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From the * Department of Environmental Health,
Occupational Health Program, Harvard School of Public Health, Boston,
Massachusetts; Simmons College, School for
Health Studies, Nursing Programs, Boston, Massachusetts;
National Center for Environmental Health,
Centers for Disease Control and Prevention, Atlanta, Georgia;
Department of Biostatistics, Harvard School of
Public Health, Boston, Massachusetts; || Department
of Biostatistical Science, Dana-Farber Cancer Institute, Boston,
Massachusetts; ¶ Vincent Memorial Obstetrics and
Gynecology Service Andrology Laboratory and In Vitro Fertilization Unit,
Massachusetts General Hospital, Boston, Massachusetts; and #
Department of Obstetrics and Gynecology, Division of
Reproductive Biology and Medicine, School of Medicine, University of
California-Davis, Davis, California.
| Correspondence to: Dr Russ Hauser, Department of Environmental Health, Occupational Health Program, Building 1, Room 1405, 665 Huntington Avenue, Boston, MA 02115. |
| Received for publication August 25, 2003; accepted for publication October 9, 2003. |
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Abstract |
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Key words: Environment, epidemiology, human phthalate
In one of the first human studies to explore associations between environmental levels of phthalates and human semen parameters, we recently demonstrated a relationship between environmental levels of phthalates and traditional semen analysis parameters categorized by World Health Organization (1999) categories for count, motility, and morphology (Duty et al, 2003). Specifically, we found dose-response relations between tertiles of monobutyl phthalate (MBP) and sperm motility (OR per tertile: 1.0, 1.8, 3.0, P value for trend = .02) and sperm concentration (1.0, 1.4, 3.3, P value for trend = .07). There was also a dose-response relation between monobenzyl phthalate (MBzP) and sperm concentration. We also found limited evidence for an association between monomethyl phthalate (MMP) with poor sperm morphology. To further explore the relationship between phthalate exposure and sperm motility, we investigated in a larger data set whether computer-aided sperm analysis (CASA) motion parameters were associated with environmental exposure to phthalates. CASA parameters are not as easily interpretable as World Health Organization semen analysis categories but may offer additional insights into whether any particular aspect of sperm movement would be differentially affected.
Phthalates are used in many consumer products such as carpet backing, soaps, shampoos, paints, glues, hair-sprays, nail polishes, insect repellents (Agency for Toxic Substances and Disease Registry, 1999), cosmetics, perfumes (Blount et al, 2000b), and medical products (Nassberger et al, 1987). Di(2-ethylhexyl) phthalate (DEHP), one of the more commonly used phthalates, leaches from blood products, and from intravenous and dialysate bags and tubing made with polyvinyl chloride (Nassberger et al, 1987).
There is consistent toxicological evidence of adverse developmental and reproductive effects of DEHP (Reel et al, 1984), butyl benzyl phthalate (BBzP) (Agarwal et al, 1985), and di-n-butyl phthalate (DBP) (Wine et al, 1997). Fewer live pups per litter, decreased live pup weights, and degeneration of the epididymis and testes have been found in pups after intrauterine DBP exposure (Wine et al, 1997). The National Toxicological Program (NTP) Center for the Evaluation of Risks to Human Reproduction (CERHR) Expert Panel recently concluded that there is a larger risk of DBP associated reproductive and developmental toxicity following gestation and lactation exposures as compared to adult exposures (NTP, 2000). This finding, together with the fact that animal studies are conducted with doses higher than exposures expected in the general population, prompted the NTP-CERHR Expert Panel to determine there was low concern for adverse reproductive effects in humans from adult exposure to DBP.
Currently, we know of only two other human studies on the possible relationship between phthalates and testicular function (Murature et al, 1987; Rozati et al, 2002). However, interpretation of these results is difficult because both measured the diesters in semen, and no information was provided to explain how contamination from diesters in laboratory equipment was avoided, and one study did not address potential confounders.
The use of CASA in the clinical andrology laboratory has become commonplace due to the speed of analysis and objectivity of measurements. However, technical problems exist for several reasons, including the dependence of parameter estimates on frame acquisition rate and the number of frames analyzed, as well as the variability in the depth of the counting chamber (European Society of Human Reproduction and Embryology, 1996; Kraemer et al, 1998). CASA generally underestimates sperm movement parameters in samples with sperm counts >100 million/mL and overestimates them in samples with low counts; however, CASA accurately measures samples with sperm counts ranging from 40 to 100 million/mL (Mortimer et al, 1995). Currently, no standard protocols exist for CASA operation, but there are recommendations from the European Society for Human Reproduction and Embryology Andrology Special Interest Group (European Society of Human Reproduction and Embryology, 1996) for optimizing the CASA instrument for the specific setting of its use. The predictive value of CASA parameters has been compared with conventional semen analysis in a donor insemination program; CASA was superior in predicting which ejaculate achieved pregnancy compared to the traditional semen analysis parameters (Macleod and Irvine, 1995).
Based on the findings from rodent toxicity studies and our previous human study on a small number of men, we hypothesized that MBP, MBzP, and possibly MEHP will be associated with sperm motion parameters, whereas MEP and MMP will not.
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Materials and Methods |
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Semen Sampling
Each subject produced a semen sample on-site by masturbation into a
sterile, wide-mouthed, plastic specimen cup. The sample was allowed to liquefy
at 37°C for 20 minutes prior to analysis. Subjects were instructed to
abstain from ejaculation for 48 hours prior to producing the semen sample and
to complete a questionnaire on health, personal habits, and the length of
their sexual abstinence period. Each subject provided a single semen
sample.
Computer-Aided Sperm Analysis
Sperm motion analysis and conventional semen analysis parameters were
measured on the same semen sample. All analyses were performed without
knowledge of the subject's phthalate levels. Within 1 hour of collection, a
5-µL aliquot of fresh semen was loaded into a 10-µm-deep Makler chamber
(Sefi Medical Instruments, Haifa, Israel), placed on a stage warmer set at
37°C, and evaluated using a Hamilton-Thorne Integrated visual optic system
(HTM-IVOS Version 10, Beverly, Mass). Setting parameters and the definition of
measured sperm motion parameters for the CASA were established by
Hamilton-Thorne Company (frames acquired, 30; frame rate, 60 Hz; straightness
[STR] threshold, 80.0%; medium average path velocity [VAP] cutoff, 25.0
µm/s; low VAP cutoff, 5.0 µm/s; count slow as motile, no; static head
size, 1.0–2.9; static head intensity, 0.6–1.4; static elongation,
0–80; and the duration of the tracking time, 0.5 seconds). A minimum of
200 sperm from at least four different fields was analyzed for each
specimen.
CASA outcomes include VAP, which is a mathematically smoothed velocity; straight line velocity (VSL); curvilinear velocity (VCL); amplitude of lateral head displacement (ALH), which corresponds to the mean width of the head oscillation as the cell swims; and beat cross frequency (BCF), which measures the frequency with which the cell track crosses the cell path in either direction. VAP, VSL, (STR = VSL/VAP x 100), and linearity (LIN = VSL/VCL x 100) are indicators of sperm progression, whereas VCL, ALH, and BCF are indicators of sperm vigor. STR and LIN are also used to describe sperm swimming pattern.
Seven CASA sperm motion parameters were measured, and as expected, many were strongly correlated with each other because they describe different aspects of the same movement. Measures of progression, VAP and VSL were highly correlated (r = .96: P < .0001), indicating they were likely measuring a similar characteristic of sperm movement. VSL was chosen over VAP as a measure of progression because it is a direct measurement as opposed to a mathematically smoothed value. VCL was chosen as a measure of vigor and was strongly and positively correlated with ALH (r = .84; P < .0001) but not correlated with BCF (r = –0.06; P = .39). The two measures of swimming pattern (LIN and STR) were strongly correlated (r = .89; P < .0001), indicating they were likely measuring a similar characteristic of sperm movement. LIN was chosen as a measure of swimming pattern because the other parameters chosen for this study (VSL and VCL) are components of LIN and not of STR. Therefore, a measure of progression (VSL), vigor (VCL), and swimming pattern (LIN) were chosen for in-depth statistical analyses. These three measures are also not as heavily dependent on the type of CASA instrument used, allowing for some comparison with results from other studies.
Urinary Phthalate Monoesters
Eight urinary phthalate monoesters were measured in a single spot urine
sample collected at MGH at the same visit that the semen sample was collected.
The sample was collected in a sterile specimen cup, aliquoted to cryovials,
frozen at –20°C within 1 hour, and archived until shipment to the
CDC for analysis. The analytical approach has been described in detail
elsewhere (Blount et al, 2000a;
Silva et al, 2003). Briefly,
urinary phthalate metabolite determination involved enzymatic deconjugation of
the metabolites from the glucuronidated form, solid-phase extraction,
separation with high-performance liquid chromatography, and detection by
tandem mass spectrometry. Detection limits were in the low
nanogram-per-milliliter range (ng/mL). Reagent blanks and
13C4-labeled internal standards were used along with
conjugated internal standards to increase precision of measurements. One
method blank, two quality control samples (human urine spiked with
phthalates), and two standards were analyzed along with every 21 unknown urine
samples. Analysts at the CDC in Atlanta, Georgia, were blind to all
information concerning subjects. The monoester phthalate metabolites were
measured because of potential sample contamination from the parent diester and
because some of the metabolites are believed to be the active toxicant as
opposed to the parent diester compounds
(Peck and Albro, 1982;
Li et al, 1998).
Urinary phthalate levels were normalized for dilution by specific gravity adjustment. In the primary analysis, we excluded samples that were considered unreliable, including samples with specific gravity less than 1.01 or greater than 1.03 (Teass et al, 1998). Specific gravity was measured using a hand-held refractometer (National Instrument Company, Baltimore, Md), which was calibrated with deionized water prior to each measurement. Phthalate concentrations were corrected for specific gravity by the following formula: Pc = P ((1.024–1)/SG-1)), where Pc is the specific gravity corrected phthalate concentration (ng/mL), P is the observed phthalate concentration (ng/mL), and SG is the specific gravity of the urine sample (Boeniger et al, 1993; Teass et al, 1998).
Statistical Methods
Statistical Analysis Software version 8.1 (SAS Institute Inc, Cary, NC) was
used for data analysis. Descriptive and summary statistics were generated,
outcomes were analyzed for normality, and predictors were explored for
evidence of nonlinearity with the outcome. Pearson correlation coefficients
were used to explore the relationships among the normally distributed CASA
parameters and to determine which CASA parameters to explore in statistical
analysis. Spearman correlation coefficients were used to explore the
relationships among the non-normally distributed phthalate levels and
traditional semen analysis parameters.
In preliminary analyses, scatter-plots and multiple linear regression analysis were used to explore the relationship among each CASA parameter and each phthalate metabolite, adjusting for appropriate covariates. In the primary analysis, phthalate levels were divided into tertiles and entered into the model as dummy variables to explore dose-response relationships. The use of tertile phthalate level cut points offers more flexibility for linear regression modeling and does not impose an assumption of linearity between exposure and outcome. In addition, the use of tertiles allowed individuals with nondetected values to be more easily included in the data analysis. Tertiles are also useful to explore nonlinear dose responses such as U-shaped responses or threshold responses.
Covariates considered for inclusion in the models included smoking status,
race, age, body mass index, and abstinence time. Their inclusion in the
multivariate models was based on statistical and biological considerations
(Hosmer and Lemeshow, 1989).
In addition, because CASA measurements are not independent (they all measure
different aspects of sperm motion in a single semen sample), covariates
considered potential confounders in one model were included in all models. Age
was modeled as a continuous independent variable after checking for
appropriateness using a quadratic term. Abstinence time was modeled as an
ordinal five-category variable (2 or fewer days, 3, 4, 5, and 6 or more days).
Smoking status was included as a dummy variable (current and former versus
never).
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Results |
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CASA Parameters
Correlation coefficients for CASA parameters and traditional semen
parameters (count, motility, and morphology) are shown in
Table 2. The strongest
correlation between CASA and traditional semen analysis parameters was found
for percent motility and both VCL (r = .44; P < .0001)
and VSL (r = .51; P < .0001). The mean (SD) VSL, VCL, and
LIN were 44.7 µm/s (9.6), 77.8 µm/s (16.8), and 58.2% (6.5),
respectively. The distribution of CASA parameters is shown in
Table 3.
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Phthalate Monoesters
Eight urinary phthalate metabolites, monoethyl (MEP), monomethyl (MMP),
mono-2-ethylhexyl (MEHP), mono-n-butyl (MBP), monobenzyl (MBzP), mono-n-octyl
phthalate (MOP), mono-3-methyl-5-dimethylhexyl phthalate (isononyl), (MINP),
and monocyclohexyl (MCHP) phthalates were measured. Because more than 75% of
the population had levels of MCHP, MOP, and MINP below the limit of detection,
the results for these metabolites were not informative and are not included in
the analysis. MEP was detected in 100% of subjects, whereas MBP and MBzP were
detected in more than 95% of subjects, and 75% had detectable levels of MEHP
and MMP. These five phthalates were used in all statistical analyses.
There was a wide distribution of both specific gravity-adjusted and unadjusted phthalate monoester levels (Table 4). The rank order of phthalate distribution is similar to the distribution found in the National Health and Nutrition Examination Survey (NHANES) 1999–2000 and III data (Blount et al, 2000b; CDC, 2003) with the highest levels (geometric mean) found for MEP (183.1 ng/mL), followed by MBP (18.0 ng/mL), MBzP (8.6 ng/mL), MEHP (7.0 ng/mL), and MMP (4.2 ng/mL). Thirty-three (15%) samples were excluded from primary analysis because of extreme specific gravity values (<1.010 or >1.030) (Boeniger et al, 1993). The final sample size for statistical modeling was 187 men.
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Covariates: Age, Race, Abstinence Time, and Smoking Status
There were suggestive relationships among smoking status and MEP, MBP, and
MMP, as well as among race and MEP, MBzP, and MBP, and between abstinence time
with MEP. For example, median MEP levels were higher in current smokers (235.9
ng/mL) and former smokers (199.4 ng/mL) than in men who had never smoked
(138.5 ng/mL). This pattern was similar for MMP and MBP (data not shown).
African American and Hispanic men had twofold to fourfold higher MEP phthalate
levels (505.5 and 347.0 ng/mL, respectively) than either white men (151 ng/mL)
or those of other races (116.5 ng/mL). This pattern was also observed for MBzP
(data not shown). In addition, men with abstinence times greater than 5 days
on average had lower MEP levels (112 ng/mL) than those with less than 5 days
of abstinence (187 ng/mL). In contrast to these data, the median and mean CASA
parameter values were nearly identical across all levels of these
covariates.
Age was related to some of the phthalate levels and sperm motion parameters. For instance, for each 1-year increase in age, VSL, VCL, and LIN decreased –0.27 µm/s (95% CI: –0.51, –0.03), –0.38 µm/s (95% CI: –0.80, 0.04), and –0.06 % (95% CI: –0.23, 0.11), respectively. In addition, for every year increase in age, MEP, MBzP, MBP, MEHP, and MMP increased 38.5 ng/mL (95% CI: 9.12, 67.90), 1.1 ng/mL (95% CI: –0.06, 2.16), 1.4 ng/mL (95% CI: –3.89, 6.61), 0.7 ng/mL (95% CI: –0.97, 2.37), and 0.01 ng/mL (95% CI: –0.33, 0.36), respectively.
Multiple Regression Analyses
Presented in Table 5 are the
regression coefficients, their 95% confidence intervals, and test for trend
P values for individual CASA parameters regressed on tertile of
specific gravity-adjusted phthalate monoester levels. These models are
adjusted for age, abstinence time, and smoking. Overall, although not
statistically significant, MBP, MBzP, and MEHP had negative relationships with
VSL, VCL, and LIN. No consistent relationship was found for MMP and any sperm
motion parameter, and unexpectedly, a generally positive relationship was
observed between MEP and both VSL and VCL. There were dose-response
relationships (shown as the predicted change in mean sperm motion parameter
for the second and third tertiles compared to the first tertile, followed by
the P value for trend) for MBzP with VSL (–2.36 µm/s,
–2.81 µm/s, P value for trend .09) and VCL (–1.67
µm/s, –2.45 µm/s, P value for trend .4), although neither
reached statistical significance. A general decrease in LIN was also evident
for higher MBzP levels. MBP was associated with a decline in VSL (–3.07
µm/s, –2.87 µm/s, P value for trend .08), VCL
(–3.25 µm/s, –3.46 µm/s, P value for trend .2), and
LIN (–1.60%, –1.00%, P value for trend .4). For MEHP
there was evidence of dose-response relationships with VSL (–1.09
µm/s, –2.73 µm/s, P value for trend .1), VCL (–0.29
µm/s, –2.93 µm/s, P value for trend .3), and LIN
(–0.96%, –1.30%, P value for trend .3), but none reached
statistical significance. In contrast to the other phthalates, for MEP there
were positive relationships with VSL (1.17 µm/s, 2.73 µm/s, P
value for trend .1) and VCL (3.16 µm/s, 6.36 µm/s, P value for
trend .03), but negative associations with LIN (–0.43 %, –0.65%,
value for trend .6).
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Sensitivity Analysis
Sensitivity analyses were conducted to explore the impact of using specific
gravity for exclusionary purposes only and not for adjustment of phthalate
levels. In a reanalysis, the urinary data were not specific
gravity–adjusted, but 33 unreliable samples, those with urine specific
gravity levels outside the acceptable range (1.01–1.03), were excluded.
The interpretation of the results was similar to the primary analyses. As in
the primary analyses, VSL, VCL, and LIN generally declined with phthalate
exposure for each phthalate except MEP. MEP again was associated with
increased VSL and VCL, but with a minor decrease in LIN. For MBzP and MBP, the
magnitude of the dose-response relationships with CASA parameters became
weaker; however, for MEHP, the dose-response relationship became stronger.
We also explored the use of quintile rather than tertile cut points, and despite the small sample size in each quintile, the results were consistent with the use of tertile cut points. Finally, we explored the relationship between CASA parameters and continuous phthalate concentrations. Overall, the interpretation of the continuous analysis data was similar and showed suggestive inverse dose-response relationships among CASA parameters with MBzP, MBP, and MEHP, and a positive dose-response relationship for MEP with VSL and VCL.
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Discussion |
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In laboratory animals, several researchers have explored the relationship between CASA parameters and chemical exposures, including epichlorohydrin and its metabolite, alpha-chlorohydrin (Slott et al, 1990, 1997). Epichlorohydrin, after 4 hours of inhalational exposure, transiently decreased path velocity despite no significant change in the percentage of motile sperm (Slott et al, 1990). Similarly, alpha-chlorohydrin given to male hamsters for 4 days resulted in a significant dose-dependent decline in VCL, VAP, and VCLselect despite no change in the percentage of motile sperm. In addition, alpha-chlorohydrin exposure was associated with a nonlinear impairment in in vitro fertilizing ability, which exhibited a threshold-like response (Slott et al, 1997). These studies suggest that CASA parameters may serve as a more sensitive maker of reproductive toxicity than semen parameters (Perreault and Cancel, 2001). One mechanism whereby sperm motion may be impaired includes oxidative stress and the production of reactive oxygen species and subsequent lipid peroxidation of sperm plasma membrane (Aitken, 1997; Storey, 1997; Armstrong et al, 1999).
Although CASA parameters may prove to be sensitive biomarkers of reproductive toxicity in humans, they are difficult to compare across studies because of the use of different CASA instruments and settings (Davis et al, 1992). Despite this limitation, human studies have shown that CASA parameters can be used to predict fertility (Aitken et al, 1982) and pregnancy (Macleod and Irvine, 1995; Larsen et al, 2000). Furthermore, there are also epidemiologic studies using CASA parameters as a marker of altered semen quality. Selevan et al (2000) examined the association between air pollution levels and VSL, VCL, and LIN and found that medium levels of air pollution adversely affected VCL but improved LIN. High air pollution levels, however, improved VSL and VCL but unexpectedly decreased LIN. The inference is that although the sperm traveled faster, the pattern was more erratic, and therefore, forward progression actually decreased.
The strengths of the present study include the availability of a reliable biomarker of phthalate exposure instead of relying on self-reported exposures. Biomarkers have the potential to quantify exposures to chemicals from all routes of exposure, including oral, dermal, inhalation, and ingestion (Teass et al, 1998). Furthermore, by measuring the monoester phthalate (ie, the metabolite), we avoided difficulties resulting from contamination from plastic products, such as the urine specimen collection cup (Blount et al, 2000a). One limitation in the present study was that if phthalates are associated with a complete lack of sperm motility, this study is not able to detect this, because by definition, we were able to explore motion parameters only on motile sperm.
Evidence of widespread exposure of the general population to phthalates comes from a recent study on phthalate metabolite levels in urine collected for the Second National Report on Human Exposure to Environmental Chemicals, NHANES 1999–2000 (CDC, 2003). The NHANES survey collected biological samples and information about the health and diet of people in the United States (National Center for Health Statistics, 2001). Four phthalate metabolites, MEP, MEHP, MBP, and MBzP were present in more than 75% of US subjects sampled (CDC, 2003). In the present study, 100% of subjects had measurable MEP, 95% had detectable MBP and MBzP, whereas 75% had detectable MMP and MEHP levels.
The NHANES 1999–2000 survey contains data on monoester levels
stratified by gender or age (6–11 years, 12–19 years, and 
20
years). The NHANES report did not include cross-stratification by age and
gender (eg, the report did not present data for a substratum consisting
entirely of adult males age 20 years). Therefore, the NHANES male stratum
included men from 6 to >60 years of age, whereas the age strata included
both men and women. This lack of cross-stratification by age and gender makes
the NHANES male stratum not entirely comparable to the present study on adult
men because of trends in phthalate monoesters with age. For instance, children
(6–11 years) and adolescents (12–19 years) had higher median
levels of MBP, MBzP, and MEHP than adults (20 years). In contrast, median
levels of MEP were lower for the age group 6–11 years than for the other
two age groups. It is unknown whether age and gender differences in monoester
concentrations reflect differences in exposure, body-size relationships, or
metabolism. Because of the absence of a stratum of adult men in the NHANES
report, the comparisons made between the present study and NHANES are meant as
qualitative guidance but should not be used to determine precise quantitative
differences. The samples in the present study and the NHANES samples were both
analyzed by the same CDC laboratory.
In the present study, unadjusted median MEP levels (152.7 ng/mL) were similar to median levels in NHANES men (154 ng/mL), whereas MEHP was higher in the present study (6.1 ng/mL) as compared to NHANES (3.40 ng/mL). In contrast, in the present study, median MBP (17.8 ng/mL) and MBzP (9.9 ng/mL) levels were lower than in the NHANES data set (23.1 ng/mL and 17.7 ng/mL, respectively). The inclusion in the NHANES male stratum of children and adolescents with higher MBP and MBzP levels than adults may account for the higher median levels of MBP and MBzP in NHANES as compared to the present study. In contrast, the high levels of MEHP found in the present study would be even higher than NHANES levels if children and adolescents, with higher MEHP levels than adults, were excluded from the NHANES male stratum. MMP was not measured in the NHANES data set.
There is controversy about the best way to correct for urine volume when using a single spot urine sample (Boeniger et al, 1993; Teass et al, 1998). In our sensitivity analysis, we used specific gravity criteria to exclude 33 samples and analyzed the data without adjusting the remaining phthalate levels. The results were consistent with the primary analysis, although the relationship between MBP and MBzP became weaker, while the relationship for MEHP and both VSL became stronger. This may indicate that the additional measurement error introduced by not using specific gravity adjustment biased the results toward the null hypothesis, making relationships more difficult to detect.
It is interesting to note that study subjects were exposed to several phthalates simultaneously, which raises the issue of how to explore relationships between semen parameters and exposures to multiple phthalates that may act via similar mechanisms. Gray and colleagues (2000) suggested that risk assessments for phthalate-induced reproductive toxicity should consider phthalates as a group and include exposures from multiple sources (Gray et al, 2000). Although they provide preliminary phthalate ester toxic equivalency factors (PE-TEFs) for reproductive toxicity induced in utero, they do so to stimulate discussion and further research about how we should estimate cumulative and aggregate risk to phthalates. Additional dose-response studies are necessary before we can apply PE-TEFs in both toxicological and epidemiological studies.
In conclusion, although we did not find statistically significant associations between CASA parameters and adult exposure to phthalates, there were trends that warrant further follow-up. These data extend the results of our previous study (Duty et al, 2003) that found an association between MBP and lower sperm motility. Although intriguing, these results are preliminary and should be explored in larger, as well as different study populations.
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Acknowledgments |
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Footnotes |
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