Our results emphasize the importance of considering the combined effects of various phthalate metabolites on respiratory function, rather than focusing solely on individual metabolites. This provides a more nuanced understanding of the potential health impacts of phthalate exposure.
Keywords: urinary phthalate metabolites, pulmonary function, children, NHANES, ppFVC
Abstract
Objective
The aim of the study was to evaluate the links between single and combined urinary phthalate metabolites and respiratory function in children.
Methods
We analyzed the data from National Health and Nutrition Examination Survey (2007–2012) with the final analysis encompassed 1644 participants, those divided into four groups on average. This study investigated the correlation between ten prevalent phthalate metabolites in pediatric urine samples and four pulmonary function indices, employing multivariable linear regression, segmented linear regression, and the weighted quantile sum methodologies for analysis.
Results
Significant negative correlations between eight phthalate metabolites. Additionally, the remaining seven phthalate metabolites showed significant negative associations with the predicted percentage of the ratio of forced expiratory volume in 1 second to forced vital capacity.
Conclusions
The combined effects of various phthalate metabolites are the primary contributors to the decline in lung function.
LEARNING OUTCOMES
Our study reveals significant associations between urinary phthalate metabolites and decreased respiratory function in children, particularly when concentrations exceed specific breakpoints. These findings contribute to the negative associations between specific urinary phthalate metabolites and respiratory function indices in children, as demonstrated through multivariable linear regression and weighted quantile sum regression analyses.
Within the context of the NHANES data (2007–2012), readers will recognize the temporal relevance of phthalate exposure and its impact on children’s respiratory health, enabling the application of these findings to future epidemiological studies or public health interventions.
Phthalates constitute chemicals predominantly utilized in the manufacturing and processing of plastics within industrial settings. Exposure of humans to phthalates can occur via diverse pathways, such as inhalation, ingestion, or skin contact. Within the human body, phthalates undergo metabolism to form various metabolites, which are excreted in urine.1 Multiple studies have suggested the existence of potentially detrimental health consequences associated with phthalates and their metabolites, encompassing disruptions in endocrine function, reproductive toxicity, and neurotoxicity.2
The respiratory system is considered one of the significant target organs for phthalate exposure, because the lungs can directly absorb phthalates from the air, and they possess substantial metabolic capabilities.3 Multiple epidemiological investigations have demonstrated notable correlations between pulmonary function indices and pulmonary health/disease risk, serving as crucial metrics for evaluating lung ventilation capacity and airway resistance.4 Additional research has established prenatal exposure as a determinant of lung function across both pediatric and adult stages.5 Nonetheless, most studies on individual phthalate effects on pediatric respiratory function have either yielded nonsignificant correlations or inconclusive results. These conflicting results underscore the urgency of conducting additional studies for validation and more extensive inquiry. It is worth noting that cross-sectional studies serve as a vital initial stage for drawing potential connections.
In order to address the existing deficiencies in knowledge, this study employed cross-sectional data sourced from the NHANES covering the period from 2007 to 2012. The study cohort comprised 7219 children civilians residing in the United States. We systematically examined the connections between individual urinary phthalate metabolites and a comprehensive amalgamation of these metabolites concerning lung function. Furthermore, our aim was to pinpoint the primary urinary phthalate metabolite predominantly accountable for the deterioration of lung function in children.
MATERIALS AND METHODS
Study Population
The present investigation the data from the National Health and Nutrition Examination Survey (NHANES), an extensive and nationwide cross-sectional examination of noninstitutionalized individuals in the United States. This survey, managed by the National Center for Health Statistics in the United States, serves as a comprehensive health and nutrition assessment and is representative of the national population. To maintain representativeness, NHANES adopts a stratified, multistage probability sampling approach for selecting participants. Ethical approval for NHANES was granted by the National Center for Health Statistics Ethics Review Board, with all study participants providing their informed consent before enrollment. The database has been deidentified by replacing all personal information with random codes, ensuring that patient identifiers are removed and anonymity is preserved. Ethical approval was waived by the ethics committee of the Kunming Children’s Hospital & Children’s Hospital Affiliated to Kunming Medical University and the clinical trial number is not applicable. The research adhered to the items outlined in the guidelines of STROBE and was explained in detail within the SDC 1 (Supplementary Digital Content 1, http://links.lww.com/JOM/B898).Comprehensive protocols and descriptions pertinent to the NHANES are accessible online at http://www.cdc.gov/nchs/nhanes.htm. The analysis was based on data from a consecutive NHANES cycle (2007–2012), which initially comprised 30,442 participants. Initially, the study encompassed 7219 participants aged 6 to 18 years. Exclusions were applied for participants with incomplete data regarding urinary phthalate metabolites and pulmonary function, as well as those with missing covariate information. Consequently, the final analysis encompassed 1644 participants, divided into four groups on average to address potential sources of bias(Q1, Q2, Q3, Q4) (Fig. 1).
FIGURE 1.
Flow chart of individuals included in our final analysis. NHANES 2007–2012.
Urinary Phthalates Exposure Assessment
For this study, we selected data from NHANES (2007–2012), which is the most recent dataset and the first to include both urinary phthalate metabolites and lung function indicators in children. We chose ten common phthalate metabolites in urine as exposure variables. These metabolites include mono-ethylphthalate (MEP), a primary metabolite of diethyl phthalate; mono-isobutyl pthalate (MiBP), a primary metabolite of di-isobutyl phthalate; mono-n-butyl phthalate (MnBP), a primary metabolite of di-n-butyl phthalate; mono-benzyl phthalate (MBzP), a primary metabolite of butyl benzyl phthalate; mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), the three secondary metabolites of bis(2-ethylhexyl) phthalate; mono-(7-carboxy-2,7-dimethylheptyl) phthalate (MCNP); and monocarboxyoctyl phthalate (MCOP), the two secondary metabolites of Di-isononyl phthalate. The concentrations of these phthalate metabolites reflect the level of exposure to phthalates in the body and serve as biomarkers for assessing the impact of phthalates on human health. For additional information regarding urine sample collection, phthalate metabolite detection, and measurement methodologies, please refer to the NHANES website at http://www.cdc.gov/Nchs/Nhanes/2007-2012/PTHTE_G.htm. Consistent with NHANES recommendations,6 values below the limit of detection (LOD) were imputed using the LOD divided by the square root of two as a substitution. The LODs for MCNP, MCOP, MECPP, MnBP, MCPP, MEP, MEHHP, MEOHP, MBzP, and MiBP were 0.2 μg/L, 0.2 μg/L, 0.2 μg/L, 0.4 μg/L, 0.2 μg/L, 0.6 μg/L, 0.2 μg/L, 0.2 μg/L, 0.3 μg/L, and 0.2 μg/L, respectively.
Lung Function Data
The study focused on four specific pulmonary function indices as outcome variables: the predicted percentage of forced vital capacity (ppFVC), the predicted percentage of forced expiratory volume in 1 second (ppFEV1), the predicted percentage of the ratio of forced expiratory volume in 1 second to forced vital capacity (ppFEV1/FVC), and the predicted percentage of the forced expiratory flow between 25% and 75% of the pulmonary volume (ppFEF 25%–75%). These indices collectively reflect lung volume, airflow speed, and airflow proportion, providing a comprehensive assessment of pulmonary function. ppFVC and ppFEV1 gauge maximum ventilatory capacity, ppFEV1/FVC assesses airflow resistance, and ppFEF 25%–75% reflects small airway patency. These indices are amenable to straightforward respiratory tests and exhibit high repeatability and comparability. All analyses concerning lung function outcomes employed percent predicted values.
Covariate Assessment
Data on potential confounding variables were gathered through a thorough assessment encompassing a home-administered questionnaire, physical examinations, and biospecimen measurements. The choice of covariates was guided by existing research concerning pulmonary function and urinary phthalate metabolite concentrations.7
Statistical Analysis
We employed various statistical analysis methods, including multiple linear regression models, segmented linear regression models, and weighted quantile sum (WQS) approaches. Lung function parameters, including ppFVC, ppFEV1, ppFEF 25%–75%, and ppFEV1/FVC, exhibited a normal distribution. However, urinary phthalate metabolite levels and creatinine concentrations were right-skewed, necessitating a natural-log transformation (ln-transformation) to achieve normality. Incorporation of ln-transformed creatinine as a covariate and application of covariate-adjusted standardization were employed to mitigate biases associated with urinary dilution in the study.8 Specifically, this study formulated a model where ln-transformed creatinine was the dependent variable, and factors like age, gender, and race/ethnicity were included as covariates. Subsequently, urinary phthalate metabolite levels were standardized by division with (Cr/⋀Cr), where Cr represented the observed creatinine, and ^⋀Cr indicated the fitted creatinine. Standardization of phthalate metabolite levels, following adjustment and ln-transformation, positioned them as the exposure variables. Threshold identification and nonlinear relationship assessment with lung function indicators were conducted using segmented regression models. This study applied generalized additive models equipped with thin plate regression spline functions to examine potential nonlinear correlations between continuous phthalate metabolite levels and lung function metrics.
The Weighted Quantile Sum (WQS) regression model quantifies cumulative chemical exposure, accounting for nonlinearity and collinearity, to estimate their combined impact on a health outcome.9 For determining the WQS index and the adaptive weights associated with selected phthalate deciles, we randomly partitioned the data into a training subset (40%) and a validation subset (60%). Through 100 iterations of holdout-repeated WQS regression with 500 bootstrap resamples each, we stabilized WQS index and weight estimates. The resultant coefficient indicates average pulmonary function change per one-decile concentration increment of exposure mixture constituents.10
In this study, all statistical tests were conducted using a two-sided approach and a significance level of 0.05 was established as the criterion for statistical significance. To determine the central tendency and dispersion of quantitative variables, we computed their respective mean (M) and standard deviation (SD). Nominal and ordinal variables were summarized using frequency counts and percentages (%). For comparisons involving more than two groups, This study employed one-way analysis of Variance, considering a P value below 0.05 as indicative of statistical significance, and all tests were conducted with two-tailed distributions. The analyses were performed using R software version 3.6.1 with the assistance of the “survey,”11 “mgcv,”12 and “gWQS” R packages.13
RESULTS
Descriptive Statistics
In our study, 411 participants distributed evenly across the four quarters. Age distribution remained consistent, with mean ages ranging from approximately 11.7 to 11.9 years (P = 0.660). Gender distribution indicated that males constituted 46.2% to 52.8% across quarters, while females ranged from 47.2% to 53.8% (P = 0.183). Racial composition displayed significant fluctuations across quarters, notably among Mexican Americans, whose representation ranged from 20.0% to 28.2% (P < 0.001). Urinary creatinine levels also demonstrated notable differences (P = 0.019<0.05). Pulmonary function index, including ppFVC, displayed minor yet significant variations (P = 0.043<0.05). However, ppFEV1, ppFEF 25% to 75%, and ppFEV1/FVC showed minor fluctuations without statistical significance (P = 0.143, 0.242, 0.078) (Table 1).
TABLE 1.
The Characteristics of Participants
| Q1 | Q2 | Q3 | Q4 | P | |
|---|---|---|---|---|---|
| N | 411 | 411 | 411 | 411 | |
| Age (years, x ± s) | 11.8 ± 3.6 | 11.9 ± 3.7 | 11.7 ± 3.6 | 11.6 ± 3.5 | 0.660 |
| Sex (%) | 0.183 | ||||
| Male | 217 (52.8%) | 190 (46.2%) | 217 (52.8%) | 204 (49.6%) | |
| Female | 194 (47.2%) | 221 (53.8%) | 194 (47.2%) | 207 (50.4%) | |
| Race (%) | <0.001 | ||||
| Mexican American | 102 (24.8%) | 106 (25.8%) | 116 (28.2%) | 82 (20.0%) | |
| Non-Hispanic White | 52 (12.7%) | 51 (12.4%) | 46 (11.2%) | 48 (11.7%) | |
| Non-Hispanic Black | 132 (32.1%) | 137 (33.3%) | 122 (29.7%) | 98 (23.8%) | |
| Other Hispanic | 109 (26.5%) | 93 (22.6%) | 91 (22.1%) | 118 (28.7%) | |
| Other | 16 (3.9%) | 24 (5.8%) | 36 (8.8%) | 65 (15.8%) | |
| Urinary creatinine (mg/dL) (x ± s) | 126.4 ± 76.5 | 125.5 ± 78.3 | 122.5 ± 79.4 | 114.5 ± 80.1 | 0.019 |
| Lung function parameters | |||||
| FVC (L) | 46.7 ± 14.0 | 46.1 ± 12.9 | 45.4 ± 12.5 | 44.2 ± 12.4 | 0.043 |
| FEV1 (L) | 117.3 ± 46.1 | 117.3 ± 43.8 | 113.9 ± 43.7 | 111.3 ± 42.0 | 0.143 |
| FEF (L/s) | 101.9 ± 10.2 | 102.8 ± 10.4 | 103.3 ± 9.6 | 102.8 ± 9.9 | 0.242 |
| FEV1/FVC | 102.0 ± 9.9 | 102.4 ± 10.4 | 103.3 ± 9.7 | 103.5 ± 9.7 | 0.078 |
FEF, the mid exhalation forced expiratory flow rate in percent predicted values; FEV1, the forced expiratory volume in the first second in percent predicted values; FEV1/FVC, ratio of FEV1 to FVC in percent predicted values; FVC, the forced vital capacity in percent predicted values.
In the study, almost all samples detected MEP at an average concentration of 55.9 ng/mL, with the highest 10% reaching up to 327 ng/mL. The GM concentrations of MEP, MnBP, MiBP, MBzP, MEOHP, MEHHP, MECPP, MEPP, MCOP, and MCNP in urine were 55.9, 20.4, 10.5, 11.2, 11.0, 17.3, 29.2, 4.1, 14.9, and 3.0, indicating that MEP, which had the highest exposure dose, significantly surpassing the other phthalates. Similarly, MnBP was found in 98.7% of samples, having a median concentration of 21.6 ng/mL and the top 10% exceeding 76.9 ng/mL. MiBP also had near-universal detection, with a median value of 11.3 ng/mL. The detection rates for most other phthalates were exceedingly high, generally above 98%, indicating widespread exposure. The geometric means varied, with some phthalates like MECPP showing higher average concentrations (29.2 ng/mL) compared to others like MCOP at just 3.0 ng/mL. The data suggests varied but prevalent exposure to these phthalate metabolites in the examined samples (Table 2).
TABLE 2.
Urinary Concentrations of Phthalate Metabolites
| Phthalate Metabolites (ng/mL) | LOD | Detection Rate (%) | GM | 10th | 25th | 50th | 75th | 90th |
|---|---|---|---|---|---|---|---|---|
| MEP | 0.4 | 99.9 | 55.9 | 10.6 | 23.1 | 50.0 | 134.9 | 327.0 |
| MnBP | 0.2 | 98.7 | 20.4 | 4.1 | 10.2 | 21.6 | 43.7 | 76.9 |
| MiBP | 0.1 | 99.8 | 10.5 | 2.5 | 5.3 | 11.3 | 21.5 | 38.9 |
| MBzP | 0.1 | 99.9 | 11.2 | 2.1 | 4.9 | 11.9 | 26.7 | 53.0 |
| MEOHP | 0.1 | 99.8 | 11.0 | 2.4 | 5.08 | 11.1 | 22.5 | 46.3 |
| MEHHP | 0.1 | 99.9 | 17.3 | 3.7 | 7.9 | 17.0 | 36.51 | 75.6 |
| MECPP | 0.5 | 99.9 | 29.2 | 7.2 | 14.6 | 28.1 | 55.8 | 112.7 |
| MEPP | 0.1 | 98.5 | 4.1 | 0.9 | 1.9 | 3.8 | 8.07 | 16.0 |
| MCOP | 0.4 | 99.7 | 14.9 | 3 | 6.2 | 13.8 | 32.5 | 79.9 |
| MCNP | 0.1 | 99.6 | 3.0 | 0.9 | 1.6 | 3 | 5.4 | 10.0 |
GM, geometric mean; LOD, limit of detection; MCNP, mono-(7-carboxy-2,7-dimethylheptyl) phthalate; MCOP, monocarboxyoctyl phthalate; MiBP, mono-isobutyl pthalate; MnBP, mono-n-butyl phthalate; MBzP, mono-benzyl phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MEP: Mono-ethylphthalate.
Relationship Between Urinary Levels of Phthalate Metabolites and Pulmonary Function Indicators
The relationship between the continuous and quartile levels of the ten phthalate metabolites and the four pulmonary function indicators is presented in Table 3. Based on the results of the multivariate linear regression models, we observed significant negative correlations between eight phthalate metabolites, except for MOiNP and MOBzP. This indicates that higher concentrations of phthalate metabolites are associated with decreased maximal ventilatory capacity in the lungs. Additionally, with the exception of MOiNP, MOBP, and MOBzP, the remaining seven phthalate metabolites showed significant negative associations with ppFEV1/FVC. This suggests that higher concentrations of phthalate metabolites are linked to increased airflow resistance. Regarding ppFEF 25% to 75%, only MMP, MEP, MBP, and MBzP displayed significant negative associations, indicating that higher phthalate metabolite concentrations are associated with decreased small airway patency. MEOHP is linked to a potential adverse impact on ppFEV1, as indicated by a coefficient of −0.039. MEPP demonstrates a significant negative effect on ppFVC, with a coefficient of −0.01, signifying its considerable influence on pulmonary function. Conversely, MBzP displays a clear positive correlation with ppFEF 25% to 75%, with a coefficient of 0.02. Lastly, MEP shows a slight negative trend with ppFEV1, marked by a coefficient of −0.01 (Table 3, Fig. 2).
TABLE 3.
Relationship Between Urinary Levels of Phthalate Metabolites and Pulmonary Function Indicators
| Predictor | ppFVC | ppFEV1 | ppFEF (25%–75%) | ppFEV1/FVC |
|---|---|---|---|---|
| MCNP | −0.02 (−0.05, 0.02) | −0.01 (−0.04, 0.02) | −0.01 (−0.04, 0.02) | −0.01 (−0.04, 0.02) |
| MCOP | 0.00 (−0.0, 0.01) | 0.00 (−0.01, 0.01) | 0.01 (−0.00, 0.01) | −0.01 (−0.01, 0.01) |
| MEHHP | 0.00 (−0.02, 0.03) | 0.01 (−0.01, 0.03) | 0.01 (−0.02, 0.03) | 0.01 (−0.02, 0.03) |
| MEOHP | −0.01 (−0.06, 0.04) | −0.04 (−0.08, 0.01) | −0.01 (−0.06, 0.04) | −0.02 (−0.07, 0.02) |
| MEPP | −0.01 (−0.02, −0.00) | −0.01 (−0.02, 0.00) | −0.01 (−0.02, 0.01) | 0.01 (−0.01, 0.02) |
| MECPP | 0.00 (−0.01, 0.01) | 0.0062 (−0.0045, 0.0169) | 0.01 (−0.01, 0.01) | 0.01 (−0.01, 0.02) |
| MBzP | 0.01 (−0.00, 0.02) | 0.01 (−0.00, 0.02) | 0.02 (0.01, 0.04) | 0.02 (0.01, 0.03) |
| MEP | −0.00 (−0.00, 0.00) | −0.01 (−0.0, −0.0) | −0.01 (−0.00, 0.00) | −0.01 (−0.00, 0.00) |
| MiBP | 0.00 (−0.01, 0.02) | 0.001 (−0.01, 0.01) | −0.01 (−0.02, 0.01) | −0.01 (−0.02, 0.01) |
| MnBP | −0.00 (−0.01, 0.01) | −0.01 (−0.01, 0.01) | −0.01 (−0.01, 0.01) | −0.01 (−0.01, 0.01) |
FEV1; the forced expiratory volume in the first second in percent predicted values; FEF; the mid exhalation forced expiratory flow rate in percent predicted values; FEV1/FVC; ratio of FEV1 to FVC in percent predicted values; FVC; the forced vital capacity in percent predicted values; MCNP, mono-(7-carboxy-2;7-dimethylheptyl) phthalate; MCOP, monocarboxyoctyl phthalate; MiBP, mono-isobutyl pthalate; MBzP, mono-benzyl phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MEP: Mono-ethylphthalate; MnBP, mono-n-butyl phthalate.
FIGURE 2.
Relationship between urinary levels of phthalate metabolites and pulmonary function indicators. The regression coefficients and their 95% confidence intervals for each phthalate metabolite with respect to each pulmonary function indicator were presented.
Relationship Between Urinary Phthalate Metabolite Co-exposure With Pulmonary Function Indicators
According to the segmented linear regression model results, we found that the impact of phthalate metabolite concentrations on pulmonary function indicators significantly increases when these concentrations surpass a certain threshold. MEP, MnBP, MiBP, MBzP, MEOHP, MEHHP, MECPP, MEPP, MCOP, and MCNP were represented as metabolite 0, metabolite 1, metabolite 2, metabolite 3, metabolite 4, metabolite 5, metabolite 6, metabolite 7, metabolite 8, and metabolite 9 in the figure. For example, when the concentration of MMP exceeds 0.03 μg/mL, the regression coefficient for its effect on ppFVC changes from −0.02 to −0.08, which demonstrates a substantial amplification of the detrimental effect of MMP on ppFVC. Similar results were observed for other phthalate metabolites and their respective pulmonary function indicators (Fig. 3).
FIGURE 3.
The results of the segmented linear regression models are presented, showing the regression coefficients and 95% confidence intervals for each phthalate metabolite with respect to each pulmonary function indicator, along with the breakpoint values of phthalate metabolite concentrations.
Through the WQS method, we found a significant negative impact on ppFEV1 due to the combined effects of various phthalate metabolites, suggesting that their synergistic action may reduce lung flow. However, for the other three pulmonary function indicators, the combined effects of multiple phthalate metabolites did not reach statistical significance, indicating that their synergistic impact may be less pronounced (Fig. 4).
FIGURE 4.
Relationship between urinary phthalate metabolite co-exposure with pulmonary function indicators.
DISCUSSION
Recent studies have explored phthalate effects on lung function, with a meta-analysis indicating elevated asthma and allergy risks linked to PVC phthalate exposure.14 Additionally, a prior investigation involving pregnant women from minority groups in New York City discovered correlations between phthalate concentrations in urine and air with respiratory symptoms and asthma diagnoses.15 Existing research has produced mixed results regarding the link between phthalate metabolites in urine and pulmonary function among children. For instance, Hsu et al16 reported a study involving children aged 3 to 9 years, MnBP was linked to diagnosed asthma and daytime respiratory symptoms. Similarly, Beko et al17 reported an increased risk of wheeze and bronchitis with metabolites of di-(2-ethylhexyl) phthalate. Gascon et al18 observed significant associations between maternal urinary levels of di-(2-ethylhexyl) phthalate and MBzP in children with asthma, suggesting that high-molecular-weight phthalates may enhance the risk of respiratory disease in childhood. Conversely, Lin et al19 noted a negative relationship between phthalate exposure and lung function parameters such as FEV1 and FVC in a birth cohort. These inconsistencies may originate from differences in cohort characteristics, exposure magnitudes, or methodological designs. These findings collectively suggest that phthalate exposure may have adverse effects on lung function and health, particularly among sensitive populations, especially in children.
Our study, based on NHANES 2007–2012, assessed the association between urinary phthalate metabolites and pediatric pulmonary function indices. The results of this study revealed that most phthalate metabolites were negatively associated with pulmonary function indicators, especially when their concentrations exceeded certain threshold values. Furthermore, the findings of this study revealed that the cumulative effects of multiple phthalate metabolites exerted a notable influence on ppFEV1, with values marginally exceeding those reported in Cakmak’s study.4 Our investigation further uncovered notable synergistic impacts among various phthalate metabolites on ppFEV1, hinting at potential interactions within the body. These results corroborate previous scholarly endeavors.20 These findings emphasize the importance of considering not only individual exposure levels but also the nonlinear and intricate nature of exposure when evaluating the impact of phthalates on lung function in children.
At present, numerous studies have delved into the mechanisms associated with the influence of phthalates on pulmonary function, the primary focus centers on the following key aspects, First, phthalates are considered endocrine disruptors, and they may affect lung development and function by interfering with hormone signaling. For instance, phthalates might disrupt the synthesis and action of thyroid hormones, which plays a critical role in lung development and differentiation.21 Phthalates could induce oxidative stress and inflammatory responses, damaging lung cells and tissues. Additionally, phthalates have the potential to elevate the production of reactive oxygen species, which can subsequently trigger lipid peroxidation, ultimately resulting in cellular demise and tissue impairment.22 Furthermore, phthalates have the capacity to stimulate immune cells, notably macrophages and lymphocytes, resulting in the secretion of inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), which in turn initiates a cascade leading to lung inflammation.23 These mechanisms might interact or synergize, collectively contributing to the impact of phthalates on lung function. Therefore, while these researches indicate a potential link between phthalate exposure and pulmonary inflammation in children, further experimental research is required to validate and elucidate the specific roles and interactions of these mechanisms. The strength of this research could help identify ways to reduce phthalate exposure and improve respiratory health outcomes in children.
Although the NHANES serves as a comprehensive national survey, our research encounters several constraints. Foremost among them is the cross-sectional design, which hampers our ability to definitively establish causal connections between phthalate metabolites and lung function. While the cross-sectional study was well-suited for identifying associations within a specific population at a particular point in time, the cross-sectional study cannot infer causality.24 Second, although we adjusted for several confounders, residual confounding may still exist due to unmeasured variables, such as environmental or genetic factors, which could introduce bias into our results. Third, Our study did not explore potential interaction effects between phthalate metabolites and other factors, such as age, gender, or race, on pulmonary function. In the future studies, longitudinal designs and incorporate interaction analyses are needed to confirm our findings and further refine our understanding of the relationship between phthalate exposure and respiratory health.
ACKNOWLEDGMENTS
All methods were performed in accordance with STROBE Guidelines.
Footnotes
Funding sources: This work was supported by Yunnan Provincial Department of Science and Technology Kunming Medical Joint Special General Project (Grant No.:202401AY070001-292).
Authors' contributions: Conceived and designed the research: Kai Liu; Hong-min Fu.
Analyzed the data: Pei-long Li; Hong-min Fu.
Writing-original draft preparation: Feng Li, Pei-long Li.
Writing-review and editing: Kai Liu.
Data availability statement: If reasonably requested, it can be obtained from the corresponding author.
AI section of information: No AI was utilized at any stage during research development, design, data collection and manuscript preparation.
Support relevant to publication: None Declared.
Conflict of interest: None declared.
Ethical Considerations & Disclosure: The NHANES data used in this analysis are accurate medical data that can be accessed for free. All personal information in the database has been deidentified, replaced with random codes instead of patient identifiers, ensuring anonymity. So, Consent for participate was waived by the local Ethics Committee of Kunming Children’s Hospital & Children's Hospital Affiliated to Kunming Medical University. The study was approved by the Ethics Committee of Kunming Children’s Hospital & Children's Hospital Affiliated to Kunming Medical University (date: 10/10/2024) and all methods were performed in accordance with relevant guidelines and regulations.
Supplemental digital contents are available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.joem.org).
Contributor Information
Pei-long Li, Email: 243874117@qq.com.
Hong-min Fu, Email: 3063753054@qq.com.
Feng Li, Email: 2594069523@qq.com.
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