Prospective associations between persistent organic pollutants and metabolic syndrome: A nested case–control study

Science of the Total Environment 496 (2014) 219–225

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Prospective associations between persistent organic pollutants and metabolic syndrome: A nested case–control study Yu-Mi Lee a,b, Ki-Su Kim a, Se-A Kim c,d, Nam-Soo Hong a, Su-Jin Lee e, Duk-Hee Lee a,d,⁎ a

Department of Preventive Medicine, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu 700-842, Republic of Korea Regional Cardiocerebrovascular Center, Kyungpook National University Hospital, 130 Dongdeok-ro, Jung-gu, Daegu 700-721, Republic of Korea Department of Biomedical Science, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu 700-842, Republic of Korea d BK21 Plus KNU Biomedical Convergence Program, Department of Biomedical Science, Kyungpook National University, Republic of Korea e Department of Epidemiology and Health Promotion, School of Public Health, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu 700-842, Republic of Korea b c

H I G H L I G H T S • Prospective evidence on the relationship between POPs and metabolic syndrome is scarce. • Most PCBs and some OCPs predicted the future risk for metabolic syndrome. • Low-dose POPs may be more harmful than high-dose POPs.

a r t i c l e

i n f o

Article history: Received 13 March 2014 Received in revised form 15 June 2014 Accepted 11 July 2014 Available online xxxx Editor: Adrian Covaci Keywords: Persistent organic pollutants Organochlorine pesticides Polychlorinated biphenyls Metabolic syndrome

a b s t r a c t Objective: Exposure to persistent organic pollutants (POPs) has recently been linked to metabolic syndrome (MetS) and some MetS components. However, prospective evidence in humans is scarce, and the nature of the dose–response relationship is unclear. We evaluated the association between POPs and MetS using a nestedcase control study within a community-based Korean cohort. Method: The study subjects were 64 patients newly diagnosed with MetS during a 4-year follow-up, and the controls were 182 subjects without MetS. Concentrations of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) were measured in stored serum collected at baseline. Results: The concentrations of most PCBs and some OCPs such as β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, and heptachlor epoxide predicted the risk for MetS. The POP exposure and MetS showed an inverted U-shaped or a linear association with plateau rather than a linear dose–response association. When the summary measure of the PCBs and OCPs was used, the adjusted odds ratios (ORs) across the quartiles of the summary measure were 1.0, 1.3, 3.8 (95% confidence interval, 1.3–10.7), and 2.1 (Pquadratic = 0.013) after adjusting for potential confounders. In the analyses of each of the five MetS components, POP exposure was mainly associated with an increased risk for glucose and lipid metabolism disturbances. Conclusion: This study demonstrated that chronic exposure to a mixture of PCBs and OCPs can increase the risk for MetS within the low-dose background exposure range of POPs. As the findings of this study suggest a nonmonotonic dose–response relationship, in vitro and in vivo experimental studies are needed to understand the underlying mechanisms. © 2014 Published by Elsevier B.V.

1. Introduction

Abbreviations: BMI, body mass index; HDL, high-density lipoprotein; NHANES, National Health and Nutrition Examination Survey; LOD, limit of detection; MetS, metabolic syndrome; OCPs, organochlorine pesticides; ORs, odds ratios; PCBs, polychlorinated biphenyls; POPs, persistent organic pollutants. ⁎ Corresponding author at: Department of Preventive Medicine, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu 700-422, Republic of Korea. Tel.: +82 53 420 4866; fax: +82 53 425 2447. E-mail address: [email protected] (D.-H. Lee).

http://dx.doi.org/10.1016/j.scitotenv.2014.07.039 0048-9697/© 2014 Published by Elsevier B.V.

Metabolic syndrome (MetS) is a cluster of major metabolic risk factors, including glucose intolerance, dyslipidemia, and high blood pressure, that promote the development of various diseases such as type 2 diabetes, cardiovascular disease, and cancer (Esposito et al., 2012; Grundy et al., 2005). The most important risk factor of MetS is abdominal obesity (Grundy et al., 2005). Insulin resistance is also involved in the underlying pathophysiological mechanism of MetS (Lann and LeRoith, 2007). Excess calorie intake and physical inactivity are major

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contributors to obesity and insulin resistance (Brown et al., 2009; Kelly, 2000). Various environmental chemicals have received attention as contributors to obesity and insulin resistance (Casals-Casas and Desvergne, 2011). Among such chemicals, persistent organic pollutants (POPs), which are lipophilic chemicals that bioaccumulate in adipose tissue and move around with serum lipids, have been closely linked to MetS components (Ruzzin et al., 2012). POPs, a group of environmental chemicals with highly lipophilic properties and resistance to biodegradation (Li et al., 2006), include several hundred different chemicals, including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and dioxins. A substantial number of cross-sectional and prospective epidemiological studies on the relationship between exposure to POPs and type 2 diabetes have been published in recent years (Lee et al., 2014; Taylor et al., 2013). However, only a few studies have focused on POPs and MetS in humans, and prospective evidence of the role of POPs in MetS is scarce. Two cross-sectional studies and one case–control study reported positive associations between environmental exposure and some POPs and MetS (Lee et al., 2007; Park et al., 2010; Uemura et al., 2009). However, increased lipolysis, which is commonly observed with insulin resistance, can increase the POP levels. Some human studies showed that weight loss increased serum POP concentrations, whereas weight gain decreased serum POP concentrations (Ibrahim et al., 2011; Imbeault et al., 2001; Lim et al., 2011). Therefore, measurement of POP concentration among subjects with MetS in cross-sectional and traditional case–control studies has a limited ability to confirm causality. In one small-scale prospective nested case–control study of POPs and diabetes, low-dose POPs were reported to be related to an increased body mass index (BMI), increased triglyceride levels, decreased highdensity lipoprotein (HDL) cholesterol level, and increased insulin resistance among young adults (Lee et al., 2011). An important finding of this prospective study showed a clearly inverted U-shaped association, suggesting that the degree of the harmful effect of POPs may not increase with an increase in POP dose within the background exposure of the general population. Meanwhile, cross-sectional or case–control studies tended to show more linear trends despite the existence of some nonmonotonic dose–response relationships, especially with PCBs (Lee et al., 2007; Park et al., 2010; Uemura et al., 2009). The possibility that the degree of harmful effect may not increase with increasing doses of chemicals represents a significant threat to public health. Therefore, evaluating this possibility is important in determining whether POP exposure is associated with the development of MetS. In this prospective study, we investigated the relationship between exposure to various serum concentrations of POPs at baseline and the development of MetS and/or MetS components during 4 years using a nested case–control study based on a community-based Uljin cohort in South Korea. Among the various POPs, we focused on OCPs and PCBs, which were strongly associated with MetS in a previous epidemiological study (Lee et al., 2011). 2. Materials and methods 2.1. Study population The Uljin cohort was established to study changes in cardiovascular risk factors among residents in a local community. A total of 1007 residents aged N40 years were recruited at a baseline examination in 2006, of whom 61.6% (n = 621) completed a follow-up examination in 2010. Of the participants, 445 were not diagnosed with MetS at baseline. Sixty-four subjects with a sufficient amount of stored serum were selected as cases from among 73 participants newly diagnosed with MetS during the follow-up examination. Among the participants without MetS at the follow-up examination, 192 were randomly selected as control subjects, but 10 subjects were later excluded because

information on POP concentrations was not available owing to an insufficient amount of serum during laboratory analyses. The controls were frequency matched to the cases according to age and sex. Written informed consent was obtained from all the subjects. This study was reviewed and approved by the institutional review board of the Kyungpook National University Hospital.

2.2. Measurements Information on clinical and demographic characteristics, health behaviors, and anthropometric dimensions was collected at baseline and follow-up examinations. BMI was derived as weight divided by the height squared (kg/m2). Waist circumference was measured at a level midpoint between the iliac crest and lowest rib. Systolic and diastolic blood pressures were measured in the sitting position after a 5-min rest. Blood pressure was measured twice at 5-min intervals, and the mean of two blood pressure measurements was used. Prior to blood sampling at baseline, the subjects were asked to fast overnight for at least 8 h. Approximately 2–5 mL of serum samples was obtained from each participant and stored frozen at −70 °C until analysis. Levels of fasting glucose, triglycerides, and HDL cholesterol were analyzed by enzymatic methods using the ADVIA 1650 analyzer (Bayer Inc., NY, USA). The serum concentrations of PCBs and OCPs were determined at the laboratory of the School of Environmental Science and Engineering, POSTECH (Pohang, South Korea), on gas chromatography (GC, 6890N, Agilent, CA, USA) and high-resolution mass spectrometry (HRMS, JMS-800D, JEOL, Tokyo, Japan), using the isotope dilution method after solid phase extraction using hydrophilic–lipophilic balanced and silica/Florisil cartridges for cleanup. The laboratory personnel were blinded to all data, including the case–control status. The limit of detection (LOD) was defined as three times the signal-to-noise ratio. Subjects with POP concentrations lower than the LOD were assigned LOD values divided by 3. Of 33 kinds of POPs (17 PCBs and 16 OCPs) included in the measurement, the concentrations of the following 15 PCBs and eight OCPs that were higher than the LOD in at least 75% of the study subjects were evaluated in the present study: PCB74, PCB99, PCB105, PCB118, PCB138, PCB153, PCB156, PCB164, PCB167, PCB172, PCB177, PCB178, PCB180, PCB183, PCB187, β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, trans-nonachlor, heptachlor epoxide, p,p′dichlorodiphenyldichloroethylene, p,p′-dichlorodiphenyltrichloroethane, and mirex. Supplementary Table 1 shows the detection rate (%) and distribution of the serum concentrations according to the quartiles of the individual PCBs and OCPs. We also presented the serum concentrations in the US general population using the data from the National Health and Nutrition Examination Survey (NHANES) 2003–2004 for comparison with those in our study subjects. The analytical results were reported in terms of wet-weight- and lipidstandardized-based concentrations of POPs. The lipid-standardized-based concentrations were calculated from the serum concentrations of POPs divided by the total serum lipid content. Total lipid concentration (mg/dL) was calculated using the following formula: (2.27 × total cholesterol) + triglyceride + 62.3. We defined MetS using the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria instead of the original NCEP ATP III criteria. The modified criteria are more appropriate for Asian populations than the original criteria, which are based on data from Caucasians (Tan et al., 2004). MetS was diagnosed when the subjects had co-occurrence of at least three of the following five criteria: 1) waist circumference ≥ 90 cm in men or ≥ 80 cm in women; 2) blood pressure of 130/85 mm Hg or under medication; 3) triglyceride level ≥ 150 mg/dL; 4) fasting blood glucose level ≥ 100 mg/dL or under medication; and 5) HDL cholesterol level b40 mg/dL in men or b50 mg/dL in women.

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2.3. Statistical analysis To determine whether exposure to the serum concentrations of each POP was significantly associated with the development of MetS, the subjects were categorized into four groups according to cutoff points of the 25th, 50th, and 75th percentile values of each POP concentration. To evaluate the associations between the summary measurements of the POP concentrations and MetS, we constructed three summary measurements based on the following: 1) all 23 POPs (both 15 PCBs and 8 OCPs), 2) 15 PCBs, and 3) 8 OCPs. Because OCPs demonstrated substantially different results depending on their kind, we additionally made a summary measurement for 4 OCPs that showed statistically significance in an analysis of individual OCPs. The summary measurements were estimated by summing the rank orders of the individual POP from ranks 1 to 246. For instance, the lowest concentration of an individual POP was assigned rank 1, and the highest was assigned rank 246. Concentrations lower than the LOD were assigned rank 1, and the remaining subjects were ranked according to concentration. The ranks for individual POPs were summed, and the summed values were categorized into four quartile groups. We did not use the summary measurements of the absolute concentrations of each POP because they were influenced only by POPs with high concentrations. In particular, when nonlinear dose–response relationships are expected, low-dose effects can be masked by several POPs with high concentrations. Multiple logistic regression analysis was used to examine whether the risks for incident MetS and five MetS components were associated with exposure to POPs. The covariates were age (years), sex, cigarette smoking (never, former, or current), alcohol drinking (never, former, or current), and exercise (average daily hours of moderate or vigorous exercise). Triglyceride and cholesterol levels were further adjusted when wet-weight-based concentrations of POPs were used but not when lipid-standardized concentrations were used. We presented the following two models: Model 1 was adjusted for age, sex, cigarette smoking, alcohol drinking, exercise, and triglyceride and total cholesterol levels. Model 2 was further adjusted for BMI. Because waist circumference, a MetS component, is highly correlated with BMI, the inclusion of BMI as a covariate can be an overadjustment in the analyses of the outcome of MetS. Ptrend and Pquadratic were estimated to test whether a linear dose–response relationship or an inverted U-shaped dose–response association existed. In the analyses of the associations between exposure to POPs and the five MetS components in the risk for the syndrome, case and control subjects with each component at baseline were excluded from the analyses. This reduced the sample sizes to 156 for increased waist circumference, 161 for high blood pressure, 238 for a high triglyceride level, 214 for high fasting blood glucose level, and 149 for low HDL cholesterol level. All the data were analyzed using SAS version 9.2 (SAS Inc., Cary, NC, USA). 3. Results

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Table 1 General and clinical characteristics of subjects at baseline. Characteristicsa

Age (years) Male Current smokers Current drinkers Physically inactiveb Body mass index (BMI, kg/m2) Triglycerides (mg/dL) Total cholesterol (mg/dL) a b

Controls

Cases

n = 182

n = 64

54.7 ± 7.3 31.9% 8.2% 36.8% 39.6% 23.3 ± 2.8 85.9 ± 30.0 193.4 ± 32.9

57.3 ± 7.6 35.9% 15.6% 45.3% 53.1% 24.8 ± 2.4 106.5 ± 41.5 203.7 ± 35.9

p-Value

0.016 0.551 0.219 0.442 0.059 b0.001 b0.001 0.036

Mean ± standard deviation or proportion. No regular exercise.

eight OCPs, β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, and heptachlor epoxide showed significant or marginally significant associations. The risk for MetS was two to four times higher among the subjects belonging to the second or fourth quartiles compared with those in the first quartile (Supplementary Table 3). The shapes of the associations varied including linear, inverted U shape, and linear with the plateau. 3.3. Associations between the summary measurements of POPs and incident MetS The results of the summary measurements of the POPs are presented in Table 2. Similar to those of the individual compounds, inverted U-shaped or linear associations with the plateau were observed. When the summary measurements of the PCBs and OCPs were used, the adjusted odds ratios (ORs) were 1.0, 1.6, 3.9 (95% confidence interval [CI], 1.4–11.0), and 2.7 (Pquadratic = 0.014), after adjusting for age, sex, cigarette smoking, alcohol drinking, exercise, triglyceride levels, and total cholesterol level. Further adjustment for BMI did not materially change the results, with adjusted ORs of 1.0, 1.3, 3.8 (95% CI, 1.3–10.7), and 2.1 (Pquadratic = 0.013). The results of the summary measurements of the 15 PCBs were similar to those of the PCBs and OCPs (Table 2). The summary measurements of all eight OCPs were not related to the risk for MetS. When the summary measurements of the four OCPs that demonstrated significant or marginally significant associations with MetS were used, the adjusted ORs were 1.0, 2.0, 3.2 (95% CI, 1.1–9.1), and 3.1 (95% CI, 1.1–8.8; Ptrend = 0.043). Further adjustment for BMI made the results pertaining to OCPs a little weaker and statistically nonsignificant. When we used the lipid-standardized-based concentrations of POPs, the general patterns were similar to those of the wet-weight-based concentrations of POPs, but the strength of the association became weaker (Table 3). 3.4. Associations between the summary measurements of POPs and the MetS components

3.1. General and clinical characteristics Table 1 shows the general and clinical characteristics of the 64 cases and 182 controls. At baseline, the cases were approximately 3 years older and more physically inactive than the controls. In addition, BMI, triglyceride level, and total cholesterol level were significantly higher in the cases than in the controls. 3.2. Associations between serum concentrations of individual POPs and incident MetS Exposure to most individual PCBs showed significant or marginally significant associations with the risk for MetS (Supplementary Table 2). Each PCB showed an inverted U-shape or linear association with the plateau rather than a clearly linear association. Among the

Table 4 shows the associations between the five MetS components and the summary measurements of POPs. The strongest association with the summary measurements of PCBs and OCPs was observed with the risk for high serum triglyceride level. The adjusted ORs were 1.0, 2.0, 5.3 (95% CI, 1.4–20.0), and 1.7 (Pquadratic = 0.004). High fasting glucose level also showed a significant association, with adjusted ORs of 1.0, 0.8, 2.6, and 3.6 (Ptrend = 0.023), whereas low HDL cholesterol level showed a marginally significant association (Pquadratic = 0.061). When we used the summary measurements of the 15 PCBs, they were most strongly associated with the risk for high fasting blood glucose and low HDL cholesterol levels. Compared with those in the first quartile, the subjects belonging to the third or fourth quartile showed two to four times higher risks for elevated fasting blood glucose level (Ptrend = 0.033). The risk for low HDL cholesterol level was about

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Table 2 Adjusted odds ratios (ORs) (95% CI) of the risk of metabolic syndrome (MetS) according to the quartiles of summary measures of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Analytes

Model

All 23 PCBs and OCPs

All 15 PCBs

All 8 OCPs

a

4 significant OCPs

Cases/no. Model 1 Model 2 Cases/No. Model 1 Model 2 Cases/No. Model 1 Model 2 Cases/No. Model 1 Model 2

Quartiles of summary measures Q1

Q2

Q3

Q4

8/61 Referent Referent 8/61 Referent Referent 13/61 Referent Referent 8/61 Referent Referent

12/62 1.6 (0.6–4.7) 1.3 (0.4–3.9) 14/62 1.9 (0.7–5.4) 1.6 (0.6–4.7) 11/62 0.8 (0.3–2.0) 0.6 (0.2–1.6) 14/62 2.0 (0.7–5.8) 1.5 (0.5–4.4)

25/62 3.9 (1.4–11.0) 3.8 (1.3–10.7) 24/62 3.8 (1.4–9.9) 4.1 (1.5–11.2) 19/62 1.6 (0.6–3.9) 1.3 (0.5–3.4) 20/62 3.2 (1.1–9.1) 2.5 (0.9–7.3)

19/61 2.7 (0.9–8.0) 2.1 (0.7–6.5) 18/61 2.2 (0.8–6.3) 1.9 (0.7–5.6) 21/61 1.4 (0.5–3.6) 1.0 (0.4–2.6) 22/61 3.1 (1.1–8.8) 2.2 (0.7–6.2)

Ptrend

Pquadratic

0.184 0.350

0.014 0.013

0.203 0.280

0.017 0.010

0.281 0.663

0.546 0.541

0.043 0.188

0.127 0.205

Model 1: adjusted for age, sex, smoking, drinking, exercise, triglycerides, and total cholesterol. Model 2: adjusted for age, sex, smoking, drinking, exercise, triglycerides, total cholesterol, and BMI. a Four significant OCPs: summary measure of four individual OCPs (β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, and heptachlor epoxide) that demonstrated significant or marginally significant associations with MetS.

five to 10 times higher among those with higher PCB levels (Pquadratic = 0.063). Meanwhile, the summary measurements of the eight OCPs did not show clear associations (data not shown), but those of the four OCPs demonstrated significant or marginally significant associations with some MetS components. The risk for abdominal obesity or high fasting blood glucose level was two or three times higher in the subjects belonging to the third or fourth quartile than those belonging to the first quartile (Ptrend = 0.084 and Ptrend = 0.044, respectively).

4. Discussion We observed that the serum concentrations of most PCBs and some OCPs predicted the future risk for MetS during the 4-year follow-up. The strength and shape of the associations were substantially different, depending on the individual POPs. In human studies, the interpretation of the influence of individual compounds has little meaning because humans are exposed to a mixture of various POPs, and serum concentrations of many PCBs and OCPs are highly correlated. The findings on one specific POP may reflect those of a POP mixture, which are correlated with that compound. Therefore, in this discussion, we focus on the mixture of POPs or mixtures of PCBs or OCPs rather than on individual POPs, unless a discussion of the individual compounds is warranted in a particular context.

In general, the present results are in agreement with findings of previous epidemiological studies that reported positive associations between some POPs and MetS or MetS components (Lee et al., 2007; Park et al., 2010; Uemura et al., 2009). However, in this nested case–control study, the association between exposure to POPs and MetS did not follow a linear dose–response relationship but an inverted U-shaped or linear association with plateau. The shape of the association is important in studies of POPs because several studies have raised the possibility that the degree of the harmful effect of POPs may not linearly increase as doses increase (Lee et al., 2014; Vandenberg et al., 2012). If this is the case, there could a huge impact on public health. One previous prospective study that evaluated changes in BMI, triglyceride levels, HDL-cholesterol level, and insulin resistance over an 18-year period also demonstrated a clear inverted U-shaped association (Lee et al., 2011). The shape of the association in that study seemed to be different from previous findings of cross-sectional or case–control studies on POPs and MetS, with those studies tending to show mostly linear dose–response relationships (Lee et al., 2007; Park et al., 2010; Uemura et al., 2009). Because the previous study was performed among 90 control subjects of the nested case–control study on POPs and diabetes who remained nondiabetic during the 18 years and the outcomes were the changes in clinical risk factors, not the incidence of MetS, the interpretation on the inverted U-shaped association was limited (Lee et al., 2011). However, the present study demonstrated a shape of the

Table 3 Adjusted ORs (95% CI) of the risk of MetS according to the quartiles of summary measures based on lipid-standardized concentrations of PCBs and OCPs. Analytes

All 23 PCBs and OCPs

All 15 PCBs

All 8 OCPs

4 significant OCPsa

Model

Cases/no. Model 1 Model 2 Cases/No. Model 1 Model 2 Cases/No. Model 1 Model 2 Cases/No. Model 1 Model 2

Quartiles of summary measures Q1

Q2

Q3

Q4

10/61 Referent Referent 8/61 Referent Referent 14/61 Referent Referent 9/61 Referent Referent

14/62 1.2 (0.5–2.9) 1.1 (0.4–2.9) 16/62 2.0 (0.8–5.2) 1.8 (0.7–4.9) 11/62 0.8 (0.3–1.9) 0.7 (0.3–1.8) 15/62 1.7 (0.7–4.4) 1.3 (0.5–3.5)

22/62 2.3 (0.9–5.8) 2.3 (0.9–5.9) 22/62 3.1 (1.2–8.1) 3.4 (1.3–8.9) 18/62 1.4 (0.6–3.2) 1.3 (0.5–3.2) 18/62 2.2 (0.9–5.8) 1.8 (0.7–4.8)

18/61 1.6 (0.6–4.1) 1.5 (0.5–4.0) 18/61 1.9 (0.7–5.4) 1.9 (0.7–5.5) 21/61 1.5 (0.6–3.6) 1.1 (0.4–2.8) 22/61 2.6 (1.0–6.6) 1.7 (0.6–4.6)

Ptrend

Pquadratic

0.542 0.644

0.120 0.131

0.298 0.287

0.030 0.029

0.209 0.557

0.992 0.807

0.189 0.511

0.863 0.662

Model 1: adjusted for age, sex, smoking, drinking, and exercise. Model 2: adjusted for age, sex, smoking, drinking, exercise, and BMI. a Four significant OCPs: summary measure of four individual OCPs (β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, and heptachlor epoxide) that demonstrated significant or marginally significant associations with MetS.

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Table 4 Adjusted ORs (95% CI) of the risk of components of MetS according to quartiles of summary measures of PCBs and OCPs. Ptrend

Pquadratic

12/39 1.4 (0.4–4.7)

0.570

0.507

12/41 1.0 (0.3–3.2) 1.0 (0.3–3.2)

13/40 1.1 (0.3–3.7) 1.1 (0.3–3.6)

0.565 0.586

0.847 0.830

16/60 5.5 (1.5–20.6) 5.3 (1.4–20.0)

8/59 1.9 (0.5–8.4) 1.7 (0.4–7.7)

0.921 0.965

0.004 0.004

11/54 2.7 (0.7–9.9) 2.6 (0.7–9.9)

15/53 3.6 (0.9–14.0) 3.6 (0.9–14.0)

0.022 0.023

0.312 0.315

9/38 8.8 (1.0–82.0) 8.6 (0.9–80.0)

8/37 7.1 (0.8–66.8) 6.8 (0.7–64.6)

0.265 0.270

0.060 0.061

12/39 1.4 (0.5–4.2)

13/39 1.8 (0.5–5.7)

0.323

0.981

15/41 1.3 (0.5–3.8) 1.3 (0.5–3.8)

12/40 1.1 (0.3–3.5) 1.1 (0.3–3.5)

0.564 0.570

0.811 0.822

12/60 1.8 (0.6–5.3) 1.7 (0.6–5.1)

9/59 1.1 (0.3–3.7) 1.0 (0.3–3.4)

0.862 0.921

0.342 0.359

14/54 3.9 (1.1–13.3) 4.0 (1.2–13.7)

13/53 3.3 (0.9–12.2) 3.2 (0.9–11.8)

0.033 0.033

0.127 0.121

8/38 9.7 (1.0–93.5) 9.5 (1.0–90.8)

8/37 6.5 (0.7–61.7) 6.4 (0.7–59.7)

0.215 0.206

0.061 0.063

13/39 3.4 (1.0–11.6)

15/40 3.5 (1.0–11.5)

0.084

0.161

9/41 0.8 (0.2–2.6) 0.7 (0.2–2.4)

11/40 1.1 (0.3–3.8) 1.1 (0.3–3.6)

0.782 0.704

0.967 0.895

10/60 2.5 (0.7–9.2) 2.4 (0.6–8.7)

12/59 2.5 (0.7–9.1) 2.2 (0.6–8.3)

0.279 0.399

0.269 0.292

13/54 2.4 (0.7–8.1) 2.3 (0.7–8.2)

13/53 2.4 (0.7–8.2) 2.4 (0.7–8.4)

0.041 0.044

0.396 0.395

10/37 2.1 (0.5–8.7) 1.9 (0.5–8.2)

6/37 0.6 (0.1–3.0) 0.6 (0.1–2.7)

0.739 0.800

0.092 0.093

Quartiles of summary measures Q1

Q2

All 23 PCBs and OCPs Waist circumference ≥90 cm in men or ≥80 cm in women Cases/no. 11/39 9/39 Model 1 Referent 0.9 (0.3–2.9) Blood pressure ≥ 130/85 mm Hg Cases/no. 9/40 8/40 Model 1 Referent 0.6 (0.2–2.0) Model 2 Referent 0.6 (0.2–1.9) Triglycerides ≥ 150 mg/dL Cases/no. 4/59 7/60 Model 1 Referent 2.1 (0.5–8.3) Model 2 Referent 2.0 (0.5–7.8) Fasting blood glucose ≥ 100 mg/dL Cases/no. 4/53 4/54 Model 1 Referent 0.8 (0.2–3.7) Model 2 Referent 0.8 (0.2–3.7) HDL cholesterol b40 mg/dL in men or b50 mg/dL women Cases/no. 1/37 5/37 Model 1 Referent 5.0 (0.5–49.5) Model 2 Referent 4.8 (0.5–47.5) All 15 PCBs Waist circumference ≥90 cm in men or ≥80 cm in women Cases/no. 10/39 10/39 Model 1 Referent 1.1 (0.4–3.4) Blood pressure ≥ 130/85 mm Hg Cases/no. 10/40 5/40 Model 1 Referent 0.4 (0.1–1.4) Model 2 Referent 0.4 (0.1–1.4) Triglycerides ≥ 150 mg/dL Cases/no. 7/59 7/60 Model 1 Referent 1.0 (0.3–3.3) Model 2 Referent 0.9 (0.3–3.0) Fasting blood glucose ≥ 100 mg/dL Cases/no. 4/53 3/54 Model 1 Referent 0.7 (0.1–3.5) Model 2 Referent 0.7 (0.1–3.3) HDL cholesterol b40 mg/dL in men or b50 mg/dL women Cases/no. 1/37 6/37 Model 1 Referent 5.6 (0.6–55.8) Model 2 Referent 5.2 (0.5–52.2) 4 significant OCPsa Waist circumference ≥90 cm in men or ≥80 cm in women Cases/no. 7/39 10/38 Model 1 Referent 2.3 (0.7–7.7) Blood pressure ≥ 130/85 mm Hg Cases/no. 8/39 14/41 Model 1 Referent 1.8 (0.6–5.4) Model 2 Referent 1.7 (0.6–5.1) Triglycerides ≥ 150 mg/dL Cases/no. 5/59 8/60 Model 1 Referent 2.1 (0.6–7.4) Model 2 Referent 1.9 (0.5–6.9) Fasting blood glucose ≥ 100 mg/dL Cases/no. 5/53 3/54 Model 1 Referent 0.5 (0.1–2.1) Model 2 Referent 0.5 (0.1–2.1) HDL cholesterol b40 mg/dL in men or b50 mg/dL women Cases/No. 5/37 2/38 Model 1 Referent 0.3 (0.0–1.8) Model 2 Referent 0.2 (0.0–1.6)

Q3

Q4

13/39 1.5 (0.5–5.0)

Model 1: adjusted for age, sex, smoking, drinking, exercise, triglycerides, and total cholesterol. Model 2: adjusted for age, sex, smoking, drinking, exercise, triglycerides, total cholesterol, and BMI. a Four significant OCPs: summary measure of four individual OCPs (β-hexachlorocyclohexane, hexachlorobenzene, oxychlordane, and heptachlor epoxide) that demonstrated significant or marginally significant associations with MetS.

dose–response relationship similar to that in the previous study (Lee et al., 2011). Despite the general similarity between the findings of the previous prospective study (Lee et al., 2011) and the present study, the shape of the inverted U-shaped association in this study seems to be

somewhat weak, with the decreased risk for MetS among those with high POPs not as clear. One difference between the two studies is the age distribution. The previous study was performed with young adults aged 20–32 years at baseline, whereas the age of the subjects at baseline in the present study was 40–70 years. A recent review (Lee et al., 2014)

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of POPs and type 2 diabetes suggested that inverted U-shaped associations may be more clearly observed among younger individuals and that the decreased trend in the risk for MetS at higher concentrations of POPs may be weaker in older individuals, thereby changing the shape of the association from an inverted U shape to a linear association with plateau. The suggested mechanism is the decreased biological responses of the physiological system with aging (Noth and Mazzaferri, 1985). The present and previous findings on MetS or MetS components agree well with this hypothesis. In contrast to prospective findings, cross-sectional and case–control findings on POPs and MetS (Lee et al., 2007; Park et al., 2010; Uemura et al., 2009) have tended to report more linear patterns. POPs stored in adipose tissue can be more easily released to the blood circulatory system among patients with insulin resistance (Irigaray et al., 2007), a key pathological condition of MetS, than in those without insulin resistance. Therefore, in cross-sectional studies, reverse causality due to disease progression bias can explain a higher prevalence of MetS among those with higher concentrations of POPs (Porta, 2008). When we analyzed the associations between exposure to various concentrations of POPs and the risk for MetS using lipid-standardized concentrations of POPs, the strengths of the associations were slightly weakened compared with those in the analysis using wet-weightbased concentrations of POPs, including lipids as covariates. Even though lipid-standardized concentrations have been commonly used to compare POP levels between populations, they can distort or underestimate true associations between exposure to POPs and biological outcomes when serum lipid levels are involved in the pathogenesis of outcomes such as MetS (Gaskins and Schisterman, 2009). Nevertheless, it is remarkable that the results of the analysis using lipid-standardized concentrations of POPs were not widely different from those of the analysis using wet-weight-based concentrations of POPs. In the present analyses of each of the five MetS components, the POPs seemed to disturb glucose and lipid metabolisms compared with the other features of MetS. The relationship with waist circumference was specifically related to some OCPs. High blood pressure was not associated with exposure to either PCBs or OCPs. One cross-sectional study that focused on POP subclasses also demonstrated that PCBs and OCPs were more strongly associated with disturbances of glucose and lipid metabolisms, whereas high blood pressure was more strongly related to exposure to POPs belonging to polychlorinated dibenzo-pdioxins or polychlorinated dibenzofurans with strong dioxin activity (Lee et al., 2007). This suggests that exposure to different kinds of POPs can be differently associated with each MetS component. However, as people are simultaneously exposed to a mixture of all these POPs, the MetS phenotype, which includes a cluster of cardiovascular factors, can be frequently observed in humans. In fact, factor analyses of MetS traits have shown that blood pressure elevation clusters less closely than other MetS traits, such as abdominal obesity, dyslipidemia, and impaired fasting glucose level (Meigs, 2000). Different associations with different classes of POPs may explain the results of the factor analyses of MetS. Human findings on POPs and MetS are strongly supported by experimental findings. For example, feeding rats or mice a diet of POP-contaminated salmon oil or filet induced visceral obesity, hepatosteatosis, dyslipidemia, glucose intolerance, and insulin resistance, all of which are phenotypes related to MetS (Ibrahim et al., 2011; Ruzzin et al., 2010). These experimental studies were unique in that their experimental conditions mimicked closely human exposure to POPs in the real world. In contrast to most experimental studies on POPs, they focused on a mixture of POPs rather than on any specific compound, and the body burden of POPs in the animals exposed to POPs was similar to that observed in humans 40–50 years of age. When a nonmonotonic dose–response relationship is expected, experimental designs with exposure similar to human exposure are critical. Moreover, there is evidence that POPs coexisting with various lipid components may not be innocent bystanders. POPs accumulated in

adipose tissue can disrupt endocrine pathways in adipocytes by impairing adipogenic/lipogenic processes, and POPs in ectopic lipidcontaining nonadipose tissues such as those of the liver, skeletal muscle, and pancreas increased diverse adverse effects on these tissues (Mullerova and Kopecky, 2007). This study had several limitations. First, the statistical power in our research was limited due to the small sample size. In particular, the sample size of our study was not large enough to evaluate the associations between exposure to various concentrations of POPs and each MetS component because when each MetS component was analyzed as the outcome, the cases and controls with that condition at baseline were excluded from the analyses. Second, as the data analyses involved a large number of statistical tests, there might be false-positive associations. Third, we could not exclude the possibility that other POPs not analyzed in this study might have contributed to the development of MetS. Although POPs encompass a variety of chemicals, we measured only PCBs and OCPs. Fourth, the follow-up rate was only 61.6%. Thus, selection bias was possible and generalization of the findings may be difficult. However, as the purpose of the present study was to evaluate associations between exposure to various concentrations of POPs and the development of MetS, internal validity is more important than external validity. In conclusion, this study indicated that chronic exposure to a mixture of PCBs and OCPs can increase the risk for MetS in the general population. It also suggests that low-dose POPs may be more harmful than high-dose POPs. Both in vitro and in vivo experimental studies are needed to understand the underlying mechanisms of the low-dose effects of POPs. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments This work was financially supported by a grant from the Korean Food and Drug Administration (11162KFDA702 and 12162KFDA733) and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI13C0715). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2014.07.039. References Brown T, Avenell A, Edmunds LD, Moore H, Whittaker V, Avery L, et al. Systematic review of long-term lifestyle interventions to prevent weight gain and morbidity in adults. Obes Rev 2009;10:627–38. Casals-Casas C, Desvergne B. Endocrine disruptors: from endocrine to metabolic disruption. Annu Rev Physiol 2011;73:135–62. Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care 2012;35:2402–11. Gaskins AJ, Schisterman EF. The effect of lipid adjustment on the analysis of environmental contaminants and the outcome of human health risks. Methods Mol Biol 2009; 580:371–81. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735–52. Ibrahim MM, Fjaere E, Lock EJ, Naville D, Amlund H, Meugnier E, et al. Chronic consumption of farmed salmon containing persistent organic pollutants causes insulin resistance and obesity in mice. PLoS One 2011;6:e25170. Imbeault P, Chevrier J, Dewailly E, Ayotte P, Despres JP, Tremblay A, et al. Increase in plasma pollutant levels in response to weight loss in humans is related to in vitro subcutaneous adipocyte basal lipolysis. Int J Obes Relat Metab Disord 2001;25: 1585–91. Irigaray P, Newby JA, Lacomme S, Belpomme D. Overweight/obesity and cancer genesis: more than a biological link. Biomed Pharmacother 2007;61:665–78. Kelly GS. Insulin resistance: lifestyle and nutritional interventions. Altern Med Rev 2000; 5:109–32.

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