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. 2025 Sep 19;4(2):212–224. doi: 10.1021/envhealth.5c00050

Urinary Organophosphate Esters and Increased Oxidative Stress in Healthy Older Adults: The China BAPE Study

Chenfeng Li †,‡, Xiaojie Guo †,§, Huimin Ren †,∥, Yibo Xu †,∥, Jianlong Fang †, Peijie Sun †,⊥, Enmin Ding †,#, Yifu Lu †, Chenlong Li †,‡, Ximiao Zhao †,∥, Keke Long †,∥, Kangning Cao †,∇, Jiankun Qian †,∥, Yuanyuan Chen †, Yu Wang †, Fuchang Deng †, Minmin Hou ○, Yali Shi ○, Yaqi Cai ○, Shilu Tong †,◆, Tong Shen ∇, Song Tang †,⊥,¶,*, Xiaoming Shi †,⊥,¶,*
PMCID: PMC12930313  PMID: 41743804

Abstract

Exposure to organophosphate esters (OPEs) is associated with the development of geriatric diseases. However, the impact of OPEs on oxidative stress, a precursor of geriatric diseases, remains unclear in older adults. Based on the China BAPE study, a panel study with multiple repeated measurements, we collected urine samples from 76 healthy older adults to measure OPEs and oxidative stress markers, aiming to investigate both individual and combined OPE exposures in relation to oxidative stress. Our results indicated that Dibutyl phosphate (DBP), Bis­(2-ethylhexyl) phosphate (BEHP), Bis­(1,3-dichloro-2-propyl) phosphate (BDCIPP), Diphenyl phosphate (DPHP), and Bis­(2-methylphenyl) phosphate (BMPP) exposure were significantly associated with increased levels of 8-hydroxy-2’-deoxyguanosine (8-OHdG), 8-hydroxyguanosine (8-OHG), and malondialdehyde (MDA). Household heating status had a modifying effect on these associations. Generalized additive mixed models revealed potential linear and nonlinear associations between OPEs and oxidative stress markers. The quantile g-computation and Bayesian kernel machine regression further found positive effects of OPEs mixtures on 8-OHdG, 8-OHG, and MDA. Weighted distribution analyses identified BEHP as a potentially contributing component affecting oxidative stress. These findings not only illuminate the health hazards of OPE exposures but also aid in understanding the link between OPEs and adverse health outcomes in older adults.

Keywords: organophosphate esters, oxidative stress, mixture exposure, older adults, panel study

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1. Introduction

The pervasive presence of chemicals within the environment poses a major threat to human health, − contributing significantly to the global burden of diseases. Despite this, only a fraction of these chemicals have undergone rigorous safety and toxicity assessments, leading to a serious underestimation of the health hazards associated with exposure to environmental chemicals. Among these, organophosphate esters (OPEs), a type of flame retardants replacing traditional brominated ones, have received considerable attention as emerging pollutants. Their application spans a diverse array of industries including electronics, furniture, plastics, and food packaging, , contributing to a steady increase in global production and consumption. , In 2015 and 2023, the global consumption of OPEs reached approximately 680,000 and 860,000 tons, respectively. OPEs, commonly integrated into products by physical addition instead of chemical combination, could easily release into the environment through volatilization or leaching, , thereby persisting for extended durations and dispersing over long-distance. OPEs have been ubiquitously found in various environmental matrices − and in various human biological samples, − with toxic effects on multiple systems such as endocrine, reproductive, hematological, and nervous systems. −

Oxidative stress epitomizes a pathological condition wherein the generation of reactive oxygen species (ROS) surpasses the endogenous scavenging activities of body, resulting in an imbalance between pro-oxidative and antioxidative systems and consequently damaging nucleic acids, proteins, and lipids. , The formation of 8-hydroxy-2’-deoxyguanosine (8-OHdG) and 8-hydroxyguanosine (8-OHG) arises from the interaction of DNA and RNA nucleobases with hydroxyl radicals, while malondialdehyde (MDA) predominantly emerges from the oxidative degradation of polyunsaturated lipids. Epidemiological evidence has linked OPE exposures with various diseases such as diabetes, metabolic syndrome, cognitive impairment, and cancer. However, the intricate pathways and mechanisms by which OPEs contribute to disease development remain unclear, even though oxidative stress is postulated to play an important role in the pathophysiology of these conditions. − Only a few epidemiological investigations have underscored the association between exposure to OPEs and increased oxidative stress. ,, For example, significant associations between exposure to Bis­(1,3-dichloro-2-propyl) phosphate (BDCIPP) and elevated levels of 8-OHdG have been observed among workers in electronic waste recycling and waste incineration. , Additionally, a cross-sectional study revealed a correlation between prenatal OPE exposures and thyroid dysfunction in mothers and their offspring, wherein DNA oxidative damage and lipid peroxidation played mediating roles.

Despite these findings, the relationship between OPE exposures and oxidative stress remains ambiguous. Notably, most existing studies have focused on exposure to single OPE compound, disregarding the fact of concurrent exposure to multiple OPE compounds. Given the continuous decline in physiological functions and the weakening of defense systems, older adults are particularly susceptible to environmental exposures. , Consequently, it is essential to explore the environmental determinants of adverse health outcomes in older adults, especially in the context of the increasing trend of population aging and the substantial disease burden in this group. , However, data pertaining to the relationship between exposure to OPEs and oxidative stress in older adults are scarce.

Based on the China Biomarkers of Air Pollutant Exposure (China BAPE) Study, this investigation aimed to systematically elucidate the association between exposure to five OPE metabolites and three oxidative stress markers in urine. In addition, we evaluated the mixture effects of these OPE metabolites on oxidative stress markers, pinpointing potentially contributors to each marker. The findings from this study are anticipated to offer epidemiological evidence for the systematic evaluation of the impact of OPE exposures on oxidative stress within the older adult population, and provide a reference for the future research to link OPE exposures to geriatric-related diseases and exploration of possible mechanisms.

2. Methods

2.1. Study Population

The China BAPE Study involved the recruitment of 76 healthy individuals aged 60 to 69 years from the Dianliu Community in Jinan City, Shandong Province, China. The investigation spanned from September 2018 to January 2019, and during this period, five epidemiological surveys were conducted alongside the collection of biological samples. A total of 353 measurements were obtained. To ensure a homogeneous cohort, the subjects were selected with good compliance, regular daily routines, and the absence of detrimental habits (e.g., smoking and drinking), acute or chronic diseases, and medication usage. A standardized diet was provided to all participants freely over a continuous period of 5 days in each survey wave in order to minimize potential confounding factors arising from varied diets among the participants. The project design and implementation process were detailed in the previous reports. , After excluding 17 measurements with missing oxidative stress marker data and two participants with only one measurement, a total of 74 older adults with a total of 334 measurements were included, all of whom participated in at least two surveys. All participants signed a written informed consent, and the study was approved by the Ethical Commissions of the National Institute of Environmental Health (NIEH), China CDC (No. 201816).

2.2. Onsite Investigation and Physical Examination

During each investigation phase, comprehensive data were gathered from all the participants. Professional health staff from the Ankang Community Hospital in Jinan conducted thorough physical examinations and collected 70 mL urine samples from each participant. − Various aspects of information were collected, encompassing general demographics, details of living environment, behavioral activity patterns, and anthropometric measurements (e.g., height, weight, and waist circumference). Additionally, a 24-h log questionnaire survey was administered three times (at the beginning, middle and end of this study) to capture comprehensive data about their additional dietary intake and lifestyles.

2.3. Assessment of Urinary OPEs concentrations

Totally, 11 OPE metabolites were measured in urine samples. The specific methods for detecting OPEs, along with the corresponding raw data and quality control procedures, have been previously reported. , In brief, the detection process involved several steps. Initially, mixed internal standards and sodium acetate buffer were added to the urine, which was then digested overnight using β-glucuronidase/aryl sulfatase enzymes. Subsequently, enrichment and purification were performed using a STRATA-X-AW cartridge (3 cm3, 60 mg; Phenomenex Inc., Torrance, CA) and a nylon filter to ensure sample integrity. Finally, the sample was separated and detected using an Acquity UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm particle size, Waters). For the subsequent statistical analysis, five OPE metabolites with detection frequencies above 50% were incorporated, including Diphenyl phosphate (DPHP), Bis (2-ethylhexyl) phosphate (BEHP), BDCIPP, Diphenyl phosphate (DBP), and Bis (2-methylphenyl) phosphate (BMPP).

2.4. Assessment of Urinary Oxidative Stress Markers and Creatinine concentrations

Urinary 8-OHdG and 8-OHG were detected using high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). Briefly, internal standard and derivatization reagent solution were added into urine samples (0.5 mL), followed by solid phase extraction (SPE) was then performed to prepare the samples. The eluate was then dried and reconstituted with water–methanol mixed solvent, after which further analysis was conducted using HPLC-MS/MS in both positive and negative ionization modes. Urinary MDA was measured with ELISA assay kits (Shanghai Enzyme-linked Biotechnology, Shanghai, China), based on its reaction with horseradish peroxidase (HRP). Aliquots of the urine samples were combined with a solution containing HRP and incubated in a water bath or incubator at 37 °C for 60 min. After discarding the solution, the plate was washed with washing solution. Subsequently, substrates were added and incubated at 37 °C in the dark for 15 min, after which absorbance values were measured at a wavelength of 450 nm. Urinary creatinine levels were measured using picric acid spectrophotometry with a spectrophotometer (Xinyue-T6, Persee Analytics, Beijing, China).

2.5. Statistical Analyses

Continuous variables were presented as the mean ± standard deviation (SD), while categorical variables were expressed in percentages (%). Considering the low detection rates of certain OPEs, the analysis focused on five OPEs with detection rates exceeding 50%. Metabolites below the limit of detection (LOD) were substituted with half of the LOD. Concentrations of OPEs and oxidative stress markers were corrected using urinary creatinine to account for individual variations in urinary dilution. Additionally, urinary OPE metabolites and oxidative stress markers underwent natural logarithm-transformation to enhance the normality of the data for subsequent analysis.

2.5.1. Single Pollutant Approach

We used linear mixed-effects model (LMM) with random terms to analyze the differences in the concentrations of OPEs and oxidative stress markers among the different survey months. In data analysis, given the nature of repeated measurements, individual subject numbers were treated as a random effect within the model. In addition, LMM was employed to examine the association between urinary OPE metabolites and oxidative stress markers at the individual level, and creatinine-adjusted concentrations of OPEs were modeled as continuous variables and quartile categorical variables for analysis. The model incorporated several confounding factors, including age (continuous, years), gender (categorical: male and female), BMI (continuous, kg/m2), educational attainment (categorical: junior school degree or less, senior high school, and college degree or higher), annual household income (categorical: ≤ 7, 7–10, and >10 × 10,000 CNY per years), and household heating condition (categorical: yes and no). Since the previous research results based on China BAPE have pointed out that other diets of healthy older adults may be the main source of urinary OPE exposures. Therefore, we also considered other diet (other dietary behaviors besides receiving a standardized diet during the survey period; categorical: yes and no) as a covariate. Results were expressed as (e β×IQR – 1) × 100%, representing the percentage change in oxidative stress markers for each interquartile range (IQR) increase in urinary OPE metabolites. P values were adjusted using the false discovery rate (FDR) with the Benjamini–Hochberg correction method.

Furthermore, a generalized additive mixed model (GAMM) with cubic regression splines was employed to visualize the exposure-response trends between exposure to OPEs and oxidative stress markers to explore potential nonlinear associations. Extreme OPE concentrations less than the first percentile and higher than the 99th percentile were excluded to reduce their disproportional impact on the spline fitting in the GAMM analysis. , The effective degree of freedom (edf) as a statistical parameter from the GAMM was used to define nonlinear associations, with edf equal to 1 reflecting the presence of a linear association, edf between 1 and 2 being considered a weakly nonlinear association, and nonlinear associations being considered to be tenable when edf was greater than or equal to 2. The adjusted covariates in the GAMM were consistent with those used in the LMM.

A series of stratified analyses were conducted according to gender (male and female), BMI (<24 and ≥ 24 kg/m2), and household heating status (yes and no) in participants. Multiplicative interaction terms between each OPE marker and each stratification variable were added to the LMM to determine whether the stratification variable had a significant effect of modification on the association between OPE exposures and oxidative stress markers.

2.5.2. Mixtures Approach

It is worth noting that the single pollution model may mask the joint effects of coexposure to pollutants on health outcomes. Therefore, we further used two mixture exposures modeling approaches, including quantile g-computation (qgcomp) and Bayesian kernel machine regression (BKMR), to elucidate the joint effects of OPEs on oxidative stress markers and to corroborate the results from the two models. Meanwhile, these two models have been proven to be suitable for the repeated measurement data. −

Qgcomp as a multipollutant mixture exposure analysis model that can simultaneously estimate the effects of each component in the mixture of OPEs on oxidative stress markers. This approach was used to unravel the associations between the OPE metabolites and oxidative stress markers. The outcomes were interpreted as (e β – 1) × 100%, representing the percentage change (%) in the oxidative stress markers for each IQR increase in the levels of urinary OPEs mixture. To identify the crucial OPE components contributing to the overall effect, a discerning screening threshold of ≥ 1/q = 1/5 was set, where q represents the number of exposed variables in the qgcomp analysis. This threshold was instrumental in determining which urinary OPE metabolites stood out as significant components of the overall effect. The qgcomp analysis underwent a rigorous 3000 iterations, with the covariates maintaining steadfast consistency with those in the LMM.

In addition, we constructed the BKMR model to evaluate the association between the mixture of OPEs and oxidative stress markers. It can depict the potential nonparametric high-dimensional exposure-response function by using kernel functions, while allowing for the existence of nonlinear and nonadditive interactions. , In the BKMR model, we normalized the log-transformed OPE concentrations to minimize the impact of outliers or extreme values on the results. All BKMR model-based analysis processes were iterated 30,000 times, and the covariates were consistent with the LMM. We first fixed all five OPEs at the median level and calculated the cumulative changes in oxidative stress markers caused by every increase of five percentiles. In addition, we fixed the levels of the other four OPEs at the median based on the exposure-response function to visualize the exposure-response curves between a specific OPE and oxidative stress markers. Finally, bivariate exposure-response relationships were established to explore potential interactions among the five OPEs.

All statistical analyses were utilized using R version 4.3.2, with a bilateral test and a significance level (α) set at 0.05.

3. Results

3.1. Characteristics of the Participants

A total of 74 participants were included in this study, with each participant attending a minimum of two follow-up visits (Table ). The overall sample size consisted of 334 measurements. The average age of the participants was 65.01 ± 2.78 years, with 37 individuals (50.0%) being male. The mean BMI of all participants was 25.05 ± 2.32 kg/m2. Among the five surveys, 211 person-time measurements (63.17%) showed adherence to other diets in addition to receiving standardized diets provided uniformly.

1. Characteristics of a Total of 334 Measurements for 74 Participants with 5 Monthly Repeated Surveys .

    visit 1
 visit 2
 visit 3
 vsit 4
 visit 5
characteristics total (N = 74, n = 334) (N = 58) (N = 65) (N = 69) (N = 71) (N = 71)
age (years) 65.01 ± 2.78 64.93 ± 2.74 64.85 ± 2.78 65.10 ± 2.80 65.07 ± 2.83 65.08 ± 2.82
sex            
male 37 (50.0) 26 (44.83) 32 (44.83) 34 (44.83) 35 (49.3) 36 (50.7)
female 37 (50.0) 32 (55.17) 33 (50.77) 35 (50.72) 36 (50.7) 35 (49.3)
BMI (kg/m2) 25.05 ± 2.32 24.81 ± 2.35 25.14 ± 2.19 25.11 ± 2.32 25.13 ± 2.30 25.02 ± 2.44
educational attainment            
junior school or below 27 (36.49) 21 (36.21) 25 (38.46) 26 (37.68) 26 (36.62) 26 (36.62)
senior high school 33 (44.59) 27 (46.55) 28 (43.08) 29 (42.03) 31 (43.66) 31 (43.66)
university or higher 14 (18.92) 10 (17.24) 12 (18.46) 14 (20.29) 14 (19.72) 14 (19.72)
other diet            
yes 211 (63.17) 28 (48.28) 44 (67.69) 42 (60.87) 47 (66.2) 50 (70.42)
no 123 (36.83) 30 (51.72) 21 (32.31) 27 (39.13) 24 (33.8) 21 (29.58)
annual household income (10,000 CNY/year)            
≤7 104 (31.14) 18 (31.03) 21 (32.30) 22 (31.88) 22 (30.99) 21 (29.58)
7 ∼ 10 114 (34.13) 18 (31.03) 22 (33.85) 24 (34.78) 25 (35.21) 25 (35.21)
>10 116 (34.73) 22 (37.94) 22 (33.85) 23 (33.34) 24 (33.80) 25 (35.21)
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Note: Characteristics of study participants presented as percentage (n %) or the mean with standard deviation (Mean ± SD). Abbreviations: SD, standard deviation; BMI, body mass index.

3.2. Overview of Urine OPE Metabolites and Oxidative Stress Markers

The detection rate for the five OPE metabolites was over 50%, including DBP, BEHP, BDCIPP, DPHP, and BMPP. Figure shows the differences in the distribution of OPEs and oxidative stress markers across the five investigation periods. After adjusting for creatinine, BEHP, DPHP, and BDCIPP were the predominant OPE metabolites in urine, with geometric mean concentrations of 0.21, 0.15, and 0.12 μg/g Cre, respectively. The geometric mean concentrations of DBP and BMPP were 0.06 and 0.01 μg/g Cre, respectively. In addition, all three oxidative stress markers were detected in every urine sample. The geometric mean concentrations of 8-OHdG, 8-OHG, and MDA were 7.06, 9.01, and 51.52 μg/g Cre, respectively (Table S1).

1.

1

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Differences in creatinine-adjusted concentrations (μg/g Cre) of OPEs and oxidative stress markers in different investigated months using linear mixed-effects models. NOTE: *: P < 0.05, **: P < 0.01, ***: P < 0.001. Abbreviations: DBP, Dibutyl phosphate; BEHP, Bis (2-ethylhexyl) phosphate; BDCIPP, Bis (1,3-dichloro-2-propyl) phosphate; DPHP, Diphenyl phosphate; BMPP, Bis (2-methylphenyl phosphate); 8-OHdG, 8-hydroxy-2’-deoxyguanosine; 8-OHG, 8-hydroxyguanine; MDA, Malondialdehyde; ns: No significant.

3.3. Associations between Exposure to Individual OPE Metabolites and Oxidative Stress Markers

After adjusting for various covariates, including age, gender, BMI, educational attainment, annual household income, other diets, and household heating status, we observed significant and positive associations between all five OPEs and the three oxidative stress markers (Table ). Specifically, DBP had the strongest association with 8-OHdG and 8-OHG, with each IQR increase in exposure leading to a 34.23% (95% CI: 23.39%, 46.03%) and 36.54% (95% CI: 24.88%, 49.29%) increase in 8-OHdG and 8-OHG, respectively. BEHP had the strongest association with MDA, with each IQR increase in exposure corresponding to a 56.14% (95% CI: 43.77%, 69.57%) increase in MDA levels. In addition, when OPEs were treated as categorical variables in the LMM, we still observed positive dose–response relationships between most OPEs quartiles and oxidative stress markers (P-trend <0.001). For example, compared with the lowest quartile of DBP, the levels of 8-OHdG in the second, third, and highest quartiles increased by 13.58% (95% CI: 0.03%, 28.96%), 28.40% (95% CI: 12.62%, 46.39%), and 53.93% (95% CI: 33.93%, 76.90%), respectively. However, the association between BMPP and MDA was not significant in the categorical model.

2. Percentage Change (95% CI) in the Levels of Urinary Oxidative Stress Markers with an Interquartile Range (IQR) Increase in Urinary OPEs Using Linear Mixed-Effects Models (334 Measurements from 74 Participants) .

OPEs 8-OHdG 8-OHG MDA
DBP (μg/g Cre)      
Q1 (≤0.02) reference reference reference
Q2 (0.02–0.07) 13.58% (0.03%, 28.96%) 16.86% (2.13%, 33.72%) 5.32% (−12.53%, 26.81%)
Q3 (0.07–0.18) 28.40% (12.62%, 46.39%)# 29.73% (12.84%, 49.16%)# 29.21% (6.63%, 56.57%)#
Q4 (≥0.18) 53.93% (33.93%, 76.90%)# 60.87% (38.86%, 86.37%)# 80.14% (47.05%, 120.68%)#
P-trend <0.001* <0.001* <0.001*
Continuous (ln) 34.23% (23.39%, 46.03%)# 36.54% (24.88%, 49.29%)# 47.23% (29.99%, 66.75%)#
BEHP (μg/g Cre)      
Q1 (≤0.10) reference reference reference
Q2 (0.10–0.22) –3.16% (−16.21%, 11.93%) –4.19% (−17.75%, 11.60%) 7.87% (−12.11%, 32.39%)
Q3 (0.22–0.42) –6.85% (−26.80%, 18.54%) –1.94% (−23.99%, 26.51%) 15.20% (−18.17%, 62.18%)
Q4 (≥0.42) 17.67% (4.16%, 32.95%)# 18.37% (4.15%, 34.52%)# 59.54% (34.36%, 89.42%)#
P-trend <0.001* <0.001* <0.001*
continuous (ln) 22.52% (15.19%, 30.32%)# 22.92% (15.19%, 31.17%)# 56.14% (43.77%, 69.57%)#
BDCIPP (μg/g Cre)      
Q1 (≤0.08) reference reference reference
Q2 (0.08–0.16) 10.28% (−2.90%, 25.26%) 16.26% (1.48%, 33.18%) 16.77% (−2.94%, 40.47%)
Q3 (0.16–0.28) 19.35% (5.05%, 35.60%)# 19.25% (4.06%, 36.65%)# 29.39% (7.51%, 55.73%)#
Q4 (≥0.28) 58.91% (39.93%, 80.45%)# 59.18% (38.98%, 82.30%)# 99.67% (66.03%, 140.12%)#
P-trend <0.001* <0.001* <0.001*
continuous (ln) 27.09% (19.95%, 34.66%)# 26.04% (18.48%, 34.08%)# 44.29% (32.78%, 56.81%)#
DPHP (μg/g Cre)      
Q1 (≤0.07) reference reference reference
Q2 (0.07–0.11) 8.07% (−4.94%, 22.85%) 4.29% (−8.93%, 19.44%) 7.51% (−11.17%, 30.12%)
Q3 (0.11–0.19) 28.23% (13.04%, 45.46%)# 25.33% (9.63%, 43.28%)# 30.32% (7.96%, 57.32%)#
Q4 (≥0.19) 66.25% (44.22%, 91.65%)# 69.97% (46.48%, 97.22%)# 91.39% (55.18%, 136.04%)#
P-trend <0.001* <0.001* <0.001*
continuous (ln) 28.24% (21.26%, 35.63%)# 29.74% (22.31%, 37.63%)# 37.48% (26.54%, 49.37%)#
BMPP (μg/g Cre)      
Q1 (≤0.01) reference reference reference
Q2 (0.01–0.02) 11.76% (−1.78%, 27.16%) 10.95% (−3.03%, 26.95%) 12.75% (−7.31%, 37.15%)
Q3 (0.02–0.03) 10.53% (−3.08%, 26.05%) 12.04% (−2.30%, 28.48%) 2.01% (−16.41%, 24.48%)
Q4 (≥0.03) 40.35% (22.34%, 61.00%)# 51.48% (31.39%, 74.63%)# 20.98% (−1.57%, 48.71%)
P-trend <0.001* <0.001* 0.856
continuous (ln) 24.99% (16.48%, 34.12%)# 28.49% (19.35%, 38.32%)# 15.94% (4.10%, 29.12%)#
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NOTE: LMM was adjusted by age, gender, BMI, educational attainment, annual household income, other diet, and household heating situation. #: P FDR < 0.05, *: P < 0.05.

We further evaluated the aforementioned significant positive associations between exposure to OPEs and oxidative stress markers using GAMM (Figure ). Monotonic and weakly nonlinear associations were observed between DPHP and 8-OHdG, 8-OHG, and MDA (1 < edf <2). BEHP, BDCIPP, and BMPP exhibited J-shaped or S-shaped nonlinear associations with 8-OHdG, 8-OHG, and MDA (edf ≥ 2). In contrast, DBP maintained a linear association with 8-OHdG and 8-OHG, while a J-shaped nonlinear trend was observed with MDA. In subgroup analysis, we observed that the household heating status of the participants modified the associations of the five OPEs with 8-OHdG and 8-OHG to some extent, with OPEs tending to perturb the levels of these markers more during the heating period (Figure ). Furthermore, BMI had a modifying effect on the association between BMPP and 8-OHG. There was no additional evidence that gender and BMI modified other associations between exposure to OPEs and oxidative stress markers found in the preceding LMM.

2.

2

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Exposure-response relationships between urinary OPEs and urinary oxidative stress markers based on generalized additive mixed models (GAMM) with cubic regression splines (303 measurements from 72 participants). Abbreviations: DBP, Dibutyl phosphate; BEHP, Bis (2-ethylhexyl) phosphate; BDCIPP, Bis (1,3-dichloro-2-propyl) phosphate; DPHP, Diphenyl phosphate; BMPP, Bis (2-methylphenyl phosphate); 8-OHdG, 8-hydroxy-2’-deoxyguanosine; 8-OHG, 8-hydroxyguanine; MDA, Malondialdehyde; ns: No significant. Abbreviations: edf, effective degree of freedom.

3.

3

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Percentage change and 95% confidence interval (CI) in the levels of urinary oxidative stress markers with an interquartile range (IQR) increase in urinary OPEs stratified by gender, BMI, and household heating status subgroups using linear mixed-effects models (334 measurements from 74 participants). NOTE: #: P FDR < 0.05, *: P < 0.05.

3.4. Association between Exposure to a Mixture of OPEs and Oxidative Stress Markers

Qgcomp was employed to comprehensively assess the combined impact of exposure to a mixture of five OPE metabolites on three oxidative stress markers. There were statistically significant and positive correlations between exposure to the mixture of OPEs metabolites and elevated oxidative stress markers. Specifically, an IQR increase in the mixture of OPEs metabolites was associated with increases in the levels of 8-OHdG, 8-OHG and MDA by 30.26% (95% CI: 21.14%, 40.08%), 33.53% (95% CI: 24.62%, 43.06%), and 53.38% (95% CI: 38.45%, 69.93%), respectively (Figure A and Table S2). Notably, the weight distribution from the qgcomp shows that the potentially OPEs contributing component to the increases in oxidative stress markers varied slightly for each marker. For example, the increase in 8-OHdG levels was related to BEHP (26.7%), BDCIPP (26.6%), DPHP (23.5%), and DBP (23.0%), while the main OPEs driving the increase in MDA levels were BEHP (39.8%) and BDCIPP (23.5%). BEHP had a largest weight ratio among all three oxidative stress markers (Figure B and Table S2).

4.

4

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Association between the mixture of OPEs and oxidative stress markers (334 measurements from 74 participants). (A) Joint effect of OPEs mixture exposure on oxidative stress markers using quantile g-computation (qgcomp); (B) positive and negative weight distributions of individual OPEs on oxidative stress markers using qgcomp; and (C) cumulative effect of OPEs mixture on oxidative stress markers using Bayesian kernel machine regression (BKMR). NOTE: Qgcomp and BKMR model as adjusted by age, gender, BMI, educational attainment, annual household income, other diet, and household heating situation.

The results from the BKMR on the mixed effects of OPEs on oxidative stress markers were consistent with those from qgcomp. Compared with fixing the concentrations of all OPEs at the 50th percentile, the effect estimates for 8-OHdG, 8-OHG, and MDA levels showed significant upward trends as the combined exposure to OPEs increased (Figure C). The univariate exposure-response functions show that DBP, BEHP, BDCIPP, and DPHP had positive trends with 8-OHdG, 8-OHG, and MDA, while BMPP exhibited a S-shaped curve trend with all three oxidative stress markers, similar to the results of GAMM (Figure S1). Finally, we found no interactions among the five OPEs. When the levels of other OPEs were fixed at the median level, the bivariate exposure-response functions at the 25th, 50th, and 75th percentiles for one OPE component were parallel to another OPE component (Figures S2–S4).

4. Discussion

To the best of our knowledge, this is the first study to systematically explore the relationship between exposure to OPE metabolites and oxidative stress in healthy older adults. In this panel study, based on a repeated measures design, we found that urinary OPEs were significantly associated with increased levels of urinary 8-OHdG, 8-OHG, and MDA. Further analyses suggest that exposure to the mixture of OPEs was associated with increased oxidative stress markers, in which BEHP potentially played important roles. These findings provide new insights into the adverse health impacts associated with OPE exposures in healthy older adults.

Previous epidemiological surveys, particularly those among occupational populations and pregnant women, have suggested that exposure to OPEs could induce oxidative stress events, such as nucleic acid damage and lipid peroxidation. ,− However, there is limited evidence concerning older adults. A cross-sectional study conducted in Southern China reported positive associations of BDCIPP, DBP, and DPHP with 8-OHdG levels in occupational populations engaged in e-waste dismantling, which is consistent with our results. Parallel findings were corroborated in a cohort of Puerto Rican pregnant women, wherein urinary concentrations of BDCIPP and DPHP were positively associated with 8-OHdG levels. Aquatic fish models have also supported the occurrence of oxidative nucleic acid damage induced by exposure to Tris­(1,3-dichloro-2-propyl) phosphate (TDCIPP) and Triphenyl phosphate (TPHP), which are metabolic precursors of BDCIPP and DPHP, respectively. , However, there is currently little epidemiological evidence on the effects of OPE exposures on MDA levels. Our results suggest that exposure to DBP, BEHP, BDCIPP, DPHP, and BMPP was associated with increased MDA levels. This finding is consistent with a study of pregnant women and neonates, which found a significant association between higher levels of DBP and DPHP during pregnancy and increased levels of MDA. Variations across different study populations and exposure periods to OPEs may contribute to the observed discrepancies. Studies based on the Escherichia coli culture model confirmed that TDCIPP and TEHP, precursors of BDCIPP and BEHP, respectively, were associated with increased MDA levels. Previous in vitro studies showed that TDCIPP resulted in a 22% increase in MDA levels in PC12 cells, without affecting cell viability. Furthermore, zebrafish studies on TPHP suggest that its potential for inducing lipid oxidative damage. Our study provides novel epidemiological evidence that urinary concentrations of OPEs were significantly and positively associated with multiple markers of oxidative damage in older adults. This contributes to the broader understanding of the potential health impacts of internal exposure to OPEs and emphasizes their roles in inducing oxidative stress, particularly among healthy older adults.

The findings from GAMM suggested positive monotonic associations between exposure to single OPE component and various oxidative stress markers, which were generally consistent with the results of the LMM analyses. Interestingly, as BMPP levels continued to rise, 8-OHdG, 8-OHG, and MDA showed an S-shaped, nonlinear trend of rising, falling, and then rising again. In contrast, the other OPEs showed a monotonical positive relationship with oxidative stress markers, including both linear and nonlinear trends. We hypothesize that these disparities may arise from the distinct physiochemical properties inherent to these OPEs, coupled with their diverse metabolic transformations and modes of action within the body. , However, the observed nonlinear trends should be interpreted with caution, given the currently unclear biological mechanisms. Further research is warranted to elucidate the potential risk threshold of OPE exposures in relation to oxidative stress.

In addition, our subgroup analysis suggests that household heating status may modify the effects of OPEs on oxidative stress markers in healthy older adults. The effects of almost all OPE exposures on 8-OHdG and 8-OHG were exacerbated under heating conditions, including more severe nucleic acid oxidative damage. This finding may be explained by seasonal variations in environmental exposure levels. Evidence suggests that the sources of OPEs in the air differ between heating and nonheating seasons. During the heating season, OPEs are likely to be adsorbed on sulfate particles emitted by coal combustion thereby increasing exposure opportunities. Furthermore, when heating equipment is running, poor indoor ventilation can lead to the accumulation of pollutants, including OPEs, which in turn raises exposure levels.

The findings of this study reveal that both the constructed mixed exposure models, qgcomp and BKMR, clarified the positive combined effects of OPEs on oxidative stress, aligning with outcomes from single exposure models and thereby fortifying the robustness of our results. Limited research has explored the relationship between mixed OPE exposure and oxidative stress markers. Tan et al., based on the BKMR model, found that exposure to OPE mixture, including DnBP, BBOEP, DPHP, BCEP, BCIPP, and BDCIPP, had a positive effect on 8-OHdG and 8-OHG. However, a previous study focusing on children in Hokkaido demonstrated that mixed exposure to OPEs led to an increase in lipid peroxidation markers, hexanoyl-lysine (HEL) and 4-hydroxynonenal (HNE), with DPHP showing a relatively higher impact, while no significant association between mixtures of OPEs and 8-OHdG was observed. Discrepancies between these findings and ours may be attributed to variations in demographic characteristics, as well as differences in the types of OPEs included in the analyses and the levels of OPEs in older adults. In addition, the weight distribution results from qgcomp suggest that BEHP was a potentially contributing component affecting the changes in 8-OHdG, 8-OHG, and MDA levels. There is evidence indicating that BEHP is one of the frequently detected OPEs in textiles, air conditioning systems, and building renovation materials. Moreover, an animal experiment based on zebrafish indicated that BEHP exposure was associated with increased levels of ROS and MDA. Evidence from in human liver cells (HepG2) indicated that TEHP may induce ROS overload, mitochondrial dysfunction, and DNA damage. The prominent oxidative stress toxicity of BEHP warrants further elaboration. Collectively, our study contributed a novel perspective to understanding both the individual and combined impacts of OPE exposures on oxidative stress. And identification of key exposures supported the development of targeted public health policies to mitigate the health impacts of OPEs on older adults.

This study possesses several notable strengths. First, to the best of our knowledge, this is the first study to examine the impact of combined urinary OPE metabolite mixtures on oxidative stress within a healthy older adult population. It leverages data from the China BAPE study. The utilization of a panel design featuring repeated measurements enhances the credibility of our findings compared to cross-sectional studies. , Second, advanced statistical methods and appropriate adjustments for potential confounders were applied, addressing some of the typical limitations in traditional epidemiological studies. We extensively explored the nonlinear trends associated with individual and combined OPE exposures and various oxidative stress markers. Several limitations of this study should be acknowledged. First, the focus on healthy older adults may limit the applicability of our findings to more diverse populations. Second, the cross-sectional nature of the study design limits the ability to draw definitive causal inferences between OPE exposures and oxidative stress biomarkers in older adults. Longitudinal or experimental research is needed to clarify the directionality of these associations and to explore underlying biological mechanisms. Third, the relatively small sample size, particularly within subgroups, may have limited the statistical power and robustness of the results. In addition, due to data constraints, we were unable to assess the potential interaction between OPEs and the antioxidant defense system. Considering the dynamic balance between oxidative stress and antioxidant responses, OPEs may exert important effects within this integrated system. Future research should incorporate larger and more heterogeneous populations as well as a broader panel of oxidative and antioxidant biomarkers to comprehensively evaluate the oxidative toxicity of OPEs.

5. Conclusions

As the first study to explore the effects of both individual and combined exposures to OPEs on oxidative stress in healthy older adults, our results indicate that OPE exposures were significantly associated with increases in multiple oxidative markers in this susceptible population and explore the modifying effects of household heating status on these associations. In addition, our findings provide empirical evidence implicating BEHP as a potential contributor to oxidative stress marker alterations. Our findings not only provide valuable insights into the potential hazards of OPE exposures, but also contribute to a better understanding of the possible link between OPE exposures and adverse health outcomes in older adults. Moreover, the results underscore the importance of enhancing OPEs control measures in the living and residential environments of older adults to mitigate related health risks.

Supplementary Material

eh5c00050_si_001.pdf (1.4MB, pdf)

Acknowledgments

The authors gratefully acknowledge all the participants of the China Biomarkers of Air Pollutant Exposure (BAPE) Study, the Dianliu Community, the Ankang Community Hospital, the Shandong Center for Disease Control and Prevention (CDC), the Jinan CDC and the China BAPE Study team. This study was financially supported by the National Key Research and Development Program of China (No. 2022YFC3702700), the National Natural Science Foundation of China (No. 82025030), and the National Research Program for Key Issues in Air Pollution Control of China (No. DQGG0401) to Prof. Xiaoming Shi.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00050.

  • Details of the concentration distribution of OPEs and oxidative stress markers (Table S1); supporting results of qgcomp analysis (Table S2); supporting results of the BKMR analysis (Figures S1–S4) (PDF)

C.L.: Conceptualization, Methodology, Software, Formal Analysis, Visualization, And Writingoriginal Draft. X.G.: Conceptualization, Methodology, and Validation. H.R.: Methodology and Validation. Y.X.: Validation. J.F.: Methodology, Investigation, and Data curation. P.S.: Methodology and Validation. E.D.: Conceptualization, Methodology, and WritingReview & Editing. Y.L.: Conceptualization, Methodology, Validation, and Investigation. C.L.: Methodology and Validation. X.Z.: Validation. K.L.: Validation. K.C.: Validation. J.Q.: Validation. Y.C.: Methodology, Investigation, and Data curation. Y.W.: Methodology. F.D.: Methodology, Investigation, Data curation, and WritingReview & Editing. M.H.: Validation. Y.S.: Validation. Y.C.: Validation. S.T.: Validation, Supervision, and WritingReview & Editing. T.S.: Validation. S.T.: Conceptualization, Methodology, Investigation, Data curation, Supervision, and WritingReview & Editing. X.S.: Conceptualization, Methodology, Data curation, Supervision, Project administration, Funding acquisition, and WritingReview & Editing.

The authors declare no competing financial interest.

⋈.

Lead contact.

Published as part of Environment & Health special issue “New Pollutants: Challenges and Prospects”.

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