Exposure to di(2-ethylhexyl) phthalate (DEHP) may be related to adverse health effects including developmental and reproductive disorders, prompting interest in strategies for reducing human exposure. We previously reported a reduction of DEHP metabolite levels in composite urine samples by more than 50% (geometric means) during a 3-day dietary intervention avoiding plastics in food packaging, preparation, and storage. In the present study, we analyzed individual spot urine samples before compositing in order to evaluate temporal variability. There were no meaningful changes in any of the previous findings when using individual rather than composited samples. Individual urine samples, like the composites, showed significant decreases of ≥50% in all three measured DEHP metabolites during the intervention. Compositing urine samples provided sufficient information to observe the effect of the intervention, whereas reducing analytical expenses compared with analyzing multiple samples individually. Low intraclass correlations (ICCs) for samples collected from the same person before the intervention indicate the importance of collecting multiple samples per exposure condition. Substantially larger ICCs during and after the intervention suggest that much of the variability observed in DEHP metabolite levels originates from dietary exposure.
Di(2-ethylhexyl) phthalate (DEHP), a common additive to polyvinyl chloride (PVC), is present in a variety of consumer goods, including food packaging,1, 2 leading to widespread exposure in the United States and elsewhere. DEHP metabolites have been detected in the urine of virtually 100% of a representative sample of the US population.3, 4 Evidence in animal studies indicates that DEHP is an anti-androgenic endocrine disruptor, with some evidence for effects on other pathways.5, 6, 7, 8 Consistent with the animal evidence, cross-sectional studies in humans have linked DEHP exposure with effects on male reproductive development,9 as well as effects on adult male hormone levels and semen quality,10, 11, 12 although the associations described in these studies may be confounded by co-occurrences of exposures to other environmental chemicals or to dietary and lifestyle factors.
Exposure estimates based on concentrations in food, air, dust, and consumer products indicate that diet is likely to be a major source of non-occupational exposure to DEHP.1, 13 Recent studies measuring phthalate metabolite excretion after fasting or changes in diet have added to the evidence that non-occupational exposures to DEHP and di-isononyl phthalate are largely driven by diet, whereas exposures to other phthalates are more influenced by other sources.14, 15
Exposure studies have had limited ability to identify what foods were responsible for participants’ phthalate exposure, with a few notable exceptions. Lorber and Calafat,16 using detailed diaries and repeated urine measurements to model intake, identified increases in phthalate metabolite concentrations after individual meals, including a single meal of prepared food (reported as “bagel w/egg, sausage, cheese, coffee bought at gas station”) that was a major contributor to one participant’s high DEHP exposure. We previously reported17 that switching to a “fresh foods” diet, with minimal exposure to plastic or epoxy-based food packaging, for 3 days reduced geometric mean concentrations of bisphenol A (BPA) and DEHP metabolites by over half in the urine of five California families. A larger-scale randomized trial of a similar intervention18 unexpectedly found increased DEHP metabolite levels in subjects following a “no packaged food” diet because of unanticipated DEHP contamination in coriander and milk, demonstrating that food contamination with DEHP is another potentially important contributor to dietary exposure.
Although using biomarkers such as urinary concentrations to estimate exposure has many advantages, temporal variability of biomarker concentrations can lead to exposure misclassification. Temporal variation is an important consideration for DEHP, as DEHP metabolites have short half-lives14, 15, 19, 20 and exposure patterns appear sporadic.16 Temporal variability in phthalate metabolite concentrations from multiple urine samples from the same individual has been well documented, with intraclass correlations (ICCs) for DEHP metabolite from multiple samples from the same individual ranging from 0.08 to 0.67,21, 22 although most of the literature reports ICCs between 0.2 and 0.4.23, 24, 25, 26, 27, 28 To minimize misclassification of typical phthalate exposure because of temporal variability, investigators have suggested including more participants in exposure studies23 or collecting repeated spot urine samples.24, 29 Existing studies of temporal variability of phthalate metabolite levels have mostly focused on using urinary measurements to classify long-term phthalate exposure, for example, for the purpose of investigating potential associations between phthalate exposures and health outcomes. The few studies that have measured phthalate metabolites during a period of fasting14, 15 provide valuable information about pharmacokinetics and exposure, but to our knowledge no published work has directly addressed strategies for accurately estimating exposure to phthalates during periods of altered exposure, as in an intervention designed to reduce potential sources of exposure.
Rudel et al.17 reported concentrations in composites of two urine samples collected after dinner on sequential days from each participant before, during, and after the dietary intervention. BPA and DEHP metabolite levels dropped substantially and significantly during the intervention. BPA levels rose significantly after the end of the fresh foods intervention, whereas DEHP metabolite levels did not. To provide a more detailed picture of exposure and variability over the period of the study, the present work measured DEHP metabolite levels in each individual sample. This reanalysis allowed us to compare the relative benefits of compositing samples as opposed to testing samples individually.
Intervention Study Design
The design of the dietary intervention and urine sample collection has been described previously.17 Briefly, in January 2010, five families in the San Francisco Bay Area, each comprising two adults aged 36–46 years and two children aged 3–11 years, participated in the three phases of the study: pre-intervention, when participants ate their typical diet; intervention, when they ate only foods provided by a caterer, prepared from fresh ingredients (no canned or frozen foods) and packaged almost exclusively without contact with plastic; and post-intervention, when the participants returned to preparing their own food and were instructed to return to their normal diet. With a few exceptions,17 all participants ate the fresh food (intervention) diet for all meals and snacks on days 3–5 of the 8-day study and collected a urine sample in the evenings of days 1 and 2 (preintervention), 4 and 5 (intervention), and 7 and 8 (post-intervention; Figure 1). Each of the 20 participants contributed six samples over the course of the study. Informed consent was obtained from all adult study participants on behalf of themselves and their children. All minor participants, with the exception of one 3-year-old, provided verbal assent.
Chemical Analysis and Quality Assurance
Urine specimen jars were stored, double bagged, in participants’ home freezers until pickup within a week of the study’s conclusion. After pickup, urine samples were stored in a freezer overnight and shipped overnight on blue ice to the laboratory, where they were stored frozen at −20 °C. In February and March 2010 (<2 and <8 weeks after collection, respectively), laboratory technicians thawed the samples and removed 40 ml aliquots for quantification of BPA (February) and phthalate metabolite (March) concentrations. These analyses used 1 ml subsamples drawn from composites of equal volumes from the two samples from each participant during each phase of the study, except for three samples that were excluded because of ambiguous labeling (the other sample from the same person during the same study phase was used instead of a composite in these cases). After the removal of aliquots for testing, samples were refrozen at −20 °C. In September–November 2011, laboratory technicians thawed the samples and removed 1 ml aliquots for reanalysis as individual (non-composited) samples, with the exception of the three ambiguously labeled samples, the three samples that had been individually analyzed in previous analyses, and two samples of which there was insufficient volume.
The individual samples were analyzed for the DEHP metabolites mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono-2-ethylhexyl phthalate (MEHP). They were also analyzed for seven other phthalate metabolites (Supplementary Table 1). As previously reported, composited samples were analyzed for MEHHP, MEOHP, MEHP, and BPA, as well as four other phthalate metabolites. Composited and individual samples were also analyzed for creatinine.
Analysis was conducted at AXYS Laboratory, Sidney, British Columbia, by HPLC-Tandem Mass Spectroscopy using isotope dilution quantification as previously described.17 Samples were analyzed in batches, each including a procedural blank (water) and two spiked reference samples (synthetic urine with phthalate metabolites and their isotopically labeled surrogates added at known concentrations), one of which was analyzed in duplicate. Data and analyses are presented only for metabolites of DEHP, with the exception of summary data in Supplementary Table 1.
In addition to a limit of detection (LOD) for each analyte in each batch based on the lowest level calibrated, the laboratory calculated an LOD for each analyte in each sample, based on recovery of isotopically labeled surrogates added to each sample. The laboratory reported the higher of these two limits for each sample.30 LODs for DEHP metabolites were mostly around 1 ng/ml (median LODs 1 ng/ml for all three metabolites), with a few elevated values for each compound (MEHHP: 90th percentile LOD 3.5 ng/ml, maximum LOD 14.2 ng/ml; MEOHP: 90th percentile 3.0 ng/ml, maximum 10.2 ng/ml; MEHP: 90th percentile 1.98 ng/ml, maximum 3.4 ng/ml). Elevated LODs were reported only in samples in which the analyte was quantified above the LOD, with the exception of three samples in which MEHP was not detected above an elevated detection limit of 2.97 ng/ml.
Eighteen MEHP measurements reported as non-detects were assigned the sample-specific LOD.30 This approach biases results up, which constitutes a bias toward the null given that non-detects were most common during the intervention, when we observed a decrease in concentrations (Supplementary Table 2).
We compared the concentrations of the DEHP metabolites measured in composite samples in 2010 and the concentrations measured in individual samples in 2011 using Spearman correlation tests.
Data Analysis and Statistics
We report phthalate metabolite concentrations in urine as ng/ml, unadjusted for creatinine. As dietary protein levels influence urinary creatinine concentrations,31, 32 changes in creatinine concentration17 during the intervention were likely unrelated to changes in urine dilution. However, to address the influence of urine dilution, we included creatinine as a variable in the mixed-effects models described below, as recommended by Barr et al.33 We used unadjusted concentrations for analyses comparing concentrations between phases, and used creatinine-corrected concentrations for ICC calculations within phases, because we expected that variation in creatinine concentration within a study phase primarily reflect changes in urine dilution. Creatinine-adjusted and unadjusted concentrations are summarized in Supplementary Table 2.
We used the sums of the geometric means of the concentrations of each DEHP metabolite on each day as a simple means of comparison between days. To evaluate the consistency of results, we estimated correlations between concentrations measured in composite samples (analyzed in 2010) and simulated composites (averages of concentrations in pairs of individual samples analyzed in 2011).
We used log-transformed data for mixed-effects models and analysis of variance models that require the assumption of normality, because the distributions of analyte concentrations appear approximately log-normal. We used non-parametric statistics based on ranks of untransformed data to test for differences (paired Wilcoxon signed-rank) and estimate correlation (Spearman).
We used a linear spline mixed-effects model to evaluate changes in DEHP metabolite concentrations in individual samples between the study phases. The model had a single knot placed at the intervention and included family and participant as multilevel random effects and creatinine as a fixed effect. This model treated the two samples collected for each participant during each phase of the study as independent. Although the assumption of independence is violated in this case, making this assumption was preferable to overfitting the small data set by adding additional terms to the model. We compared the results of this model with a similar model using the concentrations detected in composite samples (this differs slightly from the previously reported model17 in that samples were excluded if we did not have both composite and individual data), and with a model using simulated composites (averages of concentrations from paired individual samples). For purposes of comparison, we excluded the three samples that were never composited and two with insufficient volume. Similar models were used to evaluate changes between pairs of consecutive phases considering only one day of each phase, for a total of eight comparisons (day 1 vs day 4, day 4 vs day 7, etc; four comparisons between preintervention and during-intervention, four between during-intervention and post-intervention). We also used pairwise Wilcoxon signed rank tests to compare concentrations between days and between phases, using creatinine-adjusted concentrations. We used the Holm34 method to correct P-values to account for the multiple comparisons.
To evaluate the relative importance of temporal (within person, within phase) variability and interpersonal (between-person, within phase) variability, we calculated ICCs for the concentrations of each metabolite during each phase, treating participant as a random effects predictor of log (creatinine-adjusted concentration).35
All data analysis was performed in R 188.8.131.52
Analytical methods performed well and results for individual samples were consistent with the previous study measuring phthalate metabolites in composited urine samples.17 None of the reported phthalate metabolites were detected in any of the laboratory blanks. Recovery in spiked samples was within 10% of spiked concentration. Samples analyzed in duplicate agreed to within 15% of the mean, with the exception of one pair in which [MEHHP] differed by 22% of the mean, and one pair of duplicates in which [MEOHP] differed by 16% of the mean.
As would be expected, DEHP metabolite levels were correlated. MEOHP and MEHHP concentrations were strongly correlated with each other (Spearman ρ=0.94, P<0.001), and well correlated with [MEHP] (Spearman ρ=0.71–0.73, P<0.001). Creatinine and DEHP metabolite concentrations in composited samples (measured 2010) and simulated composites (average of paired individual samples analyzed 2011) were highly correlated (Spearman ρ=>0.97, P<0.001; Supplementary Figures 1 and 2).
We detected MEHHP and MEOHP in 100% of individual samples and MEHP in 84% (Table 1).
DEHP metabolite concentrations were generally lower during the intervention than before the intervention, with intermediate values after the intervention (Figure 2, Table 2; creatinine adjusted values in Supplementary Figure 3). Medians were lower on both days during the dietary intervention than on either preintervention day. Median, mean, geometric mean, third quartile, and maximum concentrations were highest on day 1 (preintervention) and lowest on day 5 (during intervention). Creatinine-adjusted concentrations followed a similar pattern (Supplementary Figure 3). MEHHP and MEOHP concentrations were three to five times higher than corresponding concentrations of MEHP. MEHP levels were much closer to the LOD (65% of reported MEHP values <5x LOD, vs 8% MEHHP, 20% MEOHP), and most likely less precise because of their closeness to the LOD.
The sum of the geometric means of the concentrations of the three DEHP metabolites ranged from 29.1 ng/ml (49 μg/g creatinine) on day 5 to 112 ng/ml (128 μg/g) on day 1. Geometric means of DEHP metabolites calculated from individual sample data were slightly lower on each day than those calculated from composite or simulated composite (average of concentrations in paired individual samples) data, consistent with the skewness of the data (Table 2).
Mixed-effects models using individual, composited, and simulated composite data all showed significant decreases (slope<0, P<0.05) in all three DEHP metabolite levels between the preintervention and intervention study phases, although all slope estimates were positive (Table 2). No model showed significant change in any phthalate metabolite concentration between the intervention and post-intervention study phases. Slope estimates for change between phases were similar whether using individual, composited, or simulated composited data, although 95% confidence intervals for slopes were smaller in the models using individual samples because of the increased power associated with the additional measurements being treated as independent. As previously reported, age and sex did not strongly influence phthalate metabolite concentrations (data not shown).
Day by Day Comparisons
To evaluate the value of collecting multiple urine samples for each exposure condition, we modeled changes in DEHP metabolite concentrations between each preintervention day and each during-intervention day, and between each during-intervention day and each post-intervention day, for a total of eight comparisons for each of the three metabolites. These mixed-effects models, like the models described above, include creatinine as a fixed effect and participant and family as nested random effects. The concentrations of all DEHP metabolites were significantly lower (upper bound of 95% confidence interval for slope<0) on day 5 than on either day 1 or day 2. MEHHP was significantly lower on day 4 compared with day 1, but not compared with day 2, and day 4 concentrations of MEOHP and MEHP were not significantly different than concentrations on either preintervention day. Concentrations of all three metabolites were significantly higher on day 7 than on day 5, but no other combination of a during-intervention day and a post-intervention day showed a significant change. Similar models using composited and simulated composited data also showed significant reductions in all three DEHP metabolites between the preintervention and during-intervention study phases.
We also compared daily levels using paired Wilcoxon tests corrected for multiple comparisons,34 with similar results: There were significant (P<0.005) differences in creatinine-uncorrected concentrations of all three DEHP metabolites between day 1 and day 5. Between day 2 and day 5, there were significant differences in MEHHP (P<0.01) and MEOHP (P<0.03) concentrations, but not MEHP concentrations (P>0.4). No other day-wise comparisons showed significant differences (P>0.1).
Correlations Between Concentrations in Samples from One Participant
We used simple random effects linear models and Spearman’s tests to evaluate the correlation of creatinine-corrected concentrations of each DEHP metabolite in the two samples from the same person during each phase of the study (Figure 3, Supplementary Table 3).
For each metabolite, ICCs based on a linear model of log (creatinine-adjusted concentrations) predicted by participant were highest after (0.70–0.87) and during (0.38–0.70) the intervention and lowest during the preintervention phase (0.30–0.50). In each phase, MEHHP and MEOHP ICCs were similar, and MEHP ICCs were lower. Lower ICCs during preintervention compared with the other two phases of this study primarily reflect decreased within person variability associated with the intervention (Supplementary Table 3). ICCs for using family as the grouping variable were generally similar to those using participant, except in the cases of MEOHP and MEHHP during and after the intervention, in which family ICCs were substantially lower (Supplementary Table 3).
The two measurements from each participant in each phase were significantly correlated (Spearman correlation coefficient=≥0.58, P<0.05), with highest coefficients after the intervention and lowest before the intervention. The one exception was MEHP during the intervention phase (Spearman correlation coefficient=0.31, P>0.19), when there was a particularly high proportion of samples in which MEHP was not quantifiable.
Effects of the Dietary Intervention
Mixed-effects models using concentrations of DEHP metabolites in individual urine samples, in samples composited by participant and study phase, and in simulated composite samples (averaged by participant and study phase) showed substantial and significant decreases in DEHP metabolite concentrations because of the dietary intervention. This analysis strengthens the evidence from our previous study of composited samples from the same intervention17 that food packaging is a major source of DEHP exposure in the United States. We observed this change using multiple statistical approaches; however, owing to limited sample size, we cannot rule out the possible role of chance in our findings. However, the longitudinal sampling in this intervention study allowed individuals to serve as their own controls, avoiding many sources of confounding that can limit cross-sectional studies.
As previously discussed,17 levels of DEHP and most other phthalate metabolites in this study were within the range of those reported in recent National Health and Nutrition Examination Survey reports,3 although this population had somewhat higher median levels of most metabolites excluding monoethyl phthalate, which was substantially lower in this group. This similarity suggests that our findings are likely to be broadly relevant to American diets, despite the small number and geographic homogeneity of our study participants.
Although concentrations of DEHP metabolites increased after the intervention, none of the increases were statistically significant, excluding a few comparisons of individual days. In contrast, BPA levels in the same group nonetheless increased significantly after the intervention, with a 200% increase in geometric mean.17 The half-life of the DEHP metabolites (5–10 h) is not long enough to explain the lack of a significant increase 30+hours after participants began eating their own food; in fact, we measured higher DEHP metabolite concentrations on the first post-intervention day than on the second. Although chance may play a role, the findings also suggest that study participants may have altered their dietary habits because of their participation in the study and that DEHP and BPA have different dietary sources. That is, the behavioral changes that the families might have adopted subsequent to the intervention, such as using glass storage containers or eating out less often, might have affected sources of DEHP more than sources of BPA. However, Sathyanarayana et al.18 did not see a decrease in urinary DEHP metabolite or BPA levels in participants given advice on avoiding dietary sources of plasticizers. Alternatively, the discrepancy between the increase in BPA and the lack of an increase in DEHP metabolites could simply reflect a greater proportion of BPA exposure than DEHP exposure coming from food packaging sources. This is consistent with the larger decrease in BPA than in DEHP metabolite levels because of the intervention.17
Notably, geometric means of urinary DEHP metabolite concentrations observed during the intervention, at least 48 h after the last exposure to packaged food, were still many times higher than the concentrations reported by Koch et al.14 in urine from volunteers fasting for as few as 12–24 h. This suggests that substantial dietary sources of DEHP remained even after eliminating exposure to food packaging, consistent with detections of phthalates in processed and unprocessed food products.37 Some of the difference could also reflect higher exposure to DEHP from non-dietary sources in our study population of Californian families than in the German adults studied by Koch et al.
The higher concentrations observed on the first day of each study phase most likely reflect random variability in exposure rather than any meaningful difference because of biological residence time between the 2 days within any phase. Studies measuring phthalate metabolites in urine from fasting subjects or from volunteers exposed to D4-DEHP have established that MEHHP, MEOHP, and MEHP have urinary half-lives of excretion between 4 and 10 h, with urinary concentrations of these metabolites leveling off between 12 and 24 h after dietary exposure.14, 15, 19, 20 For example, as participants in our study had gone at least 48 h without eating packaged food when they collected day 4 urine samples, it is unlikely that the higher DEHP metabolite concentrations on day 4 compared with day 5 represent excretion of DEHP from preintervention dietary exposures.
Variability and Implications for Sampling Strategies
By analyzing urine samples individually rather than as composites, we were able to analyze temporal variability in phthalate metabolite levels. Analysis of multiple urine samples per phase also yielded somewhat tighter confidence intervals for slope estimates in our mixed-effects models, but it did not change our overall conclusions regarding the effect of the dietary intervention on DEHP exposure.
Correlations (evaluated by ICC or Spearman’s correlation coefficient) of DEHP metabolite concentrations in multiple spot urine samples from the same person before the dietary intervention (Figure 3, Supplementary Table 3a) were squarely within the range reported by the many investigators23, 24, 25, 26, 27, 28, 38 who have previously measured DEHP metabolites in repeated urine samples from the same people. However, correlations were much higher for samples collected from the same person during the intervention (MEHHP and MEOHP) and after intervention (all metabolites) than in past reports.
The higher ICCs and lower within-participant variance during the intervention, although all participants were eating essentially identical diets, suggest that residual exposures from non-food sources vary less from day-to-day than do typical food-related exposures. Many non-dietary sources of exposure to DEHP (e.g., shower curtain, flooring, car interior) would be shared between members of the same household, and the slightly increased family ICCs for MEHHP and MEOHP during intervention (Figure 3, Supplementary Table 3b) support this interpretation.
We have demonstrated that urinary metabolites of DEHP decreased by more than 50% after 2–3 days of a dietary intervention avoiding exposure to food packaging. This supports earlier reports that DEHP exposures are dominated by DEHP in food packaging. The conclusions are similar whether using composited or individual urine samples from 2 days in each exposure condition. The considerable expense of analyzing each sample individually and relatively modest increase in statistical power from individual rather than composite measurements, even when treating samples as independent within a study phase, suggest that compositing repeat spot urine samples is an efficient strategy for biomonitoring to assess non-occupational DEHP exposure, especially in research settings with limited resources. Based on the relatively low ICCs observed before the intervention in this study and by other researchers, compositing multiple spot samples is also likely to give a more accurate estimate of exposure in the general population than analysis of a single spot sample. Further investigations of biomarker variability under different exposure conditions could help to clarify when multiple samples are necessary for accurate exposure assessment, and provide additional information about the prevalence and variability of different sources of phthalates and other chemicals of concern.
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We thank the Vassar College Institutional Review Board for assistance in study planning and human subjects research oversight. This work was supported by funding from Passport Foundation (Charlotte, North Carolina), the Susan S Bailis Breast Cancer Research Fund at Silent Spring Institute, and the American Chemistry Council Long-Range Research Initiative.
The authors were free to design, conduct, interpret, and publish research without interference or input from any funding organization. This publication has not been reviewed by the American Chemistry Council and views expressed are solely the authors'. The authors declare that they have no conflicts of interest related to this work.
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website
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Ackerman, J., Dodson, R., Engel, C. et al. Temporal variability of urinary di(2-ethylhexyl) phthalate metabolites during a dietary intervention study. J Expo Sci Environ Epidemiol 24, 595–601 (2014). https://doi.org/10.1038/jes.2013.93
- dietary exposure
- endocrine disruptors
- analytical methods
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