Biomonitoring for exposure to multiple trace elements via analysis of urine
from participants in the Study of Metals and Assisted Reproductive
Keewan Kim,aAmy J. Steuerwald,acPatrick J. Parsons,acVictor Y. Fujimoto,dRichard W. Brownee
and Michael S. Bloom*ab
Received 19th April 2011, Accepted 28th June 2011
Humans are exposed to concentrations of multiple trace elements through a variety of background
sources; many are suspected reproductive toxicants. Prior to investigating associations between trace
elements and human reproductive health, potential biomarkers of exposure should be characterized by
sources of variability in the population at risk. Factors influencing elemental exposure should also be
identified to ensure their consideration as potential confounding variables. The principal aim of this
study is to characterize sources of variability for 19 trace elements measured in urine specimens
collected from 55 women and 36 male partners completing a 1st cycle of in vitro fertilization (IVF).
Urine specimens were analyzed using a biomonitoring method based on inductively coupled plasma–
mass spectrometry (ICP–MS). Randomly selected urine specimens (?6%) were analyzed in duplicate,
and these data were used to characterize sources of variability. Nine trace elements including As, Ba,
Cd, Cs, Co, Cu, Mn, Mo, and Zn, were quantified in most specimens, indicating their utility in future
epidemiologic studies of trace elements exposure and IVF outcomes. With few exceptions, normalizing
urine using the traditional creatinine-correction procedure, or analternative approach basedon a linear
regression model, increased residual variability only slightly. Sex and race appear to be important
factors to consider in epidemiologic studies conducted in this population. Urine concentrations for
most elements are similar to those reported in the 2005–2006 National Health and Nutrition
Examination Survey (NHANES); however, differences in others may indicate regional trends or
a unique exposure history for this infertile study population.
Many elements are ubiquitous environmental contaminants,
occurring at trace (i.e., low mg g?1) concentrations; humans are
exposed through dietary sources, drinking water, and respiratory
sources even in the absence of occupational or other risk factors.1
Chronic exposures even at trace levels may have adverse effects
on reproductive health2as well as the success of assisted repro-
ductive procedures including in vitro fertilization (IVF).3The
aDepartment of Environmental Health Sciences, University at Albany,
State University of New York, Rensselaer, NY, USA. E-mail: mbloom@
albany.edu.; Fax: +(1) (518) 474-9899; Tel: +(1) (518) 473-1821
University of New York, Rensselaer, NY, USA
cLaboratory of Inorganic and Nuclear Chemistry, Wadsworth Center, New
York State Department of Health, Albany, NY, USA
dDepartment of Obstetrics, Gynecology, and Reproductive Sciences,
University of California at San Francisco, San Francisco, CA, USA
eDepartment of Biotechnical and Clinical Laboratory Sciences, University
at Buffalo, State University of New York, Buffalo, NY, USA
Humans are exposed to a variety of elements, including metals and metalloids through myriad environmental sources; these may act
as reproductive toxicants. Investigators are increasingly attentive to the possibility that low-level, yet widespread exposures to toxic
elements may be of substantial public health relevance. Even at trace concentrations, such as those due to dietary exposure, contact
with toxic elements may adversely impact reproductive processes in humans. The current study characterizes the measurement
properties and distribution of exposures to trace elements in an infertile population. This study represents the first step in
acomprehensive approachto the investigationof potential human reproductive toxicity in association with incidental environmental
exposures to toxic elements from background sources.
This journal is ª The Royal Society of Chemistry 2011 J. Environ. Monit., 2011, 13, 2413–2419 | 2413
Dynamic Article LinksC
Cite this: J. Environ. Monit., 2011, 13, 2413
detection of non-essential elements at trace concentrations in
human reproductive tissues and fluids underscores this possi-
bility.3–7Although biomonitoring programs in the U.S. report
values for a wide range of trace elements to which the general
population is exposed,1few published reports describe such
exposures for infertile U.S. populations. In this study, we char-
acterize sources of variability and distributions for 19 trace
elements measured in urine specimens collected from women and
their male partners undergoing IVF in a San Francisco clinic.
These data will be employed during the design of a future study
to investigate associations between trace elements exposure and
Between March 12th 2007 and April 29th 2008 58 female patients
and 37 male partners undergoing a 1st IVF procedure at the
University of California San Francisco (UCSF) Center for
Reproductive Health (San Francisco, CA USA) were recruited to
participate in the Study of Metals and Assisted Reproductive
Technologies (SMART). Sample selection and clinical protocols
have been described in detail elsewhere.8In brief, a single urine
specimen was obtained from 55 female patients at the time of
oocyte retrieval, following implementation of an ovarian
hyperstimulation protocol, and from 36 male partners, when
available, on that same day. Specimens were collected from
women while fasting, whereas those collected from men did not
require fasting. Following collection into a specimen cup, 1.8 mL
of urine were aliquoted into polypropylene cryovials and frozen
immediately at ?80?C. Informed consent was obtained from all
study participants and the study protocol was approved by the
UCSF Committee for Human Research, and by the respective
Institutional Review Boards of the University at Albany and the
New York State Department of Health (NYSDOH).
Urine trace element analysis
Urine specimens were transported on dry ice to the clinical trace
elements section of the Laboratory of Inorganic and Nuclear
Chemistry at the NYSDOH’s Wadsworth Center (Albany, NY
USA). Specimens were analyzed for 19 trace elements including:
Sb, As, Ba, Be, Cd, Cs, Cr, Co, Cu, Pb, Mn, Mo, Ni, Pt, Tl, Sn,
W, U and Zn. Analyses were carried out using a Perkin Elmer
Sciex ELAN DRC II inductively coupled plasma-mass spec-
trometer (PerkinElmer Life and Analytical Sciences, Shelton, CT
USA) equipped with Dynamic Reaction Cell technology (DRC-
ICP-MS). The Wadsworth Center laboratory is certified by (a)
Amendments of 1988 (CLIA’ 88) and (c) by the Occupational
Safety and Health Administration (OSHA), and successfully
participates in several proficiency testing (PT) programs and
external quality assessment (EQA) schemes for trace elements in
urine including those operated by the NYSDOH, Institut
National deSant? e Publique du Qu? ebec, Le Centre deToxicologie
du Qu? ebec, the Trace Elements External Quality Assessment
Scheme, at the University of Surrey, UK, as well as in the
German External Quality Assessment Scheme, operated by the
Institute and Outpatient Clinic for Occupational, Social and
Environmental Medicine of the Friedrich-Alexander University,
The specimen preparation and typical instrumental operating
parameters have been previously described in detail.9In brief,
urine specimens were diluted 1 + 19 with 2% (v/v) HNO3, 0.005%
Triton X-100 as a surfactant and 10 mg L?1Ga, Y, Rh and Ir as
internal standards. All specimens were prepared for analysis
under Class 100 clean conditions. A matrix-matched standard
curve was generated for each analyte using a minimum of six
calibration standards traceable to the U.S. National Institute of
Standards and Technology (NIST, Gaithersburg, MD USA).
Method accuracy was assessed by analyzing NIST Standard
Reference Material (SRM) 2670a Toxic Elements in Urine
(Freeze-Dried) and method performance monitored via partici-
pation in the four aforementioned PT/EQA schemes. In addition,
four levels of urine internal quality control (IQC) materials were
analyzed at the beginning, end and periodically throughout each
analytical run. Target values for these IQC materials were
established by circulating them as test samples in the NYSDOH
PT program for trace elements in urine, which is organized by the
Wadsworth Center. Acceptable IQC performance was observed
during analysis of the SMART study specimens. Method detec-
tion limits (MDL) were calculated according to International
Union of Pure and Applied Chemistry (IUPAC) guidelines.10The
MDL is defined as three times the standard deviation of
concentrations measured in urine matrix blanks for 10 indepen-
dent analytical runs, whereas the limit of quantitation (LOQ) is
defined as 10 times the calculated standard deviation for each
element using the same matrix blanks. In thisstudy, urineelement
concentrations below the MDL (including negative values) were
included in statistical calculations to preclude the introduction of
statistical bias.11As part of standard IQC procedures, duplicate
analysis was performed on six randomly selected study samples.
Urine creatinine analysis
A second 1.8 mL aliquot of urine was shipped on dry ice to the
Oxidative Stress Laboratory at the University at Buffalo
(Buffalo, NY USA) for measurement of creatinine concentra-
tion. A multi-step, enzymatic, endpoint colorimetric assay was
employed using diagnostic reagents, calibrators and two-level
QC materials (Diagnostic Chemical Limited, Oxford, CT USA).
Each sample was analyzed in duplicate, as a single batch on the
Cobas Fara II automated chemistry analyzer (Roche Diagnos-
tics, Indianapolis, IN USA). The mean of duplicates for each
participant was employed for analysis.
Statistical analysis was completed for the trace elements with
a minimum of 60% of values abovethe MDL.1Duplicate data for
six randomly selected urine specimens were used to conduct an
analysis of sources of variability for these measurements.12For
each analyte measured, we used the data derived from duplicate
analyses to assess the extent to which it is possible to (a) specify
the mean value (i.e., coefficient of variation (CV)) and (b) to
distinguish changes in values between-subjects (i.e., intraclass
correlation coefficient (ICC)).
2414 | J. Environ. Monit., 2011, 13, 2413–2419This journal is ª The Royal Society of Chemistry 2011
A nested ANOVA model was generated for each analyte
defined as Yij¼ m + subi+ ej(i); where Yijdescribes an element
value for the jth replicate (j ¼ 1, 2) from the ith subject (i ¼
1,.,6), m describes the grand mean for an analyte, subidescribes
the random effect of the ith subject on the grand mean, and ej(i)
describes the random effect of the jth replicate nested within the
ith subject. Errors were assumed normally distributed with mean
zero and constant variance (?N (0, s2)). Under this model
specification, between-subject variability (i.e., s2B) and residual,
or unexplained, variability (i.e., s2R) are captured; residual
variability encompasses analytic variability. The total variance
for a measured analyte in our study sample was defined by s2T¼
s2B+ s2R. Relative contributions for each component of vari-
ance to the total were determined by calculating proportions.
Coefficients of variation were calculated from the components of
variance specified as CVl ¼ Os2k/? xk in which s2k represents
a variance component and ? xkthe mean value for an analyte.
Intraclass correlation coefficients were estimated as s2B/
(s2B+ s2R) with 95% confidence intervals (CIs) calculated using
the inverse tan transformation of Smith’s variance.13Initially we
generated ICCs using urine concentration data (mg L?1) and
compared them to ICCs recalculated after normalization using
a ‘traditional’ creatinine-correction, in which the element
concentrations were divided by the concentration of creatinine
(mg dL?1). Secondly, we assessed an alternative procedure, using
the residuals generated by linear regression of element levels
(mg L?1) on creatinine (mg dL?1) and compared ICCs to those
Nine of the 19 measured trace elements, having at least 60% of
values above the MDL1and ICC > 0.80 (As, Ba, Cd, Cs, Co, Cu,
Mn, Mo, and Zn),13were characterized in terms of covariates
suspected to confound associations between exposure and IVF
endpoints. Distributions were characterized by median and their
corresponding 95% CIs. Mann-Whitney U-tests and Spearman
rank correlation coefficients were used to evaluate associations
with sex, race (‘non-Asian’ vs. ‘Asian’; ‘non-Asian’ comprised n
¼ 64 self-identified as White, n ¼ 3 self-identified as Hispanic,
and n ¼ 1 self-identified as African American. Two men without
race data were assigned the race of their female partners, non-
Asian), cigarette smoking (‘never’ vs. ‘ever’), age (years), and
BMI (kg m?2; for women only; height and weight data are not
routinely collected for male partners and thus BMI data were
unavailable) as appropriate. The racial distribution of our study
sample provided a sufficient number of subjects to facilitate
comparison of Asian to non-Asian participants, which was of
interest given higher reported seafood consumption, a significant
source of dietary exposure to some trace elements, among the
former.15–17Creatinine-corrected urine analytes were used for
categorical analysis, whereas creatinine-regressed urine analytes
were used for correlations. The latter approach precludes the
introduction of bias that has been demonstrated when normal-
izing a predictor used in a general linear model.14Statistical
significance was defined as P < 0.05 for a two-tailed test. SAS
v.9.2 (SAS Institute, Inc. Cary, NC USA) was used for statistical
Sources of variability
Table 1 describes median overall values and MDLs for each of 19
measured urine trace elements. The components of variance and
the calculated values for between-subjects and residual percent
CVs are described for 11 trace elements for which there are $60%
detectable values. Overall, residual variability is smaller than
between-subjects variability, usually comprising #10% of total
variability. Described in Table 2, ICCs exceed 0.9 for all urine
analyte concentration data (ug/L), with the exceptions of Cr
(0.61) and Tl (0.02). Creatinine-corrected ICCs are somewhat
lower than those for the urine concentration values, with the
exception of As which increases to 0.96. Differences between
ICCs using urine concentrations (ug/L) and a creatinine-correc-
tion procedure (ug/g) do not exceed 5%, except +6.7% for As,
?14.7% for Cr, and ?10.5% for Mn. With the exceptions of
?12.5% for As and ?27.4% for Mn, a similar pattern is produced
using the alternate creatinine-regression approach.
Table 3 presents the distributions for nine trace elements with
ICC >0.80 using creatinine-corrected data stratified by sex, race,
and cigarette smoking, as well as correlation coefficients for
creatinine-regressed urine element concentrations with age, and
BMI (women only). Significant differences are detected between
female IVF patients and their male partners for urine Cd, Co,
Cu, and Zn; the difference for Ba is ‘borderline’ (P ¼ 0.071). In
addition, significant differences are detected between non-Asian
and Asian study participants for As and Cu; the difference for Cd
is borderline (P ¼ 0.098). No difference is detected by self-
reported never vs. ever cigarette smoking. With the exception of
Ba and BMI (r ¼ ?0.27, P ¼ 0.059), no correlation is suggested
for urine analytes and age or BMI among women. Median
creatinine concentrations are 113.90 mg/dL (95% CI 81.54–
150.88) for women and 108.58 mg/dL (95% CI 82.42–167.68) for
men, and moderate to strong (r ¼ 0.56–0.82) correlations are
detected for each element (mg L?1) listed in Table 3. Significant
positive inter-correlations of weak to moderate magnitude (r ¼
0.19–0.69) are detected among all elements listed in Table 3 (data
The results of this study of 19 trace elements in urine specimens
collected from infertile couples undergoing IVF indicate that
measurements of As, Ba, Cd, Cs, Co, Cu, Mn, Mo, and Zn in
urine are appropriate biomarkers of background exposure in this
study population. In addition, the use of a creatinine-regression
procedure to accommodate variability in urine output within-
subject does not adversely affect the ICC compared to the
traditional creatinine-correction approach. This study also
suggests the importance of considering sex and race when con-
ducting epidemiologic studies of trace element exposures in this
This journal is ª The Royal Society of Chemistry 2011J. Environ. Monit., 2011, 13, 2413–2419 | 2415
Sources of variability
To maximize statistical power in epidemiologic studies it is
important to characterize all sources of variability for a potential
biomarker of exposure in the target population.12The CV quan-
and is an inverse function of that concentration.18Specifically, the
%CVBdescribes the average spread of individual mean values
about the group mean, and %CVRdescribes the average spread of
duplicate results about individual means. For most trace elements
considered, residual variability is (not surprisingly) much smaller
than between-subject variability, comprising less than 10% of the
total, and %CVBs generally exceed %CVRs.
The ICC describes the capability to distinguish changes in
values between subjects; a threshold of 0.80 is recommended as
the minimum for use in epidemiologic studies.13This criterion
assures variability between subjects accounts for at least 80% of
total variability. Even at this fairly conservative threshold, a 25%
increase in population-sample size is required to achieve statis-
tical power equal to that with ICC ¼ 1.00.13In this study, the
ICCs for urine As, Ba, Cd, Cs, Co, Cu, Mn, Mo, and Zn each
exceed 0.80, suggesting they are suitable as biomarkers of
exposure in this population (Table 2).
It is critical to note that when measured concentrations are
close to the MDL, it is not possible to quantitate with a large
degree of confidence. Therefore, the total variability will be
dominated by residual sources due primarily to the limitations
inherent to any analytical method as one approaches the LOQ
and thence the MDL. The concept of MDL and LOQ are well
established within analytical chemistry, and delineate the
difference between the concentrations at which an analyte is
detected versus that at which an analyte can be quantified with
some stated degree of confidence. This is an important issue for
epidemiologic studies when dealing with data that are below the
LOQ, but above the MDL. Even though Cr and Tl are
‘‘detectable’’ in our study sample, the values are too close to the
MDL to be quantified with confidence given the limitations of
current state-of-the-art laboratory technology. Notwithstanding
the number of values above the MDL (>60%), substantial
proportions of Cr (81.3%) and Tl (49.5%) are still below the
LOQ. This underscores the limitations of using urine as
a biomarker of background exposures to Cr and Tl in our study
population, although no better matrix (i.e., biomarker of expo-
sure) has been identified for monitoring exposure to these trace
Although use of a single urine specimen is frequently a more
convenient and less invasive approach to exposure assessment
than a 24-hour urine collection protocol, the biologic variability
Reproductive Technologies (SMART)a,b
Characteristics for concentrations of urine trace elements (mg L?1urine) measured for 91 participants in the Study of Metals and Assisted
Element Median MDL% > MDL% s2Bc
2.36 ? 10?4
1.00 ? 10?3
aNOTE: Reliability analysis was conducted for trace elements with $60% of values above the MDL;bCV, coefficient of variation; MDL, method
detection limit.cReliability characteristics are based on duplicate analysis conducted in a subsample of 6 randomly selected participants.
creatinine-corrected, and creatinine-regression urine trace element
concentration from six participants in the Study of Metals and Assisted
Reproductive Technologies (SMART)a,b
Comparison of ICCs (95% CIs) for creatinine-uncorrected,
aNOTE: Reliability analysis was conducted for elements with $60% of
values above the MDL.
correlation coefficient; MDL, method detection limit.cICC calculated
using urine trace element values as reported.dICC calculated following
division of reported urine element values by creatinine concentrations.
eICC calculated using residuals from linear regression models of
reported urine element values on creatinine concentrations.
bCI, confidence interval; ICC, intraclass
2416 | J. Environ. Monit., 2011, 13, 2413–2419This journal is ª The Royal Society of Chemistry 2011
in urine output within subjects necessitates a ‘correction’ for
accommodate variability in urine output variation is indicated
by prior studies,21,22as well as in this study by positive corre-
lations between urine trace element and creatinine levels.
However, an analogous study of polychlorinated biphenyls
measured in serum lipids, suggests an alternative regression
approach and indicates that traditional normalization is likely
to introduce bias.14
Comparing two different approaches for accommodating
urine output variability, creatinine-correction and creatinine-
regression procedures increase residual variability for most trace
elements measured; however, the ICC decreases only margin-
ally, likely due to the propagation of measurement error from
the creatinine assay. For some trace elements, we observe little
consistency with a priori anticipated changes in ICC incurred by
adjustment for creatinine,based
kinetics.19,23For example, while we anticipated no change in
ICC following accommodation of urine creatinine for those
elements reported to undergo passive glomerular filtration (i.e.,
Cd, Mn, Zn),23ICCs for Mn decrease more than 10% in our
study. Analogously, we expected a decrease in ICC for those
elements reported to undergo active tubular secretion (i.e., Cr,
Cu);23however, that for Cu changes by only 3% in our study. It
appears that the extent of the reduction in ICC is element-
specific and no ‘universal rule’ applies. Thus, investigators
might consider the decision to adjust for creatinine, and how on
a case by case basis, rather than relying on reported excretion
Described in Table 4, data for creatinine-corrected urine trace
elements (mg g?1creatinine) with ICC >0.80 are mostly similar to
reference values reported for the 2005–2006 U.S. population by
the National Health and Nutrition Examination Survey
(NHANES);1however, there are a few differences of note.
Concentrations of As and Cs measured in this study substantially
exceed those reported for U.S. women and men, whereas Co and
Mo concentrations are much lower. Barium levels measured for
men are somewhat lower than that for U.S. men; however levels
are similar for women. In contrast, female Cd levels marginally
exceed U.S. values, whereas values for men are similar. The
differences between our sample and the U.S. population are
generally small; however, a distinct exposure pattern is sug-
gested. This pattern may reflect regional differences in exposure
to trace elements between the San Francisco Bay Area of Cal-
ifornia and the U.S. overall. Alternately, differences might
represent variability in exposure between couples treated for
infertility by IVF and the overall U.S. population; however,
given the absence of a ‘fertile’ reference group with whom to
compare we are unable to assess this possibility in the current
Distributions of urine trace elements in this study vary by sex
as indicated by higher concentrations of Cd, Co, Cu, and Zn for
women than for men (Table 3). A similar pattern has been
reported for the U.S. population1and elsewhere.24Sex-related
discrepancies in trace element exposure might be explained in
part by diet such as consumption of seafood,25,26or foodstuffs
high in iron (Fe) like red meat. Differences in Fe stores, poten-
tially depleted during menstruation, affect gastrointestinal
absorption of Cd and Co for example, and may facilitate variable
internal doses to women and men.24,27,28This difference might
also be due in part to prenatal vitamin use;29however, we do not
have these data available for this study.
Asian participants demonstrate higher As and Cu levels, as
well as a trend towards higher Cd concentrations, compared to
non-Asians. Whereas Asians comprised about 4% of the U.S.
population as of the 2000 census,3023% (n ¼ 21) of our study
sample self-reports Asian race.8It is tempting to speculate that
race-related discrepancies in trace element concentrations,
including the aforementioned difference in As levels, are
a consequence of ethnic dietary and health-related behaviors.
Although dietary data are not available for this study, it is
reported that persons of Asian heritage in the U.S. consume
more seafood than other groups;15a source of exposure to Cd
and organic arsenic,16,17which is likely to compromise a majority
of the total urine As reported herein.
Median urine trace element concentrations by demographic factors, for participants in the Study of Metals and Assisted Reproductive
BMI (kg m?2)d,e
(n ¼ 55) (n ¼ 36)
(n ¼ 70)
(n ¼ 16) (n ¼ 75)
P-value(n ¼ 21)
3.8 ? 10?30.979
aNOTE: Exposure analysis conducted for elements with $60% of values above the MDL and ICC >0.80.bBMI, body mass index; MDL, method
detection limit.cUrine trace elements corrected using division by creatinine concentration and expressed as (mg g?1creatinine).dUrine elements
adjusted by using residuals from linear regression models of reported urine elements values on creatinine concentrations (mg/dL).en ¼ 55 women only.
This journal is ª The Royal Society of Chemistry 2011J. Environ. Monit., 2011, 13, 2413–2419 | 2417
Concentrations of urine trace elements measured in our study,
with the exception of Ba which trends towards a positive corre-
lation to BMI, do not vary by age, BMI (women only) or ciga-
rette smoking. We anticipated higher As, Ba and Cd levels for
cigarette smokers;21,31,32however, despite higher median values
for Cd in ‘ever’ smokers, differences are not significant. More-
over, we expected a positive correlation between age and urine
Cd levels;21however no such association is indicated, possibly
due to the limited age range for study participants (28–44 years
for women, and 31–48 years for men). In contrast, the absence of
correlations between age and non-persistent trace elements was
Limitations and strengths of the study
This study considers a small number of subjects and the results
should thus be interpreted with caution. In addition, participants
were highly selected to this study, comprised of only female
patients and their male partners completing a 1st IVF cycle at the
clinic. In order to avoid the introduction of a bias that might
result from inherent differences in behaviors leading to exposure
between infertile and fertile couples,34all participants were
recruited from an infertile population. The latter might be
considered as a study limitation; however, presuming participant
recruitment was independent of exposure, our study results are
A single urine specimen, less ideal than repeated specimens
collected over time,35,36was used and we are thereby unable to
differentiate sources of variability due to within-subject factors
from those due to analytic factors; a single component of residual
variance incorporates both according to our model specifica-
tion.18Moreover, clinical procedures including administration of
exogenous hormones and fasting required only for women may
have altered the absorption, distribution, metabolism, and/or
excretion of some or all of the trace elements considered and thus
biased exposure estimates.
Our use of self-report and dichotomous variables for race and
cigarette smoking are vulnerable to misclassification and may
have introduced measurement error, potentially biasing the
results toward the null. With regard to smoking, this scenario is
likely exacerbated by the presence of only a handful of active
smokers in our study sample (9.6%), which also limits statistical
power for the comparisons. In addition, some significant
associations may be a spurious result of type-1 error inflation due
to the conduct of multiple independent tests. However, consis-
tent with the hypothesis generating nature of this study we opted
for the highest sensitivity to detect potential associations for
Many trace elements measured in this study are ubiquitous,
raising the possibility for specimen contamination during
collection and storage. Field blanks were unavailable to our
laboratory to confirm the absence of systematic contamination;
however, a subsequent evaluation of the cryovials used to store
specimens indicates these containers were not a source of
contamination. In addition, interference by molybdenum oxide
(MoO) may result in overestimation of urine Cd measured by
ICP-MS, in the presence of low concentrations in conjunction
with high Mo levels.38However, our Mo values are low and Cd
data are similar to those reported by NHANES that were cor-
rected for the MoO interference.1Lastly, we considered only
a limited panel of trace elements during this preliminary study,
which precludes provision of a comprehensive profile of expo-
sures. Additional trace elements such as iodine (I), selenium (Se)
and magnesium (Mg) play critical roles in human physiology and
so are also of interest.
We suggest urine As, Ba, Cd, Cs, Co, Cu, Mn, Mo, and Zn are
appropriate as biomarkers of background exposure in this study
population. Exposure to other measured trace elements occurs at
concentrations too low to permit reliable quantification given the
limits of modern laboratory technology. Comparing creatinine
accommodation procedures, ICC values using creatinine-
regression indicate that this procedure is a suitable approach to
normalization based on urine creatinine, while minimizing bias
for most elements. However, the investigator must consider the
relative importance of measurement reliability and bias. Our
results suggest a unique trace element exposure profile for
infertile couples residing in northern California, and also indicate
sex and race as important covariates for epidemiologic studies in
this population. The results of this study will be used in
a comprehensive longitudinal investigation of exposures to trace
elements and reproductive endpoints among infertile couples
treated by IVF.
compared to the U.S. population (2005–2006)a,b
Median (95% CI) trace element concentrations for participants in the Study of Metals and Assisted Reproductive Technologies (SMART)
SMART (n ¼ 55) 95% CIU.S. 95% CISMART (n ¼ 36) 95% CIU.S.95% CI
aNOTE: Urine trace elements corrected using division by creatinine concentration and expressed as (mg g?1creatinine).b2010 U.S. Centers for Disease
Control and Prevention Fourth National Report on Human Exposure to Environmental Chemicals-Updated Tables. CI, confidence interval; MDL,
method detection limit.
2418 | J. Environ. Monit., 2011, 13, 2413–2419 This journal is ª The Royal Society of Chemistry 2011
1 CDC, Fourth National Report on Human Exposure to Environmental
Chemicals-Updated Tables, Report, U.S. Centers for Disease
Control and Prevention, Atlanta, GA, 2010.
2 P. Mendola, L. C. Messer and K. Rappazzo, Fertil. Steril., 2008, 89,
3 E. V.Younglai,W. G. Foster, E. G. Hughes,K. Trim andJ. F. Jarrell,
Arch. Environ. Contam. Toxicol., 2002, 43, 121–126.
4 J. Fiala, D. Hruba, I. Crha, P. Rezl and J. Totusek, Int. J. Occup.
Med. Environ. Health, 2001, 14, 185–188.
5 K. Paksy, I. Gati, M. Naray and K. Rajczy, J. Toxicol. Environ.
Health A, 2001, 62, 359–366.
6 N. Pant, A. K. Banerjee, S. Pandey, N. Mathur, D. K. Saxena and
S. P. Srivastava, Hum. Exp. Toxicol., 2003, 22, 125–128.
7 T. Silberstein, O. Saphier, O. Paz-Tal, L. Gonzalez, D. L. Keefe and
J. R. Trimarchi, Fertil. Steril., 2009, 91, 1771–1774.
8 M. S. Bloom, P. J. Parsons, A. J. Steuerwald, E. F. Schisterman,
R. W. Browne, K. Kim, G. A. Coccaro, G. C. Conti, N. Narayan
and V. Y. Fujimoto, Reprod. Toxicol., 2010, 29, 298–305.
9 M. G. Minnich, D. C. Miller and P. J. Parsons, Spectrochim Acta Part
B At Spectrosc, 2008, 63, 389–395.
10 M.Thompson,S. L.R. EllisonandR.Wood,PureAppl.Chem.,2002,
11 E. F. Schisterman, A. Vexler, B. W. Whitcomb and A. Liu, Am. J.
Epidemiol., 2006, 163, 374–383.
12 E. Taioli, P. Kinney, A. Zhitkovich, H. Fulton, V. Voitkun,
G. Cosma, K. Frenkel, P. Toniolo, S. Garte and M. Costa, Environ.
Health Perspect., 1994, 102, 306–309.
13 J. M. Lachin, Clin Trials, 2004, 1, 553–566.
14 E. F. Schisterman, B. W. Whitcomb, G. M. Buck Louis and
T. A. Louis, Environ. Health Perspect., 2005, 113, 853–857.
15 J. M. Hightower, A. O’Hare and G. T. Hernandez, Environ. Health
Perspect., 2006, 114, 173–175.
16 X. Le, W. Cullen and K. Reimer, Clin. Chem., 1994, 40, 617–624.
17 K. L. Caldwell, R. L. Jones, C. P. Verdon, J. M. Jarrett, S. P. Caudill
and J. D. Osterloh, J. Expo. Sci. Environ. Epidemiol., 2009, 19, 59–68.
18 C. G. Fraser, Biological variation: from principles to practice, AACC
Press, Washington, DC, 2001.
19 M. F. Boeniger, L. K. Lowry and J. Rosenberg, Am. Ind. Hyg. Assoc.
J., 1993, 54, 615–627.
20 A. H. Garde, A˜. M. Hansen, J. Kristiansen and L. E. Knudsen, Ann.
Occup. Hyg., 2004, 48, 171–179.
21 J. A. McElroy, M. M. Shafer, J. M. Hampton and P. A. Newcomb,
Sci. Total Environ., 2007, 382, 214–223.
22 A. Navas-Acien, K. A. Francesconi, E. K. Silbergeld and E. Guallar,
Environ. Res., 2011, 111, 110–118.
23 S. Araki, H. Aono, K. Yokoyama and K. Murata, Archives of
Environmental Health: An International Journal, 1986, 41, 216–221.
24 J. Kristiansen, J. M. Christensen, B. S. Iversen and E. Sabbioni, Sci.
Total Environ., 1997, 204, 147–160.
25 N. A. Clark, K. Teschke, K. Rideout and R. Copes, Chemosphere,
2007, 70, 155–164.
26 S. Satarug, S. H. Garrett, M. A. Sens and D. A. Sens, Environ. Health
Perspect., 2010, 118, 182–190.
27 M. Berglund, A. Akesson, B. Nermell and M. Vahter, Environ. Health
Perspect., 1994, 102, 1058–1066.
28 M. Vahter, A. Akesson, C. Liden, S. Ceccatelli and M. Berglund,
Environ. Res., 2007, 104, 85–95.
29 B. Avula, Y. H. Wang, N. S. Duzgoren-Aydin and I. A. Khan, Food
Chem., 2011, 127, 54–62.
30 U.S. Census Bureau, Profile of General Demographic Characteristics:
2000, Geographic Area: United States, 2002.
31 P. A. Richter, E. E. Bishop, J. Wang and M. H. Swahn, Int. J. Env.
Res. Public Health, 2009, 6, 1930–1946.
32 S. J. Stohs, D. Bagchi and M. Bagchi, Inhal. Toxicol., 1997, 9, 867–890.
33 M. Schuhmacher, J. L. Domingo and J. Corbella, Sci. Total Environ.,
1994, 148, 67–72.
34 J. Olsen, J. P. Bonde, N. H. Hjollund, O. Basso and E. Ernst, Hum.
Reprod. Update, 2005, 11, 607–611.
35 R. W. Browne, M. S. Bloom, E. F. Schisterman, K. Hovey,
M. Trevisan, C. Wu, A. Liu and J. Wactawski-Wende, Biomarkers,
2008, 13, 160–183.
36 D. A. Lacher, J. P. Hughes and M. D. Carroll, Clin. Chem., 2005, 51,
37 D. A. Savitz and A. F. Olshan, Am. J. Epidemiol., 1995, 142, 904–908.
38 J. M. Jarrett, G. Xiao, K. L. Caldwell, D. Henahan, G. Shakirovaand
R. L. Jones, J. Anal. At. Spectrom., 2008, 23, 962–967.
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