Association of Exposure to Di-2-Ethylhexylphthalate Replacements With Increased Blood Pressure in Children and AdolescentsNovelty and Significance
Phthalates are environmental chemicals widely used in consumer and personal care products. In this study, we examined associations of urinary phthalates with blood pressure, triglycerides, and lipoproteins in children and adolescents, performing a cross-sectional analysis of a subsample of US children 6 to 19 years of age who participated in the National Health and Nutrition Examination Survey between the years 2009 and 2012. We quantified exposure to common environmental phthalates, with a focus on the dietary contaminant di-2-ethylhexylphthalate and 2 increasingly used replacements, di-isononyl phthalate and di-isodecyl phthalate, based on micromolar concentration of urinary metabolites. We assessed descriptive, univariate, and multivariable associations with blood pressure and lipids. Controlling for an array of sociodemographic and behavioral factors, as well as diet and body mass, metabolites of di-2-ethylhexylphthalate, di-isononyl phthalate, and di-isodecyl phthalate were associated with higher age-, sex- and height-standardized blood pressure. For each log unit increase in di-isodecyl phthalate metabolites, a 0.105 standard deviation unit increase in systolic blood pressure z score was identified (P=0.004); for di-isononyl phthalate metabolites, a 0.113 standard deviation unit increment was identified (P=0.008). For di-2-ethylhexylphthalate metabolites, a 0.103 standard deviation unit increment (P=0.013) was detected. Metabolites of low molecular weight phthalates commonly found in cosmetics and personal care products showed an association with blood pressure (≥90th percentile) in univariate analysis, but this was no longer significant in our full multivariable model, suggesting specificity. Phthalate metabolites were not associated with triglycerides or high-density lipoproteins. Further, longitudinal studies are needed to confirm these associations and to assess opportunities for intervention.
- blood pressure
- cross-sectional studies
- endocrine disrupting chemicals
- high-density lipoproteins
Phthalates are environmental chemicals widely used in consumer and personal care products and can be classified into 2 main groups. Low-molecular weight (LMW) phthalates are frequently added to personal care products to preserve scent,1 whereas high-molecular weight (HMW) phthalates are used as plasticizers of polyvinyl chloride to increase flexibility and can be found in a variety of settings ranging from flooring, clear food wrap, and intravenous tubing (Table S1 in the online-only Data Supplement).2 Because phthalates are not covalently bound to the polyvinyl chloride polymer, they can easily leach into food, making ingestion one of the major routes for human exposure.3 Within the HMW phthalate category, di-2-ethylhexylphthalate (DEHP) is of particular interest because industrial processes to produce food frequently use plastic products containing DEHP.4 Because recognition of potential health risks related to DEHP exposure has increased,5 DEHP is being replaced by di-isononyl phthalate (DINP) and di-isodecyl phthalate (DIDP), 2 HMW phthalates with similar chemical properties.6 Specifically, DINP is used in plastic products for food packaging, and DIDP is used in furnishings, cookware, medications, and several other consumer products.7 These alternatives have not been substantially studied for toxicity in laboratory studies because these studies are not required for regulatory approval under the 1976 Toxic Substance Control Act.8
Dietary exposure to phthalates is a major concern for children because increasing laboratory evidence suggests that exposures to environmental chemicals early in life may disrupt developmental endocrine processes, permanently disturbing metabolic pathways and contributing to adverse cardiovascular profiles.9,10 Laboratory studies have found that phthalate metabolites increase release of interleukin-6, a proinflammatory cytokine,11 and expression of integrin in neutrophils12 and exhibit cytotoxic effects in endothelial cells.13 Biomarkers of phthalate exposure have been associated with increases in C-reactive protein and gamma glutamyltransferase,14 as well as oxidative stress markers malondialdehyde and 8- hydroxydeoxyguanosine.15,16 Recent findings suggest that environmental oxidant stressors, such as phthalates and bisphenol A, may produce increases in low-grade albuminuria,17,18 which, in turn, may be associated with increased cardiovascular risk.19 Phthalates can also activate nuclear receptors peroxisome proliferator-activated receptor-alpha and peroxisome proliferator-activated receptor-gamma,20 and both types of receptors are present in the arterial tree.21 Taken together, these finding suggest that there are multiple biologically plausible mechanisms by which phthalates may affect vascular function and increase cardiovascular risk, independent of body mass effects.
A previous study from our group identified a relationship between dietary phthalates exposure (DEHP metabolites) and increased systolic BP in children using data from 2003 to 2008 National Health and Nutrition Examination Survey (NHANES).22 Because DEHP is being replaced by DINP and DIDP, as reflected in biomonitoring data showing a decrease in the levels of DEHP metabolites by 17% to 37% between 2001 and 2010,23 it is appropriate to examine the relationship of urinary phthalates and blood pressure, especially in the context of increasing DINP and DIDP use. We also examined the relationship between urinary phthalates and dyslipidemia, performing cross-sectional analyses in a fasting subsample of US children and adolescents in 2009 to 2012 NHANES.
Data Source and Sample
NHANES is a biannual, multicomponent, nationally representative survey of the noninstitutionalized US population administered by the National Centers for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). Data from 2009 to 2012 questionnaire, laboratory, diet, and physical examination components were used in the present analysis. Of the 8515 children aged 6 to 19 who participated, our analytic sample comprised 1619 participants with urinary phthalate measurements. Of these, fasting triglycerides were available for 367 (measured in 12- to 19-year olds), as were 1329 for blood pressure (BP, measured in 8- to 19-year olds), and 1405 for high-density lipoproteins (HDL) levels (measured in 6- to 19-year olds). The New York University School of Medicine Institutional Review Board exempted this project from review on the basis of its analysis of an already collected and deidentified data set.
Measurement of Urinary Phthalates
Phthalates were measured in one spot urine sample from each participant and analyzed using high-performance liquid chromatography and tandem mass spectroscopy. More extensive methodological description is provided elsewhere.24
We grouped urinary biomarkers for exposure according to their use in product categories (Table S1). We calculated molar sums for LMW and HMW metabolites and, within the HMW category, molar sums for DEHP, DIDP, and DINP metabolites dividing by the creatinine (Cr) concentration of the sample to account for dilution (see Expanded Material and Methods in the online-only Data Supplement). Our primary exposure variables were log-transformed molar concentrations of LMW, HMW, DEHP, DIDP, and DINP metabolites, though secondary analyses analyzed individual metabolites to determine which metabolites are driving associations.
Measures of Cardiovascular Risks
In NHANES, using an aneroid sphygmomanometer, certified examiners assess systolic (first Korotkoff phase) and diastolic (fifth Korotkoff phase) BP 3 consecutive times in all children 8 to 19 years of age after they sit quietly for 5 minutes. A fourth attempt may be made if one or more of the initial measurements is incomplete or interrupted.25 We followed the common practice of averaging BP measurements for purposes of generating continuous and categorical BP variables. Because BP varies widely by age, sex, and height, we calculated systolic/diastolic BP Z-scores from mixed-effects linear regression models derived using data from 1999 to 2000 CDC NHANES (see Expanded Material and Methods in the online-only Data Supplement). We categorized BP outcomes into present/absent prehypertension (BP≥90th percentile for age/height Z-score/sex).
We used cut points of HDL <40 mg/dL and triglycerides ≥100 mg/dL, the same applied to assess components of the metabolic syndrome in analyses of adolescents in 2001 to 2006 NHANES.26 Triglycerides were log-transformed to account for skewed distribution.
Information on height and weight was based on measures taken by trained health technicians, who used data recorders and used standardized measurement procedures. We derived body mass index (BMI) Z-scores from 2000 CDC norms, incorporating height, weight, and sex; overweight and obese were categorized as BMI Z-score ≥1.036 and ≥1.64.27
To measure caloric intake, trained interviewers fluent in Spanish and English elicited total 24-hour calorie intake in person, using standard measuring guides to assist reporting of volumes and dimensions of food items (available on the CDC NHANES Web site). To differentiate normal from excessive caloric intake, we used age- and sex-specific US Department of Agriculture cut points for calories/day in high physical activity children.28 Self-reported data on leisure-time physical activity obtained during household interviews was also included in our analysis (see Expanded Material and Methods in the online-only Data Supplement). Because exposure to tobacco smoke is a risk factor for metabolic syndrome in adolescence,29 we included serum cotinine in multivariable models, as measured using high-performance liquid chromatography and tandem mass spectroscopy. We categorized serum cotinine levels into low (<0.015 ng/mL), medium (<2 and ≥0.015 ng/mL), and high (≥2 ng/mL) categories.
Race/ethnicity was categorized into Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other. Poverty–income ratio was categorized into quartiles, within the sample for which urinary phthalates were measured. Age was categorized into 2 groups: 6 to 11 and 12 to 19 years, in the same way as obesity prevalence estimates are produced from NHANES.27
To maximize sample size in multivariable analysis, missing categories30 were created for all potential confounders, except BMI category. Physical activity data were missing in 81.1%, serum cotinine was missing in 10.5%, and poverty–income ratio in 9.1%. Recognizing concerns raised about addition of missing categories in multiple linear regression,31 as a robustness check, we repeated our main multivariable analysis as a complete case analysis, omitting observations that had missing values for cotinine and poverty–income ratio.
We conducted univariable, bivariable, and multivariable analyses using statistical techniques that reflect the complex survey sampling design, using Stata 12.0 (College Station, TX) and following NCHS guidelines.30 Blood pressure analyses were performed using environmental sample weights. For analyses of fasting triglycerides, we performed unweighted analyses, following the practice of Stahlhut et al.32 This approach was chosen because fasting samples and urinary phthalate measurements are collected from partially overlapping subsamples for triglycerides. Although there are subsample weights for each, NCHS advises against use of either subsample weight because each accounts for different patterns of nonresponse.
Urinary metabolite concentrations were log-transformed to account for skew in the distribution of urinary phthalates. We performed univariate regressions of logs of the micromolar concentrations of metabolite groups against blood pressure, triglycerides, and lipoproteins and each of the demographic, dietary, anthropometric, and the other covariates. We used multivariable linear regression analysis to model continuous dependent variables and logistic regression to model dichotomous variables in separate models. We adjusted all multivariable models for urinary creatinine; for BMI category, demographic, and exposure characteristics (race/ethnicity, age category, poverty–income ratio, sex, serum cotinine) and lifestyle characteristics (measures of caloric intake, physical activity).
In secondary analyses, we also analyzed individual phthalate metabolites from any of the significant models to determine which metabolites were driving the association. We performed a complete case analysis to ensure that our results were not driven by artifactual associations with missing categories for caloric intake and cotinine. In addition, given the high number of missing values for physical activity data, we used multiple imputation techniques33 to generate randomly a replacement value for each missing data point (see Expanded Material and Methods in the online-only Data Supplement). Finally, to ensure that our results for nonfasting outcomes were not an artifact of statistical weighting, we also repeated our analysis in unweighted modeling.
Table 1 shows the characteristics of the study population and Table 2 reports the results of univariate regression analyses of phthalate metabolites against potential confounders while controlling for urinary creatinine. These analyses revealed increases in LMW metabolites among girls and lower concentrations of all metabolites among adolescents, except for LMW. Socioeconomic status was inversely related to LMW metabolites, with the fourth quartile (the highest) having the lowest level of urinary LMW metabolites. Socioeconomic status was not associated with any of the other urinary phthalates. LMW metabolite concentrations were higher among those categorized as overweight/obese and with higher BP (at least 90th percentile), an association that was no longer present once BMI was included in the model (Model B).
Significant differences were not identified for prehypertension, HDL, and triglycerides. However, increases in systolic BP (SBP) Z-score emerged in multivariable modeling in association with urinary HMW, DEHP, DINP, and DIDP metabolites. For each log unit increase of HMW metabolites, we identified a 0.12 SD unit increment in systolic BP Z-score (P=0.009); for DEHP metabolites, a 0.10 SD unit increment (P=0.013); for DIDP metabolites, a 0.105 SD unit increment (P=0.004); for DINP metabolites, a 0.113 SD unit increment (P=0.008). A significant 0.09 SD unit increment (P=0.041) in diastolic BP Z-score was also identified for DEHP metabolites (Table 3 and 4).
Regression analyses of individual metabolites (Table 5) suggest that associations of SBP Z-score with HMW metabolites are driven mainly by non-DEHP metabolites (DIDP and DINP). Significant association of mono-carboxyisooctyl phthalate (0.11 SD/log unit increase, P=0.006), mono-isononylphthalate (0.09 SD/log unit increase, P=0.006), mono-carboxyisononyl phthalate (0.10 SD/log unit increase, P=0.008) were identified with systolic BP Z-score. Among DEHP metabolites, significant associations of mono-(2-ethyl-5-hydroxyhexyl) phthalate (0.09 SD/log unit increase, P=0.012), mono-(2-ethyl-5-oxohexyl) phthalate (0.08 SD/log unit increase, P=0.025), mono-(2-ethyl-5-carboxypentyl) phthalate (0.11 SD/log unit increase, P=0.014) were identified with systolic BP Z-score. Association with diastolic BP Z-score were also identified for mono-(2-ethyl-5-hydroxyhexyl) phthalate (0.09 SD/log unit increase, P=0.023) and for mono-(2-ethyl-5-oxohexyl) phthalate (0.09 SD/log unit increase, P=0.045).
Unweighted analyses confirmed SBP Z-score associations in unweighted modeling for DIDP and DINP (0.09 SD unit increment per log unit DIDP increase and 0.05 SD unit increment per log unit DINP increase in full multivariable model, P=0.001 and P=0.010, respectively; Table S2) but the association with DEHP metabolites was no longer significant in unweighted modeling. Results were unchanged in complete case analyses (data not shown). Multiple imputation of physical activity data also did not change the results (data not shown).
With respect to triglyceride and HDL outcomes, no significant association was detected with metabolites of urinary phthalates in our study population (Table 5).
In this study, we identify a significant association of DINP and DIDP metabolites, currently used as DEHP replacements, with higher SBP. To provide better clinical context, a 0.11 SD increment in SBP Z-score, which reflects closely the increments detected for DEHP, DIDP, and DINP, corresponds to an increase of 1.1 mm Hg in males. Although small, the SBP increments observed in this study are significant when considered at the population level, in which even small increments can result in large increases in prehypertension and hypertension prevalence, shifting the distribution of blood pressure and increasing the number of individuals who are above the cut points for prehypertension and hypertension. A 0.115 SD increment in SBP Z-score, assuming a normal distribution, equates to a 2.2% increase in prehypertension, substantial when compared with the 5.3% prevalence in the study population. Although our study did not identify association of DINP metabolites with prehypertension, it was not powered to do so, with 5.3% weighted prevalence in our study population of 1329. Assuming a normal distribution of SBP, our study has 16.4% power to detect 1.35-log difference in prehypertension between the 10th and 90th percentiles of DINP in our sample (ie, a 0.152 SD unit increase in SBP Z-score in our linear models). With respect to hypertension, only 13 participants in our sample were hypertensive (0.7% weighted prevalence).
In our analysis, no association of urinary phthalates was identified with triglyceride or HDL. It should be noted that although HDL data were available for the full sample, triglyceride data were available only in a small number of participants (n=367), and this represents a limitation of the current study.
Phthalate exposure has been associated with oxidative stress, possibly through activation of peroxisome proliferator–activated receptors34 or through changes in mitochondrial membrane potential and permeability,35 and this could be a plausible mechanism to explain our findings. However, because oxidative stress biomarkers, such as F2-isoprostane and 8-hydroxydeoxyguanosine, are not available for 2009 to 2012 NHANES, this hypothesis could not be further substantiated in the present study.
A cross-sectional study has significant limitations, and our findings could be explained by unmeasured confounding factors or reverse causation, that is, children and adolescents with higher increments in systolic BP z-score have increased urinary excretion of phthalates because of increased consumption of packaged foods. However, the association between DIDP/DINP metabolites and increased BP remained significant after the inclusion of a rich set of information about demographics, exposures, and lifestyle variables, thus providing more convincing evidence for nonspuriousness. In particular, addition of a lifestyle variable likely to be associated with processed food consumption (excessive caloric intake) did not change our estimate of the DIDP/DINP metabolite associations with SBP Z-score.
Phthalate exposure is measured at one time point in this analysis, and monoesters of phthalates are typically known to have half-lives of 12 to 48 hours,36 which may not relate well with the sustained changes that lead to the development of hypertension. However, repeated bouts of oxidative stress associated with ubiquitous phthalate exposure may result in short-term changes in arterial tone, which can predispose to development of vasomotor dysfunction.37,38 Furthermore, phthalate accumulation has been detected in human adipose tissues39 and in phthalate-treated rats,40 thereby lengthening half-life beyond that identified in the few adult pharmacokinetic studies.41 There are no data available on DIDP/DINP and temporal variability over time but, even if current urinary phthalates are weak indices of exposure, our estimates of association should be biased toward the null for dichotomous outcomes.42
In this study, we did not identify an association between DIDP/DINP metabolites and overweight status, which was detected for LMW phthalates. LMW phthalates was also the only category for which we found a significant, inverse relationship, with socioeconomic status. This is consistent with a previous study from our group in which a similar association was detected in 2003 to 2008 NHANES data22 and, as other authors have previously noted, may reflect different lifestyle (reduced/different use of scented products for personal care) and food choices (different diets with less consumption of processed and prepackaged food), which, in turn, vary by socioeconomic factors.43,44Available evidence does not suggest a consistent association of phthalates with obesity but, among phthalates, the LMW group in particular has been more consistently associated with obesity in children and adolescents.45 The lack of association of DIDP/DINP metabolites with BMI, and the fact that the inclusion of BMI in our multivariable model did not influence the significance of the association with BP, points toward an effect that is independent of increased body mass and lends support to the notion of oxidative damage as a potential mechanisms by which phthalates can contribute to increasing cardiovascular risk. Along the same line, the inclusion in our model of caloric intake and physical activity, 2 lifestyle variables likely to be associated with processed food consumption, did not change the association between DIDP/DINP metabolites and increase in systolic BP z-score. Additional, longitudinal studies are required to substantiate our findings and to identify the underlying mechanisms through a comprehensive examination of several cardiovascular parameters and using more direct measures of cardiovascular risk, such as carotid intima-media thickness, brachial artery distensibility, and pulse wave velocity measurement.46,47
Rates of childhood hypertension have reached almost epidemic proportions48,49 and the evidence suggesting links between phthalates and adverse health effects is rapidly increasing. Phthalates simultaneously affects multiple cellular targets50 and produce changes in the metabolic profile of cardiac cells,51 as well as oxidative stress.14 Phthalates can also have additional, indirect effects on cardiovascular risk because women exposed to phthalates during pregnancy have been shown to have significantly increased odds of delivering preterm,52 and the link between preterm birth (gestational age) and hypertension risk is clear and well established.53 In light of this and the potential associated health costs, as evidenced in a recently published analysis on the burden and disease costs of exposure to endocrine disrupting chemicals in the EU,54 there is a need for increased regulatory consideration and policy initiatives to limit exposure to ubiquitous environmental chemicals with the potential to increase cardiovascular risk.
We thank Jan Blustein, Adam Spanier, and Sheela Sathyanarayana for their support in related manuscripts that examine associations of phthalate metabolites with cardiovascular and body mass outcomes.
Sources of Funding
This work was supported by grant R01ES022972. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health.
The online-only Data Supplement is available with this article at http://hyper.ahajournals.org/lookup/suppl/doi:10.1161/HYPERTENSIONAHA.115.05603/-/DC1.
- Received April 2, 2015.
- Revision received April 18, 2015.
- Accepted May 22, 2015.
- © 2015 American Heart Association, Inc.
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Novelty and Significance
What Is New?
Dietary exposure to phthalates is associated with higher blood pressure in children and adolescents.
What Is Relevant?
High blood pressure is a leading cause of heart disease and stroke, with a variety of factors contributing to its development.
Exposures to environmental chemical represent a group of understudied, but potentially important, factors.
Phthalates are chemicals widely used in consumer and personal care products, and exposure is highly prevalent.
Children and adolescents exposed to phthalates have significantly increased systolic blood pressure. Regulatory actions are needed to decrease exposure to ubiquitous environmental chemicals with the potential to increase the risk for developing cardiovascular events.