Abstract
Use of organophosphorus flame retardants (OPFRs) in consumer materials have led to widespread human exposure.Research is needed to examine the health effects attributable to the general population’s exposure to OPFRs. Using the data from the National Health and Nutrition Examination Surveys (NHANES) (2013–2014), multiple regression analyses were performed to compare the adjusted geometric means (aGMs) of serum sex hormone by quartiles of urinary metabolites of OPFRs, including diphenyl phosphate (DPhP), bis(1,3-dichloro-2-propyl) phosphate (BDCPP), bis(2-chloroethyl) phosphate (BCEP) and dibutyl phosphate(DBuP), in children (6 – 9 years old), adolescents (10 – 19 years old) and adults(≥ 20 years old), while accounting for potential confounding factors. The aGMs of sex hormone-binding globulin increased by 36% (95% CI: 6.1 – 56.7%) in female children (p = 0.03), 44% (95%CI: 16 – 63%) in female adolescents (p = 0.010), and 22% (95%CI: 3.51 – 37%) in female adults (p = 0.025), from the 1st to 4th quartiles of the levels of DPhP, BDCPP, DBUP, respectively. The aGMs of estradiol (EST) decreased by 64% and 77% from the 1st to 4th quartiles of the DBUP levels in female children (p = 0.015) and female adolescent (p = 0.020), respectively. The aGMs of EST increased by 31% (95%CI: 3.8 – 51%) from the 1st to 4th quartiles of the DBUP levels in female adults (p = 0.031). These findings suggest that exposure to certain OPFRs is associated with the altered sex hormone levels in this sample of US population. More studies are needed to examine the mechanisms responsible for these observations.
Keywords: OPFRs, sex hormone, endocrine disrupting chemicals, urinary metabolites
Introduction
Since 2005, common mixtures of polybrominated diphenyl ethers (PBDEs) (i.e., pentaBDE and octaBDE) have been voluntarily phased out for the use as flame retardants. In the last decades, organophosphate flame retardants (OPFRs) have been increasingly used as replacements in plastics, furniture, electronics, packaging materials, and other consumer goods to reduce product flammability to meet safety standards in the United States (US) [1, 2]. Because OPFRs are not chemically bonded to the consumer materials in which they are present, they can leach or outgas with time and use, and consequently, result in human exposure [3–5]. Some of the metabolites of OPFRs, including bis(1,3-dichloro-2-propyl) phosphate (BDCPP), bis(2-chloroethyl) phosphate (BCEP) and diphenyl phosphate (DPhP), have been detected in ≥ 80% of the urine samples collected from the participants in a reference sample of the US general population during the period from 2013 to 2014 [1, 6], suggesting that the exposure to OPFRs is common and widespread.
General population can be exposed to OPFRs through oral ingestion of indoor and outdoor dusts [3, 7], inhalation of polluted air [8, 9], dermally contact with the contaminated surfaces containing OPFRs [10], and dietary intake [11–13]. Recent studies found that the use of evolving consumables such as electronic vaping devices (e.g. electronic cigarettes) is also an exposure source to some OPFRs [5, 6].
In vitro and in vivo studies have suggested that OPFRs are potential endocrine disrupting chemicals which can produce adverse developmental, reproductive, neurologic, and immune effects by interfering with the function of hormones [14–19]. Human studies have linked OPFR exposure with reduced fertility [20, 21], and altered progression of adiposity [22, 23].
However, studies regarding the impact of OPFRs exposure on human health remain limited. More research is still needed to confirm the findings reported in previous studies measured using animal models under controlled exposure scenarios, and to provide scientific evidence for establishing the strategic plans for the protection of the health of the general population.
The major objective of this study was to examine the associations between the urinary metabolite levels of OPFRs and the sex hormone levels measured in the National Health and Nutrition Examination Surveys (NHANES) during the period from 2013 to 2014. The results represent a first examination of its kind using the data from this reference sample of the US general population.
Methods
Study Design and Participants
NHANES is a cross-sectional health examination survey representative of the US civilian noninstitutionalized population that is conducted by the National Center for Health Statistics (NCHS) of the US Centers for Disease Control and Prevention (CDC) [24]. The samples of participants are obtained through a complex, stratified, multistage probability design with unequal probabilities of selection of subgroups of Mexican Americans, non-Hispanic blacks, and individuals of low socioeconomic status.
Subjects included in this analysis were aged 6 years and older who participated in the NHANES during the period from 2013 to 2014. Both the laboratory and questionnaire data is publicly accessible at NHANES’s website [25]. The NCHS Research Ethics Review Board (ERB) protected the rights and welfare of NHANES participants, and the NCHS ERB reviewed and approved NHANES protocols in accordance with Federal regulations. Written informed consent was obtained from all participants or their parent/guardian prior to collecting any data. Statistical analyses were restricted to participants whose questionnaire data and laboratory measurements were concurrently available. Pregnant women and participants with self-reported history of thyroid disease were excluded to minimize the influence of potential extreme biological measurements on statistical results. Sample size and characteristics for the final participants included in this study are given in Table 1
Table 1 –
Weighted sample characteristics for children (6–9 years old), adolescents (10–19 years old) and adults (≥ 20 years old) included in this study.
| Children | Adolescents | Adults | ||
|---|---|---|---|---|
| Sample size, N | 167 | 400 | 1226 | |
| Testosterone, GM (95%CI), (ng/dL) | 2.997 (2.641, 3.401) | 55.034 (48.132, 62.925) | 99.939 (91.226, 109.485) | |
| Estradiol, GM (95%CI), (pg/mL) | 2.336 (2.199, 2.482) | 18.693 (16.398, 21.310) | 24.147 (22.850, 25.517) | |
| SHBG, GM (95%CI), (nmol/L) | 91.834 (81.644, 103.295) | 47.092 (43.036, 51.530) | 47.025 (44.994, 49.149) | |
| DPhP, GM (95%CI), (ug/L) | 1.497 (1.114, 2.011) | 1.523 (1.322, 1.759) | 0.750 (0.0.697, 0.808) | |
| BDCPP, GM (95%CI), (ug/L) | 2.272 (1.685, 3.063) | 1.459 (1.257, 1.696) | 0.7457 (0.673, 0.853) | |
| BCEP, GM (95%CI), (ug/L) | 0.713 (0.566, 0.898) | 0.619 (0.523, 0.732) | 0.380 (0.340, 0.424) | |
| DBUP, GM (95%CI), (ug/L) | 0.239 (0.197, 0.289) | 0.209 (0.181, 0.242) | 0.169 (0.148, 0.193) | |
| Serum cotinine, GM (95%CI), (ng/mL) | 0.0720 (0.0492, 0.105) | 0.068 (0.048, 0.097) | 0.245(0.170, 0.353) | |
| Urine Creatinine, GM (95%CI), (mg/dL) | 64.8 (54.7, 76.8) | 112.3 (103.3, 122.0) | 93.5 (88.8, 98.4) | |
| Race | NH White | 2.64 (1.13, 4.16) | 8.93 (7.35, 10.52) | 51.2 (45.11, 57.29) |
| NH Black | 0.64 (0.39, 0.89) | 1.86 (0.93, 2.8) | 8.77 (5.74, 11.8) | |
| Mexican American | 0.89 (0.4, 1.38) | 2.59 (1.34, 3.84) | 7.17 (4.4, 9.93) | |
| NH Asian | 0.31 (0.15, 0.47) | 0.65 (0.34, 0.96) | 5.01 (3.5, 6.52) | |
| Other Hispanic | 0.33 (0.17, 0.5) | 0.99 (0.61, 1.36) | 4.95 (3.03, 6.88) | |
| Other race | 0.31 (0, 0.61) | 0.89 (0.32, 1.45) | 1.88(0.85, 2.91) | |
| Age (years) | 6 – 9 | 5.12 (3.79, 6.45) | - | - |
| 10 – 19 | - | 15.91 (14.77, 17.05) | - | |
| 20 – 45 | - | - | 40.65 (37.99, 43.31) | |
| ≥ 46 | - | - | 38.33 (35.66, 40.99) | |
| Poverty ratio | < 1.0 | 2.08 (1.33, 2.83) | 4.47 (3.26, 5.69) | 16.68 (13.86, 19.5) |
| 1.0 ≤ and ≤ 1.93 | 1.13 (0.7, 1.56) | 3.06 (2.1, 4.02) | 13.64 (11.12, 16.17) | |
| 1.93 ≤ and ≤ 3.71 | 1.05 (0.62, 1.48) | 4.2 (3.56, 4.84) | 20.35 (17.38, 23.32) | |
| ≥3.71 | 0.86 (0.58, 1.13) | 4.18 (2.75, 5.61) | 28.3 (22.57, 34.02) | |
| Education | < High School | 5.12 (3.79, 6.45) | 14.57 (13.43, 15.71) | 11.65 (8.71, 14.6) |
| HS/GED | 1.34 (0.85, 1.82) | 15.63 (13.59, 17.68) | ||
| College or AA degree | - | - | 26.95 (23.37, 30.53) | |
| > College graduate above | - | - | 24.74 (21.63, 27.84) | |
| BMI | BMK 18.5 | 3.35 (2.38, 4.31) | 3.21 (2.4, 4.03) | 1.43 (0.69, 2.16) |
| 18.5 ≤ BMI < 25 | 1.57 (1.01, 2.13) | 7.77 (6.26, 9.28) | 23.91 (20.13, 27.68) | |
| 25 ≤ BMI <30 | 0.17 (0.01, 0.32) | 2.87 (1.64, 4.1) | 25.14 (21.92, 28.35) | |
| ≥ 30 | 0.04 (0, 0.1) | 2.06 (1.26, 2.85) | 28.5 (25.56, 31.44) | |
| Smoking Status | Nonuser | - | 2.45 (1.73, 3.17) | 0.67 (0.16, 1.19) |
| Past smoker | - | - | 3.13 (2.12, 4.13) | |
| Current smoker | - | 54.79 (50.31, 59.28) | ||
| Alcohol drink frequency Percentage (95% CI) |
Drink weekly | - | 0.05 (0, 0.15) | 47.31 (40.6, 54.03) |
| Drink monthly | 0.69 (0.02, 1.36) | 24.56 (20.33, 28.79) | ||
| Drink yearly | - | 1.04 (0, 2.39) | 26.35 (21.47, 31.22) | |
| Diabetes | Yes | 0.02 (0, 0.07) | 7.45 (6.34, 8.55) | |
| No | 5.12 (3.79, 6.45) | 15.89 (14.75, 17.02) | 71.53 (69.52, 73.53) | |
| Physical activity | Yes | - | 4.83 (3.86, 5.81) | 27.74 (23.59, 31.88) |
| No | 5.12 (3.79, 6.45) | 11.08 (9.8, 12.36) | 51.24 (46.98, 55.49) | |
Abbreviations: GM – geometric mean; NH – Non Hispanic; CI – confidence interval; BMI – Body mass index;
Laboratory Measurements
After the collection of samples (blood and urine) at the Mobile Examination Center (MEC), they were processed and shipped to the laboratories at CDC for subsequent analysis. Nine urinary metabolites of OPFRs, including DPhP, BDCPP, BCEP, bis(1-chloro-2-propyl) phosphate (BCPP), dibutyl phosphate (DBUP), di-p-cresyl phosphate (DpCP), di-o-cresyl phosphate (DoCP), dibenzyl phosphate (DBzP) and 2,3,4,5-tetrabromobenzoic acid (TBBA), were measured using a high performance liquid chromatography (HPLC) coupled with tandem mass spectrometry (MS/MS) with limits of detection (LODs) ranging from 0.05 to 0.16 μg/L [26]. Serum concentrations of testosterone (TST) and estradiol (EST) were quantified using a Agilent 1100 Series LC system (Santa Clara, CA) couple with a Sciex API 5000 MS/MS (Foster City, CA). Sex hormone binding globulin (SHBG) was measured using a chemiluminescent immunoassay on the Roche/Hitachi cobas e 411 analyzer. Urinary creatinine concentrations were measured using colorimetric methods based on Jaffé rate reaction [27].
Statistical Methods
All statistical analyses were performed using SAS (version 9.4; SAS Institute Inc., Cary, NC). Analyses were separately performed for children (6–9 years), adolescents (10–19 years) and adults (≥ 20 years) by gender, and the design effects of stratification and clustering and sampling weights were accounted for throughout the statistical analyses. To assess the associations between the urinary metabolite concentrations of OPFRs and serum sex hormone levels, multiple regression analyses were performed, and the least square geometric mean (hereafter called adjusted GMs – aGMs) were calculated for the participants categorized by the quartiles of urinary metabolite levels defined for each subgroup.
In all regression analyses, log-transformed creatinine was included to account for potential variations resulting from spot urine sampling [6, 28–31]. For children, log-transformed sCOT was included to account for potential secondhand smoke exposure. For adults, smoking status (never, past and current smoker) and average daily alcohol intake were also examined along with other covariates, including age, race/ethnicity, body mass index (BMI), income-poverty ratio, education level, and physical activity. Sex hormone and OPFR metabolite concentrations were also log-transformed to normalize their distributions. Regression models for TST and EST were evaluated after the adjustment for SHBG because of the dependence of the bioavailable TST and EST on SHBG concentration.
From the sample-weighted regression analyses, adjusted GMs of TST, EST and SHBG were calculated by quartiles as exp(least-squares means) with 95% CIs as exp(upper/lower limits on least-squares means), where the least-squares means are the quartile-specific means of TST, EST and SHBG after adjustment for other covariates. Percent changes in TST, EST and SHBG of each subgroup (e.g., from the 1st to 4th quartiles) were calculated as [exp(β) – 1] ×100% with 95% confidence intervals (CIs) estimated as [exp(upper/lower limits on β) – 1] ×100%, where β and upper/lower limits are the estimated regression coefficient and 95% CIs for β, respectively. Concentrations below LODs were assigned a value of LOD divided by the square root of 2. In all cases, significance was set at p < 0.05.
Results
In this survey cycle (NHANES 2013–2014), OPFR metabolites were measured in a random 1/3 urine samples (n=2666) of participants aged 6 years and older. There were totally 167 children (6 – 9 years old), 400 adolescents (10 – 19 years old) and 1226 adults (≥ 20 years old) whose questionnaire data and laboratory measurements met the criteria and were concurrently available (Table 1). Among nine metabolites of OPFRs, DPhP, BDCPP, BCEP and DBuP were detected in ⩾ 80% of the urine samples. In contrast, BCPP, DpCP, DoCP, DBzP and TBBA had detection rates below 60%. For this reason, analyses present in this study were focused on those four metabolites of OPFRs – DPhP, BDCPP, BCEP, and DBuP.
We observed that the serum levels of SHBG were significantly increased with the increase in the urine levels of DPhP in female adolescents, with the percentage change of 44% (95%CI: 15.6, 62.8%) from the 1st to 4th quartiles of DPhP concentrations (p=0.01) (Table 2). Urinary levels of BDCPP were also found to be positively associated with the serum levels of SHBG in female adults. Adjusted GM of SHBG increased by 22.3% (95%CI: 3.5, 37.4%) from the 1st to 4th quartiles of the urinary BDCPP levels in the female adults (p=0.025) (Table 3).
Table 2 –
Adjusted geometric means of serum TST, EST and SHBG by quartiles of the urinary DPhP levels in children, adolescents, and adults
| Adjusted geometric means (95% confidence interval)a | ||||
|---|---|---|---|---|
| SHBG | TST | EST | ||
| Child, Male | Q1: <= 0.69 | 78.2 (66.7, 91.6) | 2.6 (1.9, 3.5) | 2.1 (2, 2.2) |
| Q2: 0.69<conc<=1.22 | 101.1 (80.6, 126.7) | 2.8 (2.4, 3.4) | 2.1 (2.1, 2.2) | |
| Q3: 1.22<conc<=2.46 | 100.9 (83.3, 122.2) | 2.5 (1.8, 3.6) | 2.3 (2, 2.5) | |
| Q4: >2.46 | 79 (67.1, 93.1) | 2.1 (1.6, 2.6) | 2.1 (2.1, 2.1) | |
| Percentage change | 1.1 (20.8, −23.4) | −26 (−66.9, 4.8) | 1.7 (−2.6, 5.8) | |
| p-value | 0.91 | 0.1 | 0.4 | |
| Child, Female | Q1: <= 0.905 | 91.7 (78.6, 107) | 4.2 (2.7, 6.5) | 2.7 (2, 3.8) |
| Q2: 0.91<conc<=1.79 | 73.8 (64, 85) | 3.2 (2.4, 4.3) | 2.7 (2.2, 3.4) | |
| Q3:1.79<conc<=3.6 | 66.9 (56.3, 79.5) | 3.6 (2.6, 5) | 2.7 (2.1, 3.4) | |
| Q4: >3.6 | 93.4 (75.8, 115.1) | 3.6 (2.4, 5.2) | 2.3 (1.7, 3.1) | |
| Percentage change | 1.8 (−25.1, 22.9) | −18.5 (−92, 26.9) | −18 (−81.7, 23.3) | |
| p-value | 0.87 | 0.46 | 0.423 | |
| Adolescent, Male | Q1: <= 0.705 | 37.6 (32.1, 44) | 120.5 (94.6, 153.6) | 9.3 (8.3, 10.3) |
| Q2: 0.71<conc<=1.35 | 35.1 (29.9, 41.1) | 148.9 (111.7, 198.4) | 8.2 (7, 9.6) | |
| Q3: 1.35<conc<=2.67 | 38.9 (35.3, 42.8) | 120.1 (90.3, 159.7) | 9.5 (7.6, 11.7) | |
| Q4: >2.67 | 39 (33.6, 45.3) | 132.8 (96.3, 183) | 8.1 (7.3, 8.9) | |
| Percentage change | 3.7 (−18.9, 21.9) | 9.2 (−26.9, 35.1) | −15.1 (−32.8, 0.20) | |
| p-value | 0.71 | 0.55 | 0.053 | |
| Adolescent, Female | Q1: <= 0.66 | 37 (27.3, 50.3) | 19.1 (16, 22.8) | 21.9 (11.3, 42.5) |
| Q2: 0.66<conc<=1.765 | 45 (38.8, 52.1) | 16.9 (14.7, 19.4) | 21.5 (13.9, 33.4) | |
| Q3: 1.765<conc<=3.17 | 51.8 (44.3, 60.7) | 17.6 (14.3, 21.6) | 19.9 (10.5, 37.8) | |
| Q4: >3.17 | 66.1 (53.7, 81.4) | 21.9 (18.6, 25.9) | 10.7 (5.1, 22.4) | |
| Percentage change | 44 (15.6, 62.8) | 12.9 (−18, 35.7) | −104.8 (−382.5, 13.1) | |
| p-value | 0.01 | 0.35 | 0.10 | |
| Adult, Male | Q1: <= 0.32 | 34.8 (31.6, 38.2) | 394.3 (375.1, 414.5) | 24.2 (22.7, 25.9) |
| Q2: 0.32<conc<=0.68 | 34.4 (31.8, 37.3) | 405.6 (391, 420.7) | 22.4 (20.9, 24) | |
| Q3: 0.68<conc<=1.265 | 36.7 (32.5, 41.4) | 385.7 (364.2, 408.6) | 22.8 (20.6, 25.1) | |
| Q4: >1.265 | 37.3 (32.8, 42.5) | 385 (359.1, 412.7) | 22.6 (21.5, 23.7) | |
| Percentage change | 6.8 (−7.1, 18.9) | −2.4 (−10.8, 5.3) | −7.2 (−16.9, 1.70) | |
| p-value | 0.3 | 0.52 | 0.11 | |
| Adult, Female | Q1: <= 0.32 | 55.4 (51.2, 59.9) | 19 (16.6, 21.8) | 15.6 (12.4, 19.6) |
| Q2: 0.32<conc<=0.78 | 69.6 (61.5, 78.7) | 18.6 (15.9, 21.7) | 17.9 (13.8, 23.1) | |
| Q3: 0.78<conc<=1.76 | 67.6 (62.3, 73.4) | 18.8 (16.8, 21) | 21.7 (16.7, 28.2) | |
| Q4: >1.76 | 59.5 (55, 64.4) | 20.5 (17.4, 24.1) | 20 (15.4, 25.8) | |
| Q4: >1.265 | 7.0 (−3.4, 16.3) | 7.2 (−14.8, 24.9) | 21.9 (−13.9, 46.5) | |
| Percentage change | 0.17 | 0.47 | 0.183 | |
Abbreviations: conc – concentration;
Concentration units refer to Table 1.
Table 3 –
Adjusted geometric means of serum TST, EST and SHBG by quartiles of urinary BDCPP levels in children, adolescents, and adults
| Adjusted geometric means (95% confidence interval)a | ||||
|---|---|---|---|---|
| SHBG | TST | EST | ||
| Child Male | Q1: <= 1.48 | 73 (58.7, 90.8) | 2.8 (1.9, 4.3) | 2.1 (2.0, 2.2) |
| Q2: 1.48<conc<=2.75 | 97.8 (78.1, 122.4) | 2.8 (1.9, 4.1) | 2.1 (2.1, 2.2) | |
| Q3: 2.75<conc<=6.28 | 105.7 (91.2, 122.5) | 1.9 (1.5, 2.3) | 2.1 (2.1, 2.1) | |
| Q4: >6.28 | 91.9 (75.6, 111.6) | 2.3 (1.8, 3.1) | 2.2 (2.1, 2.4) | |
| Percentage change | 20.6 (−4.5, 39.6) | −21.2 (−103.8, 27.9) | 6.7 (−4.7, 16.9) 0.22 | |
| p-value | 0.09 | 0.441 | ||
| Child Female | Q1: <= 1.71 | 74.1 (61, 90.1) | 4.1 (3, 5.6) | 2.9 (2, 4) |
| Q2: 1.71<conc<=3.05 | 93.1 (75.4, 114.9) | 3.1 (2, 4.8) | 2.8 (2.2, 3.4) | |
| Q3: 3.05<conc<=5.62 | 72.0 (63.4, 81.7) | 2.9 (2.3, 3.6) | 3.0 (2.4, 3.8) | |
| Q4: >5.62 | 77.6 (60.3, 99.9) | 4.5 (3.5, 5.6) | 2.2 (1.6, 3.0) | |
| Percentage change | 4.5 (−41.7, 35.7) | 7.9 (−32.8, 36.1) | −29.7 (−100.3, 16.0) | |
| p-value | 0.81 | 0.64 | 0.22 | |
| Adolescent Male | Q1: <= 0.54 | 35.8 (30.9, 41.5) | 112.1 (86.3, 145.5) | 9.3 (7.9, 11.0) |
| Q2: 0.54<conc<=1.04 | 37.3 (32.9, 42.2) | 121.6 (95.3, 155.1) | 8.9 (7.7, 10.4) | |
| Q3: 1.04<conc<=1.79 | 36.5 (30.5, 43.7) | 137.5 (98, 192.9) | 7.8 (6.5, 9.4) | |
| Q4: >1.79 | 39.7 (34.9, 45.3) | 120.0 (88.8, 162.3) | 8.3 (7, 9.8) | |
| Percentage change | 9.9 (−7.9, 24.8) | 6.6 (−24.8, 30.1) | −12.4 (−37.2, 7.9) | |
| p-value | 0.24 | 0.62 | 0.23 | |
| Adolescent Female | Q1: <= 0.71 | 53.1 (36.8, 76.7) | 19.1 (16.5, 22.0) | 24.3 (14.9, 39.7) |
| Q2: 0.71<conc<=1.44 | 49.8 (38.4, 64.6) | 17.8 (15.5, 20.5) | 27.6 (16.6, 45.9) | |
| Q3: 1.44<conc<=2.49 | 56.0 (42.9, 73.1) | 17.8 (15.2, 20.8) | 28.0 (17.4, 45) | |
| Q4: >2.49 | 66.3 (51.0, 86.3) | 22.7 (19, 27.1) | 17.6 (10.7, 29.2) | |
| Percentage change | 19.9 (−15.1, 44.2) | 16.0 (−3.3, 31.7) | −37.9 (−179.7, 32) | |
| p-value | 0.21 | 0.09 | 0.35 | |
| Adult, Male | Q1: <= 0.31 | 34.7 (32.0, 37.6) | 375.2 (354.0, 397.6) | 21.3 (19.5, 23.3) |
| Q2: 0.31<conc<=0.66 | 38.3 (35.5, 41.4) | 391.3 (372.8, 410.7) | 22.9 (21.1, 24.9) | |
| Q3: 0.66<conc<=1.28 | 36.6 (34.4, 38.8) | 372.3 (341.7, 405.6) | 24.2 (22.8, 25.7) | |
| Q4: >1.28 | 36.6 (33.6, 39.8) | 385.1 (344.7, 430.3) | 23.3 (21.5, 25.3) | |
| Percentage change | 5.2 (−4.3, 13.8) | 2.6 (−10.8, 14.3) | 8.6 (−0.6, 17.0) | |
| p-value | 0.25 | 0.67 | 0.06 | |
| Adult Female | Q1: <=0.38 | 56.1 (51.4, 61.1) | 20.5 (17.2, 24.4) | 17.0 (12.3, 23.5) |
| Q2: 0.38<conc<=0.76 | 64.2 (58.8, 70.1) | 22.3 (18.4, 27.1) | 18.4 (14.2, 23.9) | |
| Q3: 0.76<conc<=1.68 | 60.1 (52.6, 68.7) | 19.5 (16.3, 23.4) | 21.6 (15.6, 30.0) | |
| Q4: >1.68 | 72.2 (61.7, 84.4) | 18.7 (15.2, 22.9) | 20.4 (15.2, 27.3) | |
| Q4: >1.28 | 22.3 (3.5, 37.4) | −9.7 (−22.9, 2.1) | 16.8 (−24.6, 44.4) | |
| Percentage change | 0.025 | 0.10 | 0.35 | |
Abbreviations: conc – concentration;
Concentration units refer to Table 1.
A significant positive association was identified between the serum levels of SHBG and the urinary DBuP levels in female children, with a percentage change of 36.2% (95%CI: 6.1 – 56.7%) from the 1st to 4th quartiles of the urinary BDCPP levels (p=0.030). Urinary DBuP levels were found to be negatively associated with the serum levels of EST in female children and female adolescents, with percentage changes of –64.5% (95%CI: –141.6, –11.9%) (p=0.015) and −77.1% (95%CI: –184.5, –10.2%) (p=0.02), respectively, from the 1st to 4th quartiles of the urinary DBuP levels (Table 4). In contrast, serum levels of EST were observed to be positively associated with the urinary DBuP levels in female adults. No significant associations were identified between sex hormone levels and urinary BCEP levels in any subgroups after accounting for the covariates including age, race/ethnicity, urinary creatinine, body mass index, education, poverty-income ratio, smoking status, physical activity, and average alcoholic drinks/day (Table 5).
Table 4 –
Adjusted geometric means of serum TST, EST and SHBG by quartiles of urinary DBuP levels in children, adolescents, and adults
| Adjusted geometric means (95% confidence interval)a | ||||
|---|---|---|---|---|
| SHBG | TST | EST | ||
| Child, Male | Q1: <= 0.16 | 93.2 (78.1, 111.3) | 2.4 (1.9, 2.9) | 2.1 (2.1, 2.2) |
| Q2: 0.16<conc<=0.35 | 90.7 (78.3, 105) | 2.3 (1.9, 2.9) | 2.1 (2.1, 2.2) | |
| Q3: 0.35<conc<=0.52 | 110.3 (93.1, 130.6) | 2.5 (1.7, 3.5) | 2.2 (2, 2.4) | |
| Q4: >0.52 | 83.9 (69.9, 100.6) | 2.4 (1.9, 3.1) | 2.1 (2, 2.2) | |
| Percentage change | −11.2 (−42.7, 13.4) | 3.5 (−27.3, 26.8) | −0.7 (−3.4, 2.0) | |
| p-value | 0.38 | 0.79 | 0.61 | |
| Child Female | Q1: <= 0.1 | 61.2 (47.7, 78.6) | 3.5 (2.4, 5.3) | 3.5 (2.5, 4.8) |
| Q2: 0.1<conc<=0.29 | 78.7 (66, 93.8) | 3.8 (2.8, 5.0) | 3.0 (2.3, 4.1) | |
| Q3: 0.29<conc<=0.46 | 83.1 (72.2, 95.6) | 3.5 (2.6, 4.8) | 2.2 (1.8, 2.6) | |
| Q4: >0.46 | 96.0 (75.9, 121.4) | 3.8 (2.8, 5.2) | 2.1 (1.6, 2.8) | |
| Percentage change | 36.2 (6.1, 56.7) | 7.6 (−56.8, 45.6) | −64.4 (−141.6, −11.9) | |
| p-value | 0.03 | 0.75 | 0.015 | |
| Adolescent Male | Q1: <= 0.095 | 39.8 (33.4, 47.4) | 116.5 (85.6, 158.4) | 8.8 (7.4, 10.4) |
| Q2: 0.095<conc<=0.28 | 36.0 (32.8, 39.5) | 110.4 (86.8, 140.4) | 8.6 (7.3, 10.1) | |
| Q3: 0.28<conc<=0.39 | 41.6 (37.2, 46.6) | 125.1 (85.7, 182.8) | 9.4 (8.6, 10.3) | |
| Q4: >0.39 | 42.9 (38, 48.4) | 101.5 (72.6, 141.9) | 8.4 (7.0, 10.1) | |
| Percentage change | 7.3 (−10.1, 21.9) | −14.8 (−63.2, 19.3) | −4.3 (−28.2, 15.2) | |
| p-value | 0.36 | 0.42 | 0.67 | |
| Adolescent Female | Q1: <= 0.065 | 66.5 (48.1, 92) | 18.3 (14.4, 23.3) | 33.0 (20.4, 53.3) |
| Q2: 0.065<conc<=0.255 | 60.0 (44.2, 81.6) | 21.0 (17.4, 25.3) | 26.9 (16.2, 44.7) | |
| Q3: 0.255<conc<=0.405 | 48.3 (35.2, 66.4) | 21.2 (18.1, 25.0) | 22.2 (14.5, 33.9) | |
| Q4: >0.405 | 50.6 (35.1, 73.0) | 23.2 (19.6, 27.6) | 18.6 (14.3, 24.4) | |
| Percentage change | −31.4 (−80, 4.0) | 21.1 (−7.9, 42.3) | −77.1 (−184.5, −10.2) | |
| p-value | 0.08 | 0.13 | 0.02 | |
| Adult-Male | Q1: <= 0.065 | 36.3 (33.1, 39.8) | 394.3 (370.6, 419.5) | 22.6 (20.6, 24.7) |
| Q2: 0.065<conc<=0.22 | 34.1 (31.3, 37.1) | 395.7 (370.3, 422.8) | 22.6 (21.1, 24.1) | |
| Q3: 0.22<conc<=0.35 | 36.7 (34.7, 38.8) | 383.4 (362.6, 405.4) | 23.4 (21.9, 25.0) | |
| Q4: >0.35 | 38.1 (34.8, 41.7) | 388 (369.4, 407.5) | 22.4 (20.8, 24.2) | |
| Percentage change | 4.7 (−7.7, 15.6) | −1.6 (−11.7, 7.5) | −0.6 (−12.0, 9.7) | |
| p-value | 0.42 | 0.72 | 0.91 | |
| Adult Female | Q1: <= 0.06 | 55.3 (46.3, 66.1) | 21.8 (18.0, 26.5) | 16.2 (12.2, 21.7) |
| Q2: 0.06<conc<=0.21 | 54.2 (47.3, 62.0) | 19.9 (15.9, 24.8) | 15.8 (11.1, 22.4) | |
| Q3: 0.21<conc<=0.33 | 61.7 (53.3, 71.4) | 20.5 (16.9, 24.8) | 18.6 (13.9, 24.9) | |
| Q4: >0.33 | 61.9 (52.3, 73.4) | 18.5 (15.2, 22.5) | 23.6 (19.3, 28.8) | |
| Percentage change | 10.7 (−9.4, 27.1) | −18.1 (−45.2, 4.0) | 31.1 (3.8, 50.6) | |
| p-value | 0.25 | 0.11 | 0.03 | |
Abbreviations: conc – concentration;
Concentration units refer to Table 1.
Table 5 –
Adjusted geometric means of serum TST, EST and SHBG by quartiles of urinary BCEP levels in children, adolescents, and adults
| Adjusted geometric means (95% confidence interval)a | ||||
|---|---|---|---|---|
| SHBG | TST | EST | ||
| Child Male | Q1: <= 0.28 | 90.6 (78.6, 104.4) | 2.6 (1.8, 3.8) | 2.1 (2.1, 2.2) |
| Q2: 0.28<conc<=0.68 | 102.4 (88.7, 118.3) | 2.4 (1.7, 3.3) | 2.1 (2.1, 2.2) | |
| Q3: 0.68<conc<=1.56 | 91.4 (76.1, 109.8) | 2.4 (2, 2.9) | 2.2 (2.1, 2.3) | |
| Q4: >1.56 | 93.4 (75.1, 116.2) | 2.1 (1.3, 3.4) | 2.1 (2.1, 2.2) | |
| Percentage change | 3.1 (−28.7, 27.0) | −23.5 (−162.6, 42) | 0.8 (−0.6, 2.2) | |
| p-value | 0.82 | 0.56 | 0.23 | |
| Child Female | Q1: <= 0.26 | 80.0 (63.9, 100.1) | 3.6 (2.6, 4.9) | 3.1 (2.3, 4.2) |
| Q2: 0.26<conc<=0.54 | 74.3 (64.4, 85.8) | 3.5 (2.7, 4.5) | 2.6 (2.1, 3.4) | |
| Q3: 0.54<conc<=1.54 | 89.0 (74.1, 107.0) | 3.6 (2.3, 5.6) | 2.8 (2.2, 3.7) | |
| Q4: >1.54 | 70.9 (55.8, 90.0) | 4.1 (3.3, 5.1) | 2.3 (1.8, 2.9) | |
| Percentage change | −12.8 (−67.2, 23.9) | 13.0 (−22.5, 38.2) | −37.5 (−109.2, 9.7) | |
| p-value | 0.52 | 0.40 | 0.13 | |
| Adolescent Male | Q1: <= 0.29 | 37.9 (33.2, 43.2) | 112.4 (83.3, 151.8) | 8.8 (8.2, 9.6) |
| Q2: 0.29<conc<=0.71 | 42.4 (37.6, 48.0) | 119.4 (91.4, 156) | 9.1 (7.8, 10.5) | |
| Q3: 0.71<conc<=1.5 | 39.1 (33.8, 45.2) | 108.5 (88.1, 133.6) | 8.9 (7.9, 10.0) | |
| Q4: >1.5 | 42.5 (36.7, 49.3) | 153.8 (124.4, 190.2) | 8.3 (6.9, 9.9) | |
| Percentage change | 10.9 (−12.1, 29.2) | 26.9 (−5.4, 49.3) | −7.1 (−26.5, 9.3) | |
| p-value | 0.3 | 0.09 | 0.4 | |
| Adolescent Female | Q1: <= 0.22 | 50.4 (38.0, 66.9) | 21.3 (17.6, 25.8) | 25.7 (13.4, 49.4) |
| Q2: 0.22<conc<=0.51 | 66.6 (47.8, 92.9) | 18.9 (16.4, 21.9) | 27.6 (16, 47.5) | |
| Q3: 0.51<conc<=1.17 | 52.9 (37.1, 75.5) | 22.3 (19.5, 25.5) | 20.3 (15.5, 26.4) | |
| Q4: >1.17 | 56.5 (47.5, 67.1) | 23.2 (20.2, 26.6) | 20.2 (14.9, 27.2) | |
| Percentage change | 10.7 (−18.4, 32.7) | 8.3 (−7.2, 21.5) | −27.6 (−150, 34.8) | |
| p-value | 0.41 | 0.26 | 0.45 | |
| Adult, Male | Q1: <= 0.17 | 36.6 (34.3, 39.1) | 391.5 (377.7, 405.8) | 22.1 (20.3, 23.9) |
| Q2: 0.17<conc<=0.4 | 36.6 (33.2, 40.4) | 408.9 (387.1, 431.9) | 23.5 (22.3, 24.8) | |
| Q3: 0.4<conc<=0.93 | 35.9 (32.9, 39.2) | 392 (373.9, 411.0) | 22.7 (20.7, 24.9) | |
| Q4: >0.93 | 37.5 (33.8, 41.6) | 370.2 (343.5, 399) | 23.4 (21.4, 25.7) | |
| Percentage change | 2.3 (−7.9, 11.6) | −5.7 (−13.1, 1.1) | 5.9 (−4.3, 15.1) | |
| p-value | 0.62 | 0.10 | 0.23 | |
| Adult Female | Q1: <= 0.14 | 60.6 (53.6, 68.7) | 19.1 (16.5, 22.1) | 19.4 (15.3, 24.6) |
| Q2: 0.14<conc<=0.33 | 53.4 (47.4, 60.2) | 22.0 (18.3, 26.4) | 16.9 (12.1, 23.5) | |
| Q3: 0.33<conc<=0.75 | 59.0 (50.4, 69.0) | 20.2 (16.7, 24.4) | 20.3 (14.6, 28.3) | |
| Q4: >0.75 | 58.1 (50, 67.4) | 19.5 (15.4, 24.5) | 22.1 (15.4, 31.7) | |
| Percentage change | −4.4 (−18.4, 8.0) | 1.8 (−17.4, 17.8) | 12.2 (−24.4, 38.0) | |
| p-value | 0.48 | 0.83 | 0.44 | |
Abbreviations: conc – concentration;
Concentration units refer to Table 1.
Discussion
High detection rates of DPhP, BDCPP, BCEP and DBuP (≥80%), among the nine urinary metabolites of OPFRs, were observed in the participants in the NHANES 2013–14, suggesting widespread exposure of the general population to triphenyl phosphate (TPhP), tris(1,3-dichloro-2-propyl) phosphate (TDCPP), tris(2-chloroethyl) phosphate (TCEP) and tributyl phosphate (TBUP), respectively. Beyond these observations, we further examined the associations between serum sex hormone levels and urinary metabolites of those four OPFRs, whose detection rates were above 60%, in children, adolescents and adults after accounting for potential influencing variables. The main contribution of the present study is that we observed significant associations between the urinary metabolite levels of certain OPFRs (i.e., TPhP, TDCPP, and TBuP) and the serum levels of sex hormone (i.e., SHBG and EST) in female children, adolescents and adults. To our knowledge, this is a first study of this kind that has been performed using a reference sample of the US general population.
The findings in the present study are consistent with the results reported in previous studies [21, 32], and provide observational evidence which support that some OPFRs are potentially endocrine disrupting chemicals (EDCs). EDCs are defined as “exogenous chemicals” that can mimic or interfere with the hormone functions, consequently leading to developmental, reproductive, neural, immune and other problems [14, 33, 34]. Several of the OPFRs, including TDCPP and TCEP, have been found to cause potential carcinogenic, mutagenic and neurodevelopmental effects in cell culture systems as well as in animal models [35–37]. The effects of OPFR exposure on human health have not been adequately documented. Within this context, our findings have provided scientific data that will contribute to a better understanding of the impact of the exposure to OPFR on human health.
It is noteworthy to emphasize that children generally had higher urinary metabolite levels of OPFRs than adults. This observation is also consistent with the results reported in previous studies [38]. Due to limited questionnaire data collected in the survey, we were unable to perform a systematic characterization of the contributions from specific exposure sources to OPFRs. On the basis of the present study and those reported by others [38], we speculate that the higher metabolite levels in children could reflect their higher exposure levels to OPFRs in contrast to the adults, although the metabolite concentrations can be potentially complicated by differences in biological mechanisms involved in the chemical absorption, distribution, metabolism and excretion processes between young children and adults. Several other factors, including special treatment on children’s toys and beddings, the time spent in indoor environments, differences in daily behavioral characteristics, are likely the main contributors to the higher exposure levels of OPFRs in children [39]. Carlsson et al. [34] found concentrations of OPFRs in indoor air can be as high as 250 ng/m3, while their concentrations in outdoor ambient air were less than 1 ng/m3, suggesting indoor air was one of the main exposure sources of OPFRs. In addition to indoor and ambient air, OPFRs have been widely detected in drinking water, indoor dusts, and foods, as a result of their increasing use in furniture, electronics, construction materials, food packaging materials, personal care products, and many other consumer goods, required to meet state and federal flammability standards and regulations [34, 38, 39]. Because many of these chemicals are not chemically bonded to the materials in which they are present, they can leach or outgas with time and use, and consequently, lead to human exposure [1, 40–42]. Young children are likely to spend a major portion of their daily time in indoor environments [43, 44], and thus are expected to have higher urinary metabolite levels of OPFRs in contrast to adults. As in the present study, we observed significant associations between the urinary levels of DPhP, BDCPP and DBuP and the serum sex hormone levels in children, there is a need to perform more studies to examine the adverse health effects on children’s growth and development resulting from the exposures to such emerging OPFRs.
Additionally, it may be noteworthy to mention that recent studies also identified several OPFRs in the emerging consumables such as electronic nicotine delivery systems (ENDS) [5, 6]. Current knowledge regarding the occurrence of OPFRs in ENDS products and especially the impact on the users’ health remains limited. As ENDS have been increasingly used and especially becoming very popular in young people over the last few years [45, 46], studies are urgently needed to investigate the health impact, resulting from the exposure to OPFRs, on the growth and development of youth ENDS users, eventually contributing to the protection of the overall health of the general population.
The findings in this study should be interpreted in the context of several limitations. First, like OPFRs, several groups of other potential EDCs, including phthalate plasticizers and phenolic compounds (e.g. bisphenol A), have been often concurrently detected in a variety of samples (e.g. air, dust, soil, water, and food) [32, 47, 48] and in human samples as these chemicals often share the similar exposure pathways associated with the use of consumable products [40, 49–52], and consequently could result in common adverse outcomes [53–55]. As the effects of complicated “chemical mixtures” were not included in the single-chemical statistical methods, the study only indirectly examined the associations between the exposure levels of OPFRs and sex hormone levels in this reference sample of US general population by assuming that effect estimates attributable to other potentially concurrent EDCs occurred at comparable levels across different categorizes. Second, all chemical measurements were performed in spot urine samples as urine has been found to be the optimal matrix for this type of biomonitoring studies considering that OPFRs have short biologic half-lives, and that their metabolite levels are significantly higher in contrast to blood samples. However, spot urine samples generally have large variability in the metabolite concentrations, mainly resulting from the dilution due to water excretion. For this reason, following traditional methods, we included log-transformed creatinine in all regression models to account for the potential variations resulting from spot urine sampling [6, 28–31]. Third, urinary creatinine levels can be highly associated with body mass, gender, age, diet and renal function [30], and thus their levels can be largely different among individual persons. As a means to minimize the variability in the estimates resulting from the significant physiological changes, the participants in this study were categorized into three categories (children, adolescents and adults), and all statistical analyses were separately performed in male and female groups among each categorize. In addition, other major demographic covariables (age, race/ethnicity and BMI) were included in the models to account for potential confounding effects [1, 6, 28–30]. Finally, although screened from a reference sample of the general US population, the sample size was small especially for children group. Thus, the results presented in this study might not be representative of the overall OPFR exposure characteristics in the US population, but rather be exploratory in understanding the health impact of OPFR exposures in human health.
Conclusion
This study examined the association between the urinary OPFR metabolites and serum sex hormone levels measured in a reference sample of the US general population during 2013 – 2014. The findings suggest that exposure to DPhP, BDCPP and DBuP are associated with the altered serum sex hormone levels in children, adolescents and adults. Given that sex hormones play critical roles in the growth and development of children and also in maintaining the overall health of human body, the findings from this study support the need to explore the mechanisms responsible for these observations, including investigating the potential exposure sources, examining potential adverse health consequences, and developing effective strategic plans to minimize or eliminate the exposure levels of OPFRs in the general population, especially those subgroups at high risks.
Funding
The research in this study was supported by Roswell Park Comprehensive Cancer Center (P30 CA 016056). Wei’s effort was also supported by NIEHS and FDA Center for Tobacco Products (CTP) (NIH Grant #: R21 ES030028). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Footnotes
Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.
Conflicts of interest/Competing interests
Competing Financial Interests: MLG has received research grant from Pfizer and served as a member of scientific advisory board to Johnson&Johnson, pharmaceutical companies that manufacture smoking cessation medications.
Availability of data and material
Both the laboratory and questionnaire data is publicly accessible at NHANES’s website at:
Ethics approval
The NCHS Research Ethics Review Board (ERB) protected the rights and welfare of NHANES participants, and the NCHS ERB reviewed and approved NHANES protocols in accordance with Federal regulations. Written informed consent was obtained from all participants or their parent/guardian prior to collecting any data.
References
- 1.Ospina M, Jayatilaka NK, Wong L-Y, Restrepo P, Calafat AM: Exposure to organophosphate flame retardant chemicals in the US general population: Data from the 2013–2014 National Health and Nutrition Examination Survey. Environment international 2018, 110:32–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kim U-J, Oh JK, Kannan K: Occurrence, Removal, and Environmental Emission of Organophosphate Flame Retardants/Plasticizers in a Wastewater Treatment Plant in New York State. Environmental Science & Technology 2017, 51(14):7872–7880. [DOI] [PubMed] [Google Scholar]
- 3.Meeker JD, Cooper EM, Stapleton HM, Hauser R: Urinary metabolites of organophosphate flame retardants: temporal variability and correlations with house dust concentrations. Environmental health perspectives 2013, 121(5):580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Carignan CC, McClean MD, Cooper EM, Watkins DJ, Fraser AJ, Heiger-Bernays W, Stapleton HM, Webster TF: Predictors of tris (1, 3-dichloro-2-propyl) phosphate metabolite in the urine of office workers. Environment international 2013, 55:56–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wei B, Goniewicz M, O’Connor RJ: Concurrent Quantification of Emerging Chemicals of Health Concern in e-Cigarette Liquids by High-Performance Liquid Chromatography–Tandem Mass Spectrometry. ACS Omega 2019, 4(13):15364–15372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Wei B, Goniewicz ML, O’Connor RJ, Travers MJ, Hyland AJ: Urinary Metabolite Levels of Flame Retardants in Electronic Cigarette Users: A Study Using the Data from NHANES 2013–2014. Int J Environ Res Public Health 2018, 15(2):201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Abdallah MA-E, Covaci A: Organophosphate flame retardants in indoor dust from Egypt: implications for human exposure. Environmental science & technology 2014, 48(9):4782–4789. [DOI] [PubMed] [Google Scholar]
- 8.Hartmann PC, Bürgi D, Giger W: Organophosphate flame retardants and plasticizers in indoor air. Chemosphere 2004, 57(8):781–787. [DOI] [PubMed] [Google Scholar]
- 9.Schreder ED, Uding N, La Guardia MJ: Inhalation a significant exposure route for chlorinated organophosphate flame retardants. Chemosphere 2016, 150:499–504. [DOI] [PubMed] [Google Scholar]
- 10.Abdallah MA-E, Pawar G, Harrad S: Human dermal absorption of chlorinated organophosphate flame retardants; implications for human exposure. Toxicology and applied pharmacology 2016, 291:28–37. [DOI] [PubMed] [Google Scholar]
- 11.Li J, Yu N, Zhang B, Jin L, Li M, Hu M, Zhang X, Wei S, Yu H: Occurrence of organophosphate flame retardants in drinking water from China. Water research 2014, 54:53–61. [DOI] [PubMed] [Google Scholar]
- 12.McGoldrick DJ, Letcher RJ, Barresi E, Keir MJ, Small J, Clark MG, Sverko E, Backus SM: Organophosphate flame retardants and organosiloxanes in predatory freshwater fish from locations across Canada. Environmental pollution 2014, 193:254–261. [DOI] [PubMed] [Google Scholar]
- 13.Poma G, Glynn A, Malarvannan G, Covaci A, Darnerud PO: Dietary intake of phosphorus flame retardants (PFRs) using Swedish food market basket estimations. Food and chemical toxicology 2017, 100:1–7. [DOI] [PubMed] [Google Scholar]
- 14.NIEHS: Endocrine Disruptors. National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services. 2010.
- 15.Diamanti-Kandarakis E, Bourguignon J-P, Giudice LC, Hauser R, Prins GS, Soto AM, Zoeller RT, Gore AC: Endocrine-disrupting chemicals: an Endocrine Society scientific statement. Endocrine reviews 2009, 30(4):293–342. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Dishaw LV, Hunter DL, Padnos B, Padilla S, Stapleton HM: Developmental exposure to organophosphate flame retardants elicits overt toxicity and alters behavior in early life stage zebrafish (Danio rerio). Toxicological Sciences 2014, 142(2):445–454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Noyes PD, Haggard DE, Gonnerman GD, Tanguay RL: Advanced morphological—behavioral test platform reveals neurodevelopmental defects in embryonic zebrafish exposed to comprehensive suite of halogenated and organophosphate flame retardants. Toxicological Sciences 2015, 145(1):177–195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kojima H, Takeuchi S, Itoh T, Iida M, Kobayashi S, Yoshida T: In vitro endocrine disruption potential of organophosphate flame retardants via human nuclear receptors. Toxicology 2013, 314(1):76–83. [DOI] [PubMed] [Google Scholar]
- 19.Farhat A, Crump D, Chiu S, Williams KL, Letcher RJ, Gauthier LT, Kennedy SW: In ovo effects of two organophosphate flame retardants—TCPP and TDCPP—on pipping success, development, mRNA expression, and thyroid hormone levels in chicken embryos. toxicological sciences 2013, 134(1):92–102. [DOI] [PubMed] [Google Scholar]
- 20.Carignan CC, Mínguez-Alarcón L, Williams PL, Meeker JD, Stapleton HM, Butt CM, Toth TL, Ford JB, Hauser R, Team ES: Paternal urinary concentrations of organophosphate flame retardant metabolites, fertility measures, and pregnancy outcomes among couples undergoing in vitro fertilization. Environment international 2018, 111:232–238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Meeker JD, Stapleton HM: House dust concentrations of organophosphate flame retardants in relation to hormone levels and semen quality parameters. Environ Health Persp 2010, 118(3):318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Boyle M, Buckley JP, Quirós-Alcalá L: Associations between urinary organophosphate ester metabolites and measures of adiposity among US children and adults: NHANES 2013–2014. Environment international 2019, 127:754–763. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hoffman K, Lorenzo A, Butt CM, Adair L, Herring AH, Stapleton HM, Daniels JL: Predictors of urinary flame retardant concentration among pregnant women. Environment international 2017, 98:96–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.US-CDC: Survey design factors—sample design, weighting and variance estimation. . 2013, 2017(November 06). [Google Scholar]
- 25.CDC: The National Health and Nutrition Examination Survey (NHANES) Questionnaires, Datasets, and Related Documentation. National Center for Health Statistics (NCHS). the Centers for Disease Control and Prevention (CDC) Accessible at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. 2017.
- 26.Jayatilaka NK, Restrepo P, Williams L, Ospina M, Valentin-Blasini L, Calafat AM: Quantification of three chlorinated dialkyl phosphates, diphenyl phosphate, 2, 3, 4, 5-tetrabromobenzoic acid, and four other organophosphates in human urine by solid phase extraction-high performance liquid chromatography-tandem mass spectrometry. Analytical and bioanalytical chemistry 2017, 409(5):1323–1332. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.CDC: Laboratory procedure manual. Creatinine in urine Accessible at: https://wwwn.cdc.gov/nchs/data/nhanes/2013-2014/labmethods/ALB_CR_H_MET_URINE_CREATINE.pdf.
- 28.Wei B, Smith D, O’Connor R, Travers MJ, Hyland A: Examining the Association between Body Burdens of Harmful Chemicals and Heaviness of Marijuana Smoking. Chemical Research in Toxicology 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Wei B, Alwis KU, Li Z, Wang L, Valentin-Blasini L, Sosnoff CS, Xia Y, Conway KP, Blount BC: Urinary concentrations of PAH and VOC metabolites in marijuana users. Environment international 2016, 88:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Barr DB, Wilder LC, Caudill SP, Gonzalez AJ, Needham LL, Pirkle JL: Urinary creatinine concentrations in the US population: implications for urinary biologic monitoring measurements. Environmental health perspectives 2005, 113(2):192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Smith DM, O’connor RJ, Wei B, Travers M, Hyland A, Goniewicz ML: Nicotine and Toxicant Exposure Among Concurrent Users (Co-Users) of Tobacco and Cannabis. Nicotine & Tobacco Research 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Meeker JD, Stapleton HM: House dust concentrations of organophosphate flame retardants in relation to hormone levels and semen quality parameters. Environmental health perspectives 2010, 118(3):318–323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Sun L, Xu W, Peng T, Chen H, Ren L, Tan H, Xiao D, Qian H, Fu Z: Developmental exposure of zebrafish larvae to organophosphate flame retardants causes neurotoxicity. Neurotoxicology and teratology 2016, 55:16–22. [DOI] [PubMed] [Google Scholar]
- 34.Carlsson H, Nilsson U, Becker G, Östman C: Organophosphate ester flame retardants and plasticizers in the indoor environment: Analytical methodology and occurrence. Environmental Science & Technology 1997, 31(10):2931–2936. [Google Scholar]
- 35.Van der Veen I, de Boer J: Phosphorus flame retardants: properties, production, environmental occurrence, toxicity and analysis. Chemosphere 2012, 88(10):1119–1153. [DOI] [PubMed] [Google Scholar]
- 36.Evidence on the Carcinogenicity of Tris(1,3-Dichloro-2-Propyl) Phosphate. Sacramento, CA. Reproductive and Cancer Hazard Assessment Branch, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency [https://oehha.ca.gov/proposition-65/chemicals/tris13-dichloro-2-propyl-phosphate-tdcpp [accessed on March 8, 2018]]
- 37.TOXICOLOGY AND CARCINOGENESIS: a STUDIES OF TRIS(2-CHLOROETHYL)PHOSPHATE [https://ntp.niehs.nih.gov/ntp/htdocs/lt_rpts/tr391.pdf [accessed on January 11, 2018]]
- 38.Butt CM, Congleton J, Hoffman K, Fang M, Stapleton HM: Metabolites of organophosphate flame retardants and 2-ethylhexyl tetrabromobenzoate in urine from paired mothers and toddlers. Environmental science & technology 2014, 48(17):10432–10438. [DOI] [PubMed] [Google Scholar]
- 39.Dodson RE, Perovich LJ, Covaci A, Van den Eede N, Ionas AC, Dirtu AC, Brody JG, Rudel RA: After the PBDE phase-out: a broad suite of flame retardants in repeat house dust samples from California. Environmental science & technology 2012, 46(24):13056–13066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Calafat AM, Ye X, Wong L-Y, Reidy JA, Needham LL: Exposure of the US population to bisphenol A and 4-tertiary-octylphenol: 2003– 2004. Environmental health perspectives 2008, 116(1):39–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Silva MJ, Barr DB, Reidy JA, Malek NA, Hodge CC, Caudill SP, Brock JW, Needham LL, Calafat AM: Urinary levels of seven phthalate metabolites in the US population from the National Health and Nutrition Examination Survey (NHANES) 1999–2000. Environmental health perspectives 2004, 112(3):331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Koch HM, Calafat AM: Human body burdens of chemicals used in plastic manufacture. Philosophical Transactions of the Royal Society of London B: Biological Sciences 2009, 364(1526):2063–2078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wei B, Blount BC, Xia B, Wang L: Assessing exposure to tobacco-specific carcinogen NNK using its urinary metabolite NNAL measured in US population: 2011–2012. Journal of exposure science and environmental epidemiology 2016, 26(3):249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.EPA U: Exposure Factors Handbook. the Office of Research and Development’s National Center for Environmental Assessments (NCEA) accessed at: https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252. 2011.
- 45.Cullen KA, Ambrose BK, Gentzke AS, Apelberg BJ, Jamal A, King BA: Notes from the field: Use of electronic cigarettes and any tobacco product among middle and high school students—United States, 2011–2018. Morbidity and Mortality Weekly Report 2018, 67(45):1276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Miech R, Johnston L, O’Malley PM, Bachman JG, Patrick ME: Trends in Adolescent Vaping, 2017–2019. New England Journal of Medicine 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Meeker JD: Exposure to environmental endocrine disruptors and child development. Archives of pediatrics & adolescent medicine 2012, 166(10):952–958. [PubMed] [Google Scholar]
- 48.Stapleton HM, Klosterhaus S, Eagle S, Fuh J, Meeker JD, Blum A, Webster TF: Detection of organophosphate flame retardants in furniture foam and US house dust. Environmental science & technology 2009, 43(19):7490–7495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Smith KW, Braun JM, Williams PL, Ehrlich S, Correia KF, Calafat AM, Ye X, Ford J, Keller M, Meeker JD: Predictors and variability of urinary paraben concentrations in men and women, including before and during pregnancy. Environmental health perspectives 2012, 120(11):1538–1543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Meeker JD, Sathyanarayana S, Swan SH: Phthalates and other additives in plastics: human exposure and associated health outcomes. Philosophical Transactions of the Royal Society B: Biological Sciences 2009, 364(1526):2097–2113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Koch HM, Calafat AM: Human body burdens of chemicals used in plastic manufacture. Philosophical Transactions of the Royal Society B: Biological Sciences 2009, 364(1526):2063–2078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Silva MJ, Barr DB, Reidy JA, Malek NA, Hodge CC, Caudill SP, Brock JW, Needham LL, Calafat AM: Urinary levels of seven phthalate metabolites in the US population from the National Health and Nutrition Examination Survey (NHANES) 1999–2000. Environmental health perspectives 2004, 112(3):331–338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Darbre PD: Endocrine disruption and human health: Academic Press; 2015. [Google Scholar]
- 54.Rajapakse N, Silva E, Kortenkamp A: Combining xenoestrogens at levels below individual no-observed-effect concentrations dramatically enhances steroid hormone action. Environmental health perspectives 2002, 110(9):917–921. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Silva E, Rajapakse N, Kortenkamp A: Something from “nothing”− eight weak estrogenic chemicals combined at concentrations below NOECs produce significant mixture effects. Environmental science & technology 2002, 36(8):1751–1756. [DOI] [PubMed] [Google Scholar]