Next Article in Journal
Fruit and Vegetable Consumption Patterns and Risk of Chronic Diseases of Lifestyle among University Students in Kenya
Previous Article in Journal
The Impact of World Trade Center Related Medical Conditions on the Severity of COVID-19 Disease and Its Long-Term Sequelae
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 12
10.3390/ijerph19126964
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Urine Di-(2-ethylhexyl) Phthalate Metabolites Are Independently Related to Body Fluid Status in Adults: Results from a U.S. Nationally Representative Survey

by
Wei-Jie Wang
1,2,†,
Chia-Sung Wang
3,†,
Chi-Kang Wang
4,
An-Ming Yang
3,5 and
Chien-Yu Lin
3,5,6,*
1
Division of Nephrology, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 330, Taiwan
2
Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan 300, Taiwan
3
Department of Internal Medicine, En Chu Kong Hospital, New Taipei City 237, Taiwan
4
Department of Environmental Engineering and Health, Yuanpei University of Medical Technology, Hsinchu 300, Taiwan
5
Department of Nursing, Yuanpei University of Medical Technology, Hsinchu 300, Taiwan
6
School of Medicine, Fu Jen Catholic University, New Taipei City 242, Taiwan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2022, 19(12), 6964; https://doi.org/10.3390/ijerph19126964
Submission received: 7 May 2022 / Revised: 28 May 2022 / Accepted: 3 June 2022 / Published: 7 June 2022
(This article belongs to the Topic Hazard Assessment of Endocrine Disrupting Chemicals)
Browse Figure
Review Reports Versions Notes

Abstract

:
Purpose: Di-(2-ethylhexyl) phthalate (DEHP) has been utilized in many daily products for decades. Previous studies have reported that DEHP exposure could induce renin–angiotensin–aldosterone system activation and increase epithelial sodium channel (ENaC) activity, which contributes to extracellular fluid (ECF) volume expansion. However, there is also no previous study to evaluate the association between DEHP exposure and body fluid status. Methods: We selected 1678 subjects (aged ≥18 years) from a National Health and Nutrition Examination Survey (NHANES) in 2003–2004 to determine the relationship between urine DEHP metabolites and body composition (body measures, bioelectrical impedance analysis (BIA)). Results: After weighing the sampling strategy in multiple linear regression analysis, we report that higher levels of DEHP metabolites are correlated with increases in body measures (body weight, body mass index (BMI), waist circumference), BIA parameters (estimated fat mass, percent body fat, ECF, and ECF/intracellular fluid (ICF) ratio) in multiple linear regression analysis. The relationship between DEHP metabolites and the ECF/ICF ratio was more evident in subjects of younger age (20–39 years old), women, non-Hispanic white ethnicity, and subjects who were not active smokers. Conclusion: In addition to being positively correlated with body measures and body fat, we found that urine DEHP metabolites were positively correlated with ECF and the ECF/ICF ratio in the US general adult population. The finding implies that DEHP exposures might increase ECF volume and the ECF/ICF ratio, which may have adverse health outcomes on the cardiovascular system. Further research is needed to clarify the causal relationship.

Graphical Abstract

1. Introduction

Phthalates, known as endocrine-disrupting chemicals (EDCs), are the most common chemicals widely used in many products. They are added to increase the flexibility of the plastic due to their hydrophobicity and non-covalent bonding of the polymer [1,2]. After exposure, phthalates undergo a series of phase I hydrolysis and phase II conjugation reactions, and their metabolites are excreted in feces and urine [3]. People are widely exposed to phthalates because their metabolites are commonly detected in urine samples around the world [4]. Although the half-life of phthalates is only approximately 12 h [5], due to continuous exposure, their effects are similar to those of persistent and bioaccumulating compounds [6]. Among the compound esters of phthalates, di-(2-ethylhexyl) phthalate (DEHP) is quantitatively one of the most important members of the phthalate ester group and has been listed in Annex XIV of REACH of the European Union since 2015 [7]. Recently, several epidemiological studies have linked DEHP exposure to several cardiovascular disease risk factors, such as diabetes mellitus [8] and higher systolic blood pressure [9].
In healthy adults, extracellular fluid (ECF) content comprises ~33–40% of total body water [10] and is regulated by the activity of the renin–angiotensin–aldosterone system (RAAS), autonomic nervous system, hormones, and natriuretic peptides [11]. The increased ECF might cause stress in the cardiovascular system, which eventually contributes to hypertension [12] and has been reported as an independent predictor of cardiovascular morbidity in patients with chronic renal failure [13]. The ECF/intracellular fluid (ICF) volume ratio also shows a significant association with chronic disease [14,15,16,17]. Recently, many environmental chemicals were reported to influence blood pressure and ECF [18]. The mechanism was related to the activation of RAAS by inhibiting 11β hydroxysteroid dehydrogenase and type 2 (11β-HSD-2) enzyme activity [18]. The activation of RAAS contributes to sodium retention and ECF expansion [19]. Previous animal and human studies have reported that DEHP exposure is associated with the activation of the RAAS by inhibiting 11β-HSD-2 [20,21]. Moreover, DEHP exposure might increase the risk of obesity [22], while adipocytes are an important source of extra-adrenal aldosterone [23]. In addition, DEHP has been reported to increase epithelial sodium channel (ENaC) activity in renal cortical collecting duct cells [21] and have an affinity with the receptors of peroxisome proliferator-activated receptor (PPAR) γ [24], which can expand the ECF volume in turn [25,26]. However, no previous epidemiological study has investigated the relationship between DEHP exposure and fluid status.
Bioimpedance analysis (BIA) is a tool for measuring the electrical impedance of human tissues and can be used to assess the proportion of lean mass, fat, and body fluids [27]. Unlike fluid volume, the association between DEHP exposure and other body measures has been extensively studied. When using body mass index (BMI), weight, and waist circumference as parameters, some results showed that higher levels of urine DEHP metabolites showed a positive correlation [28,29,30], while others showed no correlation [31] or an inverse association [32]. The relationship between DEHP exposure and body fat percentage has been most investigated in children, and the results are inconsistent [33,34,35,36]. In adults, however, there are only a few studies studying the association between DEHP exposure and lean mass/body fat percentage, and the results are also inconsistent [37,38].
Since the relationship between DEHP exposure and fluid status is unknown, we included subjects enrolled in the National Health and Nutrition Examination Survey (NHANES). In the NHANES database, BIA measurements were available between 1999 and 2004. However, detailed urine DEHP metabolites were only available in NHANES 2003–2004. We hypothesized that urine DEHP metabolites may have a positive correlation with body measures, lean mass, fat mass, and body fluids in the NHANES 2003–2004 database, which has detailed information on urine DEHP metabolites, body measurements, BIA, and various covariates.

2. Materials and Methods

2.1. Study Design and Population

The data for this study were obtained from NHANES 2003–2004. NHANES is a study of a representative sample of the U.S. population that collects information about family health and nutrition. The survey data are released every two years. Detailed contents of the NHANES 2003–2004 are available on the NHANES website [39]. Our analysis included 1678 participants over the age of 18 without missing data on basic demographics and BMI and those who had undergone urine DEHP metabolite testing. All methods were carried out in accordance with relevant guidelines and regulations.

2.2. Urine DEHP Metabolites

In NHANES 2003–2004, urine DEHP metabolites, including mono (2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), were tested using high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry. For concentrations below detection limits, a value was assigned by NHANES. We used this value in our analyses. The detailed method is available in the Supplementary Materials.

2.3. Body Measures

The body measurement assessments performed on survey participants varied according to the participants’ ages. Weight, BMI, triceps, and subscapular skinfold were measured in NHANES 2003–2004. The detailed method is available in the Supplementary Materials.

2.4. Bioelectrical Impedance Analysis (BIA)

In NHANES 2003–2004, this examination was conducted in an eligible survey with participants 8–49 years of age. The BIA data were collected with a HYDRA ECF/ICF Bio-Impedance Spectrum Analyzer (Model 4200, Xitron Technologies, Inc., San Diego, CA, USA). Detailed information is available in the Supplementary Materials.

2.5. Covariates

We considered age, gender, race/ethnicity, education level, household income, smoking status, caffeine intake, total energy intake, total protein intake, total sugar intake, total carbohydrate intake, total saturated fatty acid intake, and metabolic equivalent intensity level of activity to be potential confounders in this study. Detailed information is available in the Supplementary Materials.

2.6. Statistics

DEHP metabolite concentrations were corrected for urine creatinine and expressed as the geometric mean (geometric standard error) in different subpopulations. These variables were tested by Student’s 2-tailed t-test and one-way analysis of variance. As DEHP is metabolized primarily into MEHP, MEOHP, MEHHP, and MECPP, we divided the level of each metabolite by its molar mass and then added the concentrations of each metabolite to calculate Σ DEHP [40]. Due to the obvious deviation from the normal distribution, the DEHP metabolites were transformed by a natural logarithm. We constructed an extended model approach with body measures/bioelectrical impedance analysis as the dependent variable and individual ln-DEHP metabolites as a predictor. Model 1 adjusted for age, gender, race and ethnicity, education level, household income, smoking status, and metabolic equivalent intensity level for activity. Model 2 adjusted for Model 1 plus caffeine intake, total energy intake, total protein intake, total sugar intake, total carbohydrate intake, and total saturated fatty acid intake.
We used sample weights for analysis to understand the impact of weights. The calculation of sampling weight follows the analysis guidelines of the National Center for Health Statistics and appropriately considers the complex survey design adopted in NHANES 2003–2004 [41]. All analyses were calculated using SPSS Version 20 (SPSS Inc. Chicago, IL, USA). p < 0.05 was considered significant.

3. Results

MEHP, MEOHP, MEHHP, and MECPP were detectable in 69.9%, 99.1%, 99.7%, and 100% of study subjects, respectively. Table 1 shows the basic demographics of the study subjects. The study participants were composed of 798 men and 880 women. Subjects aged between 18 and 39 years old, women, and those with higher education levels were associated with higher DEHP metabolite concentrations and Σ DEHP. In addition to MEHP, subjects with higher income were associated with higher levels of other DEHP metabolites and Σ DEHP, while these metabolite levels were different between races. Participants with a BMI between 25 and 30 had a lower concentration of DEHP metabolites and Σ DEHP, while active smokers had a lower concentration of MEOHP, MECPP, and Σ DEHP. Those who had a body fat percentage ≥35% had higher levels of MEOHP, MECPP, and Σ DEHP.
The means and S.E. of body fluid status between the basic demographic of the sample subjects are shown in Table 2. Male gender, higher household income, higher BMI, lower body fat percentage, active smoking, and higher alcohol consumption were associated with higher ECF and ICF volumes. A higher ECF volume was also found in subjects who were non-Hispanic white. Subjects aged between 40 and 59 years old, female gender, higher education level, lower BMI, higher body fat percentage, and lower alcohol consumption were associated with a higher ECF/ICF ratio, while a lower ECF/ICF ratio was found in other Hispanics.
After weighing for sampling strategy in multiple linear analysis, the correlations between DEHP metabolite levels and various body component measures are shown in Table 3. Levels of MEOHP, MEHHP, MECPP, and Σ DEHP were positively associated with weight, BMI, and waist circumference. The correlations between DEHP metabolite levels and parameters of bioelectrical impedance analysis are shown in Table 4. The levels of MEOHP, MEHHP, MECPP, and Σ DEHP were positively correlated with estimated fat mass, estimated percent body fat, ECF, and the ECF/ICF ratio.
Linear regression coefficients between Σ DEHP, BMI, percent fat mass, and ECF/ICF ratio in different subpopulations are demonstrated in Table 5. The correlations between Σ DEHP levels and BMI were more evident in subjects with older age (≥40 years old) and nonactive smokers. The relationship between Σ DEHP and estimated percent body fat and the ECF/ICF ratio was more evident in subjects of younger age (20–39 years old), women, and non-Hispanic white ethnicity. In addition, the associations between Σ DEHP and the ECF/ICF ratio were also evident in subjects who were not active smokers.

4. Discussion

In the current study, we reported that the levels of DEHP metabolites were positively correlated with parameters of body measures (weight, BMI, and waist circumference) and parameters of BIA (fat mass, estimated percent body fat, ECF, and ECF/ICF ratio). Although the relationship between DEHP exposure and obesity parameters has been extensively studied, this is the first report identifying a positive association between urine DEHP metabolites and ECF and the ECF/ICF ratio in a nationally representative survey of U.S. adults. The representativeness of the study population is the main advantage of this study, and we were able to control many covariates in the comprehensive NHANES database.
BMI and waist circumference are generally used as screening tools for being overweight or obese with high specificity [22]. However, these two parameters are less sensitive in identifying adiposity because they cannot distinguish half of the people with high body fat [42]. Some experts suggest the value of assessing body-fat percentage, which is more direct for measuring adiposity [42]. In the current study, subjects with higher BMI and lower body fat percentage were associated with higher ECF and ICF volumes and lower ECF/ICF ratio. This discrepancy may explain by adipocytes produce 30% of the total blood angiotensinogen [43] and are also an important source of extra-adrenal aldosterone [23]. It is possible that those with higher body fat produce more angiotensinogen and aldosterone to increase ECF more and result in a higher ECF/ICF ratio. Subjects with higher BMI do not necessarily have higher body fat, as the higher BMI may due to more fat-free mass (e.g., muscle). Water is the main component of muscle, accounting for about 76% of its mass [44]. Higher muscle mass increases the proportion of ICF than ECF in body composition, which results in a lower ECF/ICF ratio.
Recently, it has been discovered that the effects of environmental toxins, especially those defined as EDCs, can promote obesity and are called “obesogens” [28,45]. In animal studies, female C3H/N mice exposed to DEHP doses similar to environmental exposures showed increased food intake, body weight, and visceral fat deposits [46]. In male C3H/He mice, chronic exposure to DEHP may induce obesity through changes in energy homeostasis. The synergistic effect of hypothyroidism and hypothalamic leptin resistance is a possible mechanism [47]. Several recent epidemiological studies have surveyed the association between DEHP exposure and obesity in adults. All the previous studies used BMI/waist/weight gain as markers of obesity. In cross-sectional studies, one study observed that higher MEHP levels were associated with increased obesity prevalence in NHANES 1999–2004 [29]. Another study using data from the 2007–2010 NHANES reported that higher levels of MECPP, MEHHP, and Σ DEHP were associated with an increased prevalence of obesity [28]. However, other studies found that MEHP levels have inverse associations with obesity in both 1999–2002 NHANES data [32] and the Nurses’ Health Study (NHS) and NHS2 cohorts [31]. By using a physiologically based pharmacokinetic model, a study reported that the relationship between BMI and DEHP may be explained by higher energy intake and consequent higher DEHP exposure [48]. In prospective analyses, MEOHP levels had positive trends with weight gain between baseline and year 3. However, no statistically significant association was observed after 6 years [30]. In the current study, we found that higher levels of DEHP metabolites were correlated with higher body measure parameters (weight, BMI, and waist circumference). Differences between studies may be due to a variety of factors, such as race, measurement method, lifestyle, and food.
Previous studies on DEHP exposure and fat mass were limited to children. Some studies reported that there was no correlation between DEHP exposure and adiposity [33,35,36]. Another report showed that the associations of DEHP exposure with body fat depended on the timing of exposure [34]. In adults, there were few studies studying the association between DEHP exposure and body fat percentage. One study found no correlation between urine MEHP levels and adiposity measures in elderly women [38]. Another study enrolled participants from NHANES 1999–2006, and the authors reported that there was no association between MEHP and body fat percentage but a negative correlation with lean mass among women [37]. The difference between our study and the above two studies is that we used many metabolites of DEHP as biological indicators instead of MEHP alone. In this study, we found that the levels of MEOHP, MEHHP, MECPP, and Σ DEHP were positively correlated with estimated fat mass and estimated percent body fat. The results provided evidence that DEHP exposure might increase fat mass/body fat percentage in adults.
The fluid volume in humans depends on age, sex, and body size and is also associated with human health. ECF is controlled by sodium balance and total body sodium content. The osmolality of the ECF is regulated by water intake and vasopressin secretion, while the ECF volume is maintained by RAAS and some natriuretic factors, including atrial/brain natriuretic peptide [11]. In addition, growth and sex hormones have been found to contribute to ECF volume [25,49]. Environmental chemicals that have the capacity to inhibit 11β-HSD-2 would be predicted to elevate blood pressure and expand ECF [18]. In mice, DEHP exposure has been reported to elevate blood pressure through activation of the RAAS [20]. In premature infants, higher levels of postnatal DEHP exposure are correlated with increased blood pressure and hypertension. Statistical analysis showed that this relationship was mediated by the cortisol-to-cortisone ratio, suggesting that the mechanism may be because DEHP inhibits the activity of 11β-HSD-2 and then activates the mineralocorticoid receptor. In this study, increased expression of ENaC and phosphorylated Na+–Cl− cotransporters were also found in hypertensive infants [21]. Moreover, we reported DEHP exposure is positively associated with fat mass, which might increase the production of angiotensinogen [10] and aldosterone [3]. It is possible that DEHP induces activation of RAAS and then contributes to sodium retention and ECF expansion [19]. In addition, the mono- and dicarboxylic acid metabolites of DEHP have affinities with the receptors of peroxisome proliferator-activated receptor (PPAR)α and PPARγ in vitro studies [24]. Activation of PPARγ results in enhanced sodium and water reabsorption by upregulation of sodium–hydrogen exchanger 3, the Na-K-2Cl cotransporter, water channels aquaporin-2, and ENaC [26]. Another possible mechanism is that DEHP has been reported to have estrogenic activity both in vitro and in vivo [50]. The high estrogen status will then increase ENaC activity via protein kinase C δ signaling in renal cortical collecting duct cells and expand the ECF volume [25]. In the current study, we provide the first evidence that the levels of DEHP metabolites are positively correlated with ECF and the ECF/ICF ratio. Increased ECF may cause stress on the cardiovascular system [13], which may be one of the possible mechanisms by which DEHP causes cardiovascular disease. However, this hypothesis still needs further study.
In the subgroup analysis, the correlation between Σ DEHP and the ECF/ICF ratio was more evident in women. Women are exposed to a higher concentration of phthalates than men because of the personal care products they use [51]. Furthermore, a report showed that there were sex differences in the regulation of RAAS. The adrenal response to exogenous angiotensin II was significantly higher in women. It is possible that the effect of DEHP-induced RAAS may be more evident in women than in men [52]. We also found that the association between Σ DEHP and the ECF/ICF ratio was more pronounced among subjects who were not current smokers. Studies show that body composition may be altered by smoking habits [53,54]. A possible explanation is that DEHP has a much weaker effect on body fluids than tobacco smoke. When considering the impact of DEHP on active smokers, the trend is too small to be statistically significant.
Our research has several limitations. First, causal inference is not suitable for cross-sectional research. Second, when DEHP is exposed, other undetected chemicals may be exposed at the same time, and the correlation found in the research is not caused by DEHP itself. Third, our study population was mainly composed of adults, so we cannot infer that this association also exists in children. Finally, the relatively small sample size, especially in subgroup analysis, is a limitation of the study. Our results need to be interpreted with caution.

5. Conclusions

Although previous reports have explored the association between DEHP exposure and obesity parameters, including BMI, waist circumference, and fat percentage in adults, we present the first report identifying a positive association of urine DEHP metabolites with body fluid status in a nationally representative survey of U.S. adults. If a causal relationship exists, DEHP exposure might increase ECF volume and the ECF/ICF ratio through its affinity for PPAR γ receptor [24], as well as increasing the production of extra-adrenal aldosterone [23], increasing ENaC activity [21], and inhibiting 11β-HSD-2 activity [20,21]. Our results provide important clues for future toxicological research. Since DEHP exposure has become a worldwide concern, further research is necessary to determine the long-term mechanism and effects of low-dose DEHP exposure on human health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19126964/s1. References [55,56,57,58,59] are cited in the supplementary materials.

Author Contributions

W.-J.W. and C.-S.W. developed the theoretical concept and manuscript drafting. C.-K.W. helped to perform the analytic calculations. A.-M.Y. helped to record and analyze covariates and critical discussion. C.-Y.L. contributed to developing the theoretical concept, recording and analyzing covariates, critical discussion, and the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grants from the Ministry of Science and Technology of Taiwan (NSC 110-2314-B-385-001-MY3).

Institutional Review Board Statement

The study was conducted according to the ethical guidelines and approved by the Ethics Committee of the En Ch Kong hospital (ECKH_W11108).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets analyzed during the current study are available at the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm (accessed on 4 June 2022)).

Acknowledgments

We would like to thank the many individuals who contributed to the National Health and Nutrition Examination Survey data, including all of the anonymous participants in the present study. We are particularly grateful to those who carried out the laboratory assays for DEHP at the Division of Environmental Health Laboratory Sciences, National Center for Environmental Health, and Centers for Disease Control and Prevention.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liao, K.W.; Kuo, P.L.; Huang, H.B.; Chang, J.W.; Chiang, H.C.; Huang, P.C. Increased risk of phthalates exposure for recurrent pregnancy loss in reproductive-aged women. Environ. Pollut. 2018, 241, 969–977. [Google Scholar] [CrossRef]
  2. Wang, W.; Leung, A.O.W.; Chu, L.H.; Wong, M.H. Phthalates contamination in China: Status, trends and human exposure-with an emphasis on oral intake. Environ. Pollut. 2018, 238, 771–782. [Google Scholar] [CrossRef]
  3. Lind, P.M.; Zethelius, B.; Lind, L. Circulating levels of phthalate metabolites are associated with prevalent diabetes in the elderly. Diabetes Care 2012, 35, 1519–1524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Meeker, J.D.; Ferguson, K.K. Urinary phthalate metabolites are associated with decreased serum testosterone in men, women, and children from NHANES 2011–2012. J. Clin. Endocrinol. Metab. 2014, 99, 4346–4352. [Google Scholar] [CrossRef] [Green Version]
  5. Hoppin, J.A.; Brock, J.W.; Davis, B.J.; Baird, D.D. Reproducibility of urinary phthalate metabolites in first morning urine samples. Environ. Health Perspect. 2002, 110, 515–518. [Google Scholar] [CrossRef] [Green Version]
  6. Wang, I.J.; Lin, C.C.; Lin, Y.J.; Hsieh, W.S.; Chen, P.C. Early life phthalate exposure and atopic disorders in children: A prospective birth cohort study. Environ. Int. 2014, 62, 48–54. [Google Scholar] [CrossRef] [PubMed]
  7. Schaedlich, K.; Gebauer, S.; Hunger, L.; Beier, L.S.; Koch, H.M.; Wabitsch, M.; Fischer, B.; Ernst, J. DEHP deregulates adipokine levels and impairs fatty acid storage in human SGBS-adipocytes. Sci. Rep. 2018, 8, 3447. [Google Scholar] [CrossRef] [PubMed]
  8. James-Todd, T.; Stahlhut, R.; Meeker, J.D.; Powell, S.G.; Hauser, R.; Huang, T.; Rich-Edwards, J. Urinary phthalate metabolite concentrations and diabetes among women in the National Health and Nutrition Examination Survey (NHANES) 2001–2008. Environ. Health Perspect. 2012, 120, 1307–1313. [Google Scholar] [CrossRef] [Green Version]
  9. Trasande, L.; Sathyanarayana, S.; Spanier, A.J.; Trachtman, H.; Attina, T.M.; Urbina, E.M. Urinary phthalates are associated with higher blood pressure in childhood. J. Pediatr. 2013, 163, 747–753. [Google Scholar] [CrossRef] [Green Version]
  10. Edelman, I.S.; Leibman, J. Anatomy of body water and electrolytes. Am. J. Med. 1959, 27, 256–277. [Google Scholar] [CrossRef]
  11. Roumelioti, M.E.; Glew, R.H.; Khitan, Z.J.; Rondon-Berrios, H.; Argyropoulos, C.P.; Malhotra, D.; Raj, D.S.; Agaba, E.I.; Rohrscheib, M.; Murata, G.H.; et al. Fluid balance concepts in medicine: Principles and practice. World J. Nephrol. 2018, 7, 1–28. [Google Scholar] [CrossRef]
  12. Seo, H.S.; Kim, E.J.; Kim, S.W.; Im, S.I.; Na, J.O.; Choi, C.U.; Lim, H.E.; Won Kim, J.; Rha, S.W.; Park, C.G. Extracellular fluid adjusted for body size is contracted in hypertension. Hypertens. Res. Off. J. Jpn. Soc. Hypertens. 2013, 36, 916–921. [Google Scholar] [CrossRef]
  13. Park, S.; Lee, C.J.; Jhee, J.H.; Yun, H.R.; Kim, H.; Jung, S.Y.; Kee, Y.K.; Yoon, C.Y.; Park, J.T.; Kim, H.C.; et al. Extracellular Fluid Excess Is Significantly Associated With Coronary Artery Calcification in Patients With Chronic Kidney Disease. J. Am. Heart Assoc. 2018, 7, e008935. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Lee, J.Y.; Ryu, H.S.; Yoon, S.S.; Kim, E.H.; Yoon, S.W. Extracellular-to-Intracellular Fluid Volume Ratio as a Prognostic Factor for Survival in Patients With Metastatic Cancer. Integr. Cancer Ther. 2019, 18, 1534735419847285. [Google Scholar] [CrossRef] [Green Version]
  15. Chen, W.; Guo, L.J.; Wang, T. Extracellular water/intracellular water is a strong predictor of patient survival in incident peritoneal dialysis patients. Blood Purif. 2007, 25, 260–266. [Google Scholar] [CrossRef] [PubMed]
  16. Kim, E.J.; Choi, M.J.; Lee, J.H.; Oh, J.E.; Seo, J.W.; Lee, Y.K.; Yoon, J.W.; Kim, H.J.; Noh, J.W.; Koo, J.R. Extracellular Fluid/Intracellular Fluid Volume Ratio as a Novel Risk Indicator for All-Cause Mortality and Cardiovascular Disease in Hemodialysis Patients. PLoS ONE 2017, 12, e0170272. [Google Scholar] [CrossRef] [Green Version]
  17. Lin, Y.-P.; Yu, W.-C.; Hsu, T.-L.; Ding, P.Y.-A.; Yang, W.-C.; Chen, C.-H. The extracellular fluid—to—intracellular fluid volume ratio is associated with large-artery structure and function in hemodialysis patients. Am. J. Kidney Dis. 2003, 42, 990–999. [Google Scholar] [CrossRef]
  18. Kirkley, A.G.; Sargis, R.M. Environmental endocrine disruption of energy metabolism and cardiovascular risk. Curr. Diabetes Rep. 2014, 14, 494. [Google Scholar] [CrossRef] [Green Version]
  19. Harrison-Bernard, L.M. The renal renin-angiotensin system. Adv. Physiol. Educ. 2009, 33, 270–274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Xie, X.; Deng, T.; Duan, J.; Ding, S.; Yuan, J.; Chen, M. Comparing the effects of diethylhexyl phthalate and dibutyl phthalate exposure on hypertension in mice. Ecotoxicol. Environ. Saf. 2019, 174, 75–82. [Google Scholar] [CrossRef]
  21. Jenkins, R.; Tackitt, S.; Gievers, L.; Iragorri, S.; Sage, K.; Cornwall, T.; O’Riordan, D.; Merchant, J.; Rozansky, D. Phthalate-associated hypertension in premature infants: A prospective mechanistic cohort study. Pediatr. Nephrol. 2019, 34, 1413–1424. [Google Scholar] [CrossRef] [Green Version]
  22. Ribeiro, C.; Mendes, V.; Peleteiro, B.; Delgado, I.; Araújo, J.; Aggerbeck, M.; Annesi-Maesano, I.; Sarigiannis, D.; Ramos, E. Association between the exposure to phthalates and adiposity: A meta-analysis in children and adults. Environ. Res. 2019, 179 Pt A, 108780. [Google Scholar] [CrossRef] [PubMed]
  23. Briones, A.M.; Nguyen Dinh Cat, A.; Callera, G.E.; Yogi, A.; Burger, D.; He, Y.; Corrêa, J.W.; Gagnon, A.M.; Gomez-Sanchez, C.E.; Gomez-Sanchez, E.P.; et al. Adipocytes produce aldosterone through calcineurin-dependent signaling pathways: Implications in diabetes mellitus-associated obesity and vascular dysfunction. Hypertension 2012, 59, 1069–1078. [Google Scholar] [CrossRef]
  24. Ito, Y.; Kamijima, M.; Nakajima, T. Di(2-ethylhexyl) phthalate-induced toxicity and peroxisome proliferator-activated receptor alpha: A review. Environ. Health Prev. Med. 2019, 24, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Yusef, Y.R.; Thomas, W.; Harvey, B.J. Estrogen increases ENaC activity via PKCδ signaling in renal cortical collecting duct cells. Physiol. Rep. 2014, 2, e12020. [Google Scholar] [CrossRef] [Green Version]
  26. Fu, Y.; Gerasimova, M.; Batz, F.; Kuczkowski, A.; Alam, Y.; Sanders, P.W.; Ronzaud, C.; Hummler, E.; Vallon, V. PPARγ agonist-induced fluid retention depends on αENaC expression in connecting tubules. Nephron 2015, 129, 68–74. [Google Scholar] [CrossRef] [Green Version]
  27. Khalil, S.F.; Mohktar, M.S.; Ibrahim, F. The Theory and Fundamentals of Bioimpedance Analysis in Clinical Status Monitoring and Diagnosis of Diseases. Sensors 2014, 14, 10895–10928. [Google Scholar] [CrossRef]
  28. Buser, M.C.; Murray, H.E.; Scinicariello, F. Age and sex differences in childhood and adulthood obesity association with phthalates: Analyses of NHANES 2007–2010. Int. J. Hyg. Environ. Health 2014, 217, 687–694. [Google Scholar] [CrossRef]
  29. Yaghjyan, L.; Sites, S.; Ruan, Y.; Chang, S.H. Associations of urinary phthalates with body mass index, waist circumference and serum lipids among females: National Health and Nutrition Examination Survey 1999–2004. Int. J. Obes. 2015, 39, 994–1000. [Google Scholar] [CrossRef] [Green Version]
  30. Díaz Santana, M.V.; Hankinson, S.E.; Bigelow, C.; Sturgeon, S.R.; Zoeller, R.T.; Tinker, L.; Manson, J.A.E.; Calafat, A.M.; Meliker, J.R.; Reeves, K.W. Urinary concentrations of phthalate biomarkers and weight change among postmenopausal women: A prospective cohort study. Environ. Health Glob. Access Sci. Source 2019, 18, 1–12. [Google Scholar] [CrossRef] [Green Version]
  31. Song, Y.; Hauser, R.; Hu, F.B.; Franke, A.A.; Liu, S.; Sun, Q. Urinary concentrations of bisphenol A and phthalate metabolites and weight change: A prospective investigation in US women. Int. J. Obes. 2014, 38, 1532–1537. [Google Scholar] [CrossRef] [Green Version]
  32. Hatch, E.E.; Nelson, J.W.; Qureshi, M.M.; Weinberg, J.; Moore, L.L.; Singer, M.; Webster, T.F. Association of urinary phthalate metabolite concentrations with body mass index and waist circumference: A cross-sectional study of NHANES data, 1999–2002. Environ. Health Glob. Access Sci. Source 2008, 7, 27. [Google Scholar] [CrossRef] [Green Version]
  33. Buckley, J.P.; Engel, S.M.; Mendez, M.A.; Richardson, D.B.; Daniels, J.L.; Calafat, A.M.; Wolff, M.S.; Herring, A.H. Prenatal Phthalate Exposures and Childhood Fat Mass in a New York City Cohort. Environ. Health Perspect. 2016, 124, 507–513. [Google Scholar] [CrossRef] [Green Version]
  34. Shoaff, J.; Papandonatos, G.D.; Calafat, A.M.; Ye, X.; Chen, A.; Lanphear, B.P.; Yolton, K.; Braun, J.M. Early-Life Phthalate Exposure and Adiposity at 8 Years of Age. Environ. Health Perspect. 2017, 125, 097008. [Google Scholar] [CrossRef] [Green Version]
  35. Maresca, M.M.; Hoepner, L.A.; Hassoun, A.; Oberfield, S.E.; Mooney, S.J.; Calafat, A.M.; Ramirez, J.; Freyer, G.; Perera, F.P.; Whyatt, R.M.; et al. Prenatal Exposure to Phthalates and Childhood Body Size in an Urban Cohort. Environ. Health Perspect. 2016, 124, 514–520. [Google Scholar] [CrossRef] [Green Version]
  36. Chang, C.H.; Chen, C.F.; Tsai, Y.A.; Wang, S.L.; Huang, P.C.; Chen, B.H.; Wu, M.T.; Chen, C.C.; Hsiung, C.A.; Chen, M.L. The sex-specific association of phthalate exposure with DNA methylation and characteristics of body fat in children. Sci. Total Environ. 2020, 737, 139833. [Google Scholar] [CrossRef]
  37. Corbasson, I.; Hankinson, S.E.; Stanek, E.J., 3rd; Reeves, K.W. Urinary bisphenol-A, phthalate metabolites and body composition in US adults, NHANES 1999–2006. Int. J. Environ. Health Res. 2016, 26, 606–617. [Google Scholar] [CrossRef]
  38. Lind, P.M.; Roos, V.; Rönn, M.; Johansson, L.; Ahlström, H.; Kullberg, J.; Lind, L. Serum concentrations of phthalate metabolites are related to abdominal fat distribution two years later in elderly women. Environ. Health Glob. Access Sci. Source 2012, 11, 21. [Google Scholar] [CrossRef] [Green Version]
  39. National Health and Nutrition Examination Survey 2003–2004. Available online: http://wwwn.cdc.gov/Nchs/Nhanes/Search/nhanes03_04.aspx (accessed on 10 December 2021).
  40. Braun, J.M.; Smith, K.W.; Williams, P.L.; Calafat, A.M.; Berry, K.; Ehrlich, S.; Hauser, R. Variability of urinary phthalate metabolite and bisphenol A concentrations before and during pregnancy. Environ. Health Perspect. 2012, 120, 739–745. [Google Scholar] [CrossRef] [PubMed]
  41. The National Health and Nutrition Examination Survey (NHANES). ANALYTIC AND REPORTING GUIDELINES. Available online: http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf (accessed on 10 December 2021).
  42. Okorodudu, D.O.; Jumean, M.F.; Montori, V.M.; Romero-Corral, A.; Somers, V.K.; Erwin, P.J.; Lopez-Jimenez, F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: A systematic review and meta-analysis. Int. J. Obes. 2010, 34, 791–799. [Google Scholar] [CrossRef] [Green Version]
  43. Padilla, J.; Vieira-Potter, V.J.; Jia, G.; Sowers, J.R. Role of perivascular adipose tissue on vascular reactive oxygen species in type 2 diabetes: A give-and-take relationship. Diabetes 2015, 64, 1904–1906. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Lorenzo, I.; Serra-Prat, M.; Yébenes, J.C. The Role of Water Homeostasis in Muscle Function and Frailty: A Review. Nutrients 2019, 11, 1857. [Google Scholar] [CrossRef] [Green Version]
  45. Grün, F.; Blumberg, B. Environmental obesogens: Organotins and endocrine disruption via nuclear receptor signaling. Endocrinology 2006, 147 (Suppl. 6), S50–S55. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Schmidt, J.S.; Schaedlich, K.; Fiandanese, N.; Pocar, P.; Fischer, B. Effects of di(2-ethylhexyl) phthalate (DEHP) on female fertility and adipogenesis in C3H/N mice. Environ. Health Perspect. 2012, 120, 1123–1129. [Google Scholar] [CrossRef] [Green Version]
  47. Lv, Z.; Cheng, J.; Huang, S.; Zhang, Y.; Wu, S.; Qiu, Y.; Geng, Y.; Zhang, Q.; Huang, G.; Ma, Q.; et al. DEHP induces obesity and hypothyroidism through both central and peripheral pathways in C3H/He mice. Obesity 2016, 24, 368–378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Campbell, J.L., Jr.; Yoon, M.; Ward, P.L.; Fromme, H.; Kessler, W.; Phillips, M.B.; Anderson, W.A.; Clewell, H.J., 3rd; Longnecker, M.P. Excretion of Di-2-ethylhexyl phthalate (DEHP) metabolites in urine is related to body mass index because of higher energy intake in the overweight and obese. Environ. Int. 2018, 113, 91–99. [Google Scholar] [CrossRef]
  49. Johannsson, G.; Gibney, J.; Wolthers, T.; Leung, K.C.; Ho, K.K. Independent and combined effects of testosterone and growth hormone on extracellular water in hypopituitary men. J. Clin. Endocrinol. Metab. 2005, 90, 3989–3994. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Chen, X.; Xu, S.; Tan, T.; Lee, S.T.; Cheng, S.H.; Lee, F.W.; Xu, S.J.; Ho, K.C. Toxicity and estrogenic endocrine disrupting activity of phthalates and their mixtures. Int. J. Environ. Res. Public Health 2014, 11, 3156–3168. [Google Scholar] [CrossRef]
  51. Parlett, L.E.; Calafat, A.M.; Swan, S.H. Women’s exposure to phthalates in relation to use of personal care products. J. Expo. Sci. Environ. Epidemiol. 2013, 23, 197–206. [Google Scholar] [CrossRef]
  52. Toering, T.J.; Gant, C.M.; Visser, F.W.; van der Graaf, A.M.; Laverman, G.D.; Danser, A.H.J.; Faas, M.M.; Navis, G.; Lely, A.T. Sex differences in renin-angiotensin-aldosterone system affect extracellular volume in healthy subjects. Am. J. Physiol. Ren. Physiol. 2018, 314, F873–F878. [Google Scholar] [CrossRef]
  53. Kleppinger, A.; Litt, M.D.; Kenny, A.M.; Oncken, C.A. Effects of smoking cessation on body composition in postmenopausal women. J. Women’s Health 2010, 19, 1651–1657. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Efendi, V.; Özalevli, S.; Naz, İ.; Kılınç, O. The effects of smoking on body composition, pulmonary function, physical activity and health-related quality of life among healthy women. Tuberkuloz Toraks 2018, 66, 101–108. [Google Scholar] [CrossRef] [PubMed]
  55. 2003–2004 Data Documentation, Codebook, and Frequencies: Phthalates—Urine. Available online: https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/L24PH_C.htm (accessed on 10 December 2021).
  56. 2003–2004 Data Documentation, Codebook, and Frequencies: Body Measures. Available online: http://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/BMX_C.htm (accessed on 10 December 2021).
  57. 2003–2004 Data Documentation, Codebook, and Frequencies: Bioelectrical Impedance Analysis. Available online: http://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/BIX_C.htm (accessed on 10 December 2021).
  58. Weitzman, M.; Cook, S.; Auinger, P.; Florin, T.A.; Daniels, S.; Nguyen, M.; Winickoff, J.P. Tobacco smoke exposure is associated with the metabolic syndrome in adolescents. Circulation 2005, 112, 862–869. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. 2003–2004 Data Documentation, Codebook, and Frequencies: Physical Activity. Available online: https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/PAQ_C.htm#Appendix_1._Suggested_MET_Scores (accessed on 10 December 2021).
Table
Table 1. Basic demographics of the sample subjects, including geometric means (geometric S.E.) of DEHP metabolites concentrations.
Table 1. Basic demographics of the sample subjects, including geometric means (geometric S.E.) of DEHP metabolites concentrations.
Unweighted No. (%)MEHP (μg/g Creatinine)MEOHP (μg/g Creatinine)MEHHP (μg/g Creatinine)MECPP
(μg/g Creatinine)		Σ DEHP
(μmol/g Creatinine)
Overall1678 (100)2.13 (1.03)12.18 (1.03)18.23 (1.03)29.44 (1.03)0.21(1.03)
Age, year
   18–39718 (42.8)2.64 (1.05) ‡13.63 (1.05) ‡20.15 (1.05) †30.88 (1.04) †0.23 (1.04) †
   40–59449 (26.8)1.90 (1.06) ‡10.67 (1.05) ‡16.33 (1.05) †25.89 (1.05) †0.19 (1.05) †
   ≥60511 (30.4)1.72 (1.05) ‡11.70 (1.04) ‡17.46 (1.04) †30.81 (1.04) †0.21 (1.04) †
Gender
   Men798 (47.6)1.85 (1.05) ‡10.74 (1.04) ‡16.52 (1.04) ‡26.08 (1.04) ‡0.19 (1.04) ‡
   Women880 (52.4)2.41 (1.04) ‡13.66 (1.04) ‡19.94 (1.04) ‡32.86 (1.03) ‡0.24 (1.03) ‡
Race
   Mexican American365 (21.8)1.96 (1.06)10.36 (1.06) *15.18 (1.06) *26.30 (1.05) *0.19 (1.05) *
   Other Hispanic48 (2.9)2.63 (1.17)12.92 (1.17) *19.59 (1.18) *32.47 (1.14) *0.24 (1.15) *
   Non-Hispanic White842 (50.2)2.09 (1.04)12.91 (1.04) *19.19 (1.04) *32.00 (1.04) *0.23 (1.04) *
   Non-Hispanic Black345 (20.6)2.37 (1.07)12.72 (1.06) *19.64 (1.06) *26.91 (1.06) *0.21 (1.06) *
   Others78 (4.5)2.02 (1.14)11.09 (1.13) *17.09 (1.13) *28.41 (1.12) *0.20 (1.12) *
Education levels
   ≤High school927 (55.3)1.94 (1.04) ‡10.85 (1.03) ‡16.04 (1.04) ‡26.54 (1.03) ‡0.19 (1.03) ‡
   >High school750 (44.7)2.37 (1.05) ‡14.06 (1.04) ‡21.35 (1.04) ‡33.47 (1.04) ‡0.25 (1.04) ‡
Annual household income
   <USD 25,000556 (35.6)2.02 (1.05)10.98 (1.05) ‡16.50 (1.05) ‡27.52 (1.04) †0.20 (1.04) ‡
   USD 25,000–55,000503 (32.2)2.00 (1.05)11.54 (1.05) ‡17.24 (1.05) ‡27.89 (1.05) †0.20 (1.05) ‡
   >USD 55,000501 (32.2)2.31 (1.06)14.07 (1.05) ‡21.06 (1.05) ‡33.01 (1.05) †0.24 (1.05) ‡
BMI, kg/m2
   <25577 (34.4)2.31 (1.05) *11.92 (1.05) ‡17.77 (1.05) ‡28.71 (1.05) *0.21 (1.05) ‡
   25–30562 (33.5)1.90 (1.05) *10.93 (1.05) ‡16.31 (1.05) ‡27.29 (1.04) *0.20 (1.04) ‡
   ≥30539 (32.1)2.19 (1.05) *13.97 (1.05) ‡21.06 (1.05) ‡32.73 (1.04) *0.24 (1.04) ‡
Body fat percentage (%)
   <25227 (31.4)2.22 (1.09)10.70 (1.08) *16.81 (1.09)25.82 (1.08) *0.19 (1.08) *
   25–35230 (31.9)2.00 (1.09)11.33 (1.08) *17.11 (1.08)25.87 (1.07) *0.20 (1.07) *
   ≥35265 (36.7)2.53 (1.08)14.19 (1.07) *20.83 (1.08)32.21 (1.07) *0.24 (1.07) *
Smoking
   Nonexposed353 (21.0)2.16 (1.07)12.10 (1.06) *18.26 (1.06)31.27 (1.05) ‡0.22 (1.06) *
   Expose to ETS849 (50.6)2.15 (1.04)12.99 (1.04) *19.04 (1.04)31.15 (1.04) ‡0.23 (1.04) *
   Active smokers476 (28.4)2.07 (1.06)10.93 (1.05) *18.23 (1.05)25.46 (1.05) ‡0.19 (1.05) *
Alcohol consumption (drinks/year)
   <12412 (29.5)2.01 (1.06)11.86 (1.05)17.38 (1.05)2.52 (1.05)0.21 (1.05)
   ≥12983 (70.5)2.05 (1.04)11.89 (1.04)17.97 (1.04)2.32 (1.03)0.21 (1.03)
*, p < 0.05; †, p < 0.01; ‡, p < 0.005 (tested with Student’s 2-tailed t-test or by one-way analysis of variance); Abbreviations: DEHP, di-(2-ethylhexyl) phthalate; ETS, environmental tobacco smoke; MEHP, mono(2-ethylhexyl) phthalate; MEHHP, mono(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono(2-ethyl-5-oxohexyl) phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate. Σ DEHP was the sum of (MEHP/278) + (MEHHP/294) + (MEOHP/292) + (MECPP/308) and corrected for urine creatinine.
Table
Table 2. Basic demographics of the sample subjects, including means (S.E.) of body fluid status.
Table 2. Basic demographics of the sample subjects, including means (S.E.) of body fluid status.
Unweighted No. (%)ECF
(L)		ICF
(L)		ECF/ICF Ratio
Overall726 (100)16.92 (0.13)23.35 (0.24)0.74 (0.00)
Age, year
   18–39524 (42.8)16.80 (0.16)23.42 (0.29)0.74 (0.00) ‡
   40–50202 (26.8)17.24 (0.24)23.16 (0.42)0.76 (0.01) ‡
Gender
   Men377 (47.6)19.00 (0.15) ‡27.61 (0.28) ‡0.70 (0.00) ‡
   Women349 (52.4)14.67 (0.15) ‡18.74 (0.22) ‡0.79 (0.00) ‡
Race
   Mexican American172 (21.8)15.95 (0.23) ‡22.32 (0.43)0.73 (0.01) *
   Other Hispanic27 (2.9)15.99 (0.64) ‡22.92 (1.28)0.72 (0.02) *
   Non-Hispanic White319 (50.2)17.45 (0.21) ‡23.78 (0.38)0.75 (0.01) *
   Non-Hispanic Black172 (20.6)17.13 (0.27) ‡23.78 (0.49)0.74 (0.01) *
   Others36 (4.5)16.59 (0.65) ‡22.70 (1.22)0.75 (0.01) *
Education levels
   ≤High school376 (55.3)16.88 (0.18)23.58 (0.33)0.73 (0.00) ‡
   >High school350 (44.7)16.97 (0.20)23.10 (0.36)0.75 (0.00) ‡
Annual household income
   <USD 25,000203 (35.6)16.40 (0.23) ‡22.50 (0.41) *0.74 (0.01)
   USD 25,000–55,000227 (32.2)16.79 (0.24) ‡23.32 (0.44) *0.74 (0.01)
   >USD 55,000248 (32.2)17.62 (0.24) ‡24.25 (0.45) *0.75 (0.00)
BMI, kg/m2
   <25301 (34.4)15.04 (0.18) ‡ 20.04 (0.31) ‡0.77 (0.01) ‡
   25–30218 (33.5)17.22 (0.20) ‡24.35 (0.38) ‡0.72 (0.01) ‡
   ≥30207 (32.1)19.35 (0.23) ‡27.10 (0.46) ‡0.73 (0.01) ‡
Body fat percentage (%)
   <25229 (31.4)18.52 (0.21) ‡27.79 (0.38) ‡0.67 (0.00) ‡
   25–35230 (31.9)16.94 (0.26) ‡23.24 (0.42) ‡0.74 (0.00) ‡
   ≥35267 (36.7)15.54 (0.19) ‡19.62 (0.29) ‡0.80 (0.01) ‡
Smoking
   Nonexposed109 (21.0)15.91 (0.35) ‡21.45 (0.60) ‡0.76 (0.01)
   Expose to ETS356 (50.6)16.74 (0.19) ‡23.23 (0.35) ‡0.74 (0.00)
   Active smokers257 (28.4)17.63 (0.21) ‡24.39 (0.39) ‡0.74 (0.01)
Alcohol consumption (drinks/year)
   <12124 (29.5)16.34 (0.36) ‡b21.87(0.62) ‡0.77 (0.01) ‡
   ≥12429 (70.5)17.43 (0.17) ‡24.23 (0.31) ‡0.74 (0.00) ‡
*, p < 0.05; ‡, p < 0.005 (tested by Student’s 2-tailed t-test or by one-way analysis of variance); Abbreviations: ETS, environmental tobacco smoke.
Table
Table 3. Linear regression coefficients (S.E.) of body measures with a one-unit increase in ln-DEHP metabolite concentrations in adults, with results weighted for sampling strategy.
Table 3. Linear regression coefficients (S.E.) of body measures with a one-unit increase in ln-DEHP metabolite concentrations in adults, with results weighted for sampling strategy.
Body Measures MEHP
(μg/g Creatinine)		pMEOHP
(μg/g Creatinine)		pMEHHP
(μg/g Creatinine)		pMECPP
(μg/g Creatinine)		pΣ DEHP
(μmol/g Creatinine)		p
Body weight (kg)
Model 11563/193,092,3050.092 (0.501)0.8562.020 (0.760)0.0182.047 (0.648)0.0072.003 (0.766)0.0192.031 (0.749)0.016
Model 2852/117,709,5130.023 (0.452)0.9611.679 (0.572)0.0101.704 (0.538)0.0061.520 (0.616)0.0261.627 (0.593)0.015
Body mass index (kg/m2)
Model 11563/193,092,3050.060 (0.157)0.7110.679 (0.211)0.0060.696 (0.190)0.0020.709 (0.227)0.0070.705 (0.217)0.005
Model 2852/117,709,5130.052 (0.145)0.7260.598 (0.174)0.0040.617 (0.177)0.0030.578 (0.200)0.0110.602 (0.189)0.006
Waist (cm)
Model 11563/193,092,305−0.012 (0.409)0.9781.549 (0.534)0.0111.532 (0.468)0.0051.512 (0.515)0.0101.533 (0.521)0.010
Model 2852/117,709,5130.059 (0.414)0.8881.374 (0.423)0.0051.342 (0.403)0.0051.209 (0.391)0.0071.305 (0.411)0.006
Subscapular Skinfold (mm)
Model 11255/1,158,342,664−0.362 (0.210)0.1040.082 (0.274)0.7700.151 (0.272)0.5860.133 (0.278)0.6400.073 (0.284)0.801
Model 2687/97,486,158−0.423 (0.206)0.0580.019 (0.258)0.9430.113 (0.258)0.6690.038 (0.244)0.8770.005 (0.260)0.985
Triceps Skinfold (mm)
Model 11390/172,886,004−0.253 (0.180)0.1810.263 (0.228)0.2670.237 (0.199)0.2540.261 (0.256)0.3230.237 (0.241)0.342
Model 2763/106,411,256−0.198 (0.164)0.2450.272 (0.216)0.2270.234 (0.200)0.2620.205 (0.234)0.3950.216 (0.223)0.347
Model 1 was adjusted for age, gender, race/ethnicity, educational level, household income, smoking status, and metabolic equivalent intensity level of activity. Model 2 was adjusted for Model 1 plus caffeine intake, total energy intake, total protein intake, total sugar intake, total carbohydrate intake, and total saturated fatty acid intake. Abbreviations: DEHP, di-(2-ethylhexyl) phthalate; MEHP, mono(2-ethylhexyl) phthalate; MEHHP, mono(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono(2-ethyl-5-oxohexyl) phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate. Σ DEHP was the sum of (MEHP/278) + (MEHHP/294) + (MEOHP/292) + (MECPP/308) and corrected for urine creatinine.
Table
Table 4. Linear regression coefficients (S.E.) of bioelectrical impedance analysis parameters with a one-unit increase in ln-DEHP metabolite concentrations in adults, with results weighted for the sampling strategy.
Table 4. Linear regression coefficients (S.E.) of bioelectrical impedance analysis parameters with a one-unit increase in ln-DEHP metabolite concentrations in adults, with results weighted for the sampling strategy.
Bioelectrical Impedance Analysis MEHP
(μg/g Creatinine)		pMEOHP
(μg/g Creatinine)		pMEHHP
(μg/g Creatinine)		pMECPP
(μg/g Creatinine)		pΣ DEHP
(μmol/g Creatinine)		p
Lean mass (kg)
   Model 1674/96,499,431−0.012 (0.320)0.9700.654 (0.444)0.1620.692 (0.382)0.0900.562 (0.481)0.2620.635 (0.444)0.173
   Model 2415/61,088,209−0.020 (0.296)0.9480.608 (0.384)0.1340.640 (0.364)0.0990.497 (0.416)0.2510.579 (0.393)0.162
Fat mass (kg)
      Model 1674/96,499,4310.806 (0.610)0.2061.646 (0.675)0.0281.579 (0.617)0.0221.639 (0.697)0.0331.666 (0.695)0.030
      Model 2415/61,088,2091.000 (0.546)0.0871.716 (0.602)0.0121.560 (0.580)0.0171.547 (0.639)0.0291.651 (0.636)0.020
Percent body fat (%)
      Model 1674/96,499,4310.419 (0.377)0.2850.800 (0.383)0.0340.718 (0.368)0.0400.808 (0.374)0.0470.794 (0.391)0.036
      Model 2415/61,088,2090.632 (0.352)0.0930.951 (0.337)0.0130.789 (0.336)0.0330.865 (0.348)0.0250.894 (0.354)0.023
Cell membrane capacitance (nF)
      Model 1674/96,499,431−0.024 (0.028)0.416−0.008 (0.022)0.716−0.001 (0.018)0.952−0.014 (0.021)0.525−0.009 (0.020)0.660
      Model 2415/61,088,209−0.022 (0.029)0.469−0.002 (0.018)0.9250.004 (0.018)0.824−0.009 (0.020)0.653−0.004 (0.020)0.843
ECF (L)
      Model 1674/96,499,4310.134 (0.101)0.2030.321 (0.149)0.0490.308 (0.133)0.0350.316 (0.167)0.0780.327 (0.156)0.044
      Model 2415/61,088,2090.147 (0.085)0.1050.289 (0.126)0.0370.271 (0.119)0.0380.279 (0.143)0.0690.292 (0.134)0.045
ICF (L)
      Model 1674/96,499,431−0.106 (0.151)0.4930.196 (0.191)0.3210.230 (0.162)0.1750.139 (0.203)0.5050.179 (0.186)0.351
      Model 2415/61,088,209−0.120 (0.147)0.4300.189 (0.168)0.2780.224 (0.160)0.1840.123 (0.178)0.4980.168 (0.169)0.337
ECF/ICF ratio
      Model 1471/68,616,2090.008 (0.003)0.0100.008 (0.002)0.0040.006 (0.002)0.0210.009 (0.003)0.0050.008 (0.003)0.007
      Model 2415/61,088,2090.009 (0.003)0.0030.007 (0.002)0.0040.005 (0.002)0.0410.009 (0.002)0.0020.008 (0.002)0.004
Model 1 was adjusted for age, gender, race/ethnicity, educational level, household income, smoking status, and metabolic equivalent intensity level for activity. Model 2 was adjusted for Model 1 plus caffeine intake, total energy intake, total protein intake, total sugar intake, total carbohydrate intake, and total saturated fatty acid intake. Abbreviations: DEHP, di-(2-ethylhexyl) phthalate; MEHP, mono(2-ethylhexyl) phthalate; MEHHP, mono(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono(2-ethyl-5-oxohexyl) phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate; ECF, extracellular fluid; ICF, intracellular fluid. Σ DEHP was the sum of (MEHP/278) + (MEHHP/294) + (MEOHP/292) + (MECPP/308) and corrected for urine creatinine.
Table
Table 5. Linear regression coefficients (S.E.) between ln-Σ DEHP and body composition parameters in different subpopulations of sample subjects with results weighted for sampling strategy.
Table 5. Linear regression coefficients (S.E.) between ln-Σ DEHP and body composition parameters in different subpopulations of sample subjects with results weighted for sampling strategy.
BMI (kg/m2)Percent Body Fat (%)ECF/ICF Ratio
βcoeff (S.E.)p-Valueβcoeff (S.E.)p-Valueβcoeff (S.E.)p-Value
Age, year
   18–390.42 (0.37)0.2671.06 (0.38)0.0140.006 (0.003)0.044
   ≥400.84 (0.31)0.016−0.03 (0.57)0.9600.010 (0.008)0.228
Gender
   Men0.48 (0.39)0.246−0.19 (0.52)0.7220.002 (0.006)0.711
   Women0.77 (0.40)0.0731.78 (0.45)0.0010.011 (0.004)0.011
Race
Non-Hispanic White0.39 (0.18)0.0480.98 (0.36)0.0160.012 (0.003)0.001
Others1.38 (0.46)0.0090.63 (0.72)0.398−0.001 (0.003)0.658
Smoking
Nonactive smokers0.50 (0.20)0.0240.65 (0.36)0.0890.009 (0.002)0.002
Active smokers0.83 (0.48)0.1030.79 (0.48)0.1180.007 (0.006)0.284
Adjusted for Model 2. Abbreviations: BMI, body mass index; DEHP, di-(2-ethylhexyl) phthalate; ECF, extracellular fluid; ICF, intracellular fluid.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wang, W.-J.; Wang, C.-S.; Wang, C.-K.; Yang, A.-M.; Lin, C.-Y. Urine Di-(2-ethylhexyl) Phthalate Metabolites Are Independently Related to Body Fluid Status in Adults: Results from a U.S. Nationally Representative Survey. Int. J. Environ. Res. Public Health 2022, 19, 6964. https://doi.org/10.3390/ijerph19126964

AMA Style

Wang W-J, Wang C-S, Wang C-K, Yang A-M, Lin C-Y. Urine Di-(2-ethylhexyl) Phthalate Metabolites Are Independently Related to Body Fluid Status in Adults: Results from a U.S. Nationally Representative Survey. International Journal of Environmental Research and Public Health. 2022; 19(12):6964. https://doi.org/10.3390/ijerph19126964

Chicago/Turabian Style

Wang, Wei-Jie, Chia-Sung Wang, Chi-Kang Wang, An-Ming Yang, and Chien-Yu Lin. 2022. "Urine Di-(2-ethylhexyl) Phthalate Metabolites Are Independently Related to Body Fluid Status in Adults: Results from a U.S. Nationally Representative Survey" International Journal of Environmental Research and Public Health 19, no. 12: 6964. https://doi.org/10.3390/ijerph19126964

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop