Multivariate models to predict human adipose tissue PCB concentrations in Southern Spain

Environment International 36 (2010) 705–713

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Environment International j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e n v i n t

Multivariate models to predict human adipose tissue PCB concentrations in Southern Spain Juan P. Arrebola a,b,⁎, Mariana F. Fernandez b,c, Miquel Porta c,d,g, Jorge Rosell e, Rafael Martinez de la Ossa f, Nicolas Olea b,c, Piedad Martin-Olmedo a,c a

Escuela Andaluza de Salud Pública, Cuesta del Observatorio s/n, Campus Universitario de Cartuja s/n, 18080 Granada, Spain Laboratory of Medical Investigations, San Cecilio University Hospital, University of Granada, 18071 Granada, Spain CIBER en Epidemiología y Salud Pública (CIBERESP), Spain d Municipal Institute of Medical Research, Spain e Department of Surgery, San Cecilio University Hospital, 18012 Granada, Spain f Department of Surgery, Hospital Santa Ana Motril, 18600 Granada, Spain g School of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain b c

a r t i c l e

i n f o

Article history: Received 13 January 2010 Accepted 9 May 2010 Available online 3 June 2010 Keywords: Exposure assessment Gender Human adipose tissue Multivariate analysis Polychlorinated biphenyls

a b s t r a c t The use of Polychlorinated biphenyls (PCBs) has been severely restricted due to their high toxicity and persistency in the environment. However, the presence of PCBs in human tissues nowadays is still been reported worldwide. Background exposure predictors of the human PCB body burden require more precise understanding. In the present study, PCB congeners 138, 153, and 180 were quantified in adult adipose tissue samples (n = 387) from Granada Province (Southern Spain) and data on potential predictors of PCB concentrations were gathered by questionnaire. Chemical analysis of the selected congeners was done by gas chromatography and mass spectrometry, and multivariate analyses were performed stratifying by gender. PCB residues were quantified in 92% (PCB 153), 90% (PCB 180), and 86% (PCB 138) of the population. Geometric mean concentrations were 161.65 ± 4.41 ng/g lipid for PCB 153, 111.62 ± 6.27 ng/g lipid for PCB 180, and 38.41 ± 8.61 ng/g lipid for PCB 138. Multivariate models explained 30–36% of the variability in PCB concentrations. Age and body mass index (BMI) predicted exposure in both males and females and were positively correlated with the concentration of the three PCB congeners. Occupation and diet predicted exposure in the males, whereas only dietary predictors were observed in the females. Further in-depth studies are required to investigate the influence of dietary habits on the bioaccumulation of PCBs and to evaluate the impact of policies aimed at reducing human exposure. © 2010 Elsevier Ltd. All rights reserved.

1. Introduction Polychlorinated biphenyls (PCBs), manufactured since 1929, have been used in a large number of industrial and commercial applications worldwide (La Rocca and Mantovani, 2006). From early 1970s, many countries, including Spain, have banned or severely restricted the production, handling, and disposal of PCBs because of their high persistency in the environment and proven or suspected contribution

Abbreviations: PCB, polychlorinated biphenyls; BMI, body mass index; OH-PCB, hydroxylated polychlorinated biphenyl; GC–MS/MS, gas chromatography with a mass spectrometry detector in tandem mode; LD, limit of detection; GM, geometric mean; GSD, geometric standard deviation; EPIC, European Prospective Investigation into Cancer and Nutrition. ⁎ Corresponding author. Laboratory of Medical Investigations, San Cecilio University Hospital, University of Granada, 18071 Granada, Spain. Tel.: +34 958 240758; fax: +34 958 249953. E-mail address: [email protected] (J.P. Arrebola). 0160-4120/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.envint.2010.05.004

to clinical effects at doses traditionally considered safe (UNEP, 2002; Porta et al., 2008a). Most PCB congeners are highly toxic to mammals and many other animal species and appear to exert biological activity via various mechanisms of action (Van den Berg et al., 2006). Major effects on human health of high acute exposure to PCB mixtures include chloracne, hyperpigmentation and other dermal conditions, hepatic and hormonal disorders, chronic bronchitis, immunosuppression, neuropathies, and effects on offspring of maternal exposure (Ahlborg et al., 1995). PCBs can be transported over long distances causing widespread pollution of different environmental media (UNEP, 2002), and they have been detected in outdoor and indoor air (Jamshidi et al., 2007; WHO, 1993), drinking-water (WHO, 1993), and food, especially of animal origin (Gómara et al., 2005; Kiviranta et al., 2005). Environmental PCBs levels have slowly decreased after legislative measures were taken to restrict their production (Noren and Meironyte, 2000). Nevertheless, although its production is banned in most Western countries, its use is permitted in electrical

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transformers and capacitors, among other equipment, until 2025. In addition, human exposure persists, mainly via diet, and the presence of PCBs in human tissues has been reported worldwide (Agudo et al., 2009; Cerna et al., 2008; Covaci et al., 2002a; Kiviranta et al., 2005). Therefore, the need remains for the comprehensive surveillance and monitoring of human exposure to PCBs and for the elucidation of factors predicting the background exposure of humans to these pollutants. PCBs concentrations in human tissues are influenced by the exposure pathways and rates of bioaccumulation, metabolism, and elimination, among other factors (Covaci et al., 2008). Despite international interest in the assessment of PCBs, few authors have focused their research in quantifying PCBs concentrations in human adipose tissue (Cok et al., 2007, 2010; Costabeber and Emanuelli, 2003; Covaci et al., 2002b, 2008; De Saeger et al., 2005; Fernandez et al., 2008; Johnson-Restrepo et al., 2005; Kalantzi et al., 2009; Kiviranta et al., 2005; Kunisue et al., 2007; Li et al., 2006; Naert et al., 2006; Pulkrabová et al., 2009; Schuhmacher et al., 2004; Shen et al., 2008; Smeds and Saukko, 2001; Tan et al., 2008; Vaclavik et al., 2006) (Table 1). Furthermore, a gender approach in the evaluation of the PCB human body burden profile is very scarce (Table 1). Research on women has mostly comprised community-based studies (Covaci et al., 2002a; Kalantzi et al., 2009; Wolff et al., 2005) and birth cohort studies (Herbstman et al., 2007), whereas studies of men have targeted individuals in specific situations, e.g. fertility clinics (Cok et al., 2008, 2010; Meeker et al., 2007). In a previous study, as part of an extensive characterization of the exposure pattern of a female population (n = 20) from Southern Spain, our group analyzed the total profile of PCB exposure to 37 PCB

congeners and 10 hydroxylated polychlorinated biphenyl (OH-PCB) congeners (Fernandez et al., 2008). We found that six congeners (PCB 138, 153, 170, 180, 187, and 194) contributed around 84% of the total PCB burden in adipose tissue. At the same time, different Spanish studies reported the PCB profile of food samples gathered in the same recruitment period (2001–2005), which was dominated by congeners 153, 138, and 180 (Bocio et al., 2007; Bordajandi et al., 2006; Gómara et al., 2005). Hence, we decided to focus on the three most abundant congeners (138, 153, and 180), which are also widely used in biomonitoring programs to assess background exposure to environmental chemicals in general populations (Agudo et al., 2009; Apostoli et al., 2005; Kiviranta et al., 2005). The present cross-sectional study was undertaken to expand knowledge of human exposure to PCBs and to investigate predictors of background concentrations of three specific PCB congeners in adipose tissue from an adult population in Southern Spain. Specific objectives were to analyze possible gender differences in exposure patterns and to determine the relationship between PCB concentrations and exposure risk factors. A multivariate approach was adopted, stratifying by gender. 2. Materials and methods 2.1. Study area The study population and experimental design were extensively described elsewhere (Arrebola et al., 2009). In summary, human samples and data were collected between July 2003 and June 2004 in Granada province (Southern Spain), in two zones separated by 70 km: a densely populated urban area, corresponding to the

Table 1 Summary of recent studies that quantified PCBs in adipose tissue. Study

Country

Recruitment period

Sampling

Subjects

Age range

PCBs analyzed

ΣPCBs

Range PCBs -138, -153, -180

Associations

Smeds and Saukko (2001)

Finland

–

Autopsies

17 males 10 females

19–88 19–95

7 groups of PCBs

504 ng/gb

–

Covaci et al. (2002b)

Belgium

2000

Autopsies

841 ng/ga

105–211 ng/ga

Spain

1996–1997

Breast surgery

19–77 30–65 15–87

35 PCBs

Costabeber and Emanuelli (2003) Kiviranta et al. (2005)

11 males 9 females 123 females

No gender differences Correlation with age in males for some PCBs Correlation with age

11 PCBs

0.56 µg/gb

0.102–0.134 μg/gb

Finland

1997–1999

Appendicitis

13–81

37 PCBs

437 ng/ga

Johnson-Restrepo et al. (2005) De Saeger et al. (2005)

USA

2003–2004

Liposuctions

18–51

37 PCBs

110 ng/ga

Belgium

2001–2003

Autopsies

Naert et al. (2006)

Belgium

2001–2003

Autopsies

Li et al. (2006) Vaclavik et al. (2006)

Singapore Denmark

2003–2005 1993–1997

Cesareans Biopsies from the buttock

214 females 206 males 40 females 12 males 57 males 47 females 31 males 22 females 36 females 402 females

Kunisue et al. (2007)

Japan

2003–2004

Autopsies

Fernandez et al. (2008)

Spain

2003

Breast surgery

Covaci et al. (2008)

Belgium

2003–2005

Autopsies

Tan et al. (2008) Kalantzi et al. (2009) Shen et al. (2008)

Singapore Brazil China

2004–2006 2004–2005 2006

Pulkrabová et al. (2009)

Czech Republic

2007

– Breast surgery Liposuctions and liver transplant Liposuctions

Cok et al. (2010) a b

Median value. Mean value.

Turkey

2002–2007

Abdominal surgery

63–116 ng/ga

Correlation with age

18 males 10 females 20 females

25–81 53–109 24–81

62 PCBs 37 PCBs

1100 ng/ga 730 ng/ga 687 ng/ga

18 males 7 females 83 females 25 females 20 males 4 females 5 males 93 females 46 males (25 + 21)

9–70

23 PCBs

490 ng/gb

83–131 ng/gb

No gender difference Correlation with age Correlation with age in males No gender differences Correlation with age No gender differences Correlation with age – Correlation with age Inverse correlation with BMI No gender differences Correlation with age Correlation with age No correlation with BMI Correlation with age

18.40 40–71 26–73 33–62 17–60

41 PCBs 4 PCBs 18 PCBs

45.4 ng/ga 51 ng/ga 154 ng/gb

6–11 ng/ga 12–19.1 ng/ga 36–52 ng/gb

Correlation with age Correlation with age No correlation with age

7 PCBs

595 ng/ga

110–230 ng/ga

No correlation with age

7 PCBs

b

2–90 17–91 19–83 22–84 22–40 50–65

21–46

–

7 PCBs

658 ng/g

b

140–271 ng/gb

7 PCBs

605 ng/ga

149–274 ng/ga

7 PCBs 10 PCBs

57.4 ng/gb 872 ng/ga

6–13 ng/gb 130–266 ng/ga

382 ng/g 351 ng/gb

– 81–168 ng/ga

55–94 ng/g

b

–

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city of Granada and metropolitan suburbs (economy based on the service sector, and non-heavy industry, with high levels of trafficrelated air pollution; and a semi-rural area, corresponding to the town of Motril and surroundings (small towns and villages with an intensive presence of greenhouses and agricultural activities).

2.2. Design and study population Participants were recruited among subjects undergoing noncancer-related surgery (47% inguinal hernia or abdominal surgery; 17% gall bladder surgery; 12% varicose vein surgery; and 24% other surgery) at San Cecilio University Hospital in Granada and Santa Ana Hospital in Motril. Surgical treatment made it ethically and practically feasible to obtain adipose tissue samples. We contacted 409 subjects, and 387 (94.6%) agreed to participate in the study. Inclusion criteria were: age over 16 years, absence of hormone-related disease or cancer, no hormone therapy, and residence in one of the study areas for ≥10 yrs. All subjects signed their informed consent to participate in the study, which was approved by the Ethics Committee of each hospital.

2.3. Independent variables Participants' height and weight were measured and body mass index (BMI) was calculated as weight/height squared (kg/m2). We derived information on possible predictors of PCBs from an ad hoc questionnaire, which was completed by each participant before surgery and conducted face-to-face by a trained interviewer during hospital stay. Socio-demographic characteristics included information on age, education, drinking and smoking habits, occupation, medical history, medication, and known exposure to chemicals. Responses on occupations practiced for ≥10 yrs were grouped into three major sectors: agriculture, construction, and industry. A subject was considered a smoker (past or present) at any level of daily tobacco consumption (≥1 cig/day). A short dietary section was included to assess food habits and eating behavior, in which subjects indicated the frequency of their consumption of the following food groups: meat, cold meats, fats, fish, eggs, dairy products (without milk and cheese), milk, cheese, vegetables, pulses, fruit, bread, and pasta. Meat and fish consumption was classified according to the type of meat or fish most frequently consumed: red (beef, veal, pork and lamb) or white (chicken, turkey, or rabbit) meat; and white fish (fat b 6% weight), blue fish (fat ≥ 6%) or indistinctly (white or blue fish). Subjects were considered consumers of a food group when they reported intake of ≥1 item in the group at least once a week. Questionnaires and research procedures were standardized and validated in a pilot study in 50 subjects, in which adipose tissue concentrations of the three PCBs were quantified and questionnaires were completed. Based on this experience, sample collection proto-

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cols, analytical methodologies, and data collection criteria were tested, refined and standardized. 2.4. Sampling and chemical analysis A 5–10 g sample of adipose tissue was collected during surgery and immediately coded and stored at − 80 º C until chemical analysis. Main sources of tissue were pelvic waist (42%), front abdominal wall (39%), and limbs (13%). A chemical extraction procedure was performed on the adipose tissue samples to isolate the analytes, as previously described (Martinez Vidal et al., 2002; Moreno-Frias et al., 2004). Briefly, 200 mg of adipose tissue were extracted using n-hexane, and the solution was then purified through 200 mg alumina in a glass column and kept in test tubes at − 80 º C. PCB congeners (PCB 138, 153, and 180) were quantified by high-resolution gas chromatography with a mass spectrometry detector in tandem mode (GC–MS/MS), using a Saturn 2000 ion trap system (Varian, Walnut Creek, CA). For the analysis, we used a 2 m × 0.25 mm silica capillary column (Bellefonte, PA) coupled to a Factor Four VF-5MS 30 m × 0.25-mm i.d. analytical column (Varian Inc., Walnut Creek, CA). The Limits of detection (LDs) were determined as the smallest amount of the analyte that gave a signal-to-noise ratio ≥3, and were set at 0.01 μg/L (PCB 138), 0.03 μg/L (PCB 153), and 0.03 μg/L (PCB 180). Values below the LD were assigned a random value between zero and the LD. ΣPCB was computed as the sum of concentrations of the three congeners. Lipid content was quantified gravimetrically and PCB concentrations were expressed in nanograms per gram of lipid (Fernandez et al., 2008). A double-blinded procedure was followed so that neither the chemical analysts nor statistical staff knew the identity or characteristics of any study subject. The recovery of PCB congeners from adipose tissue was studied to assess the extraction efficiency of the methods used, spiking 10 blank samples with target analytes at an intermediate point on the calibration curve and processing as described above. Recoveries ranged from 90 to 98%. 2.5. Statistical analysis Data were stored in a database managed with SPSS v.14.0 (SPSS, Chicago, IL, USA). Because Kolmogorov–Smirnoff and Shapiro–Wilk normality tests showed non-normal distribution of PCB congener concentration values, results were natural log-transformed, and geometric means (GM) and geometric standard deviations (GSD) were calculated. Bivariate analyses were carried out using the Mann– Whitney U-test, Kruskal–Wallis test, and Spearman's correlation test. “R” software (v. 2.4.1) was used for general linear regression multivariate analyses. Models were created using a stepwise backward elimination technique. To determine whether any single food item was a significant predictor of PCB levels, all items were given the same opportunity to enter into the multivariate model. Age and BMI were centered by their

Table 2 Concentrations (ng/g lipid) of PCB-congeners. Male

PCB-138 PCB-153 PCB-180 ΣPCBsc a b c d

Female

n

%

GMa

GSDb

Range

n

%

GMa

GSDb

Range

168 179 177 180

85.3 90.9 89.8 91.4

34.03 150.60 110.21 323.84

9.47 4.66 6.53 4.64

b LDd–564.40 b LDd–1518.81 b LDd–1360.62 0.06–3280.40

166 178 173 178

87.4 93.7 91.1 93.7

43.46 173.64 113.45 370.08

7.75 4.16 6.02 3.93

bLDd–481.07 bLDd–1219.32 0.01–859.65 0.07–2479.40

Geometric mean. Geometric standard deviation. ΣPCBs (ΣPCB 138, 153, and 180). b LD: Below limit of detection.

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Fig. 1. ΣPCBs concentration by age and BMI.

mean values to assist interpretation of the β coefficients. The coefficient of determination (r2) was calculated as the proportion of variability in the PCB concentrations that was accounted for by the predictor variables. The significance level was set at 90% (p = 0.100), as proposed by Greenland (1989), and all tests were two-tailed.

3. Results 3.1. Characteristics of study population No significant differences in sex and age were found between participants and nonparticipants. The main characteristics of the study population have been described

Table 3 Predictors of PCB concentrations (ng/g lipid). Bivariate analysis. Males. PCB-138

Area Urban Semi-rural Residence close to industry N2000 m ≤2000 m Occupationc Agriculture No Yes Construction No Yes Industry No Yes Fish consumptiond Blue (fat ≥ 6%) White (fat b 6%) White + blue Fish consumption b2 portions/week ≥2 portions/week Red meat consumere No Yes Milk consumere No Yes Cheese consumere No Yes Cheese consumption ≤2 portions/week N2 portions/week

PCB-153

n

GMa

GSDb

105 92

42.24 23.41

8.13 12.51

125 72

29.40 37.08

10.39 9.99

114 83

37.83 25.43

10.26 10.09

143 54

29.87 37.82

11.70 6.90

156 41

31.80 31.82

10.08 10.98

55 47 93

20.69 46.66 33.68

11.60 12.44 8.55

68 127

25.62 39.20

12.05 8.33

60 136

25.41 35.00

11.60 9.75

19 167

18.26 32.44

9.29 10.84

12 184

35.69 31.68

11.69 10.28

88 108

30.60 37.00

11.42 8.18

p

PCB-180

GMa

GSDb

206.20 97.89

3.26 6.54

133.25 168.65

4.94 4.87

164.40 121.88

4.48 5.50

136.60 168.43

5.62 3.23

143.41 149.27

4.85 5.24

107.05 193.40 149.04

5.53 5.67 4.24

125.02 165.80

5.23 4.40

154.80 140.49

4.14 5.33

71.62 154.47

7.87 4.75

137.24 145.22

5.86 4.92

143.40 156.80

5.25 4.26

0.247

GMa

GSDb

0.002

0.659

4.85 9.06

93.06 129.63

7.77 5.61

121.87 85.26

6.54 7.56

101.08 114.49

7.55 5.69

98.42 130.00

7.69 4.73

67.41 167.66 106.25

11.08 4.03 6.39

85.88 125.24

3.40 6.14

99.69 106.62

6.35 7.37

46.85 111.40

10.43 6.89

101.89 104.67

6.84 7.07

94.82 124.41

8.39 5.23

0.259

0.111

0.658

0.110

0.448

0.616

0.676

0.917

0.018

0.924

0.038

0.284

0.081

0.163

0.249

0.185

0.764

0.206

0.224

0.141

0.891

0.104

0.546

0.974

p 0.002

156.28 67.00 0.261

0.027

“p” values reflect the comparison between rows. a Geometric mean. b Geometric standard deviation. c Any occupation for ≥10 yrs related to any of these sectors. d Type of fish most frequently consumed. e Consumer weekly intake of any amount of milk, cheese, or red meat.

p

0.434

0.881

0.517

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elsewhere (Arrebola et al., 2009). Briefly, the mean age and BMI of study subjects was 50.7 yrs, and 27.4 kg/m2, respectively. Males from the urban area were older than those from the semi-rural area (p b 0.001). Neither males nor females showed significant differences in BMI or length of residence between subjects from urban and semi-rural areas. As expected, a higher number of participants (among both males and females) from the semi-rural area lived near greenhouses (p b 0.001) and worked in agricultural activities (p b 0.001). More people from the urban area lived near an industrial site (p ≤ 0.01). More women than men lived close (≤2000 m) to agricultural sites, whereas more men had been involved in occupational activities related to agriculture, construction, or industry. Past and present smoking habit was more frequent in males than females, although no differences in smoking habits were recorded between males or females from urban and semi-rural areas. Consumption of blue fish and red meat was higher among men whereas women consumed larger amounts of white fish.

153), 0.137 (PCB 180), and 0.190 (ΣPCB). In addition, PCB concentrations showed a positive correlation with age and BMI in both male and female populations (p b 0.001). Fig. 1 shows adipose tissue ΣPCB concentrations by age for the males and females in two BMI categories (BMI b 27 kg/m2 and BMI N 27 kg/m2). Tables 3 and 4 show the results of the bivariate analysis of selected predictors of PCB concentrations for men and women, respectively. Men living in urban areas showed higher levels of PCB 153 and PCB 180 than those from semi-rural areas (p = 0.002), while women from the semi-rural areas had bioaccumulated higher concentrations of PCB 138 (p = 0.031). Living close to industry (≤2000 m) showed a positive association with PCB 153 concentrations in women (p = 0.044), while men who had worked in agricultural activities presented lower PCB 138 levels (p = 0.027). White fish consumption was related to higher levels of the three congeners (PCB 138, PCB 153, and PCB 180) in men, while women who consumed milk showed higher levels of the three PCB congeners compared with those who did not (Tables 3 and 4).

3.2. PCBs concentrations in adipose tissue and predictors of exposure

3.3. Predictors of PCBs concentrations in adipose tissue. Multivariate analysis

In the recruited subjects, detectable residues of PCBs were found in 92% (PCB 153), 90% (PCB 180), and 86% (PCB 138) of adipose tissue samples. The geometric mean concentration of the sum of all three PCB congeners (PCB 138, 153, and 180) was 345.81 ± 4.29 ng/g lipid, ranging from 0.06 to 3290.50 ng/g lipid. The geometric mean concentration of each congener was 161.65 ± 4.41 ng/g lipid for PCB 153, 111.6 2 ± 6.27 ng/g lipid for PCB 180, and 38.41 ± 8.61 ng/g lipid for PCB 138. Adipose tissue concentrations of the three congeners were highly correlated between them (Spearman coefficient N 0.85, p b 0.001). Table 2 shows frequencies and concentrations (ng/g lipid) of selected PCB congeners, stratifying by gender. Concentrations of PCBs did not significantly differ between men and women (Table 2), even when groups of similar age range were compared (data not shown). In the whole study population, age emerged as a determinant of exposure and a positive correlation was found with the concentration of each PCB congener (p b 0.001), with Spearman coefficients of 0.422 (PCB 138), 0.496 (PCB 153), 0.468 (PCB 180), and 0.481 (ΣPCB). BMI was also positively correlated with concentrations of each PCB congener (p b 0.001), showing Spearman coefficients of 0.244 (PCB 138), 0.206 (PCB

The main determinants of adipose tissue PCB concentrations in the multivariate models for men and women are displayed in Tables 5 and 6, respectively. The results explained 30–36% of the variability in PCB concentrations in the study population. Age and BMI predicted exposure in both sexes, showing a positive correlation with the concentration of the three PCB congeners. Among men, a history of occupation in construction was positively related to the concentration of PCB 153, whereas a history of agricultural employment was negatively associated with the concentrations of all three congeners. Consumption of white fish and red meat was related to higher levels of PCB 138 and PCB 180, and milk intake was positively related to concentrations of PCB 153 and PCB 180 (Table 5). Among women, frequency of fish consumption was positively associated with the concentrations of all three congeners. Milk intake was associated with higher concentrations of PCB 138 and PCB 180 congeners, and cheese consumption was positively associated with levels of congeners 153 and 180. Furthermore, red meat consumption by women linked to higher levels of PCB 153 and PCB 180 congeners (Table 6).

Table 4 Predictors of PCB concentrations (ng/g lipid). Bivariate analysis. Females. PCB-138 n Area Urban Semi-rural Residence close to industry N2000 m ≤2000 m Occupationc Agriculture No Yes Industry No Yes Fish consumptiond Blue (fat ≥ 6%) White (fat b 6%) White + blue Fish consumption b2 portions/week ≥2 portions/week Red meat consumere No Yes Milk consumere No Yes Cheese consumere No Yes Cheese consumption ≤2 portions/week N2 portions/week

GMa

PCB-153 GSDb

GMa

p

PCB-180 GSDb

0.031 81 109

33.81 46.26

9.08 7.55

130 60

34.24 58.40

4.75 2.36

128 62

35.07 54.29

9.51 5.73

176 14

39.20 60.85

8.22 7.91

25 68 96

21.43 43.80 45.60

17.09 7.18 7.12

48 141

12.23 66.82

16.11 4.61

97 92

43.81 37.27

6.81 9.98

17 172

13.46 45.80

12.59 7.583

14 175

18.29 43.63

16.91 7.58

108 81

36.25 55.24

9.21 6.01

GSDb

0.276 174.20 166.80

2.97 4.86

146.87 233.61

4.75 2.37

159.42 192.44

4.36 3.39

172.45 138.23

3.57 13.00

135.40 163.42 186.67

3.45 5.42 3.28

68.28 239.05

8.74 2.39

177.08 162.89

3.28 4.93

130.00 175.21

3.14 4.15

74.49 183.23

9.08 3.61

158.49 197.46

4.33 3.95

0.145

5.66 6.24

96.23 147.40

7.04 3.75

106.81 116.88

5.95 6.09

108.45 132.40

6.06 5.01

108.43 132.40 128.32

8.23 6.11 5.44

44.63 155.60

11.41 4.06

115.00 116.84

6.49 5.55

107.47 112.83

13.32 5.27

46.41 121.22

10.31 5.66

100.63 132.64

6.92 4.95

0.218

0.599

0.130

0.791

0.227

0.429

0.289

0.175

b 0.001

0.462

b 0.001

0.805

0.001

0.924

0.068

0.737

0.039

0.358

0.048

0.138

0.162

p 0.238

106.22 112.89 0.044

0.540

“p” values reflect the comparison between rows. a Geometric mean. b Geometric standard deviation. c Any occupation for ≥10 yrs related to any of these sectors. d Type of fish most frequently consumed. e Consumer weekly intake of any amount of milk, cheese, or red meat.

GMa

p

0.170

0.465

0.542

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Table 5 Multivariate analysis. Males. PCB-138 (r2 = 0.30)

Intercept Age (years) BMI (kg/m2) Occupation in agriculture Occupation in construction White fish (fat b 6%) consumer Red meat consumer Milk consumer

PCB-153 (r2 = 0.31)

PCB-180 (r2 = 0.36)

β

SDd

β

SDd

β

SDd

− 0.11 0.06c 0.08c − 0.74b – 0.76a 0.78b –

0.38 0.01 0.03 0.30 – 0.39 0.31 –

1.44 0.05c 0.06c − 0.76c 0.37a – – 0.85b

0.45 0.01 0.02 0.22 0.23 – – 0.36

1.36 0.06c 0.08c − 0.77c – 0.66b 0.49a 0.72a

0.52 0.01 0.02 0.25 – 0.28 0.26 0.42

Consumer weekly intake of any amount of milk, white fish, or red meat. a p ≤ 0.10. b p ≤ 0.05. c p ≤ 0.01. d SD: standard deviation.

4. Discussion The study of specific PCB congener profiles (e.g., congeners 138,153, 180) is recommended as an efficient procedure to assess exposure assessment to these environmental chemicals in epidemiological studies (Fernandez et al., 2008). PCB congeners 138, 153, and 180 were detected in the adipose tissue of 86%, 92%, and 90% of the present study population, respectively, consistent with world-wide reports (Cerna et al., 2008; Covaci et al., 2002b; De Saeger et al., 2005; Fernandez et al., 2008; Herrick et al., 2007; Humphrey et al., 2000; Kiviranta et al., 2005; Wolff et al., 2005). These three specific congeners are included in PCB profiles widely used in biomonitoring programs to assess background exposure to environmental chemicals in general populations (Apostoli et al., 2005; Kiviranta et al., 2005), and they have been found to contribute a high proportion of the total PCB concentration in adipose tissue samples (Fernandez et al., 2008; Kiviranta et al., 2005; Naert et al., 2006). For example, in a recent study of 202 adult blood donors in the Czech Republic, PCB congeners 138, 153, and 180 accounted for around 97% of the sum of seven congeners analyzed (Cerna et al., 2008). In our study population, geometric mean concentrations of these marker PCBs ranged from 38.41 to 161.65 ng/g lipid, slightly lower than recent reports in different areas of the world (Table 1), although in the same order of magnitude. In Spain, men and women from Granada showed lower serum PCB concentrations than those from the other four regions in the Spanish cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC) (Agudo et al., 2009). Wingfors et al. found that adipose tissue levels of congeners 138, 153,

Table 6 Multivariate analysis. Females. PCB-138 (r2 = 0.36)

Intercept Age (years) BMI (kg/m2) Fish consumption ≥ 2 portions/week Red meat consumer Milk consumer Cheese consumer Cheese consumptionN 2 portions/week

PCB-153 (r2 = 0.35)

PCB-180 (r2 = 0.31)

β

SDd

β

SD

β

SDd

− 1.08 0.05c 0.07c 0.94c – 1.24c – –

0.48 0.01 0.02 0.30 – 0.44 – –

0.55 0.03c 0.04c 0.67c 0.36b – 1.02c –

0.34 − 0.04 0.01 0.04c 0.01 0.03a 0.20 0.67b 0.17 0.46b – 0.99b 0.33 – – 0.4a

0.44 0.01 0.02 0.27 0.22 0.29 – 0.23

Consumer weekly intake of any amount of milk, white fish, or red meat. a p ≤ 0.10. b p ≤ 0.05. c p ≤ 0.01. d SD: standard deviation.

and 180 ranged from 200 to 300 ng/g lipid in two general population groups, one in Spain (n = 35) and one in Sweden (n = 28) (Wingfors et al., 2000). Costabeber and Emanuelli (2003) reported that median concentrations of these congeners (PCB 138, 153, and 180) ranged from 102 to 134 ng/g lipid in mammary adipose tissue samples gathered between 1996–1997. In 2003, Fernandez et al. (2008) reported that median concentrations of these selected congeners ranged from 80 to 166 ng/g lipid in another group of Spanish women (n = 20). Porta and co-workers recently reviewed Spanish studies reporting PCB concentrations in different biological matrices (human milk, serum, and adipose tissue) (Porta et al., 2008a), finding a mean PCB 153 concentration ranging from 11.6 to 230 ng/g lipid. The present findings fall within this range. Lipid-correction of PCB concentrations usually improves the estimation of total body burden and the comparison of levels in different biological tissues (Phillips et al., 1989; Rusiecki et al., 2005). Thus, a high correlation is found between serum and adipose tissue concentrations when expressed per gram of lipid (Rusiecki et al., 2005). Recent studies that measured serum levels of individual PCBs 138, 153 and 180 in different groups of non-occupationally exposed populations reported concentrations of 10–80 ng/g lipid (Kang et al., 2008; Wang et al., 2009; Zubero et al., 2009), 100–200 ng/g lipid (Donato et al., 2008; Agudo et al., 2009) and N200 ng/g lipid (Petrik et al., 2006). Few international studies have investigated the PCB content of adipose tissue and usually in small study populations (Table 1). In Finland, Kiviranta reported a mean PCB 153 concentration of 192 ng/g fat in individuals over 46 yrs old (Kiviranta et al., 2005). Concentrations of marker PCBs (congeners PCB 28, 52, 101, 118, 138, 153 and 180) in other European studies have ranged from 389 to 855 ng/g fat, with a mean value of 606 ng/g fat (Fernandez et al., 2008; Wingfors et al., 2000). Mean concentrations in the Far-East studies have been lower, although the populations were also younger than in Europe (Li et al., 2006; Shen et al., 2008; Tan et al., 2008). The highest PCB concentration in human adipose tissue was reported in Greenland Inuit populations, attributed to the consumption of meat and blubber from marine mammals with a high PCB body burden (Kiviranta et al., 2005). Multiple regression analyses were performed to determine the factors that may explain the variability in PCB concentrations in adipose tissue. Because we stratified by sex in the present study, no interaction with this variable was tested. In addition, we investigated several interactions in each model (e.g., BMI × age and age × occupation) with null results. Gender-related differences in human concentrations of persistent organic pollutants are highly controversial, and there is a need for gender-sensitive research into environmental chemical exposure (Kennedy and Koehoorn, 2003; Silbergeld and Flaws, 2002). Females and males differ in hormonal balance throughout their lifespan, and can have very different exposure experiences resulting in distinct contaminant profiles and body burdens (Arrebola et al., 2009; Gochfeld, 2007; Vahter et al., 2007). Thus, the EPIC study in the general Spanish population showed that serum levels of three most abundant PCB congeners (153, 180, and 138) were around 30% higher in men than in women (Agudo et al., 2009). In the present study, no gender difference was observed in any of the three selected PCB congeners, similar to previous findings (Covaci et al., 2008; De Saeger et al., 2005; Kunisue et al., 2007; Naert et al., 2006; Smeds and Saukko, 2001). However, the results of our genderstratified multivariate analysis suggest differences in exposure risk factors between men and women, with a significant influence of occupation and diet in males and of dietary habits in females. Thus, men working in agricultural activities showed lower levels of all three PCB congeners in comparison to those who did not. In contrast, the men engaged in construction activities showed higher levels of PCB 153.

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Occupational exposure and/or indoor air contamination from the presence of joint sealants containing PCB (Herrick et al., 2007; Jamshidi et al., 2007; Kohler et al., 2005; Wingfors et al., 2006) are important sources of PCBs, and occupants of affected buildings and workers removing or maintaining caulking and sealing products were reported to be especially exposed (Herrick et al., 2007). In a Swedish study, plasma PCB concentrations were two-fold higher in construction workers removing old elastic sealants than in a control group (Wingfors et al., 2006). Apart from certain occupational settings and PCB-containing waste sites, food is the main source of exposure for the general population. In our study, the most important PCB exposure pathway was food consumption. Due to their high lipophilicity, PCBs tend to accumulate in lipid-rich tissues, and fatty food is expected to have higher levels of these substances. Several studies have demonstrated that diet contributes more than 90% of total PCB exposure, with fish and other animal products accounting for around 80% of overall exposure (Baars et al., 2004; Cok et al., 2008; Domingo and Bocio, 2007; Fernandez et al., 2004; Llobet et al., 2003; Pulkrabová et al., 2009; Schecter et al., 2001). In the present study, male consumers of white fish and red meat showed higher levels of congeners PCB 138 and 180, and male milk drinkers had higher levels of PCB 153 and 180. In women, PCB 138 concentrations were positively related to fish and milk consumption, higher PCB 153 concentrations were found in consumers of fish, cheese and red meat, and higher PCB 180 concentrations in consumers of fish, milk, cheese, and red meat. Age was one of the main predictors of PCB concentrations, as found in other studies (Costabeber and Emanuelli, 2003; Covaci et al., 2002b; De Saeger et al., 2005; Kiviranta et al., 2005; Naert et al., 2006; Nichols et al., 2007; Vaclavik et al., 2006), reflecting the higher body burden of PCBs in older people due to their longer exposure to these bioaccumulative chemicals. Some authors have suggested that this observation may result from a cohort effect, i.e., older subjects were born when the production and use of POPs was not restricted and therefore started with a higher body burden in comparison to younger people (Ahlborg et al., 1995; Porta et al., 2008a). We also found a positive and significant relationship between the BMI and concentrations of the three selected PCB congeners. Positive correlations between BMI and PCB body burden have been reported in some other studies (Falk et al., 1999; Fitzgerald et al., 1999), whereas others found no association between them (Fernandez et al., 2008; Laden et al., 1999; Park et al., 2007). Because the concentrations reported in the present paper are lipid-adjusted, the correlation between BMI and PCB concentrations may be related to food intake, since individuals with a higher BMI are likely to consume more food. Further in-depth studies are required to investigate the influence of dietary habits on the bioaccumulation of PCBs. We found no significant association between smoking and PCB concentrations, either as a dichotomized variable (smoker/non smoker) or when 4 quantitative categories were considered (≤1 cig/ month; 2 cig/month-2 cig/week; 3 cig/week–9 cig/day; ≥10 cig/day). Nevertheless, we did not assess the length of time with the habit; therefore, its influence may have been underestimated. According to the coefficients of determination (r2) calculated from the multivariate analyses, our statistical models explained 30–36% of the variability of PCBs concentrations. The very few studies that have analyzed adipose tissue PCB concentrations from a multivariate perspective and with gender stratification have published varied results. A similar coefficient value (r2 = 0.32) was reported by Polder et al. for exposure predictors of total PCBs in Norwegian breast milk samples (Polder et al., 2009) and by Gaffney et al. (2005) in blood samples (r2 = 0.36). In contrast, the model of Vackavik et al. explained only 9% (r2 = 0.09) of the variability in total di-ortho PCBs in adipose tissue samples (Vaclavik et al., 2006), while Porta et al. found that age, sex, and social class accounted for 3.8–5.9% of the variability in serum concentrations of PCBs 138, 153 and 180 (Porta et al., 2008b).

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Nevertheless, investigations with larger study populations have usually reported higher r2 values, including those on: adipose tissue PCB concentrations, measured as toxic equivalents (WHOPCB-TEQ) (r2 = 0.70) (Kiviranta et al., 2005); blood PCDDs/PCDFs and dioxinlike PCB concentrations (r2 = 0.43–0.57) (Uemura et al., 2008); and serum dioxin concentrations (r2 = 0.70) (Garabrant et al., 2009). Some of these authors also reported that demographic factors may explain the largest proportion of the variability (Garabrant et al., 2009). Limitations of this study include those inherent in any crosssectional study based on hospital recruitment, which may not fully represent the population from which the sample was recruited. However, previous studies performed by our group using this instrument were able to demonstrate expected relationships between reported predictors and level of contaminants measured in biological specimens (Arrebola et al., 2009; Fernandez et al., 2007). A progressive reduction in the human body burden of PCBs has been reported in time-trend studies (Choi et al., 2002; Noren and Meironyte, 2000). Nevertheless, it remains necessary to monitor concentrations of PCBs in the general population in order to assess the potential adverse effects on human health of PCBs and the impact of policies aimed at reducing human exposure (Porta et al., 2008a). Further studies of large populations using standardized analytical methods are required. Acknowledgements The authors gratefully acknowledge the scientific and technical assistance provided by Richard Davies and Silvia Geeraerd. This study was supported in part by research grants from the Spanish Ministry of Health (FIS 02/974); the Regional Government of Andalucía (SAS 01/ 264), and CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Ministry of Health, Spain. The results would not have been achieved without the selfless collaboration of the staff from Santa Ana and San Cecilio Hospitals and the participants who took part in the study. References Ahlborg UG, Lipworth L, Titus-Ernstoff L, Hsieh CC, Hanberg A, Baron J, et al. Organochlorine compounds in relation to breast cancer, endometrial cancer, and endometriosis: an assessment of the biological and epidemiological evidence. Crit Rev Toxicol 1995;25:463–531. Agudo A, Goñi F, Etxeandia A, Vives A, Millán E, López R, et al. Polychlorinated biphenyls in Spanish adults: determinants of serum concentrations. Environ Res 2009;109: 620–8. Apostoli P, Magoni M, Bergonzi R, Carasi S, Indelicato A, Scarcella C, et al. Assessment of reference values for polychlorinated biphenyl concentration in human blood. Chemosphere 2005;61:413–21. Arrebola JP, Martin-Olmedo P, Fernandez MF, Sanchez-Cantalejo E, Jimenez-Rios JA, Torne P, et al. Predictors of concentrations of hexachlorobenzene in human adipose tissue: a multivariate analysis by gender in Southern Spain. Environ Int 2009;35: 27–32. Baars AJ, Bakker MI, Baumann RA, Boon PE, Freijer JI, Hoogenboom LA, et al. Dioxins, dioxin-like PCBs and non-dioxin-like PCBs in foodstuffs: occurrence and dietary intake in The Netherlands. Toxicol Lett 2004;151:51–61. Bocio A, Domingo JL, Falco G, Llobet JM. Concentrations of PCDD/PCDFs and PCBs in fish and seafood from the Catalan (Spain) market: estimated human intake. Environ Int 2007;33:170–5. Bordajandi LR, Martín I, Abad E, Rivera J, González MJ. Organochlorine compounds (PCBs, PCDDs and PCDFs) in seafish and seafood from the Spanish Atlantic Southwest Coast. Chemosphere 2006;64:1450–7. Cerna M, Maly M, Grabic R, Batariova A, Smid J, Benes B. Serum concentrations of indicator PCB congeners in the Czech adult population. Chemosphere 2008;72: 1124–31. Choi JW, Miyabara Y, Hashimoto S, Morita M. Comparison of PCDD/F and coplanar PCB concentrations in Japanese human adipose tissue collected in 1970–1971, 1994– 1996 and 2000. Chemosphere 2002;47:591–7. Cok I, Donmez MK, Hakan SM, Aydinuraz B, Henkelmann B, Kotalik J, et al. Concentration of polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans and dioxin-like PCBs in human adipose tissue from Turkish men. Chemosphere 2007;66:1955–61. Cok I, Donmez MK, Satiroglu MH, Aydinuraz B, Henkelmann B, Shen H, et al. Concentrations of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated

712

J.P. Arrebola et al. / Environment International 36 (2010) 705–713

dibenzofurans (PCDFs), and dioxin-like PCBs in adipose tissue of infertile men. Arch Environ Contam Toxicol 2008;55:143–52. Cok I, Durmaz TC, Durmaz E, Satiroglu MH, Kabukcu C. Determination of organochlorine pesticide and polychlorinated biphenyl levels in adipose tissue of infertile men. Environ Monit Assess 2010;162(1–4):301–9. Costabeber I, Emanuelli T. Influence of alimentary habits, age and occupation on polychlorinated biphenyl levels in adipose tissue. Food Chem Toxicol 2003;41: 73–80. Covaci A, Koppen G, Van Cleuvenbergen R, Schepens P, Winneke G, van Larebeke N, et al. Persistent organochlorine pollutants in human serum of 50–65 years old women in the Flanders Environmental and Health Study (FLEHS). Part 2: correlations among PCBs, PCDD/PCDFs and the use of predictive markers. Chemosphere 2002a;48:827–32. Covaci A, de Boer J, Ryan JJ, Voorspoels S, Schepens P. Distribution of organobrominated and organochlorinated contaminants in Belgian human adipose tissue. Environ Res 2002b;88:210–8. Covaci A, Voorspoels S, Roosens L, Jacobs W, Blust R, Neels H. Polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in human liver and adipose tissue samples from Belgium. Chemosphere 2008;73:170–5. De Saeger S, Sergeant H, Piette M, Bruneel N, Van de Voorde W, Van Peteghem C. Monitoring of polychlorinated biphenyls in Belgian human adipose tissue samples. Chemosphere 2005;58:953–60. Domingo JL, Bocio A. Levels of PCDD/PCDFs and PCBs in edible marine species and human intake: a literature review. Environ Int 2007;33:397–405. Donato F, Zani C, Magoni M, Gelatti U, Covolo L, Orizio G, et al. Polychlorinated biphenyls and thyroid hormone serum concentrations among people living in a highly polluted area: a cross-sectional population-based study. Environ Res 2008;108:380–6. Falk C, Hanrahan L, Anderson HA, Kanarek MS, Draheim L, Needham L, et al. Body burden levels of dioxin, furans, and PCBs among frequent consumers of Great Lakes sport fish. The Great Lakes Consortium. Environ Res 1999;80:S19–25. Fernandez MA, Gomara B, Bordajandi LR, Herrero L, Abad E, Abalos M, et al. Dietary intakes of polychlorinated dibenzo-p-dioxins, dibenzofurans and dioxin-like polychlorinated biphenyls in Spain. Food Addit Contam 2004;21: 983–91. Fernandez MF, Arrebola J, Taoufiki J, Navalon A, Ballesteros O, Pulgar R, et al. BisphenolA and chlorinated derivatives in adipose tissue of women. Reprod Toxicol 2007;24: 259–64. Fernandez MF, Kiviranta H, Molina-Molina JM, Laine O, Lopez-Espinosa MJ, Vartiainen T, et al. Polychlorinated biphenyls (PCBs) and hydroxy-PCBs in adipose tissue of women in Southeast Spain. Chemosphere 2008;71:1196–205. Fitzgerald EF, Deres DA, Hwang SA, Bush B, Yang BZ, Tarbell A, et al. Local fish consumption and serum PCB concentrations among Mohawk men at Akwesasne. Environ Res 1999;80:S97-S103. Gaffney SH, Curriero FC, Strickland PT, Glass GE, Helzlsouer KJ, Breysse PN. Influence of geographic location in modeling blood pesticide levels in a community surrounding a U.S. environmental protection agency superfund site. Environ Health Perspect 2005;113:1712–6. Garabrant DH, Franzblau A, Lepkowski J, Gillespie BW, Adriaens P, Demond A, et al. The University of Michigan Dioxin Exposure Study: predictors of human serum dioxin concentrations in Midland and Saginaw, Michigan. Environ Health Perspect 2009;117:818–24. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989;79:340–9. Gochfeld M. Framework for gender differences in human and animal toxicology. Environ Res 2007;104:4-21. Gómara B, Bordajandi LR, Fernández MA, Herrero L, Abad E, Abalos M, et al. Levels and trends of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (PCBs) in Spanish commercial fish and shellfish products, 1995–2003. J Agric Food Chem 2005;53(21):8406–13. Herbstman JB, Sjodin A, Apelberg BJ, Witter FR, Patterson DG, Halden RU, et al. Determinants of prenatal exposure to polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) in an urban population. Environ Health Perspect 2007;115:1794–800. Herrick RF, Meeker JD, Hauser R, Altshul L, Weymouth GA. Serum PCB levels and congener profiles among US construction workers. Environ Health 2007;6:25. Humphrey HE, Gardiner JC, Pandya JR, Sweeney AM, Gasior DM, McCaffrey RJ, et al. PCB congener profile in the serum of humans consuming Great Lakes fish. Environ Health Perspect 2000;108:167–72. Jamshidi A, Hunter S, Hazrati S, Harrad S. Concentrations and chiral signatures of polychlorinated biphenyls in outdoor and indoor air and soil in a major U.K. conurbation. Environ Sci Technol 2007;41:2153–8. Johnson-Restrepo B, Kannan K, Rapaport DP, Rodan BD. Polybrominated diphenyl ethers and polychlorinated biphenyls in human adipose tissue from New York. Environ Sci Technol 2005;39:5177–82. Kalantzi OI, Brown FR, Caleffi M, Goth-Goldstein R, Petreas M. Polybrominated diphenyl ethers and polychlorinated biphenyls in human breast adipose samples from Brazil. Environ Int 2009;35:113–7. Kang JH, Park H, Chang YS, Choi JW. Distribution of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in human serum from urban areas in Korea. Chemosphere 2008;73:1625–31. Kennedy SM, Koehoorn M. Exposure assessment in epidemiology: does gender matter? Am J Ind Med 2003;44:576–83. Kiviranta H, Tuomisto JT, Tuomisto J, Tukiainen E, Vartiainen T. Polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyls in the general population in Finland. Chemosphere 2005;60:854–69.

Kohler M, Tremp J, Zennegg M, Seiler C, Minder-Kohler S, Beck M, et al. Joint sealants: an overlooked diffuse source of polychlorinated biphenyls in buildings. Environ Sci Technol 2005;39:1967–73. Kunisue T, Takayanagi N, Isobe T, Takahashi S, Nose M, Yamada T. Polybrominated diphenyl ethers and persistent organochlorines in Japanese human adipose tissues. Environ Int 2007;33:1048–56. La Rocca C, Mantovani A. From environment to food: the case of PCB. Ann Ist Super Sanità 2006;42:410–6. Laden F, Neas LM, Spiegelman D, Hankinson SE, Willett WC, Ireland K, et al. Predictors of plasma concentrations of DDE and PCBs in a group of U.S. women. Environ Health Perspect 1999;107:75–81. Li QQ, Loganath A, Chong YS, Tan J, Obbard JP. Levels of persistent organic pollutant residues in human adipose and muscle tissues in Singapore. J Toxicol Environ Health A 2006;69:1927–37. Llobet JM, Bocio A, Domingo JL, Teixido A, Casas C, Muller L. Levels of polychlorinated biphenyls in foods from Catalonia, Spain: estimated dietary intake. J Food Prot 2003;66:479–84. Martinez Vidal JL, Moreno FM, Garrido FA, Olea-Serrano F, Olea N. Determination of endocrine-disrupting pesticides and polychlorinated biphenyls in human serum by GC-ECD and GC-MS-MS and evaluation of contributions to the uncertainty of the results. Anal Bioanal Chem 2002;372:766–75. Meeker JD, Altshul L, Hauser R. Serum PCBs, p, p'-DDE and HCB predict thyroid hormone levels in men. Environ Res 2007;104:296–304. Moreno-Frias M, Jimenez TM, Garrido FA, Martinez Vidal JL, Olea-Serrano F, Olea N. Determination of organochlorine compounds in human biological samples by GCMS/MS. Biomed Chromatogr 2004;18:102–11. Naert C, Piette M, Bruneel N, Van Peteghem C. Occurrence of polychlorinated biphenyls and polybrominated diphenyl ethers in Belgian human adipose tissue samples. Arch Environ Contam Toxicol 2006;50:290–6. Nichols BR, Hentz KL, Aylward L, Hays SM, Lamb JC. Age-specific reference ranges for polychlorinated biphenyls (PCB) based on the NHANES 2001–2002 survey. J Toxicol Environ Health A 2007;70:1873–7. Noren K, Meironyte D. Certain organochlorine and organobromine contaminants in Swedish human milk in perspective of past 20–30 years. Chemosphere 2000;40: 1111–23. Park H, Lee SJ, Kang JH, Chang YS. Congener-specific approach to human PCB concentrations by serum analysis. Chemosphere 2007;68:1699–706. Petrik J, Drobna B, Pavuk M, Jursa S, Wimmerova S, Chovancova J. Serum PCBs and organochlorine pesticides in Slovakia: age, gender, and residence as determinants of organochlorine concentrations. Chemosphere 2006;65:410–8. Phillips DL, Pirkle JL, Burse VW, Bernert Jr JT, Henderson LO, Needham LL. Chlorinated hydrocarbon levels in human serum: effects of fasting and feeding. Arch Environ Contam Toxicol 1989;18:495–500. Polder A, Skaare JU, Skjerve E, Loken KB, Eggesbo M. Levels of chlorinated pesticides and polychlorinated biphenyls in Norwegian breast milk (2002–2006), and factors that may predict the level of contamination. Sci Total Environ 2009;407:4584–90. Porta M, Puigdomenech E, Ballester F, Selva J, Ribas-Fito N, Llop S, et al. Monitoring concentrations of persistent organic pollutants in the general population: the international experience. Environ Int 2008a;34:546–61. Porta M, de Basea MB, Benavides FG, Lopez T, Fernandez E, Marco E, et al. Differences in serum concentrations of organochlorine compounds by occupational social class in pancreatic cancer. Environ Res 2008b;108:370–9. Pulkrabová J, Hrádková P, Hajslová J, Poustka J, Nápravníková M, Polácek V. Brominated flame retardants and other organochlorine pollutants in human adipose tissue samples from the Czech Republic. Environ Int 2009;35:63–8. Rusiecki JA, Matthews A, Sturgeon S, Sinha R, Pellizzari E, Zheng T, et al. A correlation study of organochlorine levels in serum, breast adipose tissue, and gluteal adipose tissue among breast cancer cases in India. Cancer Epidemiol Biomark Prev 2005;14: 1113–24. Schecter A, Cramer P, Boggess K, Stanley J, Papke O, Olson J, et al. Intake of dioxins and related compounds from food in the U.S. population. J Toxicol Environ Health A 2001;63:1-18. Schuhmacher M, Domingo JL, Hagberg J, Lindstrom G. PCDD/F and non-ortho PCB concentrations in adipose tissue of individuals living in the vicinity of a hazardous waste incinerator. Chemosphere 2004;57:357–64. Shen H, Han J, Tie X, Xu W, Ren Y, Ye C. Polychlorinated dibenzo-p-dioxins/furans and polychlorinated biphenyls in human adipose tissue from Zhejiang Province, China. Chemosphere 2008;74:384–8. Silbergeld EK, Flaws JA. Environmental exposures and women's health. Clin Obstet Gynecol 2002;45:1119–28. Smeds A, Saukko P. Identification and quantification of polychlorinated biphenyls and some endocrine disrupting pesticides in human adipose tissue from Finland. Chemosphere 2001;44:1463–71. Tan J, Li QQ, Loganath A, Chong YS, Xiao M, Obbard JP. Multivariate data analyses of persistent organic pollutants in maternal adipose tissue in Singapore. Environ Sci Technol 2008;42:2681–7. Uemura H, Arisawa K, Hiyoshi M, Satoh H, Sumiyoshi Y, Morinaga K, et al. PCDDs/PCDFs and dioxin-like PCBs: recent body burden levels and their determinants among general inhabitants in Japan. Chemosphere 2008;73:30–7. UNEP. Regionally Based Assessment of Persistent Toxic Substances. Mediterranean Regional Report. 2002. United Nations Environment Programme on Chemicals. http://www.chem.unep.ch/Pts/regreports/Mediterranean.pdf [Accessed January 2010]. Vaclavik E, Tjonneland A, Stripp C, Overvad K, Weber JP, Raaschou-Nielsen O. Organochlorines in Danish women: predictors of adipose tissue concentrations. Environ Res 2006;100:363–70.

J.P. Arrebola et al. / Environment International 36 (2010) 705–713 Vahter M, Gochfeld M, Casati B, Thiruchelvam M, Falk-Filippson A, Kavlock R, et al. Implications of gender differences for human health risk assessment and toxicology. Environ Res 2007;104:70–84. Van den Berg M, Birnbaum LS, Denison M, De Vito M, Farland W, Feeley M. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol Sci 2006;93: 223–41. Wang RY, Jain RB, Wolkin AF, Rubin CH, Needham LL. Serum concentrations of selected persistent organic pollutants in a sample of pregnant females and changes in their concentrations during gestation. Environ Health Perspect 2009;117:1244–9. WHO. Polychlorinated biphenyls and terphenyls. 2nd ed. Geneva: World Health Organisation; 1993.

713

Wingfors H, Lindstrom G, van Bavel B, Schuhmacher M, Hardell L. Multivariate data evaluation of PCB and dioxin profiles in the general population in Sweden and Spain. Chemosphere 2000;40(9–11):1083–8. Wingfors H, Selden AI, Nilsson C, Haglund P. Identification of markers for PCB exposure in plasma from Swedish construction workers removing old elastic sealants. Ann Occup Hyg 2006;50:65–73. Wolff MS, Deych E, Ojo F, Berkowitz GS. Predictors of organochlorines in New York City pregnant women, 1998–2001. Environ Res 2005;97:170–7. Zubero MB, Ibarluzea JM, Aurrekoetxea JJ, Rivera J, Parera J, Abad E, et al. Serum levels of polychlorinated dibenzodioxins and dibenzofurans and PCBs in the general population living near an urban waste treatment plant in Biscay, Basque Country. Chemosphere 2009;76:784–91.