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Associations between exposure to brominated flame retardants and hyperlipidemia risk in U.S. Adults

樱花视频 volume听25, Article听number:听980 (2025) Cite this article

Abstract

Background

Environmental exposure to toxic brominated flame retardants (BFRs) has been confirmed to have detrimental effects on human health. The impact of serum BFRs on hyperlipidemia risk has not been sufficiently examined. Our objective is to identify both the individual and combined effects of serum BFRs on hyperlipidemia and to further investigate the most influential chemicals.

Methods

We included 7,009 individuals with complete details on 9 types of serum BFRs, hyperlipidemia, and other covariates from the NHANES in 2007鈥�2016. Multivariate logistic regression was conducted to evaluate the individual impact of BFRs exposure on hyperlipidemia risk. We assessed the cumulative effect of BFRs on hyperlipidemia risk through weighted quantile sum (WQS) regression, quantile g-computation (QGC), and Bayesian kernel machine regression models.

Results

PBDE 28, PBDE 47, PBDE 85, PBDE 99, PBDE 100, PBDE 154, PBDE 209, and PBB153 were found to be positively associated with hyperlipidemia risk. The results of WQS and QGC revealed consistent positive correlation. PBDE209 emerged as the most significant chemicals exerting influence. The restricted cubic splines regression further identified significant dose-response relationship.

Conclusion

Exposure to individual and combined serum BFRs has been associated with an increased risk of hyperlipidemia. The causal relationship still requires confirmation through large-scale cohort studies.

Peer Review reports

Introduction

Brominated flame retardants (BFRs), which are composed of a diverse array of organobromine compounds, have been widely employed in various applications, including electronic equipment, plastics, and textiles. Its primary function is the mitigation of product flammability [1, 2]. Nevertheless, polybrominated diphenyl ethers (PBDEs) and 2,2麓,4,4麓,5,5麓-hexabromobiphenyl (PBB153) have been recognized as hazardous BFRs and emerged as the inaugural brominated persistent organic pollutants (POPs) [3]. Identified in various matrices and organisms, BFRs have been found to present potential exposure hazards for humans [4]. Furthermore, organisms are prone to bioaccumulate elevated concentrations of BFRs from their surroundings, subsequently leading to their transfer through food webs [5,6,7]. Hence, BFRs exposure among human may occur through food of animal origin consumption and dust ingestion. The accumulation of BFRs in the human body has been discovered in a range of physiological tissues and fluids, including serum, adipose tissue, and bodily fluids [8,9,10]. Concerns surrounding BFRs have heightened due to their enduring presence, extensive distribution, and environmental biotoxicity [11]. Previous epidemiological investigations have demonstrated that BFRs have a positive association with various adverse health conditions, including thyroid disorders, neurobehavioral disorders, reproductive health issues, immune system dysfunction, endocrine system disorders, and atherosclerosis [12,13,14,15,16,17]. Nevertheless, research concentrated on the toxic effect of BFRs on the metabolism, particularly in relation to the risk of hyperlipidemia, are still relatively limited.

Hyperlipidemia is a metabolic disorder resulting from disruptions in lipid metabolism within the human body, leading to elevated concentrations of serum lipids that exceed usual levels [18]. It exhibits a high prevalence among the population, affecting more than 53% of Americans [19]. Although the traditional risk factors for hyperlipidemia (including smoking, excessive alcohol consumption, and insufficient exercise) have been well explored [20], the effects of environmental pollutants such as BFRs co-exposure remain relatively inconclusive. Previous studies have indicated a positive correlation between elevated BFR levels and the increased likelihood of hypertriglyceridemia, obesity, and dyslipidemia [17, 21]. Additionally, multiple animal studies also indicated that exposure to environmental chemicals, such as PBDEs, can potentially dysregulate lipid metabolism [22]. For instance, both male and female rats exhibited elevated blood cholesterol levels when exposed to PBDE mixtures [17, 23]. Given the ubiquitous presence of BFRs in the environment and their effects on lipid levels, it is essential to investigate the relationship between BFRs and hyperlipidemia.

Thus, we conducted a cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES) to investigate the toxic effects of exposure to both individual and multiple BFRs on the risk of hyperlipidemia. Several novel statistical models were utilized for the evaluation of BFRs co-exposure, including weighted quantile sum regression (WQS), Bayesian kernel machine regression (BKMR), and quantile-based g-computation (qgcomp). These methods have been widely used in previous studies to explore the health impacts of mixed exposures and are well-established in environmental epidemiology for their effectiveness in capturing the complex interactions among mixed exposures and health outcomes [4, 24]. Our study may provide extensive epidemiological evidence regarding the association between BFRs co-exposure and hyperlipidemia risk in the population.

Materials and methods

Study setting

NHANES was developed to assess the nutritional status and health of U.S. participants and is conducted every two years [25]. Details such as dietary, laboratory, examination, and demographic data could be accessible at . Individuals would provide written consent when they were enrolled. This research combined data from 2007鈥�2008, 2009鈥�2010, 2011鈥�2012, 2013鈥�2014, and 2015鈥�2016 survey cycles of NHANES. Participants with missing serum BFRs level variables, lacking details correlated with hyperlipidemia, and those having insufficient details on other covariates were excluded. Collectively, 7009 respondents were enrolled in this research, which was exhibited in Fig. 1.

Fig. 1
figure 1

Flowchart of the study population

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Outcome measures

Hyperlipidemia was determined by the adult National Cholesterol Education Program guidelines (2002). During individual interviews, respondents that met any of the following criteria was identified with hyperlipidemia, including: 鈶� TC鈥夆墺鈥�200 mg/dL 鈶� TG鈥夆墺鈥�150 mg/dL 鈶� LDL-C鈥夆墺鈥�130 mg/dL 鈶� HDL-C鈥夆墹鈥�40 mg/dL in males or 鈮も��50 mg/dL in females. In addition, respondents could be considered have hyperlipidemia if they have taken lipid-lowering medications for treatment.

Measurements of serum BFRs

According to the details of NHANES Laboratory Procedures Manual, PBB153 and PBDEs were extracted from serum based on automated liquid-liquid extraction [4]. The analysis of serum samples for BFRs was performed using isotope dilution gas chromatography high-resolution mass spectrometry. Table S1 illustrates the detection rates and distribution of BFRs. To enhance the representativeness of the data and the reliability of our study, we selected PBB153 and eight PBDEs with a detection rate above 75% as key exposure variables. In this study, eight PBDEs include 2,4,4麓-Tribromodiphenyl ether (PBDE28), 2,2麓,4,4麓-Tetrabromodiphenyl ether (PBDE47), 2,2麓,3,4,4麓-Tentabromodiphenyl ether (PBDE85), 2,2麓,4,4麓,5-Pentabromodiphenyl ether (PBDE99), 2,2麓,4,4麓,6-Pentabromodiphenyl ether (PBDE100), 2,2麓,4,4麓,5,5麓-Hexabromodiphenyl ether (PBDE153), 2,2麓,4,4麓,5,6麓-Hexabromodiphenyl ether (PBDE154), and Decabromodiphenyl ether (PBDE209). Concentrations of serum BFRs below the lower limit of detection (LLOD) were determined by dividing the LLOD value by the square root of two, following the methods provided in the NHANES dataset [4, 26, 27].

Covariates

To minimize the impact of confounders and provide a more accurate assessment of the relationship between BFRs and hyperlipidemia risk, we included demographic characteristics (gender, age, race, and marital status), socioeconomic characteristics (family poverty income ratio), and health-related factors (body mass index, alcohol use, and serum cotinine) as covariates, based on published research and clinical diagnostic knowledge [2, 4].The categories for covariates were as follows: gender (male, female), race (mexican american, other hispanic, non-hispanic white, non-hispanic black, other race/multi-race), marital status (married, living with partner, widowed, divorced, separated, and never married), family poverty income ratio (PIR) (<鈥�1, 1鈥�3,鈮�3), body mass index (BMI) (<鈥�18.5,18.5鈥�25, 鈮モ��25), and alcohol use (yes, no). To precisely reflect smoking status and estimate the degree of environmental tobacco smoke exposure, we prefer serum cotinine levels to self-reported survey data regarding smoking [28,29,30,31]. All these variables were included to adjust for potential confounders and to examine their effects on hyperlipidemia risk.

Statistical analysis

To conduct baseline comparisons classified by hyperlipidemia condition, Chi-square tests was utilized for categorization, whereas the t-test was employed for continuous variables. The mean value and standard deviation were used for continuous variables, while categorical variables were expressed as n (%). Data of serum BFRs were ln-transformed to achieve normality and were then divided into 4 quartiles. In order to explore the potential co-exposure patterns, we constructed a Pearson correlation analysis among nine Ln-transformed BFRs. Multivariate logistic regression was implemented to examine the association between individual BFRs and hyperlipidemia. In this analysis, concentrations of nine serum BFRs were input as either ln-transformed variables or categorical variables with four quartiles. The calculation of odds ratios and corresponding 95% confidence intervals were used to explore single serum BFRs鈥檚 effect on hyperlipidemia. Model 1 was crude. The second model have modified covariates for age, gender, race, marital status, alcohol use, BMI, family PIR, and serum cotinine.

To comprehensively investigate the toxic effects of mixed serum BFRs on hyperlipidemia risk, we employed various statistical models. Firstly, the WQS model was utilized to quantify the combined effect of 9 serum BFRs on hyperlipidemia risk. WQS regression is designed for analyzing complex environmental mixtures characterized by high collinearity among their components, as it creates a composite index that captures the cumulative impact of the mixture [25, 32, 33]. To construct this index, we categorized each component into quantiles and utilized a two-step process for weight estimation. Our data was performed with 1000 bootstrap iteration, and split into validation and training sets. Specifically, 40% of the data were randomly allocated to a training set, while the remaining 60% were assigned into a validation set. The co-exposure level of serum BFRs was represented by the WQS index, which range from 0 to 1 and comprised weighted sums of 9 single BFRs [29, 34].

In addition, BKMR model was performed to determine combined impacts of serum BFRs mixture upon hyperlipidemia due to its strength in permitting possible nonlinearity and non-additive effects among 9 serum BFRs [35, 36]. In BKMR model, the relative roles played by element in mixture towards hyperlipidemia incidence was estimated using posterior inclusion probability (PIP) [33]. Then quantile-based g computation (qgcomp) model, was introduced aiming to overcome shortcomings of WQS models in determining heterogeneity of direction towards co-exposure. The qgcomp model could calculate the weights in positive and negative direction for factors of the mixtures. The restricted cubic splines (RCS) was also adopted to assess the possible non-linear relationships between serum BFRs and hyperlipidemia risk based on the R packages (鈥渞ms鈥�) [37]. Three knots were set up at the percentiles of the 5th, 50th, and 75th in RCS regression in accordance to the criterion of minimum absolute value of Akaike information [38].

R software (version 4.2.3) has been utilized for all analyses. Significance level was considered as p-value鈥�<鈥�0.05. The analysis was conducted using the following R packages: gwqs (version 3.0.5), qgcomp (version 2.15.2), bkmr (version 0.2.2), and rcssci (version 0.3.0).

Results

Sample characteristics

In total, 7009 participants were recruited in the study, with an average age of 49.37 years (SD鈥�=鈥�17.67). The majority had higher BMI (71.34%) and had alcohol drinking (72.08%). 5036 participants were diagnosed as having hyperlipidemia, whereas 1973 were classified as non-hyperlipidemia. Age, sex, race, marital status, BMI, and alcohol use were found to differ significantly between different status of hyperlipidemia. More details were summarized in Table 1.

Table 1 Characteristics of participants by hyperlipidemia, NHANES 2007鈥�2016
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Distribution and correlation of serum BFRs

The detailed distributions for concentrations towards 9 serum BFRs were exhibited in Table S1, with detection rate above 75%. And PBDE47 had the highest concentration among 9 serum BFRs, while PBDE154 had the lowest concentration.

The illustration of correlations among the 9 serum BFRs after ln transformation was displayed in Fig. 2. The serum BFRs including PBDE85 and PBDE47 (r鈥�=鈥�0.94), PBDE99 and PBDE47 (r鈥�=鈥�0.93), PBDE154 and PBDE99 (r鈥�=鈥�0.92) exhibited strong correlations. PBDE 154 showed a correlation coefficient of 0.9 with PBDE 100, while PBDE 85 showed a correlation coefficient of 0.91 with PBDE 99. Strong correlations may indicate co-exposure or co-toxicity effect among BFRs. Significant correlations were all observed among 9 serum BFRs.

Fig. 2
figure 2

Pearson鈥檚 correlation matrix among Ln-transformed BFRs in the study population

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Associations between single BFRs and hyperlipidemia risk

The findings regarding the association of single BFRs with hyperlipidemia risk assessed by multivariate logistic regression were provided in Table 2. As continuous variables, all serum BFRs (PBDE28, PBDE47, PBDE85, PBDE99, PBDE100, PBDE153, PBDE154, PBDE209, and PBB153) with ln transformation had positive association with hyperlipidemia risk, while adjusting no covariates. In model 2, the results showed that for each unit increase in ln-transformed PBDE28, PBDE47, PBDE100, PBDE154, PBDE209, and PBB153, there shown a 21% (OR: 1.21, 95%CI: 1.09鈥�1.33), 13% (OR: 1.13, 95%CI: 1.03鈥�1.24), 14% (OR: 1.14, 95%CI: 1.05鈥�1.25), 10% (OR: 1.10, 95%CI: 1.01鈥�1.20), 37% (OR: 1.37, 95%CI: 1.22鈥�1.54), and 12% (OR: 1.12, 95%CI: 1.04鈥�1.20) increased risk of hyperlipidemia (all P value鈥�<鈥�0.05). After dividing serum BFRs into 4 quartiles, PBDE28, PBDE47, PBDE99, PBDE100, PBDE154, PBDE209 and PBB153 with concentrations in highest quartiles was all linked to greater likelihood of hyperlipidemia risk (OR: 1.28,95%CI:1.08鈥�1.52; OR: 1.24,95%CI:1.05鈥�1.46; OR: 1.19,95%CI:1.01鈥�1.41; OR: 1.30,95%CI:1.10鈥�1.53; OR: 1.24,95%CI:1.05鈥�1.47; OR: 1.46,95%CI:1.24鈥�1.73; OR: 1.39,95%CI:1.13鈥�1.71, respectively).

Table 2 Associations between single BFRs and hyperlipidemia in the NHANES
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Association of mixture BFRs exposure with hyperlipidemia risk in WQS regression and QGC analysis

Findings from WQS confirmed that doubling of serum BFRs may elevate the risk of hyperlipidemia (OR: 1.21, 95% CI: 1.09鈥�1.34) while adjusted all covariates. PBDE209 made the most single contribution to hyperlipidemia risk, followed by PBB153 and PBDE100. PBDE99 had lowest weight and had relative low contribution to hyperlipidemia risk (Fig. 3). The qgcomp model analysis found that serum BFRs co-levels were also significantly correlated with higher possibility of hyperlipidemia (OR: 1.20, 95%CI: 1.1鈥�1.32) (Figure S1).

Fig. 3
figure 3

Estimated weights of serum BFRs for hyperlipidemia by WQS models adjusted for age, gender, race, marital status, family income to poverty ratio, BMI, alcohol use and serum cotinine level. Notes: The red line indicates the threshold for identifying BFRs of concern, where the weight surpasses the reference value

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BKMR analysis to evaluate the correlations of mixed serum BFRs and hyperlipidemia

According to the results of the combined effect of BFRs on hyperlipidemia in Fig. 4A, co-exposure to BFRs was positively associated with hyperlipidemia risk. Specifically, hyperlipidemia risk was significantly increased when BFRs mixtures were at the 25th to 35th percentiles in comparison to the 50th percentile. And we also found a positive trend when all BFRs were at above the 60th percentile, relative to when all BFRs were at their 50th percentile. We further compared the single exposure-response relationship to the 25th percentile while setting the remaining BFRs at the 25th, 50th, or 75th percentile. As shown in Fig. 4B, PBDE28 and PBDE209 exhibited significantly positive associations with an increased risk of hyperlipidemia when the levels of the remaining BFRs were at the 25th, 50th, and 75th percentiles. In addition, we explored the interaction relationships among 9 serum BFRs (Figure S2), and a possible interaction between PBB153 and PBDE154, as well as PBB153 and PBDE47 was identified. Table S2 presents the PIP within the BKMR model, with PBDE209 (1.00) displaying the highest PIP value, followed by PBDE28 (0.9960) and PBDE85 (0.4458).

Fig. 4
figure 4

Analysis of hyperlipidemia risk and BFRs exposure using the BKMR model. (A) Combined effects of the serum BFRs as a mixture on hyperlipidemia risk by BKMR models. (B) Relationship of single BFRs with hyperlipidemia risk when an individual BFRs exposure was at its 75th percentile as compared to its 25th percentile, when all other BFRs held at the 25th, 50th, or 75th percentile, respectively. Models were adjusted for age, gender, race, marital status, family income to poverty ratio, BMI, alcohol use and serum cotinine level

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Figure 5 displayed the findings from RCS model. A significant dose-response relationship has been observed among PBDE28, PBDE47, PBDE100, PBDE154, PBDE209, and PBB153 and the hyperlipidemia risk (P for overall鈥�<鈥�0.05). A linear dose-response relationship for these BFRs concentrations with hyperlipidemia risk was established (P for nonlinear鈥�>鈥�0.05).

Fig. 5
figure 5

Dose-response relationship between serum BFRs (ln-transformed) and hyperlipidemia risk. The model was adjusted for covariates including age, gender, race, marital status, family income to poverty ratio, BMI, alcohol use and serum cotinine level

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Discussion

Our research represents a recent contribution to estimating the association between individual and mixtures of serum BFRs and hyperlipidemia risk in U.S. adults. Logistic regression models confirmed that individual BFRs, such as PBDE 28, PBDE 47, PBDE 85, PBDE 99, PBDE 100, PBDE 154, PBDE 209, and PBB 153, are positively correlated with hyperlipidemia. A positive association between the BFRs mixture and the risk of hyperlipidemia was also consistently observed across the WQS, qgcomp, and BKMR models. In WQS and BKMR model, PBDE209 emerged as the chemical exerting the greatest influence. The results of RCS regression provided additional support to these findings.

In this study, we observed significant positive associations between several BFRs, such as PBDE209 and PBDE100, and the risk of hyperlipidemia. Specifically, PBDE209 was identified as a chemical showing notable contribution to the association. This finding could be further validated in an experimental animal study, where adult male mice exposed to PBDE 209 for 60 days showed growing levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) in comparison to the control group [39]. This phenomenon may be attributed to the molecular-level effects of enduring organic exposure, which induces enlargement of fat cells and subsequently leads to an increase in adipose tissue volume. This disruption in lipid metabolism may lead to higher levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) [39]. Nevertheless, Zhang et al. evaluated the links between BFRS and lipid profiles in adipose tissues among female, revealing a negative association between PBDE 209 and triglycerides [40]. This may be attributed to the variations in BFRs exposure levels among studies or the influence of demographic characteristics, lifestyle factors, or other potential confounders. In addition, evidence indicated that PBDE100 may impact hyperlipidemia through influencing blood glucose levels [41]. And hyperlipidemia was the usual complication linked to inadequate glycemic control in diabetes. Insulin deficiency or resistance can result in reduced lipoprotein lipase activity in diabetic patients, subsequently causing dyslipidemia [42].

Furthermore, the findings demonstrated that PBB153 were positively link to hyperlipidemia risk. The dose of PBB mixture exposure may link to decreased levels in serum triglyceride and cholesterol, thereby influencing blood lipid levels [43]. PBB153 may contribute to hyperglycemia and dyslipidemia, increasing the risk of hyperlipidemia events. Moreover, exposure to PBB153 was a risk factor for subsequent disorders, such as abdominal obesity, and metabolic syndrome, further increasing the risk of hyperlipidemia [44, 45].

The positive correlations were also observed between PBDE 28, PBDE 47, PBDE 99, and PBDE154 and hyperlipidemia risk. As renowned endocrine disruptors, enduring presence of PBDEs in adipose tissue had the potential to interfere with the regular functions of lipid metabolism, thereby posing a health risk to humans [46, 47]. Evidence suggests that elevated serum levels of PBDEs accumulate in adipose tissue and may appear to interfere with related hormone, potentially increasing the risk of hyperlipidemia [48]. Elevated concentrations of PBDEs in blood serum can have a lasting impact on the balance of lipids in the blood and liver. This could temporarily influence lipid metabolism by reversibly inducing liver nuclear receptors. Consequently, this process may result in dyslipidemia [40, 49]. In summary, the predictable negative effects of PBDEs on lipid levels demonstrated a universal toxicological response. The accumulation of PBDEs in human tissue may be a risk factor for hyperlipidemia, disrupting the original balance and even cause serious consequences [50]. Therefore, it is crucial to concentrate on the exposure burden of BFRs and further investigate the underlying mechanisms between BFRs and hyperlipidemia risk.

The results obtained from BKMR, WQS, and the QGC model consistently indicated an upward trend between mixed BFRs and hyperlipidemia risk. Moreover, PBDE209 was found to be the most significant chemical exerting influence. Previous studies also identified the same conclusion that exposure to BFRs mixture was positively associated with low HDL and hypertriglyceridemia risk in adults [2]. Additionally, the RCS analysis also revealed the positive linear relationship between serum PBDE28, PBDE47, PBDE100, PBDE154, PBDE209, and PBB153 and hyperlipidemia risk. However, further investigation was required to examine the toxic impact of serum BFRs and to elucidate the biological processes.

This study has several strengths. Firstly, we investigated the relationships between serum BFRs and the risk of hyperlipidemia using various methods among a relatively large population. Secondly, the study meticulously examined the individual and co-exposure impact of serum BFRs exposure on hyperlipidemia risk, thereby addressing gaps in prior research. Despite above strengths, several limitations also should be acknowledged. First, we exclusively investigated the association between concentrations of 9 BFRs in serum and hyperlipidemia risk, and further analysis should be conducted to assess the impact of other serum BFRs. Moreover, the findings of these analyses may be susceptible to bias due to residual and unmeasured confounders. For example, since BFRs are strongly lipophilic compounds, poor dietary habits such as increased fat intake may lead to hyperlipidemia and higher exposure to PBDEs, as well as resulting in higher BFRs concentrations in serum. These confounding factors may affect the association between them. In addition, the potential confounding effects of other environmental chemicals on lipids, particularly Per- and Polyfluoroalkyl Substances (PFAS), could theoretically interact with BFRs. We did not examine the impact of co-exposure to PFAS and other emerging contaminants on hyperlipidemia. Future research should further consider the effects of mixed exposures to BFRs, PFAS, and other chemicals to understand their combined effects on lipid levels. Finally, the study design employed in this research was a cross-sectional study, which may limit its ability to infer causal relationships. Given the listed limitations, it is essential to conduct further large-scale prospective investigations to support these findings.

Conclusion

In summary, we identified a positive association between individual and mixed serum BFRs and an elevated risk of hyperlipidemia, with PBDE 209 emerging as the primary driver due to its higher weights and posterior inclusion probabilities. Our findings linking PBDE209 to hyperlipidemia are corroborated by experimental animal studies, confirming the concordance between human and animal data. These findings emphasized the significance of incorporating serum BFRs mixtures in epidemiological studies for evaluating the association between BFRs exposure and hyperlipidemia risk. These findings would contribute to heightening public awareness of preventing BFRs exposures. Prospective research having repeatable metrics are required to validate underlying mechanism of serum BFRs鈥檚 toxic effect on hyperlipidemia risk.

Data availability

The data that support the findings of this study are openly available in NHANES at . The data used during the current study are available from the corresponding author on reasonable request.

Abbreviations

NHANES:

National Health and Nutrition Examination Survey

BFRs:

Brominated flame retardants

PBDEs:

Polybrominated diphenyl ethers

WQS:

Weighted quantile sum

QGC:

Quantile-based g computation

BKMR:

Bayesian kernel machine regression

RCS:

Restricted cubic splines

BMI:

Body mass index

PIR:

Income-poverty ratio

LOD:

Limits of detection

References

  1. Besis A, Christia C, Poma G, Covaci A, Samara C. Legacy and novel brominated flame retardants in interior car dust - Implications for human exposure. Environ Pollut. 2017;230:871鈥�81.

    听 CAS听 听 听

  2. Che Z, Jia H, Chen R, Pan K, Fan Z, Su C, Wu Z, Zhang T. Associations between exposure to brominated flame retardants and metabolic syndrome and its components in U.S. Adults. Sci Total Environ. 2023;858(Pt 2):159935.

    听 CAS听 听 听

  3. Sindiku O, Babayemi J, Osibanjo O, Schlummer M, Schluep M, Watson A, Weber R. Polybrominated Diphenyl ethers listed as Stockholm convention pops, other brominated flame retardants and heavy metals in e-waste polymers in Nigeria. Environ Sci Pollut Res Int. 2015;22(19):14489鈥�501.

    听 CAS听 听 听

  4. Han L, Wang Q. Association between brominated flame retardants exposure and markers of oxidative stress in US adults: an analysis based on the National health and nutrition examination survey 2007鈥�2016. Ecotoxicol Environ Saf. 2023;263:115253.

    听 CAS听 听 听

  5. Lin Y, Le S, Feng C, Qiu X, Xu Q, Jin S, Zhang H, Jin Y, Wen Y, Xu H, et al. Exposure and health risk assessment of secondary contaminants closely related to brominated flame retardants (BFRs): polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/Fs) in human milk in Shanghai. Environ Pollut. 2021;268Pt A:115121.

    听 听

  6. Yang J, Xuan L, Markovic V, Zakaly HM, Ivanov DS, Bai C, Huang R. Associations between exposure to brominated flame retardants with cognitive function in US older adults: A cross-sectional study of NHANES from 2011 to 2012. Emerg Contaminants. 2023;9(4):100259.

    听 CAS听 听

  7. Zhou S, Fu M, Ling S, Qiao Z, Luo K, Peng C, Zhang W, Lei J, Zhou B. Legacy and novel brominated flame retardants in a lab-constructed freshwater ecosystem: distribution, bioaccumulation, and trophic transfer. Water Res. 2023;242:120176.

    听 CAS听 听 听

  8. Soulen BK, Venables BJ, Johnston DW, Roberts AP. Accumulation of PBDEs in stranded harp (Pagophilus groenlandicus) and hooded seals (Cystophora cristata) from the Northeastern united States. Mar Environ Res. 2018;138:96鈥�101.

    听 CAS听 听 听

  9. Feiteiro J, Mariana M, Cairr茫o E. Health toxicity effects of brominated flame retardants: from environmental to human exposure. Environ Pollut. 2021;285:117475.

    听 CAS听 听 听

  10. Gimbrone MA Jr, Garc铆a-Carde帽a G. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circul Res. 2016;118(4):620鈥�36.

    听 CAS听 听

  11. Ding X, Liu J, Wen W. Brominated flame retardants in edible fishes from three Gorges reservoir, China. Tecnolog铆a Y Ciencias Del Agua. 2019;10(4):46鈥�65.

    听 听

  12. Zhao X, Chen T, Yang B, Wang D, Sun W, Wang Y, Yang X, Wen S, Li J, Shi Z. Serum levels of novel brominated flame retardants (NBFRs) in residents of a major BFR-producing region: occurrence, impact factors and the relationship to thyroid and liver function. Ecotoxicol Environ Saf. 2021;208:111467.

    听 CAS听 听 听

  13. Cope RB, Kacew S, Dourson M. A reproductive, developmental and neurobehavioral study following oral exposure of tetrabromobisphenol A on Sprague-Dawley rats. Toxicology. 2015;329:49鈥�59.

    听 CAS听 听 听

  14. Hales BF, Robaire B. Effects of brominated and organophosphate ester flame retardants on male reproduction. Andrology. 2020;8(4):915鈥�23.

    听 CAS听 听 听

  15. Hood RB, Terrell ML, Mardovich S, Somers EC, Pearson M, Barton H, Tomlinson MS, Marder ME, Barr DB, Marcus M. Polybrominated biphenyls (PBBs) and prevalence of autoimmune disorders among members of the Michigan PBB registry. Environ Res. 2023;239(Pt 1):117312.

    听 CAS听 听 听 听

  16. Zhang Q, Gu S, Yu C, Cao R, Xu Y, Fu L, Wang C. Integrated assessment of endocrine disrupting potential of four novel brominated flame retardants. Ecotoxicol Environ Saf. 2022;232:113206.

    听 CAS听 听 听

  17. Wu HD, Yang LW, Deng DY, Jiang RN, Song ZK, Zhou LT. The effects of brominated flame retardants (BFRs) on pro-atherosclerosis mechanisms. Ecotoxicol Environ Saf. 2023;262:115325.

    听 CAS听 听 听

  18. He N, Ye H. Exercise and hyperlipidemia. Phys Exerc Hum Health 2020:79鈥�90.

  19. T贸th PP, Potter D, Ming EE. Prevalence of lipid abnormalities in the united States: the National health and nutrition examination survey 2003鈥�2006. J Clin Lipidol. 2012;6(4):325鈥�30.

    听 听 听

  20. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despr茅s JP, Fullerton HJ, et al. Executive summary: heart disease and stroke Statistics鈥�2016 update: A report from the American heart association. Circulation. 2016;133(4):447鈥�54.

    听 听 听

  21. Lim JS, Lee DH, Jacobs DR Jr. Association of brominated flame retardants with diabetes and metabolic syndrome in the U.S. Population, 2003鈥�2004. Diabetes Care. 2008;31(9):1802鈥�7.

    听 听 听 听

  22. Zhu Y, Jing L, Li X, Zhou G, Zhang Y, Sang Y, Gao L, Liu S, Shi Z, Sun Z, et al. Decabromodiphenyl ether-induced PRKACA hypermethylation contributed to glycolipid metabolism disorder via regulating PKA/AMPK pathway in rat and L-02 cells. Environ Toxicol Pharmacol. 2022;90:103808.

    听 CAS听 听 听

  23. Oberg M, Westerholm E, Fattore E, Stern N, Hanberg A, Haglund P, Wiberg K, Bergendorff A, H氓kansson H. Toxicity of Bromkal 70-5DE, a technical mixture of polybrominated Diphenyl ethers, following 28 d of oral exposure in rats and impact of analysed impurities. Chemosphere. 2010;80(2):137鈥�43.

    听 听 听

  24. Chen Y, Wu J, Li R, Kang W, Zhao A, Yin Y, Tong S, Yuan J, Li S. Individual and joint association of phenols, Parabens, and phthalates with childhood lung function: exploring the mediating role of peripheral immune responses. J Hazard Mater. 2023;454:131457.

    听 CAS听 听 听

  25. Chen L, Zhao Y, Liu F, Chen H, Tan T, Yao P, Tang Y. Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. Adults. 樱花视频 Med. 2022;20(1):207.

    听 CAS听 听 听 听

  26. Bai T, Li X, Zhang H, Yang W, Lv C, Du X, Xu S, Zhao A, Xi Y. The association between brominated flame retardants exposure with bone mineral density in US adults: A cross-sectional study of the National health and nutrition examination survey (NHANES) 2005鈥�2014. Environ Res. 2024;251(Pt 1):118580.

    听 CAS听 听 听

  27. Cheng D, Chen Z, Zhou J, Cao Y, Xie X, Wu Y, Li X, Wang X, Yu J, Yang B. Association between brominated flame retardants (PBDEs and PBB153) exposure and hypertension in U.S. Adults: results from NHANES 2005鈥�2016. Environ Health. 2024;23(1):64.

    听 CAS听 听 听 听

  28. Zhao J, Li F, Wu Q, Cheng Y, Liang G, Wang X, Fang S, Wang Q, Fan X, Fang J. Association between Trichlorophenols and neurodegenerative diseases: A cross-sectional study from NHANES 2003鈥�2010. Chemosphere. 2022;307(Pt 2):135743.

    听 CAS听 听 听

  29. Chen Y, Pan Z, Shen J, Wu Y, Fang L, Xu S, Ma Y, Zhao H, Pan F. Associations of exposure to blood and urinary heavy metal mixtures with psoriasis risk among U.S. Adults: A cross-sectional study. Sci Total Environ. 2023;887:164133.

    听 CAS听 听 听

  30. Chen L, Sun Q, Peng S, Tan T, Mei G, Chen H, Zhao Y, Yao P, Tang Y. Associations of blood and urinary heavy metals with rheumatoid arthritis risk among adults in NHANES, 1999鈥�2018. Chemosphere. 2022;289:133147.

    听 CAS听 听 听

  31. Liang M, Guo X, Ding X, Song Q, Wang H, Li N, Su W, Liang Q, Sun Y. Combined effects of multiple metals on hearing loss: A bayesian kernel machine regression approach. Ecotoxicol Environ Saf. 2022;247:114279.

    听 CAS听 听 听

  32. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. J Agric Biol Environ Stat. 2015;20(1):100鈥�20.

    听 听 听

  33. Ma Y, Hu Q, Yang D, Zhao Y, Bai J, Mubarik S, Yu C. Combined exposure to multiple metals on serum uric acid in NHANES under three statistical models. Chemosphere. 2022;301:134416.

    听 CAS听 听 听

  34. Huang Q, Wan J, Nan W, Li S, He B, Peng Z. Association between manganese exposure in heavy metals mixtures and the prevalence of sarcopenia in US adults from NHANES 2011鈥�2018. J Hazard Mater. 2024;464:133005.

    听 CAS听 听 听

  35. Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 2015;16(3):493鈥�508.

    听 听 听

  36. Wang G, Fang L, Chen Y, Ma Y, Zhao H, Wu Y, Xu S, Cai G, Pan F. Association between exposure to mixture of heavy metals and hyperlipidemia risk among U.S. Adults: A cross-sectional study. Chemosphere. 2023;344:140334.

    听 CAS听 听 听

  37. Huang S, Zhong D, Lv Z, Cheng J, Zou X, Wang T, Wen Y, Wang C, Yu S, Huang H, et al. Associations of multiple plasma metals with the risk of metabolic syndrome: A cross-sectional study in the mid-aged and older population of China. Ecotoxicol Environ Saf. 2022;231:113183.

    听 CAS听 听 听

  38. Johannesen CDL, Langsted A, Mortensen MB, Nordestgaard BG. Association between low density lipoprotein and all cause and cause specific mortality in Denmark: prospective cohort study. BMJ 2020, 371.

  39. Alimu A, Abudureman H, Wang YZ, Li MY, Wang JS, Liu ZL. Decabromodiphenyl ether causes insulin resistance and glucose and lipid metabolism disorders in mice. World J Diabetes. 2021;12(8):1267鈥�81.

    听 听 听 听

  40. Zhang Q, Peng J, Huang A, Zheng S, Shi X, Li B, Huang W, Tan W, Wang X, Wu K. Associations between polybrominated Diphenyl ethers (PBDEs) levels in adipose tissues and blood lipids in women of Shantou, China. Environ Res. 2022;214(Pt 3):114096.

    听 CAS听 听 听

  41. Ren Y, Luo X, Wang C, Yin L, Pang C, Feng T, Wang B, Zhang L, Li L, Yang X, et al. Prevalence of hypertriglyceridemic waist and association with risk of type 2 diabetes mellitus: a meta-analysis. Diabetes Metab Res Rev. 2016;32(4):405鈥�12.

    听 CAS听 听 听

  42. Naqvi S, Naveed S, Ali Z, Ahmad SM, Khan RA, Raj H, Shariff S, Rupareliya C, Zahra F, Khan S. Correlation between glycated hemoglobin and triglyceride level in type 2 diabetes mellitus. Cureus 2017, 9(6).

  43. Toxicology. Carcinogenesis studies of a polybrominated biphenyl mixture (Firemaster FF-1) in F344/N rats and B6C3F1 mice (Gavage studies). Natl Toxicol Program Tech Rep Ser. 1983;244:1鈥�106.

    听

  44. Vasiliu O, Cameron L, Gardiner J, Deguire P, Karmaus W. Polybrominated biphenyls, polychlorinated biphenyls, body weight, and incidence of adult-onset diabetes mellitus. Epidemiology. 2006;17(4):352鈥�9.

    听 听 听

  45. Han L, Wang Q. Associations of brominated flame retardants exposure with chronic obstructive pulmonary disease: A US population-based cross-sectional analysis. Front Public Health. 2023;11:1138811.

    听 听 听 听

  46. Lee D-H, Steffes MW, Sj枚din A, Jones RS, Needham LL, Jacobs DR Jr. Low dose of some persistent organic pollutants predicts type 2 diabetes: a nested case鈥揷ontrol study. Environ Health Perspect. 2010;118(9):1235鈥�42.

    听 CAS听 听 听 听

  47. Lee D-H, Steffes M, Jacobs D. Can persistent organic pollutants explain the association between serum 纬-glutamyltransferase and type 2 diabetes? Diabetologia. 2008;51:402鈥�7.

    听 CAS听 听 听

  48. Robledo DAR, Prudente MS, Aguja SE, Iwata H. A meta-analysis of randomized controlled studies on the hepatoxicity induced by polybrominated Diphenyl ethers (PBDEs) in rats and mice. Curr Res Toxicol. 2023;5:100131.

    听 CAS听 听 听 听

  49. Dingemans MM, Kock M, van den Berg M. Mechanisms of action point towards combined PBDE/NDL-PCB risk assessment. Toxicol Sci. 2016;153(2):215鈥�24.

    听 CAS听 听 听

  50. Boutot ME, Whitcomb BW, Abdelouahab N, Baccarelli AA, Boivin A, Caku A, Gillet V, Martinez G, Pasquier J-C, Zhu J. In utero exposure to persistent organic pollutants and childhood lipid levels. Metabolites. 2021;11(10):657.

    听 CAS听 听 听 听

Acknowledgements

We thank NHANES for its data support.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No.72274047).

Author information

Author notes

  1. Junping Liu, Nan Wang and Lin Wu contributed equally to this work.

Authors and Affiliations

  1. School of Health Management, Harbin Medical University, Harbin, China

    Junping Liu,听Zhaoyue Liu,听Xinru Liu,听Nan Wang,听Lin Wu,听Yu Cui,听Lu Wang,听Huanyu Zhang,听Yajie Feng,听Xinle Yin,听Yaping Liu,听Yue Zhou,听Yanhua Hao听&听Libo Liang

  2. School of Public Health, Fudan University, Shanghai, China

    Huanyu Zhang

  3. Chengde Central Hospital, Chengde, China

    Yajie Feng

  4. Institute for Medical Demography, Harbin Medical University, Harbin , China

    Libo Liang

Authors
  1. Junping Liu

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  2. Zhaoyue Liu

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  3. Xinru Liu

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  4. Nan Wang

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  5. Lin Wu

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  13. Yanhua Hao

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Contributions

Conceptualization: J.L, and N. W; Data curation: J. L, Z.L, L. W and L. W; Investigation: X. L and Y. C; Methodology: J. L and N. W; Resources: L. L and Y. H; Software: J. L, N. W, X. Y, Y. L and Y. Z; Supervision: H. Z and Y. F; Validation: L. L and Y. H; Visualization: X. L and Y. C; Writing鈥搊riginal draft: J. L, Z. L, L. W and L. W; Writing鈥搑eview & editing: L. L and Y. H.

Corresponding authors

Correspondence to Yanhua Hao or Libo Liang.

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Liu, J., Liu, Z., Liu, X. et al. Associations between exposure to brominated flame retardants and hyperlipidemia risk in U.S. Adults. 樱花视频 25, 980 (2025). https://doi.org/10.1186/s12889-025-22010-0

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  • DOI: https://doi.org/10.1186/s12889-025-22010-0

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樱花视频

ISSN: 1471-2458

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