Diethyl phthalate

Data source and sample. NHANES is a continuous, multicomponent, nationally representative survey of the noninstitutionalized U.S. population administered by the National Centers for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC 2012b ). We used data from the questionnaire, laboratory, diet, and physical examination components in the present analysis, for which data are available in biennial groupings. Our analytic sample comprised 2,884 nonpregnant participants 6–19 years of age with urinary phthalate measurements. NHANES is approved by the NCHS Research Ethics Review Board, and written informed consent and child assent (as appropriate) was obtained from participants. The NYU School of Medicine Institutional Review Board exempted the present study from review because it is based on previously collected and deidentified data.
Measurement of urinary phthalates. Phthalates were measured in a spot urine sample collected from a randomly selected subsample of NHANES participants using high-performance liquid chromatography and tandem mass spectroscopy, as previously described (Silva et al. 2004 (link)). Phthalate concentrations below the level of detection [5.1% for mono(2-ethylhexyl) phthalate (MEHP), < 1% for all other metabolites studied] were assigned the limit of detection divided by the square root of 2, as recommended by NHANES. All models included urinary creatinine to adjust for urine dilution, following usual practice (Barr et al. 2005 (link); Stahlhut et al. 2009 (link)).
We grouped biomarkers according to use. Low-molecular-weight (LMW) phthalates (diethyl phthalate, di-n-butyl phthalate, di-n-octylphthalate and di-n-isobutyl phthalate) are predominantly used in shampoos, cosmetics, lotions and other personal care products to preserve scent (Hauser and Calafat 2005 (link); Sathyanarayana 2008 (link); Sathyanarayana et al. 2008 (link)), whereas HMW phthalates [di(2-ethylhexyl) phthalate (DEHP), di-n-octyl phthalate and butylbenzyl phthalate) are used to produce vinyl plastic for flooring, clear food wrap, intravenous tubing, and other products (Schettler 2006 (link)). DEHP is of particular interest because industrial processes to produce food frequently use plastic products containing DEHP (Fromme et al. 2007 (link); Wormuth et al. 2006 (link)).
We expressed the concentration of LMW phthalate metabolites as the sum of molar concentrations of MEP, MBP, and MiBP. The concentration of HMW metabolites was calculated as the sum of molarities of mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), MCPP, mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), MEHP, and monobenzyl phthalate (MBzP). Finally, we calculated the DEHP metabolite concentration by adding molarities of MEHP, MECPP, MEHHP, and MEOHP.
Our primary exposure variables were the natural log-transformed total molar concentrations of LMW, HMW, and DEHP metabolites. In addition, we estimated associations with metabolite groups categorized into tertiles, and with selected individual phthalate metabolites.
Body mass outcomes. In the NHANES, trained health technicians assessed body measurements, following published, standardized measurement procedures (Lohman et al. 1998 ). BMI was calculated by dividing the weight in kilograms by the height in meters squared, and z-scores were derived from 2000 CDC reference growth curves (Kuczmarski et al. 2002 (link))using the zanthro command in Stata 12.0 (StataCorp., College Station, TX). Overweight and obese were classified as BMI z-score ≥ 85th percentile for age and sex and ≥ 95th percentile, respectively (Grummer-Strawn et al. 2010 (link); Ogden et al. 2002 (link)). Study outcomes were obesity (BMI z-score ≥ 95th percentile vs. < 95th percentile), overweight (BMI z-score ≥ 85th percentile vs. < 85th percentile), and BMI z-score (as continuous variable).
Potential confounders and other covariates. Trained interviewers fluent in Spanish and English elicited two total 24-hr dietary recalls using standard measuring guides to assist reporting of volumes and dimensions of food items, and responses were converted to energy and nutrients by appropriate nutritional software (CDC 2012a ). We used the first of the two 24-hour recalls in the present analysis. Because the measurement of physical activity changed during the study period, we were unable to categorize physical activity into low, medium, and high groups normally used to derive caloric needs based on age- and sex-specific U.S. Department of Agriculture (USDA) guidelines (USDA 2010 ). Therefore, as a conservative measure, we categorized participants into “normal” or “excessive” caloric intake groups based upon daily caloric guidelines for high physical activity children, recognizing that this probably underestimates the proportion who exceeded USDA calorie intake guidelines. We dichotomized self-reported television watching as < 2 or ≥ 2 hr/day, in light of previous associations with obesity (American Academy of Pediatrics Committee on Public Education 2001 (link)), and associations with urinary phthalates in our study sample. Because exposure to tobacco smoke is a risk factor for metabolic syndrome in adolescence (Weitzman et al. 2005 (link)), and because serum cotinine was positively associated with urinary phthalate metabolites in our study population, we included serum cotinine in multivariable models categorized as low (< 0.015 ng/mL), medium (0.015–2 ng/mL), or high (≥ 2 ng/mL).
Race/ethnicity was categorized as Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other, based on self-report by 17- to 19-year-olds and caregiver report for 6- to 16-year-olds. Caregiver education was categorized as less than 9th grade, 9th–12th grade, high school/graduate equivalency diploma, some college, and college or greater. Poverty–income ratio (annual household income/poverty level) was categorized into quartiles. Age was categorized as 6–11 or 12–19 years. To maximize sample sizes in multivariable analyses, we included “missing” categories for all potential confounders. Television watching was missing for 24.4%, and serum cotinine was missing for 9.6%. Otherwise, < 5% of values were missing for any covariate. Recognizing concerns raised about potential bias due to the use of missing data categories in regression models (Jones 1996 ), we repeated our main model as a complete case analysis, omitting participants with missing values for any of the covariates.
Statistical analysis. We conducted univariable, bivariable, and multivariable analyses using statistical techniques that account for the complex survey sampling design, using Stata 12.0, and following NCHS guidelines (CDC 2012b ). We used multivariable linear regression analysis to model BMI z-score, and logistic regression to model categorical overweight and obesity in separate models.
We used log-transformed LMW, HMW, and DEHP urinary metabolite concentrations in our analyses to account for skew in the distribution of urinary phthalates. We performed separate univariate regressions of each exposure against BMI z-score, overweight, obesity, and covariates. We adjusted all multivariable models for urinary creatinine (model A). Next we added demographic and exposure characteristics (race/ethnicity, age, caregiver education, poverty–income ratio, sex, serum cotinine) (model B), and then lifestyle characteristics (measures of caloric intake, television watching) (model C).
We also developed univariate and multivariable regression models of the phthalate-obesity association stratified by sex, for which differences in urinary phthalates have also been noted, age (6–11 or 12–19 years), poverty–income ratio (< 1.6 or ≥ 1.6), cotinine level (< 2.0 or ≥ 2.0 ng/mL), parent education (no college or at least some college), caloric intake (excessive or appropriate), and television watching (< 2 or ≥ 2 hr/day). In addition, we stratified on race/ethnicity, classified as non-Hispanic black, Hispanic (Mexican-American and other Hispanic combined), and non-Hispanic white to maintain large stratum-specific samples. As a test of robustness, we estimated associations according to race/ethnicity by modeling product interaction terms for the exposure and potential modifier, in addition to lower-order terms and covariates, in whole-sample regression models controlling for all covariates. These models did not combine Hispanics into one group, maintaining the subgroupings of other Hispanics and Mexican Americans used by NHANES. In secondary analyses, we analyzed individual phthalate metabolites according to race/ethnicity.
To ensure that our results were not an artifact of statistical weighting, we also repeated our analysis of race/ethnicity–stratified models in unweighted modeling. We also reprised our models substituting continuous kilocalories in lieu of categorized excessive/appropriate caloric intake for age and sex. Finally, we repeated our analyses, substituting continuous for categorized age, and recalculated LMW, HMW, and DEHP concentrations by weighting molar concentrations using each metabolite’s molecular weight, following published practice (Teitelbaum et al. 2012 (link)).

Methanol (MeOH), acetonitrile (MeCN), chloroform, dichloromethane, petroleum ether, and diethyl ether (HPLC-grade purity) were obtained from Macron Fine Chemicals (Barcelona, Spain), and acetic acid (99.9% purity) from J.T. Baker (Madrid, Spain). The analytical standards of bisphenol S (BPS, purity ≥ 98%), bisphenol F (BPF, purity ≥ 98%), bisphenol A (BPA, purity ≥ 99.9%), di(2-ethylhexyl) phthalate (DEHP, purity ≥ 99.5%), dibutyl phthalate (DBP, purity ≥ 99%), and diethyl phthalate (DEP, purity ≥ 99%) were obtained from Sigma Aldrich (Madrid, Spain). Individual standard stock solutions of the selected compounds were prepared in methanol and maintained in darkness at 4 °C until use. A concentration of 1000 mg/L was prepared for BPA, BPS, and phthalates (DEHP, DEP, and DBP), and a concentration of 100 mg/L for BPF. Daily working standard solutions were prepared by appropriate dilution with the mixture MeCN/water (85:15, v/v). A Milli-Q water system (Merck Millipore, Madrid, Spain) was used to obtain ultrapure water (18 MΩ/cm, 25 °C).
Florisil (60–100 mesh) from Acros Organics (Madrid, Spain), sodium sulfate anhydrous (Na₂SO₄, purity ≥ 99.9%), and washed sea sand (0.25–0.30 mm) from Panreac (Barcelona, Spain) were employed as sorbents for the sample extraction method. The silanized glass wool used was purchased by Panreac (Barcelona, Spain). Glass SPE cartridges (6 mL) used for packing the sorbent material were obtained from J.T. Baker (Deventer, The Netherlands). Evaporator/concentrator equipment (TECHNE, Long Branch, NJ, USA) was used to evaporate the samples.

The phthalates of interest for this study are commonly described in the literature (Benjamin et al., 2017 (link); Radke et al., 2020 (link)) and include metabolites of the parent compounds di(2-ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), butyl benzyl phthalate (BBP), and diethyl phthalate (DEP). Thus, the measured metabolites for this study were mono-n-butyl phthalate (MBP), monobenzyl phthalate (MBzP), mono-2-carboxymethylhexyl phthalate (MCMHP), mono-2-ethyl-5-carboxypentyl phthalate (MECPP), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP), mono-2-ethylhexyl phthalate (MEHP), mono-2-ethyl-5-oxohexyl phthalate (MEOHP), mono-ethylhexyl phthalate (MEP), mono-isobutyl phthalate (MiBP).
The metabolites measured in this study include 1-napthol, 2-naphthol, 2- and 3-hydroxyfluorene, 1-hydroxypyrene, and 1-, 2-, 3-, and 4-hydroxyphenanthrene. PAHs like these that are composed four rings or less (classified as low molecular weight PAHs) are more readily excreted in the urine (Yang et al., 2021 (link)), and thus were the target metabolites for this study.
Participants were instructed to collect the first urine in the morning at the end of the 24-h sample period in a provided plastic urine collection container (previously tested to be BPA and phthalate-free). Field blanks were collected alongside of urine samples for quality assurance purposes to assess potential field contamination. Urine samples were collected from all participants when the technicians returned to pick up air monitoring equipment. Samples were transported to the field laboratory and were processed within 8 h of collection. Samples were stored in the laboratory's freezer at −20 °C until shipment to Emory University within 6 months of collection (Atlanta, GA, USA).
All urine samples were randomized using a Fisher-Yates shuffling algorithm prior to analysis to reduce any potential batch effects. A 0.5-mL aliquot of urine was spiked with isotopically labeled analogues of the target phthalates and phenols and then was subjected to an enzyme hydrolysis to liberate glucuronide-bound conjugates. The hydrolysate was extracted using an ABS Elut-NEXUS solid phase extraction column, eluting with acetonitrile and ethyl acetate. The extract was concentrated to dryness and reconstituted in mobile phase for analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) using two separate injections and acquisition methods.
Analyte concentrations were calculated using isotope dilution calibration. Two quality control materials (one high and one low) and one blank sample were analyzed concurrently with each set of 28 unknown samples. Further quality assurance measures were included in the sample analyses including the analysis of NIST SRM 3672 and 3673 (one of each per 50 samples), and bi-annual participation in the German External Quality Assessment Scheme (G-EQUAS). Specific gravity was measured using a refractometer.
All metabolite concentrations were adjusted for measured creatinine concentrations to account for variability in the volume of urine and the concentrations of endogenous and exogenous chemicals from void to void (Barr et al., 2005 (link)).