Exposure to OPFRs is associated with obesity and dysregulated serum lipid profiles: data from 2017-2018 NHANES

doi:10.21203/rs.3.rs-3091535/v1

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Exposure to OPFRs is associated with obesity and dysregulated serum lipid profiles: data from 2017-2018 NHANES

https://doi.org/10.21203/rs.3.rs-3091535/v1

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Background

Exposure to organophosphate flame retardant (OPFRs) is widespread in general population. Emerging studies revealed OPFRs have endocrine disturbing property. However, research investigating OPFRs exposure, obesity and serum lipid profiles is limited. We aim to evaluate the association between urinary metabolites of OPFRs, BMI, and serum lipid profiles.

Methods

Data from National Health and Nutrition Examination Survey (NHANES) 2017–2018 was obtained. 1334 adults were enrolled in the current study. Urinary concentrations of bis (1-chloro-2-propyl) phosphate (BCIPP), bis(2-chloroethyl) phosphate (BCEP), bis(1,3-dichloro-2-propyl) phosphate (BDCPP), dibutyl phosphate (DBUP), and diphenyl phosphate (DPHP) were quantified to assess OPFRs exposure. Covariate-adjusted linear and logistic regression models were conducted to explore the associations between log₂-transformed concentrations of OPFRs metabolites, BMI, obesity and serum lipid profiles. Stratified analyses were performed to assess the heterogeneity of associations by age, gender, race, etc.

Results

Increased urinary concentrations of BCEP and BDCPP were positively associated with 0.27 (95% CI: 0.02–0.52, p = 0.0338) and 0.56 (95% CI: 0.25–0.87, p = 0.0004) higher BMI value, respectively. One log₂-unit increase in urinary BCEP concentrations was associated with 10% higher risk for developing obesity (95% CI: 1.02–1.18, p = 0.0096). Additionally, one log₂-unit increase in BDCPP was associated with 19% higher risk for obesity (95% CI: 1.09–1.30, p = 0.0001). Multivariable linear regression showed urinary DPHP concentrations were inversely correlated with serum TG levels (β=-7.41, 95% CI: -12.13 to -2.68, p = 0.0022).

Conclusion

Environmental exposure to OPFRs might contribute to obesity and dysregulated serum lipid in adults. Future prospective researches are warranted to confirm the causal relationship between metabolites of OPFRs and obesity.

Flame retardant

Obesity

BMI

Serum lipid

NHANES

Flame retardants are a class of chemical additives which can reduce the flammability of products[1]. Polybrominated diphenyl ethers (PBDE) have been phased out since 2004 due to its toxicity, environmental persistence, and bio-accumulative property[2]. Organophosphorus flame retardants (OPFRs) have introduced to the market and acted as substitutes for PBDE. Nowadays, OPFRs are widely applied to plastics, furniture, textiles, glues, vehicles, electrical equipment, and electronic devices[3]. Epidemiological study revealed OPFRs exposure was widespread among general population. They can be ubiquitously detected in water[4], air[5], and sediment[6]. Regrettably, emerging evidence suggested the exposure to OPFRs exerted adverse effect on human health[7], such as endocrine disturbance[8], nephrotoxicity[9], neurotoxicity[10], and carcinogenicity[11]. Widespread exposure to OPFRs and these toxicity properties have raised concern whether OPFRs are safer replacement for PBDE.

Obesity has emerged as a global public health concern due to its dramatically increasing prevalence rate during the past two decades[12]. According to World Health Organization (WHO), more than 650 million adults have obesity globally. Obesity is associated with the development of various kinds of cancers, cardiovascular diseases, and diabetes[13]. Notably, it has ranked as the 5th most common leading cause of death[14]. Body mass index (BMI) is a commonly used and simple index to evaluate obesity[15], and the cut-off value for obesity is 30kg/m²[16] according to the WHO criteria. Environmental factors are increasingly recognized as major contributor to obesity epidemic because excessive energy intake and insufficient physical activity could not fully account for the sharply increased prevalence of obesity[17]. Of note, exposure to endocrine disrupting chemicals, such as perfluoroalkyl and polyfluroalkyl substances (PFAS) and di-2-ethylhexyl phthalate (DEHP) was associated with weight gain in adulthood[18]. Obesity is associated with dyslipidemia in adults, and the prevalence of dyslipidemia is increasing[19]. Intriguingly, accumulated evidence indicated exposure to endocrine disrupting chemicals, could disturb lipid homeostasis. Increased hepatic TG levels were observed after tricresyl phosphate (TCP) exposure[20]. Li and his colleagues suggested PFAS exposure was positively associated with total cholesterol and LDL-c[21].

However, epidemiological studies evaluating the relationship between OPFRs exposure and obesity among general population are limited. Herein, this study aims to evaluate whether exposure to FR metabolites contribute to obesity among 1334 participants undergoing the National Health and Nutrition Examination Survey (NHANES). Additionally, the effect of FRs metabolites on obesity was evaluated stratified by age, sex, race, etc. Furthermore, the associations between urinary concentrations of FRs metabolites and blood lipid profiles were explored.

Study population

Briefly, NHANES is a nationally cross-sectional program on the health and nutritional status of adults and children in the United States. NHANES is a representatively survey of the general non-institutionalized population through a complex, multistage probability sampling design[22]. Demographic characteristics, anthropometric data, dietary supplements, questionnaire information, and laboratory parameters are all collected. Recently, NHANES developed a method to measure the metabolites of OPFRs, which can be used as exposure biomarkers: Bis (1-chloro-2-propyl) Phosphate (BCIPP), Bis(2-chloroethyl) phosphate (BCEP), Bis(1,3-dichloro-2-propyl) phosphate (BDCPP), Dibutyl Phosphate (DBUP), Diphenyl phosphate (DPHP), and 2,3,4,5-tetrabromobenzoic Acid (TBBA). Therefore, NHANES 2017–2018 cycle data was obtained in the present study because the metabolites of OPFRs were measured using this novel method. The flow chart of the study population was shown in Fig. 1. Adults aged over 18 who had complete data for analyses were included in the current study. In detail, 9254 participants were enrolled in NHANES 2017–2018. Subjects who were absent for urinary concentrations of OPFRs were excluded (n = 6466). Individuals aged less than 18 and pregnant females were also excluded. In the remaining 1767 participants, only participants with complete data on BMI, HDL-c, TG, and without taking medicine for hyperglycemia were finally included in the study. All procedures were approved by the National Center for Health Statistics Research Ethics Committee and written informed consent were obtained from all participants at recruitment.

Detection of urinary concentrations of OPFRs biomarkers

0.2 ml spot urine specimens were collected, processed, and stored at -30℃ conditions until they were shipped to CDC National Center for Environmental Health for analysis. 6 kinds of urinary OPFRs metabolites were quantified as exposure biomarkers of OPFRs: BCIPP, BCEP, BDCPP, DBUP, DPHP, and TBBA. Briefly, the analytical method is based on enzymatic hydrolysis of urinary conjugates of target analytes, automated off-line solid phase extraction, and isotope dilution high-performance liquid chromatography-tandem mass spectrometry detection[23]. The lower limits of detection (LLOD) for urinary BCIPP, BDCPP, DBUP, and DPHP were 0.1 ng/mL, and LLOD for urinary TBBA was 0.05 ng/mL. TBBA was excluded in subsequent analyses because 93.78% samples were below the LLOD. Urinary creatinine concentrations were measured to account for dilution-dependent sample variation in biomarker concentrations.

The definition of outcomes

The primary outcomes of the present study were BMI value and the occurrence of obesity. Obesity was defined as BMI ≥ 30 kg/m²[24]. The secondary end points were the serum lipid profiles, which included triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c).

Covariates

Age, sex, race, smoking status, drinking status, physical activity, education level, marital status, family poverty income ratio (PIR), and urinary creatinine were included as covariates on the basis of previous literature[25, 26]. The participants included 5 ethnic groups: Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race: including Multi-Racial. Smoking status, drinking status, and physical activity were all obtained from self-reported questionnaire data. Smoking status was divided into current smoker (smoke at least 100 cigarettes and has quitted smoking currently), former smoker (smoke at least 100 cigarettes and quitted smoking currently) and non-smoker (smoke less than 100 cigarettes in the entire life). Drinking status was dichotomized into drinker (drink at least 12 alcoholic drinks per year) and non-drinker (drink less than12 alcoholic drinks per year). Physical activity was referred as none, moderate activity, and vigorous activity [25]. Education level was binned as less than 9th grade, 9-11th grade, high school graduate/GED or equivalent, some college or AA degree, and College graduate or above. Marital status was classified into married/living with partner, widowed/divorced/separated, and never married. Family PIR was divided into 4 groups: <1, 1-1.99, 2-3.99, ≥ 4[27].

Statistical analysis

Mean ± standard deviation were used to present normally distributed variables, whereas median and interquartile range (Q1-Q3) were used to present non-normally distributed data. Especially, urinary concentrations of OPFRs metabolites were expressed as geometric means (GM) and 95% confidence interval (CI). Number and percentage were utilized to describe categorical variables. Student t test, Mann-Whitney test, and Chi-square test were used to compare differences of continuous and categorical variables. Log₂-transformed concentrations of these metabolites were calculated to normalize their distribution and used in subsequent statistical analyses. Biomarker concentrations below the LOD were replaced with LOD divided by the squared root of 2[28]. Spearman’s correlation coefficients among these log₂-transformed concentrations of OPFRs metabolites were calculated. Considering the complex sampling design in NHANES, survey weights were used in statistical regression models[29]. Multivariate linear regression models were conducted to evaluate the associations between urinary metabolites of OPFRs, BMI and serum lipid profiles. Log₂-transformed concentrations of urinary concentrations of metabolites of OPFRs were regarded as independent variables. β and 95% CI were calculated. Multivariate logistic regression analyses were performed to assess the relationship between urinary concentrations of OPFRs metabolites and obesity. Odds ratio (OR) and 95% CI were calculated. Non-linear relationships between urinary FR metabolites and obesity were described using generalized additive model (GAM) and smooth curve fitting. All data analyses were conducted using R software (version 4.1.3) and GraphPad with NHANES-provided sampling weights. Significant levels were set at a two-tailed p = 0.05.

Baseline characteristics of study participants

As shown in Table 1, a total of 1334 individuals were enrolled in the present research. The mean age of included subjects was 45.19 ± 17.59. 656 (49.18%) of them were male, and 678 (50.82%) were female. The majority of the subjects were Non-Hispanic White and Non-Hispanic Black. Over 60% smoked currently, and 11% reported alcohol consumption. 1 in 4 reported vigorous physical activity, more than half of participants received college education, and nearly 3 in 5 were married or lived with partner. The proportion of PIR＜1 was 18.59% and 1 in 4 had hypertension. Other detailed could be obtained from Table 1.

Overview of OPFRs metabolites in urine samples

The concentrations of urinary creatinine levels and log₂-transformed urinary OPFRs metabolites were provided in Table 2. The median concentration of creatinine was 118 mg/dL in urine samples. Figure 2 also displayed the distribution of log₂-transformed urinary concentrations of OPFRs metabolites in the whole participants. BCEP exhibited the highest GM (1.046 ng/mL) compared with other kinds of FR metabolites. Of note, only weakly to moderately significant correlations were found among these biomarkers for FR, with DPHP and BDCPP having the highest correlation (r=0.579, p<0.01) (Figure 3).

Associations of urinary OPFRs metabolites, BMI, and obesity

As shown in Table 3, multiple linear regression model indicated that BCIPP, BCEP, BDCPP, and DBUP were all independently associated with increased BMI value after adjusting age, sex and race, with β coefficient of 0.32 (95% CI:0.03-0.62, p=0.0323), 0.33 (95% CI: 0.09-0.57, p=0.0068), 0.71 (95% CI: 0.46-0.96, p<0.0001), and 0.35 (95% CI: 0.03-0.67, p=0.00310), respectively. However, in fully-adjusted model, only BCEP and BDCPP were positively associated with BMI. A unit increase in log₂BCEP was associated with 0.27 (95%CI: 0.02-0.52) higher BMI value (p=0.0338), and a unit increase in log₂BDCPP was associated with 0.56 (95%CI: 0.25-0.87) higher BMI value (p=0.0004). Then, participants were divided into obese (BMI≥30 kg/m²) and non-obese (BMI＜30 kg/m²) groups. We further explored the effect of these metabolites on the risk of obesity by multivariable logistic regression, and the results were presented in Table 4. Specifically, a log₂ unit increase in BCEP, BDCPP, DBUP, and DPHP were associated with increased 12% (95%CI: 1.05-1.20, p=0.0005), 21% (95%CI: 1.09-1.30, p<0.0001), 10% (95%CI: 1.01-1.20, p=0.0273), and 9% (95%CI: 1.01-1.17, p=0.0206) risk for obesity in minimally-adjusted model, respectively. BCEP and BDCPP were significantly associated with 1.10 (95%CI: 1.02-1.18, p=0.0096) and 1.19 (95%CI: 1.09-1.30, p=0.0001) odds ratio for obesity in fully-adjusted model. Additionally, the relationship between urinary BCEP/BDCPP concentrations and obesity were displayed in Figure Supplementary 1 using generalized additive model (GAM) and smooth curve fitting analysis.

Stratification analyses of associations of urinary BCEP/BDCPP concentrations and obesity

Subgroup analyses stratified by age, sex, race, smoking status, drinking status, activity, education level, marital status, and family PIR were performed. Significant positive associations between urinary concentrations of BCEP and obesity remained in subjects aged over 60, non-smoker, non-drinker, participants received college education, widowed/divorced/separated, and those who with PIR<1. In addition, urinary BDCPP concentrations remained associated with higher risk for obesity among those who aged less than 60, Non-Hispanic White, non-smoker, non-drinker, individuals with moderate activity, received at least high school education, married/lived with partner or never married, family PIR ranged from 1-1.99 and ≥4. The detailed information could be obtained from Figure 4 and Figure 5. GAM and smooth curve fitting analysis were further performed to explore the associations between urinary BCEP/BDCPP and obesity. Nonlinear relationships between urinary BCEP/BDCPP concentrations and obesity were presented in Figure Supplementary 2-10.

Association of urinary concentrations of OPFRs metabolites and serum lipid profiles

We then explored the effect of OPFRs exposure on serum lipid profiles, including TG, TC, HDL-c, and LDL-c. The findings were presented in Table 5. Interestingly, inverse association was found between urinary BCEP concentrations and HDL-c (β=-0.52, 95% CI: -1.00 to -0.09, p=0.0199), urinary BDCPP concentrations and HDL-c (β=-0.55, 95% CI: -1.04 to -0.07, p=0.0250), urinary DPHP concentrations and TG (β=-5.67, 95% CI: -9.34 to -2.00, p=0.0025), urinary DPHP concentrations and HDL-c (β=-0.59, 95% CI: -1.08 to -0.08, p=0.0226) in model adjusted age, sex, and race. In fully-adjusted multivariable linear regression models, only DPHP was found inversely associated with serum TG levels. One unit elevation of urinary log₂DPHP was related to 7.41 mg/dL lower level of TG.

In the present study, cross-sectional associations between BMI, obesity, serum lipid profiles, and urinary biomarker concentrations of 5 OPFRs metabolites were evaluated among general adults. These 5 metabolites of OPFRs were frequently detected in urine samples. Both urinary BCEP and BDCPP were found positively associated with BMI value and the prevalence of obesity. In contrast, inverse association was observed between urinary DPHP concentrations and serum TG levels. No significant relationships between other OPFRs, BMI, and serum lipid profiles were observed.

Previous epidemiological and laboratory studies on the associations between urinary OPFRs metabolites concentrations and obesity are limited and inconsistent. Moreover, they could not provide a comprehensive understanding of the effect of OPFRs metabolites on obesity among general adult population. Two studies in pregnant women indicated positive associations between select OPFRs metabolites and BMI value. Obese pregnant women had higher concentrations of BCEP and BDCPP compared with those with normal weight[30, 31]. In vivo evidence showed perinatal and postnatal exposure to Firemaster 550, a kind of FR mixture, resulted to increased body weight in rats[32, 33]. A recent study suggested subjects with detectable BCPP had higher risk for developing obesity NHANES 2013–2014 in comparison with participants whose urinary BCPP concentrations were below LLOD[34]. These findings supported our observed positive associations with higher BMI value and obesity for select urinary OPFRs metabolites after adjusting confounders. In contrast, two studies aiming to explore the relationship between certain OPFRs metabolites and adiposity suggested DBUP concentrations in urine samples were inversely associated with several adiposity markers (BMI, obesity, and waist circumference) among both children and adults[34, 35]. The underlying mechanisms are not yet clarified. Tri-n-butyl phosphate (TNBP) acts as the parent compound of DBUP, and it tends to accumulate in adipose tissue, which might result to decreased urinary concentrations of DBUP[34].

Epidemiological study from nine European countries detected 5 times higher concentrations of DPHP than other OPFRs metabolites[36]. We also observed relatively higher DPHP levels (GM:0.764 ng/mL) in urine samples in comparison with other metabolites of OPFRs. Therefore, it is of great significance to explore the role of DPHP in lipid metabolism. The effect of OPFRs exposure on serum lipid profiles among general population remains uninvestigated. To our best of knowledge, this is the first research indicating that higher urinary DPHP concentrations were associated with lower serum TG levels on the basis of large-sample cross-sectional study. Previous study has indicated DPHP could disturb cholesterol metabolism. Specifically, 6 kinds of dysregulated genes associated with cholesterol metabolism were observed upon DPHP exposure in chicken embryonic hepatocytes[37]. Of note, laboratory studies revealed similar association between DPHP exposure and inhibited lipid metabolism. Ruby[38] demonstrated a significant reduction of fatty acid catabolic process in mice chronic exposure to DPHP. Multi-omics analysis of liver tissue showed decreased expression of genes involved in lipid catabolic processes and downregulation of target gene of peroxisome proliferator-activated receptor gamma (PPARγ).

OPFRs are a class of emerging endocrine disturbing chemicals. The disrupted lipid metabolism might give an explanation to OPFRs induced obesity. In vitro and in vivo evidence showed numerous kinds of OPFRs could lead to lipid accumulation[39, 40]. In detail, activation of PPARγ and subsequent adipocyte differentiation participated in dysregulation of fat metabolism resulted from certain OPFRs exposure[41, 42]. Additionally, oxidative stress and lipid peroxidation also participated in altered fat metabolism[43]. Yang and his colleges revealed tris (2-chloroethyl) phosphate exposure induced lipid accumulation in livers through interaction with nuclear reporter farnesoid X receptor and resulted to its downregulation[44]. Altered activity of pregnane X receptor (PXR) and androgen receptor might also involve in metabolic disruption caused by OPFRs exposure[45]. Xiang suggested PXR mediated increased synthesis of fatty acid and suppressed β-oxidation played a role in TCP induced disrupting lipid homeostasis[20]. Disorders of biosynthesis of unsaturated fatty acids and steroid hormones, alterations in activity of cytochrome P450 enzyme subfamily all contributed to lipid metabolism disorders[46]. Researches are needed to further elucidate the underlying mechanisms of BCEP and BDCPP induced obesity.

The present research has some strengths. We explored the effect of 5 OPFRs metabolites on both obesity and serum lipid profiles based on a representatively non-institutional large-sample population. Furthermore, various confounding factors were considered and included in the fully-adjusted regression models. Besides, stratification analyses were performed to evaluate the role of OPFRs exposure in different subgroups. However, the study has several limitations. Firstly, no causal relationship could be obtained due to the cross-sectional study design. Secondly, the assessment of OPFRs exposure might be inaccurate because only spot urine samples were provided to measure the concentrations of OPFRs metabolites. Thirdly, a large portion of participants were excluded due to missing indispensable data for the present research. Therefore, selection bias is inevitable.

In summary, the present study indicated exposure to BCEP and BDCPP was associated with higher BMI value and increased prevalence of developing obesity. Unexpectedly, inverse associations between urinary DPHP concentrations and serum TG levels were observed. Given the cross-sectional design of the current study, future prospective studies are needed to elucidate the causal relationship between OPFRs exposure and obesity. In addition, more laboratory evidence is warranted to further explore the biological mechanisms for OPFRs-induced adiposity and DPHP associated dysregulation in lipid metabolism.

Acknowledgement

We thank the research staff of the National Center for Health Statistics of the Centers for Disease Control (CDC) and Prevention for their time and effort, and the participants who enrolled in the NHANES project, we also appreciate CDC for publicizing the HNANES database.

Authors’ contributions

Conception and design: Weilong Xing and Lei Wang. Data acquisition: Weilong Xing, Mengyuan Liang, Wen Gu. Analysis and interpretation of data: Weilong Xing, Zhen Wang, Deling Fan, Bing Zhang, Shuai Sun. Drafting of the manuscript: Weilong Xing. Critical revision of the manuscript for important intellectual contents: Weilong Xing, Lei Wang. Supervision: Lili Shi. Obtained funding: Weilong Xing and Lei Wang. All authors read and approved the final manuscript.

Funding

The study was supported by the grants from Central Scientific Research Projects for Public Welfare Research Institutes (ZX2023QT003) and the Doctoral Program of Entrepreneurship and Innovation in Jiangsu Province (JSSCBS20211323).

Data and material availability

Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm.

Ethics approval and consent to participant

The program was approved by the National Center for Health Statistics Ethics Review Board. All of the participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

They authors declare no potential conflicts of interests.

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Table 1. Sociodemographic, hematological, and health status characteristics of included participants in NHANES 2017-2018.

Variables	All participants (n=1334)
Age, years, mean ± SD	45.19 ± 17.59
Sex, n (%)
Male	656 (49.18)
Female	678 (50.82)
BMI, kg/m², mean ± SD	29.42 ± 7.88
Race, n (%)
Mexican American	206 (15.44%)
Other Hispanic	122 (9.15%)
Non-Hispanic White	449 (33.66%)
Non-Hispanic Black	297 (22.26%)
Other Race: including Multi-Racial	260 (19.49%)
Smoking status, n (%)
Current smoker	839 (62.89%)
Former smoker	206 (15.44%)
Non-smoker	289 (21.66%)
Drinking, n (%)	158 (11.84)
Physical activity, n (%)
None	675 (50.60%)
Moderate activity	299 (22.41%)
Vigorous activity	360 (26.99%)
Education level, n (%)
Less than 9th grade	103 (8.25%)
9-11th grade	146 (11.70%)
High school graduate/GED or equivalent	281 (22.52%)
Some college or AA degree	406 (32.53%)
College graduate or above	312 (25.00%)
Marital status, n (%)
Married/living with partner	747 (59.76%)
Widowed/divorced/separated	241 (19.28%)
Never married	262 (20.96%)
Family PIR, n (%)
＜1	248 (18.59%)
1-1.99	322 (24.14%)
2-3.99	296 (22.19%)
≥4	289 (21.66%)
Missing	179 (13.42%)
Hypertension, n (%)	334 (25.04%)
Diabetes, n (%)	105 (7.87%)
Stroke, n (%)	32 (2.40%)
Coronary artery disease, n (%)	20 (1.50%)
Heart failure, n (%)	20 (1.50%)
Heart attack, n (%)	237 (13.41)
Abbreviations: BMI: body mass index; PIR: poverty income ratio.

Table 2. Urinary creatinine and metabolites of FR of included participants in NHANES 2017-2018.

Variables	All participants (n=1334)
Urinary creatinine (mg/dL), median (Q1-Q3)	118.00 (63.00-181.00)
Urinary metabolites of FR(ng/mL), GM (95%CI)
BCIPP	0.335 (0.313 0.357)
BCEP	1.046 (0.979 1.118)
BDCPP	0.136 (0.129 0.143)
DBUP	0.132 (0.125 0.138)
DPHP	0.764 (0.718 0.812)

Table 3.Associations between urinary FR metabolites concentrations and BMI.

Variables	Model 1		Model 2
	β (95% CI)	p value	β (95% CI)	p value
BCIPP	0.32 (0.03, 0.62)	0.0323	0.17 (-0.15, 0.49)	0.3030
BCEP	0.33 (0.09, 0.57)	0.0068	0.27 (0.02, 0.52)	0.0338
BDCPP	0.71 (0.46, 0.96)	<0.0001	0.56 (0.25, 0.87)	0.0004
DBUP	0.35 (0.03, 0.67)	0.0310	0.07 (-0.28, 0.41)	0.6987
DPHP	0.26 (-0.00, 0.52)	0.0528	-0.08 (-0.40, 0.24)	0.6195
Abbreviations: CI: confidence interval. Model 1: adjusted for age, sex, and race. Model 2: adjusted for age, sex, race, smoking status, drinking status, physical activity, education level, marital status, family PIR, and urinary creatinine.

Table 4.Associations between urinary FR metabolites concentrations and obesity.

Variables	Models	OR (95% CI)	p value
BCIPP	Model 1	1.05 (0.97, 1.13)	0.2557
	Model 2	1.02 (0.93, 1.11)	0.7364
BCEP	Model 1	1.12 (1.05, 1.20)	0.0005
	Model 2	1.10 (1.02, 1.18)	0.0096
BDCPP	Model 1	1.21 (1.13, 1.30)	<0.0001
	Model 2	1.19 (1.09, 1.30)	0.0001
DBUP	Model 1	1.10 (1.01, 1.20)	0.0273
	Model 2	1.04 (0.95, 1.14)	0.4258
DPHP	Model 1	1.09 (1.01, 1.17)	0.0206
	Model 2	1.00 (0.92, 1.09)	0.9820
Abbreviations: OR: odds ratio; CI: confidence interval. Model 1: adjusted for age, sex, and race. Model 2: adjusted for age, sex, race, smoking status, drinking status, physical activity, education level, marital status, family PIR, and urinary creatinine.

Table 5. Associations between urinary FR metabolites concentrations and lipid profiles.

Variables	Models	TG		TC		HDL-c		LDL-c
		β (95% CI)	p value	β (95% CI)	p value	β (95% CI)	p value	β (95% CI)	p value
BCIPP	Model 1	-2.74 (-6.65, 1.18)	0.1714	-0.08 (-1.59, 1.42)	0.9125	-0.09 (-0.66, 0.48)	0.7540	-0.70 (-2.55, 1.16)	0.4628
	Model 2	-3.31 (-7.75, 1.13)	0.1444	0.05 (-1.63, 1.73)	0.9519	0.24 (-0.36, 0.84)	0.4345	-1.22 (-3.31, 0.86)	0.2499
BCEP	Model 1	0.61 (-2.62, 3.84)	0.7117	-0.51 (-1.71, 0.70)	0.4090	-0.54 (-1.00, -0.09)	0.0199	-0.41 (-1.94, 1.12)	0.6018
	Model 2	0.16 (-3.43, 3.75)	0.9305	-0.39 (-1.71, 0.92)	0.5580	-0.32 (-0.79, 0.16)	0.1887	-1.20 (-3.37, 0.97)	0.2794
BDCPP	Model 1	-0.88 (-4.45, 2.70)	0.6304	-0.78 (-2.06, 0.50)	0.2309	-0.55 (-1.04, -0.07)	0.0250	-0.41 (-2.11, 1.28)	0.6315
	Model 2	-2.25 (-6.88, 2.38)	0.3418	-0.82 (-2.46, 0.82)	0.3279	0.29 (-0.30, 0.89)	0.3299	-1.20 (-3.37, 0.97)	0.2794
DBUP	Model 1	1.04 (-3.56, 5.64)	0.6579	-0.45 (-2.05, 1.15)	0.5831	-0.40 (-1.01, 0.21)	0.1998	-0.31 (-2.49, 1.86)	0.7787
	Model 2	0.69 (-4.55, 5.93)	0.7961	-0.76 (-2.55, 1.03)	0.4055	0.10 (-0.54, 0.75)	0.7587	-1.30 (-3.75, 1.15)	0.2994
DPHP	Model 1	-5.67 (-9.34, -2.00)	0.0025	-0.67 (-1.99, 0.66)	0.3221	-0.59 (-1.09, -0.08)	0.0226	-1.03 (-2.78, 0.73)	0.2527
	Model 2	-7.41 (-12.13, -2.68)	0.0022	-0.17 (-1.83, 1.49)	0.8420	-0.06 (-0.65, 0.54)	0.8545	-1.20 (-3.44, 1.03)	0.2925
Abbreviations: TG: triglyceride; TC: total cholesterol; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol; Model 1: adjusted for age, sex, and race. Model 2: adjusted for age, sex, race, BMI, smoking status, drinking status, physical activity, education level, marital status, family PIR, and urinary creatinine.

No competing interests reported.

FigureS1.tif
Figure Supplementary 1. Association between log2-transformed BCEP (A) and BDCPP (B) concentrations and odds ratio for obesity.
FigureS2.tif
Figure Supplementary 2. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by age.
FigureS3.tif
Figure Supplementary 3. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by sex.
FigureS4.tif
Figure Supplementary 4. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by race.
FigureS5.tif
Figure Supplementary 5. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by smoking status.
FigureS6.tif
Figure Supplementary 6. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by drinking status.
FigureS7.tif
Figure Supplementary 7. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by physical activity.
FigureS8.tif
Figure Supplementary 8. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by educational level.
FigureS9.tif
Figure Supplementary 9. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by marital status.
FigureS10.tif
Figure Supplementary 10. Association between log2-transformed BCEP (A) and BDCPP (B) and odds ratio for obesity stratified by family PIR.

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Exposure to OPFRs is associated with obesity and dysregulated serum lipid profiles: data from 2017-2018 NHANES

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