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Influence of persistent organic pollutants on the complement system in a population-based human sample
Environment International 71 (2014) 94–100
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Influence of persistent organic pollutants on the complement system in a population-based human sample Jitender Kumar a,⁎, P. Monica Lind b, Samira Salihovic c, Bert van Bavel d, Kristina N. Ekdahl e,f, Bo Nilsson f, Lars Lind c, Erik Ingelsson a a
Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden d MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden e Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden f Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, Sweden b c
a r t i c l e
i n f o
Article history: Received 4 March 2014 Accepted 5 June 2014 Available online xxxx Keywords: Complement system markers Epidemiology Persistent organic pollutants TEQ
a b s t r a c t Background: Persistent organic pollutants (POPs) are toxic compounds generated through various industrial activities and have adverse effects on human health. Studies performed in cell cultures and animals have revealed that POPs can alter immune-system functioning. The complement system is part of innate immune system that helps to clear pathogens from the body. We performed a large-scale population-based study to find out associations between summary measures of different POPs and different complement system markers. Methods: In this cross-sectional study, 16 polychlorinated biphenyls (PCBs), 3 organochlorine (OC) pesticides, octachloro-p-dibenzodioxin, and 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) were analyzed for their association with levels of protein complement 3 (C3), 3a (C3a), 4 (C4) and C3a/C3 ratio. A total of 992 individuals (all aged 70 years, 50% females) were recruited from the Prospective Investigation of the Vasculature in Uppsala Seniors cohort. Regression analysis adjusting for a variety of confounders was performed to study the associations of different POP exposures (total toxic equivalency value or TEQ and sum of 16 PCBs) with protein complements. Results: The TEQ values were found to be positively associated with C3a (β = 0.07, 95% CI = 0.017–0.131, p = 0.01) and C3a/C3 ratio (β = 0.07, 95% CI = 0.015–0.126, p = 0.01) taking possible confounders into account. The association observed was mainly driven by PCB-126. Conclusion: In this study involving 992 elderly individuals from the general population, we showed that POPs, mainly PCB-126, were associated with levels of complement system markers indicating that the association of these toxic compounds with downstream disease could be mediated by activation of immune system. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Persistent organic pollutants (POPs) are synthetic compounds that, once released in the environment, can persist for a long time due to their resistance to degradation. They are lipophilic in nature and can bio-accumulate in the fat tissue of different organisms through the food chain. They may reach high concentrations in exposed organisms and can cause detrimental health effects. A wide range of compounds such as polychlorinated biphenyl (PCB) congeners, organochlorine (OC) pesticides (hexachlorobenzene or HCB, trans-nonachlordane or
⁎ Corresponding author at: Department of Medical Sciences, Molecular Epidemiology, Dag Hammarskjölds Väg 14B, First Floor, MTC Building, Science Park, Uppsala University, SE-751 85, Uppsala, Sweden. Tel.: +46 704250724. E-mail address: [email protected] (J. Kumar).
http://dx.doi.org/10.1016/j.envint.2014.06.009 0160-4120/© 2014 Elsevier Ltd. All rights reserved.
TNC and 2,2-bis (4-chlorophenyl)-1,1-dichloroethene or p,p′-DDE), polychlorinated dibenzo-p-dioxins, and brominated diphenyl ether (BDE) congeners are among the well-known POPs. During the industrial revolution, a large number of such chemicals were in use for commercial purposes. Due to their widespread use in the past, almost every individual has been exposed to POPs at some point in time (Hansen, 1998). The primary source of exposure is through a contaminated food that involves consuming meat, fish or dairy products (Johnson et al., 2010). In order to measure relative toxic effect of different POPs, a concept of total toxic equivalency (TEQ) value was invented by Van den berg et al. that considers the toxicity of different POPs in comparison to 2,3,7,8-tetrachlorodibenzo-p-dioxin, which thus has a toxic equivalency factor of 1 (Van den Berg et al., 1998). Different studies have confirmed the validity of using this approach in order to measure toxic effects of a mixture of different PCBs together (Bradlaw et al., 1980; Harris et al.,
J. Kumar et al. / Environment International 71 (2014) 94–100
1993). Since human health can be influenced by exposure to different POPs, studies have already shown the association between POPs and disease caused by altered immune or inflammatory responses (Barrett, 2012; Gascon et al., 2013; Glynn et al., 2008; Noakes et al., 2006). The immune system is a biological system that protects any organism from diseases. It can be divided into two main categories — the innate and the adaptive parts. The complement system is a major humoral component of innate immunity that forms the first line of defense and protects the body from infections (Ricklin et al., 2010). The complement system is activated by binding recognition proteins to carbohydrates or antibodies present on foreign microbes in order to kill these pathogens. Studies involving transgenic as well as knockout mice have shown the importance of the complement system in maintaining health. Approximately 50 different proteins work together in the complement system, which provides a vital triage system that aids in rapid killing of foreign bodies by triggering a cascade of events. However, improper or uncontrolled activation of the complement system may lead to attack the host cells. Several studies have shown the adverse effects of exposure to various POPs on the immune system (Baccarelli et al., 2004; Davis and Safe, 1990; Kim et al., 2003; Lee et al., 2007b; Park et al., 2008; Vos and Moore, 1974). An early study by Vos and Moore showed that when pregnant rodents were administered with dioxins, the offspring were born with altered immune parameters (Vos and Moore, 1974). These effects may lead either to reduced capacity to fight infections or to the risk of developing autoimmune manifestation in later life. Complement 3 (C3) is the most abundant complement protein present in blood and plays a central role in the complement system. Enzyme complexes called C3-convertases cleave and activate multiple C3 molecules into the C3a and C3b fragments that lead to a cascade of further events. C3b is an opsonin that mediates phagocytosis and is also a part of a powerful amplification loop that promotes further activation of C3. C3a is an anaphylatoxin, which can bind to receptors on various inflammatory cells, and it is also the precursor of an adipokine called acylation-stimulating-protein that acts as mediator of inflammation and causes increased permeability of blood vessels. C4 is located upstream in the complement cascade and is predominately activated in response to antigen–antibody complexes. It is another major component of complement system that is highly homologous to C3 and is activated into an opsonin (C4b) and anaphylatoxin (C4a). C4a is less potent than C3a. C4b combines with another component called C2a to form C3-convertase. To the best of our knowledge, no effort has yet been made to understand the impact of various POPs on complement system in humans from the general population. In order to test the hypothesis that there might be associations between POP exposure and various components of complement system, we performed this large population-based cross-sectional study in Swedish men and women (all aged 70 years) from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort. We have recently demonstrated a strong correlation between risk factors for cardiovascular disease and plasma levels of C3, C3a and C4 in this cohort (Nilsson et al., 2014). Our primary aim here was to study associations of TEQ and summary measure of polychlorinated biphenyls (sum of PCBs) with complement components (C3, C3a, the C3a/C3 ratio and C4). Our secondary aim was to explore associations of different individual POPs with these complementary system components. 2. Materials and methods 2.1. Study participants All individuals who were aged 70 years and living in the community of Uppsala, Sweden in 2001–2005 were invited to participate in the study. Approximately 50% (n = 1016) of the invitees agreed to participate in the study and were further investigated. All participants
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provided their written informed consent and study was approved by the Ethics Committee of Uppsala University, Uppsala. The participants were asked to fill out a detailed questionnaire about, among other things, smoking habits, medications and medical history. Standard laboratory techniques were employed to measure routine clinical variables like fasting blood glucose and different lipids following standard procedures. Only 992 individuals were employed for this study due to the availability of data about different POPs in these individuals. Further information about the recruitment and clinical characteristics has been published previously (Lind et al., 2005) and can be found on the Internet as well (http://www.medsci.uu.se/pivus/). Large variations of population characteristics due to age as well as various lifestyle factors may influence outcomes. The prime motivation of selecting individuals of same age from general population was to control the confounding by age. Further, we have studied these elderly individuals of same age group where many of these lifestyle factors become attenuated and can be controlled for by extensive lifestyle data. 2.2. Measurement of POPs and quality control A modified method by Sandau et al. was followed to measure POPs in the study participants (Sandau et al., 2003), and further details can be seen elsewhere (Salihovic et al., 2012). Following a method by Rylander et al., lipid content of plasma was considered to normalize the levels of POPs measured (Rylander et al., 2006). Quality control plasma samples as well as procedural blank samples were incorporated in every batch of 10 samples so as to ensure the quality. The blank samples had no target compounds at N5% of levels present in the samples except for cis-chlordane and trans-chlordane. In the samples analyzed, both cis and trans-chlordane were present below the detection limit in N 90% of samples. The internal standard recovery was satisfactory, ranging from 60 to 110%. More details about the quality controls have been described in another article by our group (Salihovic et al., 2012). Samples with POP concentration falling below the limit of detection (LOD) were imputed and given value of LOD/2−0.5. 2.3. Summary measures of POPs Two different summary measures — TEQ and sum of PCBs were used as our primary exposure variables. Seven mono- and non-orthosubstituted dioxin-like PCBs with assigned TEF values (PCB-105, 118, 126, 156, 157, 169, 189) and OCDD were utilized to calculate the TEQ values as suggested by Van den Berg et al. (2006). The sum of PCBs was measured by adding concentration of 16 PCBs. 2.4. Measurement of complement system markers Three different complement components (C3, C3a and C4) were considered in the present study. All components were analyzed in EDTA-plasma, which had been stored at −70 °C as reported. Briefly, C3 and C4 were measured by nephelometry (Immage, BeckmanCoulter Inc., CA, USA), and C3aC3adesArg was quantified by ELISA as described earlier (Nilsson Ekdahl et al., 1992). The ratio between C3a/C3 was also calculated since it indicates the degree to which complement protein C3 is proteolytically activated. 2.5. Statistical analysis Variables were evaluated for their skewed distribution and if found to have a non-normal distribution, log-transformation was done in order to achieve normal distribution. The association between summary measures of POPs, i.e. TEQ, and sum of PCBs (independent variables) was analyzed for their association with complement system markers (dependent variables) by performing linear regression analysis. A number of potential confounders including age, sex, education (three levels), physical activity (four levels), waist circumference (cm), smoking
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(smokers, non-smokers), kidney function (glomerular filtration rate), fasting blood glucose, systolic blood pressure, body mass index (BMI), lipid profile (levels of high- and low-density lipoproteins cholesterol, triglycerides) and any medication (for diabetes or cardiovascular disease) were adjusted for in the analysis. These confounders were used based on their physiological importance. Previous studies have demonstrated the association of confounders with both POPs as well as complement system markers. The duration of exposure to POPs increases with age and their concentrations show strong positive correlations with age (Lee et al., 2006). A study by Watkins et al. has shown the association between POPs and kidney function (Watkins et al., 2013). In many renal diseases, the activation of complement and its deleterious consequences has been observed (Inal et al., 2003). Medication was used as a confounder since almost 45% of the individuals studied were on medication for cardiovascular disorders. A study by our group has shown the association between POPs exposure and hypertension (Lind et al., 2014). Patients with hypertension have shown the altered cellular and humoral immunity (Dzielak, 1992). Further, several other studies have shown the association of POP exposure with different outcomes like altered lipid levels, hypertension, and obesity (Lee et al., 2007a, 2008, 2012a; Lind et al., 2014; Ronn et al., 2011; Roos et al., 2013). Studies have revealed the link between obesity and complement system (Oberbach et al., 2011). Socio-economic status and physical activity are major confounders because of their known links to many health outcomes (Adler and Stewart, 2010). Two statistical models (models A and B) were employed. Model A was minimally adjusted considering age, sex and kidney function as confounders only, while model B was a fully-adjusted model with all confounders included. In order to evaluate the potential nonlinear relationship, a squared term of POPs was considered in the regression analysis. A two-way interaction term between POP and sex was introduced to explore the possible interaction between POPs and sex on complement system markers. Further, regression spline curves were generated to understand the relation between POPs and complement system markers studied. To confirm the association in presumably healthier individuals, the association analysis was performed in a sub-sample of the studied individuals who were non-smokers, non-diabetic and had no prior cardiovascular disease. The statistical software package STATA (version 12; StataCorp, College Station, TX, USA) was used to perform all the statistical analyses. 3. Results The study population characteristics, along with distribution of complement system markers studied, are shown in Table 1. Ratio of male and female participants in the study was equal. Out of 992 individuals, 10.6% were current smokers and 11.6% had diabetes, while 16.3% were diagnosed with cardiovascular disorders. Further, among the participants included in analysis, 67% were overweight (BMI ≥ 25.0 kg/m2) while 22% were obese (BMI ≥ 30.0 kg/m2). Table 2 shows distribution statistics of all 21 POPs along with their summary measures (TEQ and sum of PCBs) analyzed. Briefly, the highest median concentrations detected among all the POPs studied were of p,p′-DDE (308 ng/g of lipid). This pesticide was detected in the serum sample of all the participants. PCB-153 showed the highest median concentrations (232 ng/g of lipid) among all the PCBs studied. Further, the lowest median concentration observed was for OCDD (0.4 ng/g of lipid). Median levels of sum of PCBs were higher than that of p,p′-DDE (Table 2). 3.1. Association between POP exposure and complement protein C3 In regression model A, sum of PCBs showed significant association with protein complement C3 (beta = − 0.003, 95% CI = − 0.01 to −0.001, p-value = 0.002, Table 3). The association was attenuated in model B when adjusting for all confounders (Table 3). Exploratory
Table 1 Clinical characteristics and inflammatory markers of the individuals (n = 992) studied. Variable Clinical variables Age (years) Sex (female) Smokers Current Former BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) Glucose (mmol/L)a LDL (mmol/L) HDL (mmol/L) TG (mmol/L)a GFR (mL/min/1.73 m2)a Exercise habits No Low Medium High Education Low Medium High Diabetes mellitus Hypertensive CVD diagnosed CVD medication Complement system markers C3 C3a C3a/C3 C4
Median (IQR) or n (%)
Min, Max
70.1 (70.0, 70.3) 495 (50)
69.8, 70.7 –
105 (10.6) 408 (41.2) 26.6 (24.0, 29.6) 90 (84, 98) 148 (134, 164) 78 (72, 86) 5.0 (4.6, 5.4) 3.3 (2.8, 3.9) 1.4 (1.2, 1.8) 1.15 (0.87, 1.51) 78.9 (65.8, 94.9)
– – 16.6, 49.8 60, 134 84, 230 50, 114 2.8, 19.9 0.8, 6.9 0.6, 3.8 0.4, 4.8 23.2, 210.8
109 (11.3) 570 (59.1) 218 (22.6) 68 (7.0)
– – – –
555 (56.6) 175 (17.9) 250 (25.5) 87 (8.8) 11 (1.1) 162 (16.3) 445 (45)
– – – – –
0.95 (0.84, 1.07) 160.5 (129.7, 213.7) 168.2 (136.3, 216.9) 0.21 (0.18, 0.25)
0.52, 1.61 3.4, 605.4 4.4, 590.2 0.06, 0.51
Abbreviations: n, number; IQR, interquartile range; Min, minimum; Max, maximum; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; TG, triglyceride; GFR, glomerular filtration rate. Number (percentage) is shown for discrete variables while median (IQR) is shown for continuous variables. a Log transformed values are reported.
analyses showed that the attenuation was primarily due to BMI (data not shown). No significant association was observed between TEQ values and C3 in both of the models (Table 3). In the secondary analysis considering individual POPs, both BDE-47 (beta = −0.02, 95% CI = −0.03 to − 0.001) and PCB-157 (beta = − 0.02, 95% CI = − 0.1 to − 0.001) were found to be significantly negatively associated with C3 (Table 4). Other PCBs or OC pesticides did not show any significant associations. 3.2. Association between POP exposure and complement protein C3a TEQ value was found to be positively associated with C3a in a fully adjusted model (beta = 0.07, 95% CI = 0.02 to 0.13, Table 3). Sum of PCBs did not show any evidence of association with C3a in any of the models (Table 3). When individual POPs were analyzed for their association with complement markers studied, PCB-74, 105, 126, and 194 were found to be positively associated while PCB-157 was negatively associated with levels of C3a (Table 4). 3.3. Association between POP exposure and C3a/C3 ratio TEQ values were found to be positively associated with C3a/C3 ratio in both minimally-adjusted (beta = 0.07, 95% CI = 0.02 to 0.13, p-value = 0.008) and fully adjusted models (beta = 0.07, 95% CI = 0.01 to 0.13, p-value = 0.01; Table 3). Sum of PCBs showed a borderline significant positive association with C3a/C3 ratio in model A (beta = 0.005, 95% CI = 0.001 to 0.01, p-value = 0.03) and model B (beta = 0.004, 95% CI = − 0.0003 to 0.1, p-value = 0.07; Table 3). When
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3.6. Interaction analysis
Table 2 Distribution of POPs studied along with the summary measures. POPs
Mean (SD)a
Median (IQR)
% bLOD
PCB-74 PCB-99 PCB-105 PCB-118 PCB-126 PCB-138 PCB-153 PCB-156 PCB-157 PCB-169 PCB-170 PCB-180 PCB-189 PCB-194 PCB-206 PCB-209 OCDD HCB p,p′-DDE BDE-47 TNC TEQ Sum PCBs
2.7 (0.5) 2.7 (0.6) 1.6 (0.6) 3.5 (0.5) 1.8 (0.9) 4.9 (0.5) 5.4 (0.4) 3.2 (0.4) 1.5 (0.5) 3.3 (0.5) 4.4 (0.4) 5.2 (0.4) 1.2 (0.7) 2.8 (0.8) 1.4 (0.5) 1.4 (0.5) −0.8 (0.6) 3.7 (0.4) 5.7 (0.9) 0.9 (0.7) 3.1 (0.6) 0.4 (0.5) 46.9 (6.1)
91.4 (63.8–128.2) 90.7 (62.4–132.0) 32.0 (21.0–46.8) 200.6 (136.4–281.0) 40.4 (21.6–71.8) 819 (619–1115) 1428 (1114–1846) 154.2 (118.6–197.6) 28.0 (21.4–37.0) 171.4 (130.6–219.8) 497.2 (385.4–632.8) 1165 (917–1487) 19.2 (14.6–25.8) 119.4 (87.6–158.8) 26.8 (20.8–35.2) 26.2 (19.6–34.6) 2.6 (1.4–4.1) 254.0 (189.2–336.6) 1858 (1024–3451) 12.6 (9.0–19.4) 139.2 (91.6–211.2) 9.8 (6.7–13.9) 4988 (3942–6300)
0 0.5 0 0 4.5 0.4 0 0 0 0.3 0 0 0 1.4 0 0 19.4 1.4 0 27.8 0 – –
Abbreviations: SD, standard deviation; IQR, interquartile range; LOD, limit of detection; Min, minimum; Max, maximum; PCB, polychlorinated biphenyls; OCDD, octachlorodibenzo-p-dioxin; HCB, hexachlorobenzene; TNC, trans-nonachlordane; p,p′-DDE, 2,2-bis (4-chlorophenyl)-1,1-dichloroethene; BDE, bromodiphenyl ether; TEQ, total toxic equivalency value. a Values shown for POPs are lipid-normalized and log transformed (given in ng/g of lipid).
analyzed individually, POPs namely PCB-74, 105, 126, and 194 were found to be significantly positively associated with C3a/C3 ratio (Table 4). 3.4. Association between POP exposure and complement protein C4 When analyzed for two summary measures (TEQ and sum of PCBs), no significant association was observed with complement protein C4 in any of the models studied (Table 3). Further, when exposure to individual POP was analyzed for their association with C4, we did not observe any significant findings (Table 4).
When the interaction analysis between POPs and sex was performed, no significant interactions were observed (p-value N 0.05 of all associations). Further, we generated spline curves utilizing regression spline function but did not find any evidence of significant nonlinear association between levels of POPs and complement system markers in this study (data not shown). 4. Discussion In the present study, we report the association between TEQ values and sum of PCBs with different complement system markers in 70 year old participants recruited as a part of large-scale populationbased cohort from Uppsala, Sweden. We observed significant positive association between TEQ values and complement system markers C3a and C3a/C3. The sum of PCBs showed borderline significant positive association with C3a/C3 ratios. The associations observed were mainly due to PCB-74, 105, 126 and 194. 4.1. Complement protein C3 The protein C3 plays a key role in activation of complement system. A few studies have been performed to find the association between different POP exposures and immune system (Saberi Hosnijeh et al., 2011; Van Den Heuvel et al., 2002; Ye et al., 2011). In a small epidemiological study performed in humans, the effect of exposure of one of the dioxins called 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on humoral immunity has been analyzed (Saberi Hosnijeh et al., 2011). No significant difference in the levels of C3 has been observed between individuals exposed to TCDD and controls in this study. Another epidemiological study involving 200 adolescents, serum concentration of different PCBs and dioxin-like compounds has shown a significant association with different components of humoral immunity (Van Den Heuvel et al., 2002). In a study involving fish Oryzias melastigma, the effect of one of the POPs called BDE-47 has been observed on expression of a few of complement system pathway genes (Ye et al., 2011). When exposed to varying concentrations of BDE-47, the C3 gene expression has been found to be gender specific and down-regulated only in males as compared to controls. No such significant difference is observed in female fishes. In the current study, both PCB-157 and BDE47 showed association with levels of C3.
3.5. Sub-sample analysis 4.2. Complement protein C3a In the sub-sample of presumably healthier individuals (nonsmokers, non-diabetic and no prior cardiovascular disease, n = 687), TEQ value showed significant positive association with both C3a and C3a/C3 ratio (beta = 0.09, 95% CI = 0.02–0.16, p-value = 0.01 and beta = 0.10, 95% CI = 0.03–0.16, p-value = 0.004, respectively). The sum of PCBs showed a weak positive association with C3a/C3 ratio (beta = 0.01, 95% CI = 0.001–0.01, p-value = 0.04). Similar effect sizes were observed when total or sub-samples were analyzed (Table 5).
Complement protein C3 generates C3a and C3b components after activation. C3a part is also known as anaphylatoxin and can cause contraction of smooth muscles as well as increased vascular permeability. Further, C3a can bind to receptors present on mast cells and activate these cells to release histamine as part of an immunological response against any foreign antigen (Hugli, 1981). This in effect can lead to a local inflammatory response. All of these may trigger the initiation of adaptive immune response. C3a is also known to induce the synthesis
Table 3 Associations of the summary measures (TEQ values and sum of PCB concentrations) with complement system markers. TEQ
Sum of PCBs
Markers
Model A
p-Value
Model B
p-Value
Model A
p-Value
Model B
p-Value
C3 C3a C3a/C3 C4
−0.02 (−0.04, 0.01) 0.06 (0.003, 0.11) 0.07 (0.02, 0.13) −0.02 (−0.05, 0.02)
0.12 0.04 0.008 0.33
0.002 (−0.02, 0.02) 0.07 (0.02, 0.13) 0.07 (0.01, 0.13) 0.001 (−0.03, 0.03)
0.88 0.01 0.01 0.98
−0.003 (−0.01, −0.001) 0.002 (−0.002, 0.01) 0.005 (0.001, 0.01) −0.003 (−0.01, 0.0001)
0.002 0.35 0.03 0.06
−0.001 (−0.003, 0.001) 0.003 (−0.001, 0.01) 0.004 (−0.0003, 0.01) −0.001 (−0.003, 0.002)
0.20 0.17 0.07 0.60
Abbreviations: TEQ, total toxic equivalency value; PCB, polychlorinated biphenyls; C3a/C3, ratio between C3a and C3. Model A — Linear regression adjusted for age, sex and kidney function; Model B — Linear regression adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) from linear regression models is reported. Significant p-Values are shown in bold.
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Table 4 Association of individual POPs with complement system markers studied. POPs
C3
p-Value
C3a
p-Value
C3a/C3
p-Value
C4
p-Value
PCB-74 PCB-99 PCB-105 PCB-118 PCB-126 PCB-138 PCB-153 PCB-156 PCB-157 PCB-169 PCB-170 PCB-180 PCB-189 PCB-194 PCB-206 PCB-209 OCDD HCB p,p′-DDE BDE-47 TNC
−0.002 (−0.02, 0.01) −0.01 (−0.03, 0.004) −0.001 (−0.02, 0.02) −0.002 (−0.02, 0.02) 0.004 (−0.007, 0.01) −0.02 (−0.04, 0.003) −0.02 (−0.04, 0.005) −0.01 (−0.04, 0.01) −0.02 (−0.1, −0.001) −0.006 (−0.03, 0.02) −0.02 (−0.05, 0.006) −0.03 (−0.05, 0.001) −0.02 (−0.03, 0.003) −0.02 (−0.03, −0.003) −0.01 (−0.03, 0.01) −0.02 (−0.04, 0.01) 0.006 (−0.01, 0.02) −0.003 (−0.03, 0.02) 0.001 (−0.01, 0.01) −0.02 (−0.03,−0.001) −0.004 (−0.02, 0.01)
0.83 0.13 0.98 0.80 0.48 0.08 0.12 0.31 0.04 0.59 0.13 0.06 0.13 0.02 0.31 0.15 0.50 0.79 0.90 0.03 0.66
0.07 (0.01, 0.12) 0.01 (−0.04, 0.06) 0.05 (−0.001, 0.10) 0.05 (−0.01, 0.10) 0.05 (0.02, 0.08) 0.01 (−0.05, 0.07) 0.03 (−0.04, 0.10) 0.02 (−0.05, 0.109) −0.07 (−0.14,−0.01) −0.01 (−0.07, 0.06) 0.07 (−0.005, 0.15) 0.04 (−0.04, 0.11) −0.01 (−0.05, 0.04) 0.04 (0.005, 0.08) 0.03 (−0.04, 0.10) −0.01 (−0.07, 0.06) 0.04 (−0.01, 0.09) 0.03 (−0.04, 0.10) 0.02 (−0.02, 0.05) 0.02 (−0.02, 0.07) −0.05 (−0.10, 0.001)
0.02 0.64 0.05 0.11 0.002 0.76 0.44 0.51 0.02 0.89 0.07 0.38 0.83 0.03 0.39 0.82 0.11 0.39 0.34 0.34 0.09
0.07 (0.01, 0.12) 0.03 (−0.02, 0.07) 0.05 (0.005, 0.10) 0.05 (−0.004, 0.10) 0.05 (0.02, 0.08) 0.03 (−0.03, 0.09) 0.04 (−0.02, 0.11) 0.03 (−0.04, 0.10) −0.05 (−0.11, 0.01) 0.003 (−0.06, 0.07) 0.09 (0.01, 0.16) 0.06 (−0.02, 0.13) 0.005 (−0.04, 0.05) 0.06 (0.02, 0.09) 0.04 (−0.03, 0.10) 0.007 (−0.05, 0.07) 0.03 (−0.02, 0.08) 0.04 (−0.03, 0.10) 0.02 (−0.01, 0.05) 0.04 (−0.004, 0.08) −0.04 (−0.09, 0.01)
0.02 0.30 0.03 0.07 0.004 0.35 0.19 0.34 0.12 0.94 0.02 0.15 0.84 0.003 0.25 0.82 0.26 0.30 0.27 0.08 0.12
−0.003 (−0.04, 0.03) 0.0005 (−0.03, 0.03) 0.01 (−0.02, 0.04) 0.004 (−0.03, 0.03) −0.001 (−0.02, 0.02) 0.002 (−0.03, 0.04) −0.02 (−0.05, 0.02) 0.001 (−0.04, 0.04) −0.02 (−0.06, 0.01) 0.007 (−0.03, 0.04) −0.01 (−0.05, 0.03) −0.02 (−0.07, 0.02) −0.01 (−0.03, 0.02) −0.01 (−0.03, 0.01) −0.03 (−0.07, 0.007) −0.02 (−0.05, 0.02) 0.01 (−0.01, 0.04) 0.01 (−0.03, 0.05) 0.003 (−0.01, 0.02) −0.02 (−0.04, 0.01) 0.005 (−0.02, 0.03)
0.88 0.97 0.47 0.82 0.98 0.92 0.44 0.95 0.19 0.70 0.64 0.29 0.56 0.28 0.12 0.29 0.32 0.65 0.71 0.16 0.75
Abbreviations: PCB, polychlorinated biphenyls; OCDD, octachlorodibenzo-p-dioxin; HCB, hexachlorobenzene; TNC, trans-nonachlordane; p,p′-DDE, 1,1-bis-(4-chlorophenyl)-2,2dichloroethene; BDE, brominated diphenyl ether congener; C3a/C3, ratio between C3a and C3. Linear regression performed and adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) is reported from the linear regression models. Significant p-Values are shown in bold.
of adhesion molecules in endothelial cells lining blood vessels. In a separate paper by our group, analyzing the impact of POPs on various downstream inflammatory markers, we found an association between POPs (PCBs 126, 169, 189, 206 and 209) and adhesion molecules ICAM-1 and VCAM-1 (Influence of persistent organic pollutants on inflammatory markers in a population-based human sample. EHP, Manuscript Accepted). Complement is one of the important factors in inflammation and several other studies have also shown the activation of inflammation mediators due to POP exposure (Imbeault et al., 2012; Sipka et al., 2008). Significant association between TEQ values and C3a was observed in the present study.
4.3. C3a/C3 ratio The positive association between C3a/C3 ratio with TEQ values and a trend toward sum of PCBs may reflect the increased conversion of complement C3a from complement C3, thereby pointing to augmentation of the complement immune response. Activation of the complement system has been found to contribute to cardiovascular disorders and stroke (Hill and Ward, 1969; Rossen et al., 1985; Stokowska et al., 2013). A cross-sectional study performed in the same cohort showed that Table 5 Associations of the summary measures (TEQ values and sum of PCB concentrations) with complement system markers in individuals without potential recent infection. Markers
TEQ
p-Value
Sum of PCBs
p-Value
C3 C3a C3a/C3 C4
0.01 (−0.01, 0.03) 0.09 (0.03, 0.15) 0.08 (0.02, 0.13) 0.005 (−0.03, 0.04)
0.33 0.004 0.01 0.75
−0.001 (−0.002, 0.001) 0.004 (−0.001, 0.009) 0.005 (0.000, 0.01) 0.000 (−0.003, 0.002)
0.29 0.09 0.04 0.73
Abbreviations: TEQ, total toxic equivalency value; PCB, polychlorinated biphenyls; C3a/C3, ratio between C3a and C3. Individuals with possible recent infection (C-reactive protein N 10 mg/L, n = 45) were excluded from the analysis. Linear regression adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) is reported from the linear regression analysis. Significant p-Values are shown in bold.
circulating levels of PCBs were associated with atherosclerotic plaques as well as echogenicity of intima-media independently of cardiovascular risk factors like lipids (Lind et al., 2012). Another study by our group has also discovered that the exposure of POPs (PCBs with 4, 5 or 6 chlorides, p,p′-DDE and TNC) is associated with stroke in this population (Lee et al., 2007b).
4.4. Complement protein C4 In an epidemiological study by Saberi Hosnijeh et al., factory workers exposed to dioxin, no significant difference is observed between TCDD concentrations in these workers or controls and C4 (Saberi Hosnijeh et al., 2011). Other studies have also shown weak or no association between dioxin and C4 (Halperin et al., 1998; Ott et al., 1994). We did not observe any significant association between POP exposure and serum C4 concentrations. The associations observed for C3a or C3a/C3 ratio were mainly due to PCB-126. Among all the PCBs in whole family of chlorinated biphenyls, PCB-126 is the most potent agonist of aryl hydrocarbon receptor (AhR). The AhR plays an important role in physiological process including immune responses. The immunotoxicity of PCB-126 is not fully understood. Several mechanisms including altered cell proliferation, inflammation, and cytokine response could contribute to differential immune response. Different studies including ours have shown these responses due to POP exposure (Baccarelli et al., 2004; Imbeault et al., 2012; Lind et al., 2012; Ott et al., 1994; Stokowska et al., 2013; Van Den Heuvel et al., 2002; Vos and Moore, 1974). A few of the individual PCBs showed significant association with complement markers studied while sum of PCBs was found insignificant. This may probably be due to the fact that toxicity of different PCB congeners varies considerably among themselves. PCB toxicity is dependent on different factors like its structure, type of PCB (whether it is a mixture or congener), dose, and route of exposure. Further, toxicity due to mechanism that is Ah receptor dependent is different than Ah receptor independent. Studies have shown that complement system may be involved in the pathology of cardiovascular disorders as well as diabetes (Oksjoki et al., 2007; Yasuda et al., 1990). Previous studies from our group have shown significant association between POP exposure and diabetes and cardiovascular disorders (Lee et al., 2011, 2012b; Lind et al., 2012). Further,
J. Kumar et al. / Environment International 71 (2014) 94–100
smoking can also activate the alternative pathway of complement (Kew et al., 1985). Therefore, we performed sub-sample analysis to understand the association between exposures of POPs and complement system markers in presumably healthier individuals by excluding participants who were smokers or had disease conditions (diabetes or prevalent cardiovascular disease). The small effect sizes in an association between TEQ and complement system markers could potentially lead to large downstream effects on disease outcomes. These findings are important for guiding policy work regarding pollutants. The absolute effects of this study are small and therefore may be of limited clinical significance. 4.5. Strengths and limitations One of the main strength of this study is inclusion of large number of POPs and different complement system markers in a community-based sample from general population. Individuals from the cohort studied have been carefully phenotyped. Since the study individuals were all 70 years old and recruited from a specific area; this limits the generalization of results to other age groups or ethnicities. Further, the study design is cross-sectional, thereby of limited value in establishing the causal relationship between POPs and complement system markers. Therefore, mechanistic studies are needed to establish the causality. Finally, we have not performed multiple-testing adjustment to balance false positive and false negative findings. Also, as there are no other cohorts with these kinds of data as far as we are aware of, we have not been able to replicate the findings. Hence, we suggest that these findings should be considered exploratory, and that they need validation in further studies. 5. Conclusion The TEQ values were found to be associated both with C3a and C3a/ C3 ratio, thereby suggesting increased activity of the complement system due to POP exposure, mainly PCB-126. Our results indicate that the association of these toxic compounds with downstream disease could be mediated by immune system activation products. Further studies involving larger cohorts are warranted to confirm these findings. Competing financial interest The authors declare no conflict of interest. Acknowledgments We acknowledge all the individuals who participated in this study. This study was supported by the Swedish Research Council (VR, Grant number 2012-2407) and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS, Grant number 2007-2047). JK was supported by fellowships from the Swedish Heart-Lung Foundation (Grant number 20120197), Sweden, and Clinical Research at Uppsala University, Uppsala, Sweden. References Adler NE, Stewart J. Health disparities across the lifespan: meaning, methods, and mechanisms. Ann N Y Acad Sci 2010;1186:5–23. Baccarelli A, Pesatori AC, Masten SA, Patterson Jr DG, Needham LL, Mocarelli P, et al. Arylhydrocarbon receptor-dependent pathway and toxic effects of TCDD in humans: a population-based study in Seveso, Italy. Toxicol Lett 2004;149:287–93. Barrett JR. Another piece of the obesity-environment puzzle: potential link between inflammation and POP-associated metabolic diseases. Environ Health Perspect 2012; 120:A164. Bradlaw JA, Garthoff LH, Hurley NE, Firestone D. Comparative induction of aryl hydrocarbon hydroxylase activity in vitro by analogues of dibenzo-p-dioxin. Food Cosmet Toxicol 1980;18:627–35.
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Environment International journal homepage: www.elsevier.com/locate/envint
Influence of persistent organic pollutants on the complement system in a population-based human sample Jitender Kumar a,⁎, P. Monica Lind b, Samira Salihovic c, Bert van Bavel d, Kristina N. Ekdahl e,f, Bo Nilsson f, Lars Lind c, Erik Ingelsson a a
Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden d MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden e Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden f Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, Sweden b c
a r t i c l e
i n f o
Article history: Received 4 March 2014 Accepted 5 June 2014 Available online xxxx Keywords: Complement system markers Epidemiology Persistent organic pollutants TEQ
a b s t r a c t Background: Persistent organic pollutants (POPs) are toxic compounds generated through various industrial activities and have adverse effects on human health. Studies performed in cell cultures and animals have revealed that POPs can alter immune-system functioning. The complement system is part of innate immune system that helps to clear pathogens from the body. We performed a large-scale population-based study to find out associations between summary measures of different POPs and different complement system markers. Methods: In this cross-sectional study, 16 polychlorinated biphenyls (PCBs), 3 organochlorine (OC) pesticides, octachloro-p-dibenzodioxin, and 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) were analyzed for their association with levels of protein complement 3 (C3), 3a (C3a), 4 (C4) and C3a/C3 ratio. A total of 992 individuals (all aged 70 years, 50% females) were recruited from the Prospective Investigation of the Vasculature in Uppsala Seniors cohort. Regression analysis adjusting for a variety of confounders was performed to study the associations of different POP exposures (total toxic equivalency value or TEQ and sum of 16 PCBs) with protein complements. Results: The TEQ values were found to be positively associated with C3a (β = 0.07, 95% CI = 0.017–0.131, p = 0.01) and C3a/C3 ratio (β = 0.07, 95% CI = 0.015–0.126, p = 0.01) taking possible confounders into account. The association observed was mainly driven by PCB-126. Conclusion: In this study involving 992 elderly individuals from the general population, we showed that POPs, mainly PCB-126, were associated with levels of complement system markers indicating that the association of these toxic compounds with downstream disease could be mediated by activation of immune system. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Persistent organic pollutants (POPs) are synthetic compounds that, once released in the environment, can persist for a long time due to their resistance to degradation. They are lipophilic in nature and can bio-accumulate in the fat tissue of different organisms through the food chain. They may reach high concentrations in exposed organisms and can cause detrimental health effects. A wide range of compounds such as polychlorinated biphenyl (PCB) congeners, organochlorine (OC) pesticides (hexachlorobenzene or HCB, trans-nonachlordane or
⁎ Corresponding author at: Department of Medical Sciences, Molecular Epidemiology, Dag Hammarskjölds Väg 14B, First Floor, MTC Building, Science Park, Uppsala University, SE-751 85, Uppsala, Sweden. Tel.: +46 704250724. E-mail address: [email protected] (J. Kumar).
http://dx.doi.org/10.1016/j.envint.2014.06.009 0160-4120/© 2014 Elsevier Ltd. All rights reserved.
TNC and 2,2-bis (4-chlorophenyl)-1,1-dichloroethene or p,p′-DDE), polychlorinated dibenzo-p-dioxins, and brominated diphenyl ether (BDE) congeners are among the well-known POPs. During the industrial revolution, a large number of such chemicals were in use for commercial purposes. Due to their widespread use in the past, almost every individual has been exposed to POPs at some point in time (Hansen, 1998). The primary source of exposure is through a contaminated food that involves consuming meat, fish or dairy products (Johnson et al., 2010). In order to measure relative toxic effect of different POPs, a concept of total toxic equivalency (TEQ) value was invented by Van den berg et al. that considers the toxicity of different POPs in comparison to 2,3,7,8-tetrachlorodibenzo-p-dioxin, which thus has a toxic equivalency factor of 1 (Van den Berg et al., 1998). Different studies have confirmed the validity of using this approach in order to measure toxic effects of a mixture of different PCBs together (Bradlaw et al., 1980; Harris et al.,
J. Kumar et al. / Environment International 71 (2014) 94–100
1993). Since human health can be influenced by exposure to different POPs, studies have already shown the association between POPs and disease caused by altered immune or inflammatory responses (Barrett, 2012; Gascon et al., 2013; Glynn et al., 2008; Noakes et al., 2006). The immune system is a biological system that protects any organism from diseases. It can be divided into two main categories — the innate and the adaptive parts. The complement system is a major humoral component of innate immunity that forms the first line of defense and protects the body from infections (Ricklin et al., 2010). The complement system is activated by binding recognition proteins to carbohydrates or antibodies present on foreign microbes in order to kill these pathogens. Studies involving transgenic as well as knockout mice have shown the importance of the complement system in maintaining health. Approximately 50 different proteins work together in the complement system, which provides a vital triage system that aids in rapid killing of foreign bodies by triggering a cascade of events. However, improper or uncontrolled activation of the complement system may lead to attack the host cells. Several studies have shown the adverse effects of exposure to various POPs on the immune system (Baccarelli et al., 2004; Davis and Safe, 1990; Kim et al., 2003; Lee et al., 2007b; Park et al., 2008; Vos and Moore, 1974). An early study by Vos and Moore showed that when pregnant rodents were administered with dioxins, the offspring were born with altered immune parameters (Vos and Moore, 1974). These effects may lead either to reduced capacity to fight infections or to the risk of developing autoimmune manifestation in later life. Complement 3 (C3) is the most abundant complement protein present in blood and plays a central role in the complement system. Enzyme complexes called C3-convertases cleave and activate multiple C3 molecules into the C3a and C3b fragments that lead to a cascade of further events. C3b is an opsonin that mediates phagocytosis and is also a part of a powerful amplification loop that promotes further activation of C3. C3a is an anaphylatoxin, which can bind to receptors on various inflammatory cells, and it is also the precursor of an adipokine called acylation-stimulating-protein that acts as mediator of inflammation and causes increased permeability of blood vessels. C4 is located upstream in the complement cascade and is predominately activated in response to antigen–antibody complexes. It is another major component of complement system that is highly homologous to C3 and is activated into an opsonin (C4b) and anaphylatoxin (C4a). C4a is less potent than C3a. C4b combines with another component called C2a to form C3-convertase. To the best of our knowledge, no effort has yet been made to understand the impact of various POPs on complement system in humans from the general population. In order to test the hypothesis that there might be associations between POP exposure and various components of complement system, we performed this large population-based cross-sectional study in Swedish men and women (all aged 70 years) from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort. We have recently demonstrated a strong correlation between risk factors for cardiovascular disease and plasma levels of C3, C3a and C4 in this cohort (Nilsson et al., 2014). Our primary aim here was to study associations of TEQ and summary measure of polychlorinated biphenyls (sum of PCBs) with complement components (C3, C3a, the C3a/C3 ratio and C4). Our secondary aim was to explore associations of different individual POPs with these complementary system components. 2. Materials and methods 2.1. Study participants All individuals who were aged 70 years and living in the community of Uppsala, Sweden in 2001–2005 were invited to participate in the study. Approximately 50% (n = 1016) of the invitees agreed to participate in the study and were further investigated. All participants
95
provided their written informed consent and study was approved by the Ethics Committee of Uppsala University, Uppsala. The participants were asked to fill out a detailed questionnaire about, among other things, smoking habits, medications and medical history. Standard laboratory techniques were employed to measure routine clinical variables like fasting blood glucose and different lipids following standard procedures. Only 992 individuals were employed for this study due to the availability of data about different POPs in these individuals. Further information about the recruitment and clinical characteristics has been published previously (Lind et al., 2005) and can be found on the Internet as well (http://www.medsci.uu.se/pivus/). Large variations of population characteristics due to age as well as various lifestyle factors may influence outcomes. The prime motivation of selecting individuals of same age from general population was to control the confounding by age. Further, we have studied these elderly individuals of same age group where many of these lifestyle factors become attenuated and can be controlled for by extensive lifestyle data. 2.2. Measurement of POPs and quality control A modified method by Sandau et al. was followed to measure POPs in the study participants (Sandau et al., 2003), and further details can be seen elsewhere (Salihovic et al., 2012). Following a method by Rylander et al., lipid content of plasma was considered to normalize the levels of POPs measured (Rylander et al., 2006). Quality control plasma samples as well as procedural blank samples were incorporated in every batch of 10 samples so as to ensure the quality. The blank samples had no target compounds at N5% of levels present in the samples except for cis-chlordane and trans-chlordane. In the samples analyzed, both cis and trans-chlordane were present below the detection limit in N 90% of samples. The internal standard recovery was satisfactory, ranging from 60 to 110%. More details about the quality controls have been described in another article by our group (Salihovic et al., 2012). Samples with POP concentration falling below the limit of detection (LOD) were imputed and given value of LOD/2−0.5. 2.3. Summary measures of POPs Two different summary measures — TEQ and sum of PCBs were used as our primary exposure variables. Seven mono- and non-orthosubstituted dioxin-like PCBs with assigned TEF values (PCB-105, 118, 126, 156, 157, 169, 189) and OCDD were utilized to calculate the TEQ values as suggested by Van den Berg et al. (2006). The sum of PCBs was measured by adding concentration of 16 PCBs. 2.4. Measurement of complement system markers Three different complement components (C3, C3a and C4) were considered in the present study. All components were analyzed in EDTA-plasma, which had been stored at −70 °C as reported. Briefly, C3 and C4 were measured by nephelometry (Immage, BeckmanCoulter Inc., CA, USA), and C3aC3adesArg was quantified by ELISA as described earlier (Nilsson Ekdahl et al., 1992). The ratio between C3a/C3 was also calculated since it indicates the degree to which complement protein C3 is proteolytically activated. 2.5. Statistical analysis Variables were evaluated for their skewed distribution and if found to have a non-normal distribution, log-transformation was done in order to achieve normal distribution. The association between summary measures of POPs, i.e. TEQ, and sum of PCBs (independent variables) was analyzed for their association with complement system markers (dependent variables) by performing linear regression analysis. A number of potential confounders including age, sex, education (three levels), physical activity (four levels), waist circumference (cm), smoking
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(smokers, non-smokers), kidney function (glomerular filtration rate), fasting blood glucose, systolic blood pressure, body mass index (BMI), lipid profile (levels of high- and low-density lipoproteins cholesterol, triglycerides) and any medication (for diabetes or cardiovascular disease) were adjusted for in the analysis. These confounders were used based on their physiological importance. Previous studies have demonstrated the association of confounders with both POPs as well as complement system markers. The duration of exposure to POPs increases with age and their concentrations show strong positive correlations with age (Lee et al., 2006). A study by Watkins et al. has shown the association between POPs and kidney function (Watkins et al., 2013). In many renal diseases, the activation of complement and its deleterious consequences has been observed (Inal et al., 2003). Medication was used as a confounder since almost 45% of the individuals studied were on medication for cardiovascular disorders. A study by our group has shown the association between POPs exposure and hypertension (Lind et al., 2014). Patients with hypertension have shown the altered cellular and humoral immunity (Dzielak, 1992). Further, several other studies have shown the association of POP exposure with different outcomes like altered lipid levels, hypertension, and obesity (Lee et al., 2007a, 2008, 2012a; Lind et al., 2014; Ronn et al., 2011; Roos et al., 2013). Studies have revealed the link between obesity and complement system (Oberbach et al., 2011). Socio-economic status and physical activity are major confounders because of their known links to many health outcomes (Adler and Stewart, 2010). Two statistical models (models A and B) were employed. Model A was minimally adjusted considering age, sex and kidney function as confounders only, while model B was a fully-adjusted model with all confounders included. In order to evaluate the potential nonlinear relationship, a squared term of POPs was considered in the regression analysis. A two-way interaction term between POP and sex was introduced to explore the possible interaction between POPs and sex on complement system markers. Further, regression spline curves were generated to understand the relation between POPs and complement system markers studied. To confirm the association in presumably healthier individuals, the association analysis was performed in a sub-sample of the studied individuals who were non-smokers, non-diabetic and had no prior cardiovascular disease. The statistical software package STATA (version 12; StataCorp, College Station, TX, USA) was used to perform all the statistical analyses. 3. Results The study population characteristics, along with distribution of complement system markers studied, are shown in Table 1. Ratio of male and female participants in the study was equal. Out of 992 individuals, 10.6% were current smokers and 11.6% had diabetes, while 16.3% were diagnosed with cardiovascular disorders. Further, among the participants included in analysis, 67% were overweight (BMI ≥ 25.0 kg/m2) while 22% were obese (BMI ≥ 30.0 kg/m2). Table 2 shows distribution statistics of all 21 POPs along with their summary measures (TEQ and sum of PCBs) analyzed. Briefly, the highest median concentrations detected among all the POPs studied were of p,p′-DDE (308 ng/g of lipid). This pesticide was detected in the serum sample of all the participants. PCB-153 showed the highest median concentrations (232 ng/g of lipid) among all the PCBs studied. Further, the lowest median concentration observed was for OCDD (0.4 ng/g of lipid). Median levels of sum of PCBs were higher than that of p,p′-DDE (Table 2). 3.1. Association between POP exposure and complement protein C3 In regression model A, sum of PCBs showed significant association with protein complement C3 (beta = − 0.003, 95% CI = − 0.01 to −0.001, p-value = 0.002, Table 3). The association was attenuated in model B when adjusting for all confounders (Table 3). Exploratory
Table 1 Clinical characteristics and inflammatory markers of the individuals (n = 992) studied. Variable Clinical variables Age (years) Sex (female) Smokers Current Former BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) Glucose (mmol/L)a LDL (mmol/L) HDL (mmol/L) TG (mmol/L)a GFR (mL/min/1.73 m2)a Exercise habits No Low Medium High Education Low Medium High Diabetes mellitus Hypertensive CVD diagnosed CVD medication Complement system markers C3 C3a C3a/C3 C4
Median (IQR) or n (%)
Min, Max
70.1 (70.0, 70.3) 495 (50)
69.8, 70.7 –
105 (10.6) 408 (41.2) 26.6 (24.0, 29.6) 90 (84, 98) 148 (134, 164) 78 (72, 86) 5.0 (4.6, 5.4) 3.3 (2.8, 3.9) 1.4 (1.2, 1.8) 1.15 (0.87, 1.51) 78.9 (65.8, 94.9)
– – 16.6, 49.8 60, 134 84, 230 50, 114 2.8, 19.9 0.8, 6.9 0.6, 3.8 0.4, 4.8 23.2, 210.8
109 (11.3) 570 (59.1) 218 (22.6) 68 (7.0)
– – – –
555 (56.6) 175 (17.9) 250 (25.5) 87 (8.8) 11 (1.1) 162 (16.3) 445 (45)
– – – – –
0.95 (0.84, 1.07) 160.5 (129.7, 213.7) 168.2 (136.3, 216.9) 0.21 (0.18, 0.25)
0.52, 1.61 3.4, 605.4 4.4, 590.2 0.06, 0.51
Abbreviations: n, number; IQR, interquartile range; Min, minimum; Max, maximum; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; TG, triglyceride; GFR, glomerular filtration rate. Number (percentage) is shown for discrete variables while median (IQR) is shown for continuous variables. a Log transformed values are reported.
analyses showed that the attenuation was primarily due to BMI (data not shown). No significant association was observed between TEQ values and C3 in both of the models (Table 3). In the secondary analysis considering individual POPs, both BDE-47 (beta = −0.02, 95% CI = −0.03 to − 0.001) and PCB-157 (beta = − 0.02, 95% CI = − 0.1 to − 0.001) were found to be significantly negatively associated with C3 (Table 4). Other PCBs or OC pesticides did not show any significant associations. 3.2. Association between POP exposure and complement protein C3a TEQ value was found to be positively associated with C3a in a fully adjusted model (beta = 0.07, 95% CI = 0.02 to 0.13, Table 3). Sum of PCBs did not show any evidence of association with C3a in any of the models (Table 3). When individual POPs were analyzed for their association with complement markers studied, PCB-74, 105, 126, and 194 were found to be positively associated while PCB-157 was negatively associated with levels of C3a (Table 4). 3.3. Association between POP exposure and C3a/C3 ratio TEQ values were found to be positively associated with C3a/C3 ratio in both minimally-adjusted (beta = 0.07, 95% CI = 0.02 to 0.13, p-value = 0.008) and fully adjusted models (beta = 0.07, 95% CI = 0.01 to 0.13, p-value = 0.01; Table 3). Sum of PCBs showed a borderline significant positive association with C3a/C3 ratio in model A (beta = 0.005, 95% CI = 0.001 to 0.01, p-value = 0.03) and model B (beta = 0.004, 95% CI = − 0.0003 to 0.1, p-value = 0.07; Table 3). When
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3.6. Interaction analysis
Table 2 Distribution of POPs studied along with the summary measures. POPs
Mean (SD)a
Median (IQR)
% bLOD
PCB-74 PCB-99 PCB-105 PCB-118 PCB-126 PCB-138 PCB-153 PCB-156 PCB-157 PCB-169 PCB-170 PCB-180 PCB-189 PCB-194 PCB-206 PCB-209 OCDD HCB p,p′-DDE BDE-47 TNC TEQ Sum PCBs
2.7 (0.5) 2.7 (0.6) 1.6 (0.6) 3.5 (0.5) 1.8 (0.9) 4.9 (0.5) 5.4 (0.4) 3.2 (0.4) 1.5 (0.5) 3.3 (0.5) 4.4 (0.4) 5.2 (0.4) 1.2 (0.7) 2.8 (0.8) 1.4 (0.5) 1.4 (0.5) −0.8 (0.6) 3.7 (0.4) 5.7 (0.9) 0.9 (0.7) 3.1 (0.6) 0.4 (0.5) 46.9 (6.1)
91.4 (63.8–128.2) 90.7 (62.4–132.0) 32.0 (21.0–46.8) 200.6 (136.4–281.0) 40.4 (21.6–71.8) 819 (619–1115) 1428 (1114–1846) 154.2 (118.6–197.6) 28.0 (21.4–37.0) 171.4 (130.6–219.8) 497.2 (385.4–632.8) 1165 (917–1487) 19.2 (14.6–25.8) 119.4 (87.6–158.8) 26.8 (20.8–35.2) 26.2 (19.6–34.6) 2.6 (1.4–4.1) 254.0 (189.2–336.6) 1858 (1024–3451) 12.6 (9.0–19.4) 139.2 (91.6–211.2) 9.8 (6.7–13.9) 4988 (3942–6300)
0 0.5 0 0 4.5 0.4 0 0 0 0.3 0 0 0 1.4 0 0 19.4 1.4 0 27.8 0 – –
Abbreviations: SD, standard deviation; IQR, interquartile range; LOD, limit of detection; Min, minimum; Max, maximum; PCB, polychlorinated biphenyls; OCDD, octachlorodibenzo-p-dioxin; HCB, hexachlorobenzene; TNC, trans-nonachlordane; p,p′-DDE, 2,2-bis (4-chlorophenyl)-1,1-dichloroethene; BDE, bromodiphenyl ether; TEQ, total toxic equivalency value. a Values shown for POPs are lipid-normalized and log transformed (given in ng/g of lipid).
analyzed individually, POPs namely PCB-74, 105, 126, and 194 were found to be significantly positively associated with C3a/C3 ratio (Table 4). 3.4. Association between POP exposure and complement protein C4 When analyzed for two summary measures (TEQ and sum of PCBs), no significant association was observed with complement protein C4 in any of the models studied (Table 3). Further, when exposure to individual POP was analyzed for their association with C4, we did not observe any significant findings (Table 4).
When the interaction analysis between POPs and sex was performed, no significant interactions were observed (p-value N 0.05 of all associations). Further, we generated spline curves utilizing regression spline function but did not find any evidence of significant nonlinear association between levels of POPs and complement system markers in this study (data not shown). 4. Discussion In the present study, we report the association between TEQ values and sum of PCBs with different complement system markers in 70 year old participants recruited as a part of large-scale populationbased cohort from Uppsala, Sweden. We observed significant positive association between TEQ values and complement system markers C3a and C3a/C3. The sum of PCBs showed borderline significant positive association with C3a/C3 ratios. The associations observed were mainly due to PCB-74, 105, 126 and 194. 4.1. Complement protein C3 The protein C3 plays a key role in activation of complement system. A few studies have been performed to find the association between different POP exposures and immune system (Saberi Hosnijeh et al., 2011; Van Den Heuvel et al., 2002; Ye et al., 2011). In a small epidemiological study performed in humans, the effect of exposure of one of the dioxins called 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on humoral immunity has been analyzed (Saberi Hosnijeh et al., 2011). No significant difference in the levels of C3 has been observed between individuals exposed to TCDD and controls in this study. Another epidemiological study involving 200 adolescents, serum concentration of different PCBs and dioxin-like compounds has shown a significant association with different components of humoral immunity (Van Den Heuvel et al., 2002). In a study involving fish Oryzias melastigma, the effect of one of the POPs called BDE-47 has been observed on expression of a few of complement system pathway genes (Ye et al., 2011). When exposed to varying concentrations of BDE-47, the C3 gene expression has been found to be gender specific and down-regulated only in males as compared to controls. No such significant difference is observed in female fishes. In the current study, both PCB-157 and BDE47 showed association with levels of C3.
3.5. Sub-sample analysis 4.2. Complement protein C3a In the sub-sample of presumably healthier individuals (nonsmokers, non-diabetic and no prior cardiovascular disease, n = 687), TEQ value showed significant positive association with both C3a and C3a/C3 ratio (beta = 0.09, 95% CI = 0.02–0.16, p-value = 0.01 and beta = 0.10, 95% CI = 0.03–0.16, p-value = 0.004, respectively). The sum of PCBs showed a weak positive association with C3a/C3 ratio (beta = 0.01, 95% CI = 0.001–0.01, p-value = 0.04). Similar effect sizes were observed when total or sub-samples were analyzed (Table 5).
Complement protein C3 generates C3a and C3b components after activation. C3a part is also known as anaphylatoxin and can cause contraction of smooth muscles as well as increased vascular permeability. Further, C3a can bind to receptors present on mast cells and activate these cells to release histamine as part of an immunological response against any foreign antigen (Hugli, 1981). This in effect can lead to a local inflammatory response. All of these may trigger the initiation of adaptive immune response. C3a is also known to induce the synthesis
Table 3 Associations of the summary measures (TEQ values and sum of PCB concentrations) with complement system markers. TEQ
Sum of PCBs
Markers
Model A
p-Value
Model B
p-Value
Model A
p-Value
Model B
p-Value
C3 C3a C3a/C3 C4
−0.02 (−0.04, 0.01) 0.06 (0.003, 0.11) 0.07 (0.02, 0.13) −0.02 (−0.05, 0.02)
0.12 0.04 0.008 0.33
0.002 (−0.02, 0.02) 0.07 (0.02, 0.13) 0.07 (0.01, 0.13) 0.001 (−0.03, 0.03)
0.88 0.01 0.01 0.98
−0.003 (−0.01, −0.001) 0.002 (−0.002, 0.01) 0.005 (0.001, 0.01) −0.003 (−0.01, 0.0001)
0.002 0.35 0.03 0.06
−0.001 (−0.003, 0.001) 0.003 (−0.001, 0.01) 0.004 (−0.0003, 0.01) −0.001 (−0.003, 0.002)
0.20 0.17 0.07 0.60
Abbreviations: TEQ, total toxic equivalency value; PCB, polychlorinated biphenyls; C3a/C3, ratio between C3a and C3. Model A — Linear regression adjusted for age, sex and kidney function; Model B — Linear regression adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) from linear regression models is reported. Significant p-Values are shown in bold.
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Table 4 Association of individual POPs with complement system markers studied. POPs
C3
p-Value
C3a
p-Value
C3a/C3
p-Value
C4
p-Value
PCB-74 PCB-99 PCB-105 PCB-118 PCB-126 PCB-138 PCB-153 PCB-156 PCB-157 PCB-169 PCB-170 PCB-180 PCB-189 PCB-194 PCB-206 PCB-209 OCDD HCB p,p′-DDE BDE-47 TNC
−0.002 (−0.02, 0.01) −0.01 (−0.03, 0.004) −0.001 (−0.02, 0.02) −0.002 (−0.02, 0.02) 0.004 (−0.007, 0.01) −0.02 (−0.04, 0.003) −0.02 (−0.04, 0.005) −0.01 (−0.04, 0.01) −0.02 (−0.1, −0.001) −0.006 (−0.03, 0.02) −0.02 (−0.05, 0.006) −0.03 (−0.05, 0.001) −0.02 (−0.03, 0.003) −0.02 (−0.03, −0.003) −0.01 (−0.03, 0.01) −0.02 (−0.04, 0.01) 0.006 (−0.01, 0.02) −0.003 (−0.03, 0.02) 0.001 (−0.01, 0.01) −0.02 (−0.03,−0.001) −0.004 (−0.02, 0.01)
0.83 0.13 0.98 0.80 0.48 0.08 0.12 0.31 0.04 0.59 0.13 0.06 0.13 0.02 0.31 0.15 0.50 0.79 0.90 0.03 0.66
0.07 (0.01, 0.12) 0.01 (−0.04, 0.06) 0.05 (−0.001, 0.10) 0.05 (−0.01, 0.10) 0.05 (0.02, 0.08) 0.01 (−0.05, 0.07) 0.03 (−0.04, 0.10) 0.02 (−0.05, 0.109) −0.07 (−0.14,−0.01) −0.01 (−0.07, 0.06) 0.07 (−0.005, 0.15) 0.04 (−0.04, 0.11) −0.01 (−0.05, 0.04) 0.04 (0.005, 0.08) 0.03 (−0.04, 0.10) −0.01 (−0.07, 0.06) 0.04 (−0.01, 0.09) 0.03 (−0.04, 0.10) 0.02 (−0.02, 0.05) 0.02 (−0.02, 0.07) −0.05 (−0.10, 0.001)
0.02 0.64 0.05 0.11 0.002 0.76 0.44 0.51 0.02 0.89 0.07 0.38 0.83 0.03 0.39 0.82 0.11 0.39 0.34 0.34 0.09
0.07 (0.01, 0.12) 0.03 (−0.02, 0.07) 0.05 (0.005, 0.10) 0.05 (−0.004, 0.10) 0.05 (0.02, 0.08) 0.03 (−0.03, 0.09) 0.04 (−0.02, 0.11) 0.03 (−0.04, 0.10) −0.05 (−0.11, 0.01) 0.003 (−0.06, 0.07) 0.09 (0.01, 0.16) 0.06 (−0.02, 0.13) 0.005 (−0.04, 0.05) 0.06 (0.02, 0.09) 0.04 (−0.03, 0.10) 0.007 (−0.05, 0.07) 0.03 (−0.02, 0.08) 0.04 (−0.03, 0.10) 0.02 (−0.01, 0.05) 0.04 (−0.004, 0.08) −0.04 (−0.09, 0.01)
0.02 0.30 0.03 0.07 0.004 0.35 0.19 0.34 0.12 0.94 0.02 0.15 0.84 0.003 0.25 0.82 0.26 0.30 0.27 0.08 0.12
−0.003 (−0.04, 0.03) 0.0005 (−0.03, 0.03) 0.01 (−0.02, 0.04) 0.004 (−0.03, 0.03) −0.001 (−0.02, 0.02) 0.002 (−0.03, 0.04) −0.02 (−0.05, 0.02) 0.001 (−0.04, 0.04) −0.02 (−0.06, 0.01) 0.007 (−0.03, 0.04) −0.01 (−0.05, 0.03) −0.02 (−0.07, 0.02) −0.01 (−0.03, 0.02) −0.01 (−0.03, 0.01) −0.03 (−0.07, 0.007) −0.02 (−0.05, 0.02) 0.01 (−0.01, 0.04) 0.01 (−0.03, 0.05) 0.003 (−0.01, 0.02) −0.02 (−0.04, 0.01) 0.005 (−0.02, 0.03)
0.88 0.97 0.47 0.82 0.98 0.92 0.44 0.95 0.19 0.70 0.64 0.29 0.56 0.28 0.12 0.29 0.32 0.65 0.71 0.16 0.75
Abbreviations: PCB, polychlorinated biphenyls; OCDD, octachlorodibenzo-p-dioxin; HCB, hexachlorobenzene; TNC, trans-nonachlordane; p,p′-DDE, 1,1-bis-(4-chlorophenyl)-2,2dichloroethene; BDE, brominated diphenyl ether congener; C3a/C3, ratio between C3a and C3. Linear regression performed and adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) is reported from the linear regression models. Significant p-Values are shown in bold.
of adhesion molecules in endothelial cells lining blood vessels. In a separate paper by our group, analyzing the impact of POPs on various downstream inflammatory markers, we found an association between POPs (PCBs 126, 169, 189, 206 and 209) and adhesion molecules ICAM-1 and VCAM-1 (Influence of persistent organic pollutants on inflammatory markers in a population-based human sample. EHP, Manuscript Accepted). Complement is one of the important factors in inflammation and several other studies have also shown the activation of inflammation mediators due to POP exposure (Imbeault et al., 2012; Sipka et al., 2008). Significant association between TEQ values and C3a was observed in the present study.
4.3. C3a/C3 ratio The positive association between C3a/C3 ratio with TEQ values and a trend toward sum of PCBs may reflect the increased conversion of complement C3a from complement C3, thereby pointing to augmentation of the complement immune response. Activation of the complement system has been found to contribute to cardiovascular disorders and stroke (Hill and Ward, 1969; Rossen et al., 1985; Stokowska et al., 2013). A cross-sectional study performed in the same cohort showed that Table 5 Associations of the summary measures (TEQ values and sum of PCB concentrations) with complement system markers in individuals without potential recent infection. Markers
TEQ
p-Value
Sum of PCBs
p-Value
C3 C3a C3a/C3 C4
0.01 (−0.01, 0.03) 0.09 (0.03, 0.15) 0.08 (0.02, 0.13) 0.005 (−0.03, 0.04)
0.33 0.004 0.01 0.75
−0.001 (−0.002, 0.001) 0.004 (−0.001, 0.009) 0.005 (0.000, 0.01) 0.000 (−0.003, 0.002)
0.29 0.09 0.04 0.73
Abbreviations: TEQ, total toxic equivalency value; PCB, polychlorinated biphenyls; C3a/C3, ratio between C3a and C3. Individuals with possible recent infection (C-reactive protein N 10 mg/L, n = 45) were excluded from the analysis. Linear regression adjusted for age, sex, kidney function, smoking, body mass index, waist circumference, blood glucose, systolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, medication, exercise habits and education. Beta (95% confidence interval) is reported from the linear regression analysis. Significant p-Values are shown in bold.
circulating levels of PCBs were associated with atherosclerotic plaques as well as echogenicity of intima-media independently of cardiovascular risk factors like lipids (Lind et al., 2012). Another study by our group has also discovered that the exposure of POPs (PCBs with 4, 5 or 6 chlorides, p,p′-DDE and TNC) is associated with stroke in this population (Lee et al., 2007b).
4.4. Complement protein C4 In an epidemiological study by Saberi Hosnijeh et al., factory workers exposed to dioxin, no significant difference is observed between TCDD concentrations in these workers or controls and C4 (Saberi Hosnijeh et al., 2011). Other studies have also shown weak or no association between dioxin and C4 (Halperin et al., 1998; Ott et al., 1994). We did not observe any significant association between POP exposure and serum C4 concentrations. The associations observed for C3a or C3a/C3 ratio were mainly due to PCB-126. Among all the PCBs in whole family of chlorinated biphenyls, PCB-126 is the most potent agonist of aryl hydrocarbon receptor (AhR). The AhR plays an important role in physiological process including immune responses. The immunotoxicity of PCB-126 is not fully understood. Several mechanisms including altered cell proliferation, inflammation, and cytokine response could contribute to differential immune response. Different studies including ours have shown these responses due to POP exposure (Baccarelli et al., 2004; Imbeault et al., 2012; Lind et al., 2012; Ott et al., 1994; Stokowska et al., 2013; Van Den Heuvel et al., 2002; Vos and Moore, 1974). A few of the individual PCBs showed significant association with complement markers studied while sum of PCBs was found insignificant. This may probably be due to the fact that toxicity of different PCB congeners varies considerably among themselves. PCB toxicity is dependent on different factors like its structure, type of PCB (whether it is a mixture or congener), dose, and route of exposure. Further, toxicity due to mechanism that is Ah receptor dependent is different than Ah receptor independent. Studies have shown that complement system may be involved in the pathology of cardiovascular disorders as well as diabetes (Oksjoki et al., 2007; Yasuda et al., 1990). Previous studies from our group have shown significant association between POP exposure and diabetes and cardiovascular disorders (Lee et al., 2011, 2012b; Lind et al., 2012). Further,
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smoking can also activate the alternative pathway of complement (Kew et al., 1985). Therefore, we performed sub-sample analysis to understand the association between exposures of POPs and complement system markers in presumably healthier individuals by excluding participants who were smokers or had disease conditions (diabetes or prevalent cardiovascular disease). The small effect sizes in an association between TEQ and complement system markers could potentially lead to large downstream effects on disease outcomes. These findings are important for guiding policy work regarding pollutants. The absolute effects of this study are small and therefore may be of limited clinical significance. 4.5. Strengths and limitations One of the main strength of this study is inclusion of large number of POPs and different complement system markers in a community-based sample from general population. Individuals from the cohort studied have been carefully phenotyped. Since the study individuals were all 70 years old and recruited from a specific area; this limits the generalization of results to other age groups or ethnicities. Further, the study design is cross-sectional, thereby of limited value in establishing the causal relationship between POPs and complement system markers. Therefore, mechanistic studies are needed to establish the causality. Finally, we have not performed multiple-testing adjustment to balance false positive and false negative findings. Also, as there are no other cohorts with these kinds of data as far as we are aware of, we have not been able to replicate the findings. Hence, we suggest that these findings should be considered exploratory, and that they need validation in further studies. 5. Conclusion The TEQ values were found to be associated both with C3a and C3a/ C3 ratio, thereby suggesting increased activity of the complement system due to POP exposure, mainly PCB-126. Our results indicate that the association of these toxic compounds with downstream disease could be mediated by immune system activation products. Further studies involving larger cohorts are warranted to confirm these findings. Competing financial interest The authors declare no conflict of interest. Acknowledgments We acknowledge all the individuals who participated in this study. This study was supported by the Swedish Research Council (VR, Grant number 2012-2407) and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS, Grant number 2007-2047). JK was supported by fellowships from the Swedish Heart-Lung Foundation (Grant number 20120197), Sweden, and Clinical Research at Uppsala University, Uppsala, Sweden. References Adler NE, Stewart J. Health disparities across the lifespan: meaning, methods, and mechanisms. Ann N Y Acad Sci 2010;1186:5–23. Baccarelli A, Pesatori AC, Masten SA, Patterson Jr DG, Needham LL, Mocarelli P, et al. Arylhydrocarbon receptor-dependent pathway and toxic effects of TCDD in humans: a population-based study in Seveso, Italy. Toxicol Lett 2004;149:287–93. Barrett JR. Another piece of the obesity-environment puzzle: potential link between inflammation and POP-associated metabolic diseases. Environ Health Perspect 2012; 120:A164. Bradlaw JA, Garthoff LH, Hurley NE, Firestone D. Comparative induction of aryl hydrocarbon hydroxylase activity in vitro by analogues of dibenzo-p-dioxin. Food Cosmet Toxicol 1980;18:627–35.
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