Exposure to cadmium, a heavy metal present in cigarettes, can be assessed in both urine and blood. Few studies have compared the properties of concurrent measurements of urine cadmium (uCd) and blood cadmium (bCd) in relation to the duration and timing of a known exposure. In this study, bCd and uCd were modeled with data from the National Health and Nutrition Examination Survey (1999–2010). Adjusted geometric mean bCd and uCd were estimated from regression results. Each 1% higher geometric mean uCd was associated with 0.50% (95% confidence interval: 0.47%–0.54%; R2=0.30) higher bCd. In male never-smokers, bCd was 69% (59%–81%) and uCd was 200% (166%–234%) higher at age ≥70 years versus 20–29 years. Ten pack-years (py) of smoking were associated with 13.7% (10.0%–17.4%) higher bCd and 16.8% (12.6%–21.1%) higher uCd in male smokers. The first year after smoking cessation was associated with 53% (48%–58%) lower bCd and 23% (14%–33%) lower uCd in representative males aged 55 years with 20 py smoking. Smoking in the previous 5 days was associated with 55% (40%–71%) higher bCd and 7% (−3%–18%) higher uCd. Results were similar for women. uCd mainly measures long-term exposure and bCd recent exposure, but with noticeable overlap. Epidemiological studies should base the choice of uCd or bCd on the timing of cadmium exposure relevant to the disease under study.
Cadmium is a toxic and carcinogenic heavy metal dispersed in the environment largely due to human activity.1 In regions where cadmium contaminates agricultural soil and water, it is taken up by plants. Tobacco, in particular, accumulates cadmium to concentrations much higher than soil concentrations;2 edible crops including soybean, rice, and leafy greens also extract cadmium from soil.3 The general population can therefore be exposed through food and tobacco use. Contamination of drinking water and direct airborne exposure typically contribute only a small fraction of exposure.4, 5
Bio-monitoring of cadmium exposure in occupational settings first led to the understanding that cadmium accumulates in the kidneys and other tissues, and that urine cadmium (uCd) reflects this body burden.6, 7, 8, 9, 10 In contrast, blood cadmium (bCd) has been recognized to primarily reflect recent exposure but may also include a contribution from long-term body burden.9, 10, 11, 12, 13 Studies in environmentally exposed populations have corroborated these findings.14, 15, 16, 17, 18 However, these studies were of modest size, often restricted to a narrow demographic group or convenience sample; few studies have directly compared blood and uCd levels within a large, representative population.
We therefore accessed the US National Health and Nutrition Examination Survey (NHANES) data to assess the properties of cadmium concentrations in blood and urine as measures of cadmium exposure, in a diverse and representative population sample. Cigarettes present a prevalent cadmium exposure and, thus, an opportunity to observe the accumulation and excretion of cadmium during exposure and following cessation of exposure.
Materials and methods
Data from the NHANES for 1999–2010 were accessed from publicly available sources.19 NHANES is a continuously conducted, complex cross-sectional survey designed to produce a sample representative of the non-institutionalized, civilian US population. NHANES includes a range of questionnaires, a physical exam by medical professionals, and laboratory analyses that have been described in detail.19
Between 1999 and 2010, a total of 32 464 adults aged 20 years or older participated in NHANES. uCd and bCd were measured for 10 266 and 29 142 of these participants, respectively. For the analyses described in this report, participants 20 years of age or older with complete data on weight and height for calculation of body mass index (BMI), smoking habits, education, and race/ethnicity, urine cadmium, urine creatinine, and blood cadmium were included.
Respondents answering affirmatively to “Have you smoked 100 or more cigarettes in your life?” were considered ever-smokers.19 Ever-smokers indicating smoking cigarettes “every day” or “some days” were categorized as current smokers. NHANES questions on tobacco use among ever-smokers included current and former use, ages of initiation of regular smoking, age of smoking cessation, average number of cigarettes smoked, and smoking within the past 5 days. Former smokers were ever-smokers who self-reported currently smoking “not at all.” Years of smoking were calculated for ever-smokers from age of initiation to age of cessation or age at interview, for former and current smokers, respectively. Smoking rate was ascertained by NHANES as cigarettes per day and converted to packs by assuming 20 cigarettes per pack. Smoking dose was quantified in pack-years (py) with 1 py defined as smoking one pack per day for 1 year. Former and never-smokers who reported living with one or more current smokers in the household were classified as exposed to secondhand smoke. Self-reported tobacco use in NHANES has been validated, with <2% of adults under-reporting use.20
uCd and bCd Measurement
NHANES blood and urine sample collection and processing have been described.19, 21 Concentrations of cadmium were measured in urine using inductively coupled plasma mass spectrometry. From 1999 to 2002, uCd results were manually corrected for molybdenum oxide interference after initial collection; in subsequent years, results were corrected at the collection laboratory.19 Measurements below the limit of detection (LOD) were assigned a value of the LOD divided by square root of 2. From 1999 to 2004, the LOD was 0.060 μg/l following molybdenum oxide correction (assigned value 0.042 μg/l); from 2005 to 2010, the LOD was 0.042 μg/l following molybdenum oxide correction (assigned value 0.030 μg/l).22 A total of 510 (5.6%) of all participants in these analyses were at or below the uCd LOD. Spot urine samples collected from participants were assayed for creatinine (cr) as described.19, 23
bCd was measured with either electrothermal atomic-absorption spectrophotometry (1999–2002) or inductively coupled plasma mass spectrometry techniques (2003–2010).19 Values at or below LOD (1999–2002: 0.3 μg/l; 2003–2004: 0.14 μg/l; and 2005–2010: 0.2 μg/l)22 were assigned a value of LOD divided by the square root of 2. A total of 1799 (20%) of all participants in these analyses were at or below the bCd LOD.
uCd and bCd were graphically examined to detect potential outliers and to determine whether logarithmic transformation of uCd or bCd was necessary. Three outliers (uCd=8.2, 10.9, and 36.8 μg/l) were noted, but removal of these data from analyses did not materially change results and therefore they were retained in all analyses. Histograms of uCd and bCd showed positive skew, and scatter plots of uCd and bCd with age of smoking demonstrated heteroscedasticity, which were both reduced by logarithmic transformation of uCd and bCd.
To directly examine the relationship between bCd and uCd, uCd was corrected for urine cr for each participant, by dividing uCd by cr. uCd-cr (μg-Cd/g cr) was then log-transformed and used as the independent variable in linear regression of log-transformed bCd.
For analysis of the relationships between uCd or bCd and participant characteristics, separate multivariable linear regression models were constructed with either log-transformed bCd or log-transformed uCd as the dependent variable. The models included smoking status (never, former, or current), smoking dose (py; linear and quadratic terms), and years since smoking cessation (among former smokers only; linear and quadratic terms), age (years; linear and quadratic terms), sex, BMI (kg/m2; linear), race/ethnicity (non-Hispanic white, Mexican–American, other Hispanic, non-Hispanic black, or other), education (secondary or less; some college/university; college/university degree or more) and NHANES cycle. To adjust uCd measurements for variation in spot samples, urine cr (log-transformed) was included as a separate term in multivariable linear regression models of uCd.24, 25
In separate models for bCd and uCd, an additional term was added categorizing reported smoking within the last 5 days (yes/no). The ratio of geometric means and corresponding 95% confidence interval (CI) comparing recent smokers with individuals reporting no smoking in the last 5 days was obtained by exponentiating the linear regression coefficients and 95% CI end points.26
In addition, multiplicative interaction terms of age and age squared with sex and smoking status; smoking dose (py; linear and quadratic terms) with sex; and smoking status with sex were included in each regression model. Therefore, this approach was similar to modeling each of uCd and bCd in six separate strata defined by sex and smoking status, except that the coefficient of smoking dose was constrained to be equal for current and former smokers of the same sex, and coefficients of nuisance variables (BMI, race/ethnicity, education, and NHANES cycle) were constrained to be equal among all participants. Statistical significance of regression parameters was assessed with adjusted Wald tests appropriate to complex survey analysis.27 Additional variables that were examined but not included in the final model were annual household income, secondhand smoke exposure, quadratic terms in BMI, interactions of BMI with sex and smoking status, and cubic terms in age and py of smoking; these additional terms were not significantly different from zero (adjusted Wald test P>0.05) and did not substantially change other results.
To facilitate comparison of bCd and uCd models, following linear regression, adjusted geometric means at representative values were calculated (also known as margins of responses, predictive margins, or in some circumstances least-square adjusted means28). Using the fitted linear regression parameters, adjustment covariates were set to a fixed common value for each participant: urine cr of 100 mg/dl, BMI of 27 kg/m2, attained education “some college,” non-Hispanic white ethnicity/race, and NHANES cycle of 2005–2006. When age or smoking variables were adjusted, we selected an age of 55 years, 20 py of smoking, and among former smokers, 20 years since smoking cessation. These values were close to sample mean values and avoided selecting unlikely or impossible values (e.g., age 20 years with 30 years since cessation). To calculate geometric means, the predicted log-transformed bCd and uCd were averaged over the subpopulation of interest (e.g., female never-smokers) and back transformed. Adjusted geometric means were always calculated separately for men and women.
To estimate the fraction of uCd or bCd attributable to long-term accumulation, a counterfactual approach was taken. For example, we assumed the entire difference between adjusted uCd at age 20–29 and 60–69 was due to accumulation, and calculated the attributable fraction (AF) as AF=(uCd65−uCd25)/uCd65, where uCd65 and uCd25 are adjusted geometric mean uCd at age 60–69 and 20–29, respectively. Similarly, to estimate the uCd or bCd attributable to smoking, we assumed that the difference in adjusted uCd or bCd between smokers and never-smokers was due to smoking and applied an analgous formula.
All analyses used Stata 12 (StataCorp, College Station, TX, USA) survey data analysis features to account for the sampling structure of NHANES,28 following NCHS analytic guidelines for combining data from multiple cycles of NHANES.19
After exclusions, the study sample included 9191 adults aged 20–85 years with both bCd and uCd measurements available (Table 1), representing ∼190 million US adults. Never-smokers were more likely female and college-educated than ever-smokers. Current smokers were typically younger, less educated, and had slightly lower BMI than former or never-smokers. Among smokers, average reported years smoked, smoking rate (cigarettes/day), and smoking dose (py) were similar for former and current smokers.
In the US population represented by NHANES, unadjusted geometric mean (95% CI) bCd was 0.37 μg/l (0.36–0.39 μg/l) and 0.41 μg/l (0.40–0.42 μg/l) in men and women, respectively; uCd was 0.25 μg/l (0.24–0.26 μg/l) in each sex. Among never-smokers, unadjusted uCd was 0.18 μg/l (0.17–0.19 μg/l) in men and 0.21 μg/l (0.20–0.22 μg/l) in women; and unadjusted bCd was 0.23 μg/l (0.22–0.24 μg/l) in men and 0.31 (0.30–0.32 μg/l) in women.
Results of regression of bCd as a function of uCd (Figure 1 and Table 2) indicated that, in the entire population, each 1% increase in geometric mean uCd was associated with 0.50% (95% CI: 0.47%–0.54%) higher geometric mean bCd; the association was somewhat stronger in men (0.55% (95% CI: 0.57%–0.58%) than in women (0.49% (95% CI: 0.44%–0.54%). Among all participants and separately within never-, former-, and current smokers, variation in uCd explained ∼30% of the variation in bCd (all participants, R2=0.31; men: R2=0.23, 0.41, and 0.22, and women R2=0.31, 0.32, and 0.21 among never-, former, and current smokers, respectively).
Results from separate regression models of uCd and bCd indicated that these measures were differently associated with age, depending on smoking status (adjusted Wald test of interaction terms, P<0.002 in each sex). Coefficients of the quadratic age terms were significantly <0 for male never-smokers for both bCd and uCd (P<0.001 for each, adjusted Wald test); thus, bCd and uCd appreciably leveled off after attaining an age of ∼55, but less so in former or never-smokers than in current smokers (Table 3 and Figure 2). In general, adjusted geometric mean bCd and uCd were higher in women than in men, in older participants, and within each sex, in smokers than in never-smokers (Table 3 and Figure 2). For example, among male never-smokers, predicted geometric mean bCd and uCd were 69% (95% CI: 59%–81%) and 200% (166%–234%) higher, respectively, in individuals aged ≥70 years than in individuals aged 20–30 years (Figure 2). Results in women were similar.
Figure 3 illustrates the relationships of uCd and bCd with smoking dose for former and current smokers by sex, adjusted to age 55 years. By assumption in our regression model, within each sex, the association of smoking dose with log-transformed bCd or uCd was the same for current and former smokers. Among both former and current smokers, higher smoking dose was associated with elevated mean uCd and bCd (Table 4 and Figure 3). The first 10 py of smoking was associated with 13.7% (10.0%–17.4%) higher bCd and 16.8% (12.6%–21.1%) higher uCd in men. The relationship between smoking dose and uCd or bCd did not differ significantly between sexes (uCd: P=0.26; and bCd: P=0.90 for interaction terms, adjusted Wald test); nonetheless, adjusted geometric mean bCd and uCd concentrations were higher in female than male smokers (Figures 2 and 3). Although terms in the regression model quadratic in smoking dose were significantly different than 0 (P<0.001 in each bCd and uCd, adjusted Wald test), little appreciable deflection was observed in geometric mean cadmium concentrations (Figure 3).
Both uCd and bCd were lower in former smokers than current smokers with the same smoking dose (Table 4 and Figure 3). Among representative male smokers aged 55 years with a smoking dose of 20 py, adjusted geometric mean bCd was 53% (48%–58%) lower and uCd 23% (14%–33%) lower in former smokers than in current smokers, less than 1 year after cessation (Figure 3). In comparable male smokers who quit 20 years before NHANES participation, predicted geometric mean bCd was 65% lower and uCd was 45% lower than in comparable current smokers (Figure 3). However, in this representative group, adjusted geometric mean uCd remained ∼50% higher and bCd 25% higher than in comparable never-smokers (Table 4). Similar results were observed for women.
Report of smoking within the 5 days before NHANES interview was associated with a 55% higher (95% CI: 40–71%) geometric mean bCd. In contrast, recent smoking was not associated with elevated uCd (ratio of geometric means (95% CI): 1.07 (0.97–1.18)).
In this study, we were able to compare blood and uCd concentrations measured in the same individuals to demonstrate that each matrix uniquely described cadmium exposure. These analyses took advantage of the strong relation between smoking and cadmium, and data from the US NHANES. Prior studies have generally been limited to occupational bio-monitoring and smaller studies of convenience samples from non-occupationally exposed populations. Thus, they have had limited ability to examine a wide range of exposure strength and recency.
Nonetheless, autopsy studies have demonstrated the accumulation of cadmium in the kidney and other organs over decades.29 Together with occupational bio-monitoring studies that show increasing uCd associated with duration of exposure,14 a general understanding that uCd reflects long-term exposure over decades was developed.13, 30 In contrast, bCd tended to reflect more recent exposure.13, 30 Thus, we were motivated to examine the relationship between bCd and uCd, differences in uCd and bCd associated with differences in age, and the relationship between bCd and uCd and the established cadmium exposure source tobacco in a large, well-described, representative population of adults.
Direct comparison of bCd and uCd confirmed that these measures are well-correlated, but that overall, bCd explained about 30% of the variation in uCd among all participants. This is similar to reports from other non-occupationally exposed population samples,15, 16 but a somewhat weaker correlation than in occupationally exposed workers.6 Thus, if for chronic diseases with long latency, we believe uCd is a more appropriate biomarker of exposure, the use of bCd would likely attenuate risk estimates. For example, given our result that R2 ∼0.3, a relative risk that would have been 2 had exposure been assessed with uCd would be observed to be ∼1.2.31 Similarly, dividing participants into sex-specific ordinal categories based on bCd would result in substantial misclassification if uCd is taken to be the “gold standard” for assessment of cadmium dose. These results apply to men and women, aged 20–75; in other groups, especially children, bCd and uCd may be differently related or have a different relationship to exposure.32
We hypothesized that misclassification between bCd and uCd may be higher in former smokers, because we anticipated that following smoking cessation, uCd but not bCd would remain elevated. Surprisingly, correlation between uCd and bCd was strongest in former smokers and weakest in current smokers, which was driven mainly by differences among men. Apparently, current smoking broadens the distribution of bCd much more than uCd, so that the overall correlation is attenuated. Removing participants with bCd below the LOD did not change the observed patterns.
To investigate further, we explored the relationships between uCd, bCd, age, and smoking in detail. We noted that women had higher uCd and bCd than men of the same age, and taking into account smoking history. This result is consistent with previous observations from diverse populations including the NHANES.14, 16, 23, 26, 33, 34, 35 Previous studies suggest that cadmium exposure through diet does not increase substantially with age during adulthood.36, 37 Thus, the association of bCd with age among never-smokers in particular suggests that bCd, similar to uCd, partially reflects accumulated or long-term exposure. Among never-smokers, we can roughly estimate the proportion of uCd and bCd that could be ascribed to long-term accumulation. For example, among never-smokers, in women aged 60–69 years, uCd was 2.7-fold and bCd was 1.6-fold values in women aged 20–29 years. Therefore, whether the excess uCd and bCd in older women is entirely attributed to accumulation with age, ∼63% of uCd and 35% of bCd measured at age 60–69 years could be interpreted as representing past exposure.
Smoking was associated with higher uCd and bCd, consistent with previously published reports.23, 38 Regression coefficients showed that 10 py of smoking was associated with an ∼15% higher geometric mean bCd and uCd. This relationship was approximately fixed with increasing smoking dose, suggesting no “saturation” of cadmium in blood or urine with increased exposure at these levels.
Our results of smoking cessation suggest that changes in uCd follow a much slower time-course than changes in bCd, and that uCd remains a measure of total cigarette dose, and therefore total cadmium dose, for decades. However, both uCd and bCd appear to substantially decline within a year of cessation: bCd declined by about 70% of the range between current and never-smokers, in both sexes; uCd declined by about 25 and 40%, in women and men, respectively (Figure 3). Subsequent years were associated with further declines in both bCd and uCd, but our results indicate that neither returns to the level observed in comparable never-smokers. For example, 20 years after smoking cessation, following a smoking dose of 20 py, bCd and uCd in 55-year-old men were 28% and 47% higher than in never-smokers. Thus, from a counterfactual perspective, in this example roughly 22% of bCd (i.e., 0.28/1.28) and 32% of uCd in former smokers are possibly attributable to smoking exposure that ended 20 years earlier.
To examine short-term changes in uCd and bCd due to smoking, we examined the influence of reported smoking in the 5 days before NHANES interview. Recent smoking was associated with a ∼60% higher geometric mean bCd, in stark contrast to uCd, regardless of overall smoking status. Thus, the decline in bCd observed within a year of smoking cessation (Figure 3) likely occurs within days, whereas uCd is a much less sensitive measure of recent exposure.
Our results demonstrating that an appreciable portion of bCd is associated with long-ago exposure stands in contrast to typical interpretation of bCd as a marker of recent exposure, as previously noted. In fact, however, a few studies have noted sustained elevation of bCd in industrially exposed workers.10, 11, 39 We hypothesized that cadmium is released from tissues. Liver is well-known to accumulate cadmium; lower levels of cadmium in muscle, making up a larger fraction of total body mass, may also contribute.14, 29, 40, 41
Limitations to our analysis and interpretation include the cross-sectional design of NHANES. Our comparisons by age may be influenced by cohort effects to the extent that cadmium exposure has changed over time. Recent evidence shows that cadmium exposure reflected in uCd has decreased over time possibly due to decreased smoking and secondhand smoke exposure,26, 42 which could increase the apparent “long-term” component of uCd and bCd we observed, even though we adjusted for NHANES cycle (i.e., year of interview). Secular trends in diet or smoking habits, such as types of cigarettes smoked,43, 44 may similarly have affected our results. Furthermore, 24-hour urine collection would ideally be used for uCd assessment, but this is impractical; the collection and analysis of single “spot” urines and a single blood sample may have introduced noise into the relationship between uCd and bCd.5 However, only a single sample is typically available in an epidemiological study, so our analysis applies directly to such studies.
In summary, we exploited the prevalent cadmium exposure through cigarette use and available data representative of the US population to examine in detail the relationship between cadmium exposure, bCd, and uCd levels. Our results indicate that the interpretation of bCd and uCd in the context of chronic exposure in the general population is largely consistent with studies of small occupational cohorts, experiencing very high levels of exposure; bCd is dominated by recent exposure, whereas uCd reflects a much longer history of exposure. These results should be of value for future population studies of this heavy metal in human health and disease.
blood cadmium concentration
body mass index
inductively coupled plasma mass spectrometry
limit of detection
National Health and Nutrition Examination Survey
pack-year of cigarette smoking
urine cadmium concentration
- 95% CI:
95% confidence interval
Agency for Toxic Substances and Disease Registry (ATSDR) Toxicological Profile for Cadmium., Dept. of Health and Human Services, 2012. http://www.atsdr.cdc.gov/toxprofiles/tp5.pdf.
Pappas RS, Polzin GM, Zhang L, Watson CH, Paschal DC, Ashley DL . Cadmium, lead, and thallium in mainstream tobacco smoke particulate. Food Chem Toxicol 2006; 44: 714–723.
Clemens S, Aarts MG, Thomine S, Verbruggen N . Plant science: the key to preventing slow cadmium poisoning. Trends Plant Sci 2013; 18: 92–99.
Vahter M, Berglund M, Slorach S, Friberg L, Saric M, Zheng XQ et al. Methods for integrated exposure monitoring of lead and cadmium. Environ Res 1991; 56: 78–89.
Gunier RB, Horn-Ross PL, Canchola AJ, Duffy CN, Reynolds P, Hertz A et al. Determinants and within-person variability of urinary cadmium concentrations among women in Northern California. Environ Health Perspect 2013; 121: 643–649.
Alessio L, Apostoli P, Forni A, Toffoletto F . Biological monitoring of cadmium exposure—an Italian experience. Scand J Work Environ Health 1993; 19 (Suppl 1): 27–33.
Kawada T, Shinmyo RR, Suzuki S . Changes in urinary cadmium excretion among pigment workers with improvement of the work environment. Ind Health 1993; 31: 165–170.
Borjesson J, Bellander T, Jarup L, Elinder CG, Mattsson S . In vivo analysis of cadmium in battery workers versus measurements of blood, urine, and workplace air. Occup Environ Med 1997; 54: 424–431.
Lauwerys R, Roels H, Regniers M, Buchet JP, Bernard A, Goret A . Significance of cadmium concentration in blood and in urine in workers exposed to cadmium. Environ Res 1979; 20: 375–391.
Mason HJ, Williams N, Armitage S, Morgan M, Green S, Perrin B et al. Follow up of workers previously exposed to silver solder containing cadmium. Occup Environ Med 1999; 56: 553–558.
Jarup L, Persson B, Elinder CG . Blood cadmium as an indicator of dose in a long-term follow-up of workers previously exposed to cadmium. Scand J Work Environ Health 1997; 23: 31–36.
Jarup L, Rogenfelt A, Elinder CG, Nogawa K, Kjellstrom T . Biological half-time of cadmium in the blood of workers after cessation of exposure. Scand J Work Environ Health 1983; 9: 327–331.
Lauwerys RR, Bernard AM, Roels HA, Buchet JP . Cadmium: exposure markers as predictors of nephrotoxic effects. Clin Chem 1994; 40 (Pt 2): 1391–1394.
Kjellstrom T . Exposure and accumulation of cadmium in populations from Japan, the United States, and Sweden. Environ Health Perspect 1979; 28: 169–197.
Shimbo S, Zhang ZW, Moon CS, Watanabe T, Nakatsuka H, Matsuda-Inoguchi N et al. Correlation between urine and blood concentrations, and dietary intake of cadmium and lead among women in the general population of Japan. Int Arch Occup Environ Health 2000; 73: 163–170.
Olsson IM, Bensryd I, Lundh T, Ottosson H, Skerfving S, Oskarsson A . Cadmium in blood and urine—impact of sex, age, dietary intake, iron status, and former smoking—association of renal effects. Environ Health Perspect 2002; 110: 1185–1190.
Moon CS, Paik JM, Choi CS, Kim DH, Ikeda M . Lead and cadmium levels in daily foods, blood and urine in children and their mothers in Korea. Int Arch Occup Environ Health 2003; 76: 282–288.
Moon CS, Zhang ZW, Shimbo S, Watanabe T, Moon DH, Lee CU et al. Evaluation of urinary cadmium and lead as markers of background exposure of middle-aged women in Korea. Int Arch Occup Environ Health 1998; 71: 251–256.
Centers for Disease Control and Prevention (CDC). National Health and Nutrition Examination Survey data, protocols, and analytic guidelines. U.S. Department of Health and Human Services: Hyattsville, MD, USAhttp://www.cdc.gov/nchs/nhanes.htm2011.
Yeager DS, Krosnick JA . The validity of self-reported nicotine product use in the 2001-2008 National Health and Nutrition Examination Survey. Med Care 2010; 48: 1128–1132.
Centers for Disease Control and Prevention (CDC). Fourth National Report on Human Exposure to Environmental Chemicals: Centers for Disease Control and Prevention. Dept. of Health and Human Services, 2009, 1–529 http://www.cdc.gov/exposurereport/pdf/FourthReport.pdf.
Centers for Disease Control and Prevention (CDC). Fourth National Report on Human Exposure to Environmental Chemicals: Updated Tables. Centers for Disease Control and Prevention. Department of Health and Human Services, 2013, 315–317 http://www.cdc.gov/exposurereport/pdf/FourthReport_UpdatedTables_Mar2013.pdf.
Paschal DC, Burt V, Caudill SP, Gunter EW, Pirkle JL, Sampson EJ et al. Exposure of the US population aged 6 years and older to cadmium: 1988-1994. Arch Environ Contam Toxicol 2000; 38: 377–383.
Barr DB, Wilder LC, Caudill SP, Gonzalez AJ, Needham LL, Pirkle JL . Urinary creatinine concentrations in the US population: implications for urinary biologic monitoring measurements. Environ Health Perspect 2005; 113: 192–200.
Suwazono Y, Akesson A, Alfven T, Jarup L, Vahter M . Creatinine versus specific gravity-adjusted urinary cadmium concentrations. Biomarkers 2005; 10: 117–126.
Tellez-Plaza M, Navas-Acien A, Caldwell KL, Menke A, Muntner P, Guallar E . Reduction in cadmium exposure in the United States population, 1988-2008: the contribution of declining smoking rates. Environ Health Perspect 2012; 120: 204–209.
Heeringa S, West BT, Berglund PA . Applied survey data analysis. Chapman & Hall/CRC: Boca Raton, FL, USA. 2010 xix 467 pp.
Williams R . Using the margins command to estimate and interpret adjusted predictions and marginal effects. Stata J 2012; 12: 308–331.
Benedetti JL, Samuel O, Dewailly E, Gingras S, Lefebvre MA . Levels of cadmium in kidney and liver tissues among a Canadian population (province of Quebec). J Toxicol Environ Health A. 1999; 56: 145–163.
Jarup L, Akesson A . Current status of cadmium as an environmental health problem. Toxicol Appl Pharmacol 2009; 238: 201–208.
White E, Armstrong BK, Saracci R . Principles of Exposure Measurement in Epidemiology 2nd ed. Oxford University Press: Oxford, UK. 2008.
Chaumont A, Nickmilder M, Dumont X, Lundh T, Skerfving S, Bernard A . Associations between proteins and heavy metals in urine at low environmental exposures: evidence of reverse causality. Toxicol Lett 2012; 210: 345–352.
Baecklund M, Pedersen NL, Bjorkman L, Vahter M . Variation in blood concentrations of cadmium and lead in the elderly. Environ Res 1999; 80: 222–230.
Kido T, Nogawa K, Ohmichi M, Honda R, Tsuritani I, Ishizaki M et al. Significance of urinary cadmium concentration in a Japanese population environmentally exposed to cadmium. Arch Environ Health 1992; 47: 196–202.
Shaham J, Meltzer A, Ashkenazi R, Ribak J . Biological monitoring of exposure to cadmium, a human carcinogen, as a result of active and passive smoking. J Occup Environ Med 1996; 38: 1220–1228.
Egan SK, Bolger PM, Carrington CD . Update of US FDA’s Total Diet Study food list and diets. J Expo Sci Environ Epidemiol 2007; 17: 573–582.
Choudhury H, Harvey T, Thayer WC, Lockwood TF, Stiteler WM, Goodrum PE et al. Urinary cadmium elimination as a biomarker of exposure for evaluating a cadmium dietary exposure—biokinetics model. J Toxicol Environ Health A. 2001; 63: 321–350.
Richter PA, Bishop EE, Wang J, Swahn MH . Tobacco smoke exposure and levels of urinary metals in the U.S. youth and adult population: the National Health and Nutrition Examination Survey (NHANES) 1999-2004. Int J Environ Res Public Health 2009; 6: 1930–1946.
McDiarmid MA, Freeman CS, Grossman EA, Martonik J . Follow-up of biologic monitoring results in cadmium workers removed from exposure. Am J Ind Med 1997; 32: 261–267.
Baba H, Tsuneyama K, Yazaki M, Nagata K, Minamisaka T, Tsuda T et al. The liver in itai-itai disease (chronic cadmium poisoning): pathological features and metallothionein expression. Mod Pathol 2013 (e-pub ahead of print, 5 April 2013).
Min KS, Ueda H, Kihara T, Tanaka K . Increased hepatic accumulation of ingested Cd is associated with upregulation of several intestinal transporters in mice fed diets deficient in essential metals. Toxicol Sci 2008; 106: 284–289.
Riederer AM, Belova A, George BJ, Anastas PT . Urinary cadmium in the 1999–2008 US National Health and Nutrition Examination Survey (NHANES). Environ Sci Technol 2013; 47: 1137–1147.
Elinder CG, Kjellstrom T, Lind B, Linnman L, Piscator M, Sundstedt K . Cadmium exposure from smoking cigarettes: variations with time and country where purchased. Environ Res 1983; 32: 220–227.
Jones MR, Apelberg BJ, Tellez-Plaza M, Samet JM, Navas-Acien A . Menthol cigarettes, race/ethnicity, and biomarkers of tobacco use in US adults: the 1999-2010 National Health and Nutrition Examination Survey (NHANES). Cancer Epidemiol Biomarkers Prev 2013; 22: 224–232.
Investigators gratefully acknowledge the Centers for Disease Control and Prevention and National Center for Health Statistics; and Michael Passarelli and Sabah Quraishi for helpful comments on the manuscript. Supported by National Institutes of Health, National Institute of Environmental Health Sciences R01ES019667.
The authors declare no conflict of interest.
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Adams, S., Newcomb, P. Cadmium blood and urine concentrations as measures of exposure: NHANES 1999–2010. J Expo Sci Environ Epidemiol 24, 163–170 (2014). https://doi.org/10.1038/jes.2013.55
- National Health and Nutrition Examination Survey
Metal biomarker mixtures and blood pressure in the United States: cross-sectional findings from the 1999-2006 National Health and Nutrition Examination Survey (NHANES)
Environmental Health (2021)
Exposure and Health (2021)
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