetS (adj. OR for Q5 versus Q1: 0.7; 95% CI: 0.5–1.0). Sex stratified models were similar. When examining individual components of MetS, participants with higher levels of urinary cadmium had decreased odds of abdominal obesity (adj. OR for Q5 versus Q1 0.4; 95% CI: 0.3–0.6), but increased odds for low HDL (adj. OR for Q5 versus Q1 2.1; 95% CI: 1.4–3.1). Among current smokers, higher urinary cadmium was associated with increased odds of MetS, hypertension, and low HDL even after accounting for serum cotinine—a marker of smoking intensity.
Conclusions:
Higher levels of urinary cadmium, a marker of long term exposure, were not associated with an increased risk of MetS in the overall study population. However, higher urine cadmium was associated with altered MetS components. Current smokers were the most vulnerable group, with higher long-term cadmium exposure being associated with increased risk of MetS, low HDL, and hypertension.
Keywords: Cadmium, metabolic syndrome, obesity, lipids, hypertension, hyperglycemia
1. Background:
Metabolic syndrome (MetS) affects over one-third of adults living in the United States[1]. As a cluster of interrelated metabolic anomalies, MetS includes central obesity, hypertriglyceridemia, reduced HDL cholesterol, hypertension and hyperglycemia [2]. Importantly, individuals who meet the criteria for MetS are at a two-fold increased risk of cardiovascular disease [3]. MetS varies in the U.S. population, differing by sex, potentially owing to differences in hormone-mediated pathways and underlying pathophysiology [4, 5]. Additionally, MetS differs by race/ethnicity and socioeconomic status [4].
While lifestyle and genetic factors largely determine the risk for MetS, a growing body of evidence suggests that environmental factors, including heavy metals exposures, are associated with an increased risk of this syndrome [6–8]. Specifically, lead and mercury have been shown to be positively associated with MetS and its individual components [9–11]. Arsenic is also associated with an increased risk of MetS [11]. However, other commonly occurring metals and metalloids have been less well studied.
Cadmium is a ubiquitously occurring toxic metal[12, 13], which has several major sources of exposure, including cigarette smoking, diet (e.g. whole grains, fish, and green leafy vegetables), occupational exposures, as well as living near an industrial area that is more likely to be polluted with this metal [14–17]. Cadmium may affect glucose and lipid metabolism through a variety of pathways, including hormonal and cellular differentiation mechanisms [3, 18–21]. While some epidemiological studies have shown associations between cadmium and adverse health outcomes, such as cardiovascular disease [16, 22, 23], chronic kidney disease[24, 25], and all-cause mortality [26], others have been less conclusive due to differences in cadmium measurements. For example, the association between cadmium and hypertension depended on whether urine or blood cadmium levels were evaluated [7, 27, 28]. These differences may be attributed to differences in half-life based on biospecimen measurement, with blood representing shorter-term exposure to cadmium, while urine represents longer-term exposure [7, 21, 28].
In the current investigation, we conducted one of the first population-based studies on the association between urinary cadmium levels and MetS in the National Health and Nutrition Examination Survey (NHANES 2001–2014). We evaluated effect modification by sex and age for the association between long-term cadmium exposure and MetS. Finally, given that smoking is a source of cadmium and a risk factor for cardiovascular disease, we conducted a restricted analysis to evaluate urinary cadmium exposure and MetS risk by smoking status.
2. Methods
2.1. Study population
This study utilized data from NHANES, an on-going cross-sectional survey conducted by the U.S. National Center for Health Statistics that consists of a representative sample of the U.S. civilian population. Employing a complex multistage cluster probability sampling strategy reported in 2-year cycles [29], participants are recruited into the study and information is collected on sociodemographic, behavioral, and nutrition factors from questionnaires, as well as examinations and laboratory data to assess a variety of health outcomes, including anthropometry and biomarker data.
Data were merged from 7 biannual NHANES cycles, from the years 2001–2014, resulting in a pooled sample size of 35,881 participants, aged 20-<80 years. Of these, 11,573 individuals had data on urinary cadmium. Among this subset, 5984 individuals did not have information on high density lipoprotein (HDL), low density lipoprotein (LDL) and glucose levels, which left 5,589 participants with data on all five components of the MetS. Among the 5,589 participants with cadmium and MetS data, we excluded pregnant and/or lactating women (n=179) and people with fasting time <8 hours (n=506). We also excluded subjects with self-reported cardiovascular disease (CVD) (n=239), as well as those with chronic kidney disease, assessed as glomerular filtration rate <60 (CKD) (n=259), as this condition can affect urine cadmium concentrations [24]. Finally, there were about 424 participants with missing covariate information. These exclusions resulted in a final sample size of 3982 adults aged 20-<80 years for whom complete data was available on urine cadmium, MetS and its components, as well as all covariates of interest.
2.2. Urinary Cadmium
Cadmium levels were measured in spot urine samples in a random one-third sample of the overall study population [30]. Analyses of urinary cadmium levels occurred at the Environmental Health Sciences Laboratory of the Centers for Disease Control and Prevention’s National Center for Environmental Health. A detailed protocol on NHANES specimen collection, processing, quality control is provided in the NHANES Laboratory/Medical Technologists Procedures Manual [31]. Briefly, urinary cadmium levels were measured using inductively coupled plasma-mass spectrometry multi-element analytical technique. In NHANES survey cycles 2001 to 2004, the LOD for urinary cadmium was 0.042 μg/L; for 2005 to 2010, the LOD was 0.030 μg/L; for 2011–2012 the LOD was 0.056 μg/L and finally for 2013–2014 the LOD was 0.036. For study participants who had measurements of urine cadmium concentrations that were below the limit of detection (LOD: 0.056 ug/L), [32] a level equal to LOD divided by the square root of 2 was imputed [33]. A total of 212 (4.8%) of all participants in this analysis were at or below the urinary cadmium LOD. Urine dilution was accounted for by adjusting for urine creatinine.
2.3. Metabolic syndrome
MetS was defined using the National Cholesterol Education Program’s Adult Treatment Panel III report (NCEP/ATPIII) [34]. In accordance with this definition, individuals were classified as having MetS if they met at least 3 of the following 5 criteria: 1) waist circumference ≥ 102 cm in men or ≥ 88 cm in women; 2) triglycerides ≥ 150 mg/dL; 3) high density lipid (HDL) cholesterol <40 mg/dL in men or <50 mg/dL in women; 4) blood pressure ≥ 130/85 mmHg or treatment for hypertension; and 5) fasting blood glucose ≥100 mg/dL or treatment for diabetes. Blood pressure levels for each participant were taken as the average of four blood pressure measurements. Data on blood pressure, as well as waist circumference, were collected by a trained examiner in the mobile examination center [35]. Detailed information on methods used to collect information on fasting glucose, HDL cholesterol and triglyceride levels has been described in the NHANES Laboratory/Medical Technologists Procedures Manual [31].
2.4. Covariates
The following variables were identified as potential confounders: age, sex, race/ethnicity, physical activity, smoking status, education, poverty status, alcohol use, and BMI. The latter variable was included in all models, except for the model with central adiposity as the main outcome. We also adjusted for estimated glomerular filtration rate, given that kidney function can impact urinary cadmium concentrations. This information was collected via self-reported questionnaires. Age was examined as a continuous variable, as well as dichotomized at 60 years of age, given that urinary cadmium concentrations differed for those who were <60 years of age compared to those ≥ 60 years of age. Individuals <20 years of age or those 80 years of age and over were excluded. The latter exclusion was done due to NHANES’ classification of participants age ≥80 years as 80 or 85 in certain surveys for the purpose of anonymity. Race/ethnicity was categorized as non-Hispanic white (referent), non-Hispanic black, Mexican-American and others. Physical activity was classified as having vigorous, moderate or no physical exercise (referent). Smoking status was coded as current, past or never smoker (referent). Education was classified as high school graduate or less, some college, or college-graduate or higher (referent). An income-to-poverty ratio was used to infer poverty status; if ratio<1, individuals were classified as below poverty level based on federal poverty threshold and household income information [36]. Alcohol consumption was assessed as never, historical or current drinkers. Participants who reported no alcohol drinking in their entire lifetime were classified as ‘never drinkers’. Those who reported drinking at least 12 drinks of alcohol in their life time, but none in the past 12 months were classified as historical drinkers, whereas those who reported at least 12 drinks of alcohol in the past 12 months were classified as ‘current drinkers’ [37]. BMI and estimated glomerular filtration rate were taken as continuous variables. Given that diet is a primary source of cadmium exposure and that diet is also associated MetS [28], we also accounted for dietary factors. For this, participants provided a single 24-hour dietary recall through in-person interviews to obtain total caloric and individual food intake. Dietary data in NHANES was then linked to the Food Patterns Equivalents Database (FPED) provided by US Department of Agriculture (USDA), Agriculture Research Service, which disaggregates common foods on the markets into 37 ingredients in USDA Food Patterns components, including whole grains and green leafy vegetables[38].
2.5. Statistical Analysis
We accounted for the complex multistage probability sampling strategy of NHANES by using the appropriate sampling weights for all statistical analyses [33]. Descriptive analyses were performed with either weighted means/standard errors (continuous variables) or numbers/percentages (categorical variables) to describe demographic, behavioral and anthropometric characteristics of study participants by MetS status. We log-transformed urinary cadmium levels due to skewedness. For descriptive statistics, we calculated weighted geometric means and 95% confidence intervals for urinary cadmium levels by MetS status. For multivariable models, urinary cadmium levels were log transformed and adjusted for creatinine by adding urinary creatinine as a covariate in the models [7, 23]. As an additional analysis, we also adjusted for creatinine using a separate approach that has been described previously, by performing covariate-adjusted standardization of creatinine [39].
When evaluating the association between creatinine-adjusted urinary cadmium levels and MetS and its components, we categorized cadmium into quintiles and used logistic regression analysis, along with the survey series of commands in STATA to calculate odds ratios (ORs) and 95% confidence intervals (95%CIs). In addition to evaluating single components of MetS, we used Poisson regression models to determine if there was an association between urinary cadmium concentrations and the number of individual components of MetS. Multivariate models were adjusted for previously identified confounding factors: age, sex, race/ethnicity, education, physical activity, smoking, total caloric intake and poverty status, BMI, and GFR. Age strongly confounded the association between urinary cadmium levels and MetS, we therefore examined age using multiple strategies, including as a continuous term, in restricted cubic splines, as well as by dichotomizing at 60, while including continuous age in the strata to account for heterogeneity within strata. To assess effect modification, we constructed stratified models to assess effect modification by age and sex. We also included interaction terms in the adjusted models, if p for interaction was <0.10, we concluded that there was statistically significant interaction. We also calculated the p for trend by using the median of the log cadmium concentrations in each of the quintiles as a continuous variable in the regression models.
Finally, we performed several sensitivity analyses. First, we conducted an analysis of urinary cadmium levels and MetS stratified by cigarette smokers, past smokers and never smokers, as smoking is one of the major sources of cadmium exposure [14]. Second, we conducted a stratified analysis of whole grain and green leafy vegetable consumption, as these are also reported as a source of cadmium exposure [34]. We further conducted stratified analysis by menopausal status in women only, given that postmenopausal women are at high-risk of MetS and a previous study found differences by menopausal status when looking at environmental exposures and MetS risk [40]. As individuals with CVD are likely to be affected by one or more components of MetS, we performed a sensitivity analysis by including those that self-reported CVD in the analysis to examine any potential differences in the overall results. A similar sensitivity analysis was done for those with chronic kidney disease. Finally, for comparison to previous studies using acute measures of cadmium (i.e. blood cadmium levels) [22, 27, 28, 41], we provide supplemental analyses of the associations between blood cadmium and MetS and its components using the present study population. All statistical analyses were performed using R version 3.3.2 and STATA 14.0.
3. Results
Among the 3972 study participants, 32% met the NCEP/ATPIII criteria for MetS. Of those with MetS, 30% were men and 34% women (Table 1). Individuals with MetS were older (p<0.001) and less educated (p<0.001), and among women, more were postmenopausal (p<0.001). The weighted proportion of participants who met the criteria for each MetS component was 49% for abdominal obesity, 69% for hypertriglyceridemia, 60% for low HDL, 67% for hypertension and 54% for hyperglycemia.
Table 1:
Population characteristics by sex and metabolic syndrome status (n=3981)
| Men N=1996 | Women N=1985 | |||
|---|---|---|---|---|
| No MetS N=1384 | MetS N=612 | No MetS N=1307 | MetS N=678 | |
| Mean (95% CI) | ||||
| Age (yrs)*,** | 40.8 (39.9–41.8) | 48.5(47.1–49.9) | 43.1(42.0–44.1) | 52.4 (51.2–53.6) |
| BMI (kg/m2)*,** | 26.7(26.4–27.1) | 32.2(31.6–32.8) | 26.7(26.3–27.1) | 33.9(33.1–34.7) |
| Waist circumference (cm)*,** | 93.7(92.5–94.9) | 109.8(107.7–111.8) | 85.7(84.2–87.2) | 105.7(103.7–107.7) |
| Diastolic blood pressure (mmHg)* | 69(68–70) | 74(72–75) | 65(64–67) | 68(67–70) |
| Systolic blood pressure (mmHg)*,** | 117(116–119) | 124(121–127) | 110(108–112) | 121(118–124) |
| Triglycerides*,** | 110(105–115) | 219(201–238) | 94(86–101) | 170(158–182) |
| HDL*,** | 51(50–52) | 40(39–41) | 62(61–63) | 47(46–49) |
| Glucose*,** | 100(99–101) | 118(115–120) | 94(93–95) | 113(111–115) |
| Total caloric intake (kcal)* | 2724.8 (41.6) | 2546.7 (50.5) | 1834.7 (22.7) | 1804.7 (30.5) |
| Urinary creatinine (mg/dL) | 148 (3) | 154 (5) | 106 (2) | 109 (3) |
| Urinary cadmium (ug/L) | 0.22 (0.20–0.23) | 0.29 (0.26–0.31) | 0.26 (0.24–0.28) | 0.33 (0.30–0.36) |
| N (%) a | ||||
| Age*,** | ||||
| <60 years | 1020(73.4) | 277(53.0) | 1011(66.7) | 299(40.8) |
| >=60 years | 496(26.6) | 319(46.9) | 520(33.2) | 462(59.1) |
| Race*,** | ||||
| Mexican American | 298(10.7) | 142(10.3) | 251(7.1) | 156(8.4) |
| White | 613(65.3) | 292(75.7) | 707(69.7) | 317(67.8) |
| Black | 318(10.6) | 87(6.7) | 302(10.9) | 188(15.3) |
| Other | 287(13.3) | 75(7.1) | 271(12.1) | 100(8.3) |
| Smoking*,** | ||||
| Never | 755(48.8) | 249(44.4) | 1004(62.8) | 444 (56.5) |
| Current | 403(27.4) | 145(21.8) | 265(17.9) | 165(22.7) |
| Past | 388(23.7) | 213(33.7) | 262(19.2) | 152(20.6) |
| Education *,** | ||||
| High school or less | 772(43.0) | 333(45.6) | 612(33.1) | 428(47.9) |
| Some college | 416(30.5) | 167(32.9) | 499(32.8) | 233(34.4) |
| College graduates or higher | 327(26.4) | 95(21.4) | 420(34.0) | 100(17.6) |
| Poverty status | ||||
| No | 1244(87.6) | 496(89.5) | 1238(86.8) | 582(82.9) |
| Yes | 272(12.3) | 100(10.4) | 293(13.1) | 179(17.0) |
| Physical activity*,** | ||||
| Vigorous | 325 (23.2) | 156 (27.5) | 394 (48.7) | 198 (28.8) |
| Moderate | 515 (37.9) | 159 (30.6) | 309 (23.4) | 118 (15.9) |
| No | 676 (38.9) | 281 (41.9) | 827 (27.9) | 445 (55.3) |
| Menopause** | ||||
| No | - | - | 1041(68.5) | 333(47.8) |
| Yes | - | - | 490(31.4) | 428(52.1) |
p<0.05 in men;
p < 0.05 in women
Weighted percentages after accounting for the sampling design
Individuals with MetS had higher concentrations of urinary cadmium than those without MetS (Geometric mean: 0.24 μg/L creatinine; 95% CI: 0.22–0.25 creatinine versus 0.31 μg/L creatinine; 95% CI: 0.29–0.33). Cadmium levels were also higher for individuals who met each component criteria for MetS, with individuals with hypertension having the highest levels of cadmium (0.34 μg/L). In addition, geometric means of urinary cadmium levels were higher among women compared to men (0.27 μg/L and 0.23 μg/L, respectively), and higher in current smokers (0.37 μg/L) compared to past (0.29 μg/L) and never smokers (0.20 μg/L).
3.1. Urinary cadmium and metabolic syndrome
In the overall population (Table 2), creatinine-only adjusted associations showed significant findings for urinary cadmium and MetS. However, after adjusting for all potential confounders, there was no significant association between the higher creatinine-adjusted urinary cadmium levels (Q4 and Q5) and MetS compared to the lowest level of urinary cadmium (Q1)(adj. OR for Q4 v. Q1: 1.0; 95%CI: 0.7–1.4 and adj OR for Q5 v. Q1: 0.7; 95% CI: 0.5–1.0). Age was the main confounder driving the difference between unadjusted and adjusted associations. Sex did not appear to modify the association, with p for interaction >0.10 (p=0.64). Sex stratified models did not exhibit any clear associations. Additional analyses that included age stratification or age as a dichotomous variable were also not significant (data not shown).
Table 2:
Prevalence odds ratios and 95% CIs for metabolic syndrome according to log transformed and creatinine adjusted urinary cadmium in total population and stratified by gender
| Overall | Men | Women | ||||
|---|---|---|---|---|---|---|
| No. of cases/non-cases | 1290/2691 | 612/1384 | 678/1307 | |||
| Urine cadmium (ug/L)* | Model 1 1 | Model2 2 | Model 1 1 | Model2 2 | Model 1 1 | Model 2 2 |
| Q1 (<0.13) N=721 | Ref | Ref | Ref | Ref | Ref | Ref |
| Q2 (0.13–0.23) N=801 | 1.7(1.2–2.2) | 1.1(0.8–1.5) | 1.8(1.2–2.7) | 1.1(0.7–1.6) | 1.6(1.0–2.4) | 1.1(0.7–1.7) |
| Q3 (0.23–0.35) N=844 | 2.0(1.5–2.8) | 1.0(0.7–1.5) | 2.4(1.5–3.8) | 1.2(0.8–1.9) | 1.8(1.2–2.7) | 0.9(0.6–1.4) |
| Q4 (0.35–0.60) N=763 | 2.5(1.9–3.3) | 1.0(0.7–1.4) | 2.5(1.6–3.7) | 1.0(0.6–1.7) | 2.8(1.8–4.2) | 1.1(0.7–1.7) |
| Q5 (≥0.60) N=852 | 2.4(1.8–3.2) | 0.7(0.5–1.0) | 2.8(1.8–4.2) | 0.9(0.5–1.6) | 2.4(1.6–3.6) | 0.6(0.3–1.0) |
| P-trend | <0.001 | 0.32 | <0.001 | 0.14 | <0.001 | 0.67 |
Quintile cut-offs are based on raw data
Adjusted for creatinine
Adjusted for creatinine, age (restricted cubic splines), sex, race/ethnicity, BMI, EGFR, alcohol, education, smoking, physical activity, poverty, and total caloric intake
3.2. Urinary cadmium and metabolic syndrome components
We examined the association between cadmium exposure and individual components of MetS (Table 3). In the overall population, higher urinary levels of cadmium were associated with decreased odds of abdominal obesity (adj. OR for Q5 versus Q1: 0.5; 95 % CI: 0.3–0.7). When stratified by sex, both men and women had similar decreased odds of abdominal obesity [men (adj. OR for Q5: 0.4; 95 % CI: 0.2–0.7) and women (adj. OR for Q5: 0.5; 95 % CI: 0.3–0.8)]. On the other hand, there was a 2-fold increased odds of low HDL among men and women in the highest quintile of cadmium compared to those in the lowest quintile [men (adj. OR for Q5 v. Q1: 2.3; 95% CI: 1.1–4.7) and women (adj. OR for Q5 v. Q1: 2.1; 95% CI: 1.2–3.6)]. No significant p for interaction existed, but p-for-trend for both central obesity and low HDL were significant (p-for-trend for central obesity and low HDL were 0.01 and 0.004, respectively). Further, no significant associations were seen between urinary cadmium levels and the total number of MetS components.
Table 3:
Adjusted prevalence odds ratios and 95% CIs for individual components of metabolic syndrome according to urinary cadmium levels
| Individual components of metabolic syndrome | |||||
|---|---|---|---|---|---|
| Urine cadmium a | Central Obesity 1988 | Hypertriglyceridemia 1042 | Low HDL 1024 | Hypertension 1019 | Hyperglycemia 984 |
| Overall* | |||||
| Q1 | Ref | Ref | Ref | Ref | Ref |
| Q2 | 0.8(0.6–1.1) | 1.2(1.0–1.7) | 1.4(1.1–1.9) | 0.9(0.6–1.3) | 0.7 (0.5–0.9) |
| Q3 | 0.7(0.5–1.0) | 1.0(0.7–1.4) | 1.6(1.2–2.1) | 0.7(0.5–1.1) | 0.7 (0.5–1.0) |
| Q4 | 0.7(0.5–1.1) | 1.3(0.9–2.0) | 1.5(1.1–2.2) | 0.7(0.5–1.1) | 0.8 (0.5–1.1) |
| Q5 | 0.4(0.3–0.6) | 1.1(0.7–1.8) | 2.1(1.4–3.1) | 0.6(0.3–1.1) | 0.7 (0.4–1.1) |
| p-trend | 0.001 | 0.68 | <0.001 | 0.07 | 0.49 |
| Men | |||||
| Q1 | Ref | Ref | Ref | Ref | Ref |
| Q2 | 0.7(0.5–1.1) | 1.1(0.7–1.7) | 1.4(0.9–2.2) | 1.1(0.5–2.0) | 0.7 (0.5–1.1) |
| Q3 | 0.7(0.5–1.1) | 0.9(0.5–1.5) | 1.6(1.0–2.5) | 1.0(0.5–1.8) | 0.7 (0.4–1.1) |
| Q4 | 0.8(0.5–1.4) | 1.3(0.7–2.3) | 1.3(0.7–2.5) | 0.9(0.4–1.8) | 0.8 (0.5–1.4) |
| Q5 | 0.4(0.2–0.8) | 0.9(0.4–2.1) | 2.3(1.1–4.7) | 0.8(0.3–1.9) | 1.0 (0.5–1.8) |
| p-trend | 0.04 | 0.94 | 0.04 | 0.59 | 0.92 |
| Women | |||||
| Q1 | Ref | Ref | Ref | Ref | ref |
| Q2 | 0.9(0.6–1.4) | 1.5(0.9–2.5) | 1.4(0.9–2.2) | 0.7(0.5–1.2) | 0.6 (0.3–1.0) |
| Q3 | 0.7(0.4–1.1) | 1.1(0.7–2.0) | 1.6(1.0–2.4) | 0.6(0.3–1.0) | 0.8 (0.5–1.3) |
| Q4 | 0.7(0.4–1.1) | 1.5(0.9–2.8) | 1.9(1.2–3.1) | 0.7(0.4–1.2) | 0.8 (0.5–1.3) |
| Q5 | 0.4(0.2–0.7) | 1.3(0.7–2.6) | 2.1(1.2–3.6) | 0.6(0.3–1.1) | 0.6 (0.3–1.1) |
| p-trend | 0.01 | 0.48 | 0.004 | 0.10 | 0.47 |
Adjusted for creatinine, age, sex, race/ethnicity, BMI, EGFR, alcohol, education, smoking, physical activity, poverty, and total caloric intake
Urine cadmium quintiles (ug/L): Q1 (<0.13), Q2 (0.13–0.23), Q3 (0.23–0.35), Q4 (0.35–0.60), Q5(>0.60)
3.3. Urinary cadmium levels and metabolic syndrome: sensitivity analyses
We conducted a number of sensitivity analyses. First, we performed a stratified analysis by smoking status. When restricting to current smokers, we found higher cadmium concentrations compared to the overall study population. When assessing the association between urinary cadmium concentrations and MetS in current smokers, we found strong positive associations between urinary cadmium levels and MetS (adj. OR for Q4 versus Q1: 3.1; 95 % CI: 1.4–7.1 and adj. OR for Q5 versus Q1: 2.4; 95 % CI: 1.1–5.4). No such positive associations were seen among past or never smokers (Table 4). Furthermore, among current smokers there was also a significant positive association between urinary cadmium levels and low HDL (adj. OR for Q5 versus Q1: 4.1; 95 % CI: 1.8–9.2). Whereas among never smokers, we found significant inverse associations between urinary cadmium and central obesity (adj. OR for Q5 versus Q1: 0.4; 95% CI: 0.3–0.7); hypertension (adj. OR for Q5 versus Q1: 0.4; 95% CI: 0.2–0.8); and hyperglycemia (adj. OR for Q5 versus Q1: 0.5; 95% CI: 0.3–0.9. P for trends were all <0.05 for these three inverse findings. In additional analyses, we accounted for serum cotinine, as this is a biomarker for intensity of smoking among study participants and found similar associations. In this study, urinary cadmium levels were moderately correlated with serum cotinine levels (r=0.30). Only one significant interaction between past smoking status and the second quartile of urinary cadmium was found for hypertriglyceridemia (p for interaction=0.002). All other interactions were not significant (p for interaction ≥0.10) (Table 4). Second, when adjusting for whole grain and green leafy vegetable consumption, associations were similar to the overall analyses (data not shown). Furthermore, when stratifying by whole grain and green leafy vegetable consumption, we found no significant associations in any strata. Third, stratified analyses by menopausal status did not yield significant associations in either status (i.e. pre- and post-menopausal) (data not shown). Lastly, we saw similar associations between cadmium and MetS, as well as MetS components when including individuals with CVD or CKD in the analyses. Findings using restricted cubic splines generated similar associations (data not shown). Also, see Supplemental Tables 1 and 2 for the associations between acute cadmium levels and MetS and its components.
Table 4:
Adjusted prevalence odds ratios and 95% CIs for individual components of metabolic syndrome according to urinary cadmium levels among current smokers
| Urine cadmium a | Metabolic syndrome and Individual | |||||
|---|---|---|---|---|---|---|
| Current smokers b | MetS | Central Obesity | Hypertriglycerid emia | Low HDL | Hypertension | Hyperglycemia |
| No. of cases | 270 | 384 | 265 | 283 | 189 | 381 |
| Q1 | Ref | Ref | Ref | Ref | Ref | Ref |
| Q2 | 1.1(0.4–2.8) | 0.8(0.4–1.6) | 1.0(0.4–2.3) | 1.0(0.5–2.1) | 1.0 (0.3–3.0) | 0.9 (0.4–2.1) |
| Q3 | 1.6(0.7–3.7) | 1.3(0.6–2.7) | 1.1(0.4–2.8) | 1.9(1.2–3.9) | 1.9 (0.7–5.2) | 0.6 (0.3–1.2) |
| Q4 | 2.7(1.0–7.5) | 1.7(0.8–3.5) | 2.0(0.9–4.8) | 2.4(1.1–5.5) | 2.6 (1.0–7.1) | 0.9(0.4–1.9) |
| Q5 | 3.0(1.1–8.7) | 0.9(0.4–2.1) | 1.6(0.6–4.5) | 4.1(1.8–9.2) | 2.3 (07–7.0) | 0.6 (0.3–1.5) |
| p-trend | 0.01 | 0.61 | 0.15 | <0.001 | 0.12 | 0.43 |
| Never smokers b | ||||||
| No. of cases | 599 | 1105 | 509 | 556 | 514 | 906 |
| Q1 | ||||||
| Q2 | 1.0 (0.7–1.6) | 0.9 (0.6–1.2) | 1.4 (0.9–2.1) | 1.6 (1.0–2.3) | 0.5 (0.4–1.0) | 0.6 (0.4–0.9) |
| Q3 | 1.2 (0.7–2.1) | 0.7 (0.5–1.0) | 1.1 (0.7–1.7) | 1.6 (1.0–2.4) | 0.4 (0.3–0.9) | 0.7 (0.4–1.1) |
| Q4 | 1.1 (0.6–1.8) | 0.6(0.3–1.0) | 1.2 (0.7–2.2) | 1.5 (0.9–2.5) | 0.4 (0.2–0.8) | 0.6 (0.4–1.1) |
| Q5 | 0.9 (0.5–1.8) | 0.4 (0.3–0.7) | 1.0 (0.5–2.2) | 1.7 (1.0–2.9) | 0.4 (0.2–0.8) | 0.5 (0.3–0.9) |
| P-trend | 0.70 | 0.01 | 0.74 | 0.04 | 0.006 | 0.18 |
| Past smokers b | ||||||
| No. of cases | 312 | 499 | 268 | 185 | 316 | 489 |
| Q1 | Ref | |||||
| Q2 | 1.2 (0.4–3.5) | 0.9 (0.5–1.7) | 0.8 (0.4–1.6) | 1.2 (0.6–2.6) | 1.8 (0.8–3.9) | 0.5 (0.3–1.0) |
| Q3 | 0.9 (0.3–2.5) | 0.8 (0.4–1.4) | 0.4 (0.2–0.9) | 1.3 (0.6–2.5) | 1.4 (0.6–3.2) | 0.6 (0.3–1.3) |
| Q4 | 0.6 (0.2–1.8) | 0.9 (0.5–1.7) | 0.5 (0.3–1.0) | 0.9 (0.4–2.1) | 0.9 (0.4–2.0) | 0.5 (0.2–1.0) |
| Q5 | 0.5 (0.1–1.8) | 0.6 (0.3–1.3) | 0.4 (0.2–1.0) | 1.1 (0.5–2.6) | 0.7 (0.4–2.7) | 0.3 (0.1–0.9) |
| p-trend | 0.12 | 0.11 | 0.08 | 0.78 | 0.09 | 0.06 |
Adjusted for creatinine, serum cotinine, age, sex, race/ethnicity, BMI, EGFR, alcohol, education, physical activity, poverty, and total caloric intake
Urine cadmium quintiles (ug/L): Q1 (<0.13), Q2 (0.13–0.23), Q3 (0.23–0.35), Q4 (0.35–0.60), Q5(>0.60)
Overall p-for-interaction between cadmium and smoking for MetS=0.31; central obesity=0.34; hypertriglyceridemia=0.05; low HDL=0.26; hypertension=0.72; hyperglycemia=0.91
4. Discussion
In this study of a representative sample of U.S. adult population, we found no association between higher long-term cadmium exposure — as assessed by urinary cadmium levels — and MetS, but inverse associations between urinary cadmium levels and central obesity, in the overall population. The associations between urinary cadmium levels and MetS, as well as individual components of MetS, did not differ between men and women. The strongest positive associations were seen when evaluating smokers only. In this group, individuals with the highest levels of urinary cadmium were at increased odds of MetS, as well as low HDL. These findings held even after additional adjustment for dietary factors and serum cotinine.
Research investigating the effect of long-term cadmium exposure on MetS by the measure of urinary levels remains sparse and to our knowledge, few studies have looked at these associations incorporating sex differences. A study from the Korean NHANES, which used blood cadmium levels as a marker of acute cadmium exposure, showed that men with the highest levels of blood cadmium had an increased risk of MetS, but no significant associations were observed in women [41]. In the present study, we found no association between urinary cadmium levels and MetS in the overall population, nor do we find significant differences in the sex-specific subgroup analysis.
Several studies have looked at long-term cadmium exposure by evaluating urine concentrations on individual components of MetS (such as central obesity [42–44], hypertension [7, 27, 28, 45–47], low HDL cholesterol [48–50] and hyperglycemia [18, 44, 51]. Our findings are in agreement with some studies showing significant inverse associations between chronic exposure to cadmium and central obesity [42, 52, 53], as well as several previous studies of urinary cadmium levels and hypertension [7, 47, 54], hyperglycemia[18, 44, 51], and low HDL[48–50]. While some previous studies evaluating the association between cadmium and obesity are somewhat conflicting, this could be due to differences in study population, matrix used (i.e. blood, urine, hair, etc.), and method used to assess obesity and its related measures [28]. With respect to the findings of central obesity, based on experimental studies, cadmium exposure was shown to effect adipocyte differentiation [55] and size [20], which could lead to central obesity. Findings have also shown that incubation of adipocytes with cadmium decreases leptin and adiponectin, as well as resistin [20, 56].
Cadmium is suggested to negatively impact blood pressure and glucose metabolism by acting on various organs, including the pancreas, kidney, liver, adipose tissue and adrenal glands, leading to glucose intolerance[3, 18, 19, 21] and hypertension[7, 27, 28, 45–47]. For hypertension, our study findings are in-line with the majority of the studies evaluating urinary cadmium concentrations and hypertension, where inverse associations were seen [7, 47, 54]. In fact, a meta-analysis found urinary cadmium associations to be associated with a significantly decreased odds of hypertension [28]. On the other hand, results evaluating blood pressure have been less consistent, which could be due to evaluating blood pressure in different ways, sample size, as well as not accounting potential effect modification by smoking status and treatment for hypertension[28]. One potential mechanism that could explain the inverse association between chronic cadmium exposure and hypertension is that long-term cadmium exposure could have a depressor effect on blood pressure by binding to calcium-binding sites on calmodulin, a regulatory protein that can increase dopamine synthesis in the brain and lead to decreased blood pressure. That said, these findings should be cautiously interpreted, as more research is needed to evaluate reasons for this inverse association in prospective studies.
Our study found positive associations with HDL. A number of previous studies also suggest that chronic exposure to cadmium increases risk of low HDL in rodents [49, 50, 57] and in humans [47, 58]. Occupational exposure to cadmium was shown to be positively associated with prevalence of lipid abnormalities [58]. While the exact mechanisms for how chronic cadmium exposure might be involved in altered HDL are not fully understood, increased lipid peroxidation may be involved, given that cadmium can deplete glutathione and protein-bound sulfhydryl groups [58, 59]. Future work is needed to better understand how cadmium may be a metabolic disruptor that impacts lipid functioning.
With respect to hyperglycemia, urinary cadmium and hyperglycemia has been less extensively studied [3, 18, 19, 21, 44, 51]. Urinary cadmium was not associated with impaired glucose tolerance and type 2 diabetes in a study that was also able to assess insulin resistance [51]. Furthermore, a recent meta-analysis did not find a significant association between urinary cadmium and type 2 diabetes [60]. There is experimental data that suggests that moderate exposure to cadmium may lead to increased glucose uptake by adipocytes and fibrolasts due to a possible insulin-mimicking effect of this metal [61–63]. It is also able to stimulate insulin release from the pancreas [51]. As such, the present study’s inverse association with hyperglycemia may be supported by these experimental studies. That said, more research is needed to confirm the present findings.
A positive association was seen between urinary cadmium levels and MetS in smokers. Tobacco contains high concentrations of cadmium, along with other toxic chemicals, metals, and metalloids [64]. These chemical and metal mixtures could work synergistically with cadmium to impact MetS. However, co-exposures of cigarette smoke may not be easily disentangled and future studies may want to consider different degrees of chemical and metal mixtures in the context of MetS. As to be expected, cadmium concentrations were higher among current smokers compared to the overall population. Current smokers may be at particularly high-risk population, as they may have more lifestyle factors related to an increased risk of MetS compared to the overall population. As such, the finding that higher chronic exposure to cadmium is associated with MetS and several of its components may be particularly important when considering additional risk factor identification in this high-risk population.
There are limitations of this study. First, despite the fact that our study population comes from a survey of a large, representative sample of the U.S. population, this is a cross-sectional study of the association between urinary cadmium levels and MetS and its components. As such, we are unable to establish temporality and make any causal inference about this association. Possible reverse causation cannot be fully excluded. For example, there may be differences in cadmium metabolism among individuals with MetS or its components. That said, the present study evaluated chronic cadmium exposures, which account for exposure up to a few decades [65, 66]. Second, dietary data was based on a 24-hour recall questionnaire; this can be subject to recall bias and also may not be representative of a person’s regular diet. As this is a source of cadmium exposure, [17] error in reporting of this could be partially accounted for in the urinary cadmium biomarker. However, we anticipate the degree of difficulty recalling dietary information to be the same across the population, resulting in non-differential misclassification. Third, in this study, we were unable to perform a restricted analysis by occupation, owing to lack of precise information (i.e. job codes for high cadmium exposure occupations, such as welding, farming etc.). Most occupations do not yield cadmium exposure, but certain populations may have significantly higher or lower concentrations based on occupation. As such, these findings may not extend to those populations. Fourth, not all potential confounders were available. Thus, there is the possibility for residual confounding. Lastly, to account for urine dilution when examining urinary cadmium levels, we adjusted for creatinine concentrations. This adjustment may be influenced by differences in creatinine levels between men and women [67]; however, we used both a traditional and newer approach for creatinine adjustment [2, 39], and found similar associations. Further, cadmium exposure in this study was measured using spot urine samples and may not account for within-person variation of urinary cadmium concentration leading to potential exposure misclassification; however, a previous work shows good temporal stability for urinary cadmium [66].
While these limitations exist, this study had several strengths. First, by evaluating urinary cadmium, we were able to evaluate the association between chronic cadmium exposure and MetS, along with its components in a large, representative sample of the U.S. population, which allowed for the assessment of sex differences in exposures. Second, we were able to adjust for a variety of potential confounders, including lifestyle and sociodemographic factors. Third, we were able to evaluate a particularly vulnerable population for both higher cadmium exposure and higher MetS risk—current smokers, finding a significantly higher risk of MetS among those with higher chronic cadmium exposure, even after accounting for intensity of cigarette smoking.
In conclusion, to our knowledge, this is among the first studies to evaluate the association between long-term cadmium exposure and MetS and its components in a representative U.S. population. We found no associations between urinary cadmium levels and MetS in the overall population, However, we did find inverse associations between chronic cadmium exposure and abdominal obesity, along with a positive association and low HDL in the overall study population. We did not find sex-specific differences when looking at the associations between urinary cadmium concentrations and MetS, or its components. The strongest positive associations were seen among current smokers, with significant associations between urinary cadmium levels and MetS, as well as low HDL. Large-scale, prospective studies are needed to further evaluate these associations. If replicated, these findings may suggest that smokers may be vulnerable to the deleterious effects of high chronic cadmium exposure, with implications for risk of MetS.
Supplementary Material
Highlights.
Overall, long-term cadmium exposure did not increase metabolic syndrome (MetS) risk
Sex differences existed for associations between cadmium and MetS components
Urine cadmium levels were associated with a 2–3 fold increased MetS risk in smokers
Acknowledgments
Funding: This work was supported by the National Institute of Environmental Health Sciences [grant numbers T32ES007069 and P30ES000002]; and the National Heart Lung and Blood Institute [Grant number K24RR018613].
Footnotes
Declaration of Interest: The authors declare no conflict of interest.
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