Internal exposure to organochlorine pollutants and cadmium and self-reported health status: A prospective study

International Journal of Hygiene and Environmental Health 218 (2015) 232–245

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International Journal of Hygiene and Environmental Health journal homepage: www.elsevier.com/locate/ijheh

Internal exposure to organochlorine pollutants and cadmium and self-reported health status: A prospective study Nik Van Larebeke a,∗,1 , Isabelle Sioen b , Elly Den Hond c , Vera Nelen d , Els Van de Mieroop d , Tim Nawrot e,f , Liesbeth Bruckers g , Greet Schoeters c , Willy Baeyens a a

Free University of Brussels (VUB), Department of Analytical and Environmental Chemistry (ANCH), Pleinlaan 2, 1050 Brussels, Belgium Ghent University, Department of Public Health, UZ-2 Blok A, De Pintelaan 185, 9000 Ghent, Belgium c Flemish Institute for Technological Research (VITO), Environmental Health and Risk, Boeretang 200, 2400 Mol, Belgium d Provincial Institute for Hygiene, Kronenburgstraat 45, 2000 Antwerp, Belgium e Centre for Environmental Sciences, Hasselt University, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium f School of Public Health, Occupational & Environmental Medicine, K.U. Leuven, Herestraat 49 (O&N 706), 3000 Leuven, Belgium g Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium b

a r t i c l e

i n f o

Article history: Received 4 March 2014 Received in revised form 14 November 2014 Accepted 24 November 2014 Keywords: Dioxin-like activity Hexachlorobenzene Cancer Diabetes Hypertension Osteoporosis

a b s t r a c t In this paper, based on the Flemish biomonitoring programs, we describe the associations between internal exposure to organochlorine pollutants and to cadmium (measured in 2004–2005 for adults aged 50–65 years) and self-reported health status obtained through a questionnaire in November 2011. Dioxin-like activity in serum showed a significant positive association with risk of cancer for women. After adjustment for confounders and covariates, the odds ratio for an exposure equal to the 90th percentile was 2.4 times higher than for an exposure equal to the 10th percentile. For both men and women dioxin-like activity and serum hexachlorobenzene (HCB) showed a significant positive association with risk of diabetes and of hypertension. Detailed analysis suggested that an increase in BMI might be part of the mechanism through which HCB contributes to diabetes and hypertension. Serum dichlorodiphenyldichloroethylene (p,p -DDE) concentration showed a significant positive association with diabetes and hypertension in men, but not in women. Serum polychlorinated biphenyl (PCB) 118 showed a significant positive association with diabetes in both men and women, and after adjustment for correlated exposures, also with hypertension in men. Urinary cadmium concentrations showed a significant positive association with hypertension. Urinary cadmium concentrations were (in 2004–2005) significantly higher in persons who felt in less than good health (in 2011) than in persons who felt in very good health. After adjustment for correlated exposures (to HCB, p,p -DDE and PCB118) marker PCBs showed a significant negative association with diabetes and hypertension. Serum p,p -DDE showed in men a significant negative association with risk of diseases based on atheromata. Our findings suggest that exposure to pollutants can lead to an important increase in the risk of diseases such as cancer, diabetes and hypertension. Some pollutants may possibly also decrease the risk of some health problems, although this requires confirmation by other approaches. © 2014 Elsevier GmbH. All rights reserved.

Introduction Flanders is one of the most densely populated areas in Europe, with intensive traffic, industrial activities and intensive farming

Abbreviations: BMI, body mass index; p,p -DDE, dichlorodiphenyldichloroethylene: 1,1-bis-(4-chlorophenyl)-2,2-dichloroethene; HCB, hexachlorobenzene; IARC, International Agency for Research on Cancer; PCB, polychlorinated biphenyl. ∗ Corresponding author. Tel.: +32 0475449955. E-mail address: [email protected] (N. Van Larebeke). 1 Retired from Ghent University Hospital, Study Centre for Carcinogenesis and Primary Prevention of Cancer, De Pintelaan 185, 9000 Ghent, Belgium. http://dx.doi.org/10.1016/j.ijheh.2014.11.002 1438-4639/© 2014 Elsevier GmbH. All rights reserved.

close to habitation. The Flemish biomonitoring programs showed differences in internal exposure to pollutants in function of area of residence and indicated that small differences in pollutant levels were associated with observable differences in effects (Koppen et al., 2002; Staessen et al., 2001; Van Den Heuvel et al., 2002; van Larebeke et al., 2006). The biomonitoring program organised by the Flemish Centre for Environment and Health in 2007–2011 comprised a follow up study (November 2011) on the adults that participated in the previous program (2002–2006). These adults were between 50 and 65 years at sampling which was scheduled between September 2004 and June 2005.

N. Van Larebeke et al. / International Journal of Hygiene and Environmental Health 218 (2015) 232–245

Here, we investigate prospectively the associations between self-reported health status obtained through a questionnaire in November 2011 and internal exposure to organochlorine pollutants and to cadmium measured between September 2004 and June 2005. We selected these pollutants because they reflect long term (possibly life-long) exposure. Blood lead concentration was not included because no significant associations were found, only some weak trends. In particular, we wanted to investigate whether higher internal exposures to these pollutants, all known to be endocrine disruptors (De Coster et al., 2012), were associated with an increased risk of cancer, diabetes, hypertension, diseases based on atheromata or osteoporosis. Methods Selection and recruitment of participants In the Flemish Environment and Health Survey (FLEHS) of the period 2002–2006 a stratified clustered multi-stage design was used to select 775 men and 808 women (n = 1583), aged 50–65 years, as a random sample of the population of the areas under study, as described by De Coster et al. (2008). These area comprised the port areas of Antwerp and Ghent, the industrial basins of the Albert canal and Olen, Antwerp, the largest city in Flanders with 404,000 inhabitants and very dense traffic, Ghent, the second largest city in Flanders with 213,000 inhabitants, the ‘fruit area’ around Sint-Truiden, neighbourhoods close to waste incinerators in six municipalities, and rural areas. In the selected areas approximately 1.2 million inhabitants are living which is 20% of the Flemish population. For these adults, aged between 50 and 65 at sampling, internal exposure was measured between September 2004 and June 2005. In October 2011 (6–7 years after the baseline survey) letters were sent to 1574 of these adults. Of these letters, 74 returned because people had moved, 16 people were reported to have died, and of the remaining 1484 letters 973 questionnaires were returned completed (838 on paper and 135 electronically), resulting in a response rate of 65.6%. Sampling, measurements of internal exposures and measurement of BMI At the time of the sampling (September 2004–June 2005), height and weight were measured by study nurses and body mass index (BMI) was calculated. 200 mL of urine and 40 mL of blood were collected from each participant. Immediately after sampling, serum was separated. Samples of serum, whole blood, and urine were stored at 4 ◦ C for maximum one week, or immediately deep frozen. All laboratory analyses were performed blindly in specialized laboratories that met national and international quality-control standards. Parameters obtained through a questionnaire in 2004–2005 A self-administered questionnaire was used, as described by De Coster et al. (2008) to collect information on many items including education, smoking and alcohol consumption. Measured internal exposure Isotope Cd114 was used to quantify the amount of cadmium in urine using ICP-MS. Urine samples were diluted in nitric acid (0.7%). Rhodium was used as an internal standard. The detection limit for urinary cadmium was 0.002 ppb as described by Schroijen et al. (2008). Serum PCBs (PCB118, PCB138, PCB153, PCB180 (ng/g lipid)), hexachlorobenzene (ng HCB/g lipid), and p,p -DDE (ng/g lipid) were

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measured in serum using procedures published by Covaci and Schepens (2001). After solid phase extraction from plasma and further cleaning on an acid silica column, the target compounds were separated and detected with gas chromatography–mass spectrometry (GC–MS) in a negative chemical ionization mode. The detection limit of all chlorinated compounds in serum was 0.02 ␮g/L. CALUX (Chemical-Activated LUciferase gene eXpression) analyses of dioxin-like activity in blood plasma was performed as described by Van Wouwe et al. (2004) and Schroijen et al. (2006). Briefly, dioxin-like compounds were quantified on the basis of their binding affinity to the aryl hydrocarbon (Ah) receptor. The dioxinlike compounds were first extracted from the plasma lipids by acid digestion. The extract was added to in vitro cultured liver cells, i.e. mouse hepatoma H1L6.1 cell line developed by Xenobiotic Detection System, Inc. The activated Ah complex binds to the promoter of the luciferase gene which produces a light signal upon activation. TEQ-values were calculated after comparison of the obtained signals to a 2,3,7,8-TCDD calibration curve. Parameters obtained in November 2011 through a questionnaire Participants filled out a questionnaire concerning their health status. They reported to feel “in very good health”, “in good health”, “reasonably healthy”, “in bad health” or in “very bad health”. They also reported their weight (November 2011), detailed information on their smoking habits and use of alcoholic beverages, and detailed information concerning diseases, complaints, symptoms and use of medication. The questionnaire was put together with the help of clinical specialists. As to the health status of participants in November 2011 based on the questionnaire, we included in our study a series of binomial parameters defined as indicated in Table 1. To emphasize the fact that the health status parameters were self-reported they were put between quotation marks. Statistical analyses Statistical analyses were performed with the Statistica 8.0 program (Statsoft, Tulsa, OK, USA). Multiple logistic regression was used to evaluate the association between the self-reported health parameters (as dependent variables) and parameters of internal exposure as independent variables. The exposure parameters did not show a Gaussian distribution, so the natural logarithm of these parameters was used in statistical analysis to reduce the impact of outliers. As we did in our previous studies (Van Larebeke et al., 2004; Schroijen et al., 2008), we used the parameter ‘marker PCBs’ (the sum of serum concentrations of the three most abundant PCBs: PCB138 + PCB153 + PCB180) in our analysis to reflect exposure to PCBs in general whereas PCB118, a marker PCB of dioxin-like PCBs (Park et al., 2007), was considered individually. Confounding factors (gender, age, and duration of smoking) were taken into account in all regression analyses. In separate analyses additional adjustment for a priori predetermined covariates, chosen on the basis of medical expert opinion, was included as mentioned in Table 1. In analyses comprising one single pollutant there is always the possibility that another pollutant, the concentration of which is correlated with the pollutant under study, co-determines the associations or lack thereof observed for the pollutant under study. In an effort to disentangle the effects of the pollutants included in our study we performed a series of statistical analyses in which more than one exposure parameter was included. Marker PCBs, PCB118, HCB and p,p -DDE showed a quite strong correlation (see results) with R2 (squared correlation coefficient) values above 0.1. Therefore, associations with one of these “correlated organochlorines” were studied after additional adjustment for the three others. As the variance inflation factors for these correlated organochlorines, calculated from linear regressions of the serum concentration of

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Table 1 Health status parameters obtained in November 2011 through a questionnaire and list of predetermined (a priori) covariates per parameter. Dependent variable Multiple linear regression Weight 2011–Weight 2005 Multiple logistic regression Subjective health status “In very good health”

Definition

Covariates

Weight reported in 2011 minus weight measured at sampling in 2004–2005

Exercise in minutes per week, level of education

A positive answer to the question “do you feel in very good health”

BMI, exercise in minutes per week, level of education, glasses alcoholic beverages per week BMI, exercise in minutes per week, level of education, glasses alcoholic beverages per week BMI, level of education, exercise in minutes per week BMI, level of education, exercise in minutes per week

Subjective health status: “In less than good health”

The person reported to be “reasonably healthy”, “in bad health” or “in very bad health”

“Osteoporosis”

A positive answer to the question “do you suffer or have you suffered from osteoporosis” A positive answer to one of the questions “do you suffer or have you suffered from osteoporosis”, “did you suffer a bone fracture after age 50”, “did your length decrease with more than 3 cm”, “did you take medication” (against osteoporosis, a list was provided) A positive answer to the question “do you suffer or have you suffered from diabetes”. A positive answer to one of the questions “do you suffer or have you suffered from diabetes”, “were you told to have too much sugar in your blood”, “do you suffer from wounds on legs or feet that tend to heal badly”, “do you take medication (against diabetes), a list was provided”. A positive answer to the question: “do you suffer or have you suffered from one or another form of cancer”

“Osteoporosis or related condition”

“Diabetes” “Diabetes or related condition”

“Cancer”

“Hypertension”

“Atheromata”

A positive answer to the question “do you suffer or have you suffered in the last ten years from arterial hypertension?” A positive answer to one of the questions “did you have a problem with the blood circulation in the brain (cerebrovascular incident, ischemic episode)”, “have you ever experiences a heart infarction?”, “do you sometimes suffer from pain in the thorax during physical effort?”

each of these organochlorines on the serum concentration of the three others were ≤0.196 (marker PCBs: 1.96; HCB:1.54; p,p -DDE 1.58; PCB118: 1.96) inclusion of the serum concentrations of these organochlorines in multiple regression and ANCOVA analyses was considered to be acceptable. As internal exposure to dioxin-like activity, to HCB and to cadmium were most consistently associated with adverse health effects in analyses with only one pollutant, we wanted to asses associations with combined exposure to these pollutants. For each of the relevant biomarkers of exposure a standard or z score for each individual was calculated by dividing the difference between the value for that individual and the mean value for the entire subject population (the 1583 persons who participated in the 2004–2005 campaign) by the standard deviation for the entire subject population. Next, for each subject in this follow-up study an index of internal exposure ITHC defined as the sum of the z scores for dioxin-like activity (pg TEQ/g fat) in serum measured through the calux bioassay, serum concentration of HCB (ng/g fat) and urinary excretion of cadmium per g creatinine (ITHC = zdioxin-like activity + zHCB + zurinary Cd ) was estimated. p-values below 0.05 were considered as significant and p values below 0.1 were considered as marginally significant, indicative of a trend. Results Personal characteristics and internal exposure. Comparison between participants and non-participants The relevant observations as to internal exposure (September 2004–June 2005), made on the persons who later participated in the questionnaire based follow-up study in November 2011, and

BMI, exercise in minutes per week, level of education BMI, exercise in minutes per week, level of education

BMI, exercise in minutes per week, level of education, glasses alcoholic beverages per week. BMI, exercise in minutes per week, level of education, glasses alcoholic beverages per week. BMI, Level of education, cholesterol concentration in blood

some of their personal characteristics are summarized in Table 2. Table 2 also contains a comparison between participants and non-participants in the follow-up study. The characteristics of participants and non-participants were very similar. After adjustment for gender, there were no significant differences regarding internal exposure or personal characteristics between participants and non-participants. There were however significantly more smokers among non-participants. Concentrations in serum of participants of Marker PCBs, PCB118, HCB and p,p -DDE showed a quite strong correlation (Table 3) with R2 (squared correlation coefficient) values above 0.1. Health status of participants The number and percentages of men and women who reported in November 2011 to suffer from certain health conditions is given in Table 4. Table 5 gives an overview of the associations found between internal exposure to pollutants and parameters of health status. A more detailed description of the associations is given below and in Tables 6–9. Weight gain: associations with internal exposure Serum concentration of marker PCBs showed a significant positive association with weight gain between 2004 and 2005 and November 2011 (Table 6). For genders separately, this positive association was significant only for women, also after additional adjustment for the covariates (listed under methods, Table 1). After adjustment for correlated exposures with R2 values above 0.1 in addition to adjustment for confounders and covariates, the positive association between weight gain and internal exposure to marker PCBs was stronger than without adjustment for correlated

Table 2 Observations (September 2004–June 2005) on the participants and non-participants of the follow-up study performed in November 2011 and some parameters reported by participants in November 2011. Parameter

Total (lifetime) number of cigarettes smoked by smokers (at sampling in 2004–2005) Number of ex-smokers (at sampling in 2004–2005) Duration of smoking reported in 2011 (years) Exercise in minutes per week reported in 2011 Number (%) of persons abstaining from alcohol Glasses alcoholic beverages per week Cholesterol, mg/dl Triglycerides, mg/dl Dioxin-like activity, pg TEQ/g fat Marker PCBs, ng/g fat PCB118, ng/g fat HCB, ng/g fat p,p -DDE, ng/g fat Urinary Cadmium, ␮g/g creatinine Weight reported in November 2011

Non-participants in the follow-up study a

Comparison participants versus non-participants p-value

N

Geometric mean

Median

P10

P90

n

Geometric mean a

Median

P10

P90

973

57.35

57.77

51.70

62.77

610

57.51

57.85

51.39

63.28

0.40

469

80.86

80.00

68.00

95.00

306

81.52

82.50

68.00

99.00

0.36

503

68.06

68.00

55.00

84.50

304

69.05

68.00

55.00

90.00

0.17

469 503 469 503 133 (13.7% of participants) 133

173.3 161.7 26.9 26.0

173.0 162.0 26.8 25.6

166.0 154.0 23.1 21.4

180.0 170.0 31.5 32.0

173.0 161.4 27.2 26.5

173.0 161.0 27.7 26.1

165.0 154.0 22.6 21.8

180.0 170.0 32.4 33.7325

0.45 0.47 0.14 0.098 <0.0001

168,000

139,000

7,300

352,000

306 304 306 304 148 (24.3% of nonparticipants) 148

204,000a

183,000

800

383,000

0.032

363 (37.3% of participants) 973

198 (32.5% of nonparticipants)

0.052

9.6a

0

0

38

n.a.b

n.a.b

n.a.b

n.a.b

973

260a

120

0

650

n.a.b

n.a.b

n.a.b

n.a.b

7.95

4.00

0.00

20.0

0.53

340 (34.9% of participants) 973

7.49a

5.00

0.00

20.00

214 (35.1% of nonparticipants) 610

972 972 904

218.6 137.6 19.7

220.0 133.0 23.7

174.0 75.0 5.5

275.0 267.0 47.1

609 609 533

220. 3 145.0 18.2

222.0 140.0 21.6

174.0 77.0 5.4

274.0 285.0 43.4

0.37 0.11 0.072

970 970 970 970 973

339 29.2a 56.1 483 0.625

343 26.0 54.6 486 0.615

212 11.9 28.6 147 0.323

527 48.2 113.5 1575 1.3

607 607 607 607 608

344 29.2a 58.7 460 0.650

350 25.3 58.7 501 0.618

215 11.4 27.5 132 0.323

546 52.9 140.4 1608 1.4

0.55 0.78 0.051 0.40 0.097

962

74.0

75.0

59.0

92.0

n.a.b

n.a.b

n.a.b

n.a.b

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Age (at sampling in 2004–2005) Men weight (at sampling in 2004–2005), kg Women weight (at sampling in 2004–2005), kg Men length, cm Women length, cm Men BMI, kg/m2 Women BMI, kg/m2 Number of smokers

Participants in the follow-up study

a For the parameters “Duration of smoking (years)”, “Exercise in minutes per week”, “Glasses alcoholic beverages per week”, “Total number of cigarettes smoked by smokers” and “PCB118 ng/fat” arithmetic means are reported instead of geometric means. b n.a. = not available.

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Table 3 Correlations between internal exposure of participants to different substances: Pearson correlation coefficient R (and R2 ).

Dioxin-like activity, pgTEQ/g fat Marker PCBs, ng/g fat

Dioxin-like activity, pg TEQ/g fat

Marker PCBs, ng/g fat

HCB, ng/g fat

PCB118, ng/g fat

p,p -DDE, ng/g fat

Urinary cadmium, ␮g/g creatinine

1.0000 (1.0000) 0.1040* (0.0108)

0.1040* (0.0108) 1.0000 (1.0000)

0.1364* (0.0186) 0.3653* (0.1334) 1.0000 (1.0000)

0.1027* (0.0105) 0.6327* (0.4003) 0.4272* (0.1824) 1.0000 (1.0000)

0.0938* (0.0088) 0.4368* (0.1908) 0.3826* (0.1464) 0.3213* (0.1032) 1.0000 (1.0000)

−0.0317 (0.0010) −0.0564 (0.0032) 0.0710* (0.0050) −0.0191 (0.0004) 0.1038* (0.0108) 1.0000 (1.0000)

HCB, ng/g fat PCB118, ng/g fat p,p -DDE, ng/g fat Urinary cadmium, ␮g/g creatinine *

p < 0.05.

Table 4 Number of men and women who reported in November 2011 to suffer from certain health conditions. Parameter

Felt “In very good health” Felt they were “In less than good health” “Osteoporosis” “Osteoporosis or related condition” “Diabetes” “Diabetes or related condition” “Cancer” “Myocardial infarction” “Problems with cerebral circulation” “Hypertension” “Atheromata”

Number of men (%) (n = 469) positive for the parameter

Number of women (%) (n = 504) positive for the parameter

89 (19.0)

94 (18.7)

87 (18.6)

115 (22.8)

7 (1.5) 83 (17.7)

76 (15.1) 178 (35.3)

30 (6.4) 62 (13.2)

29 (5.8) 55 (10.9)

34 (7.2) 27 (5.8) 18 (3.8)

65 (12.9) 10 (2.0) 17 (3.4)

162 (34.5) 71 (15.1)

172 (34.1) 62 (12.3)

exposures as well for both genders considered together as for women separately, and became also significant for men separately (Table 6). Adjustment for correlated exposures unveiled a significant negative association between p,p -DDE and weight gain in men, and between HCB and weight gain in women (Table 6). Dioxin-like activity: associations with health status Serum dioxin-like activity showed a significant positive association with risk of “Cancer” for women separately (Table 8). For men, a positive association was observed, that was however not significant. Serum dioxin-like activity showed a significant positive association with “Diabetes” and with “Diabetes or related condition” for both genders together (Table 7) and significantly or marginally significantly also for men (Table 9) and women separately (Table 8). Dioxin-like activity also showed a positive association with risk of “Hypertension”, significantly so for both genders together (Table 7) and marginally significantly for men separately (Table 9). HCB: associations with health status Serum concentration of HCB showed, for men (Table 9), women (Table 8) and both genders together (Table 7) a significant positive association with risk of “Diabetes” and of “Diabetes or related condition” which remained significant after additional adjustment for covariates. After adjustment for confounders, or confounders and covariates, and additionally for correlated exposures with R2

values above 0.1, the positive association between HCB and “Diabetes” and “Diabetes or related condition” remained significant for women and for both genders together but was weaker for men and only marginally (“Diabetes”) or not (“Diabetes or related condition”) significant any more. HCB showed for both genders together (Table 7), as well as for men (Table 9) and women (Table 8) separately, a significant positive association with risk of “Hypertension”, which remained significant (both genders together, men) or marginally significant (women) after additional adjustment for covariates. After adjustment for correlated exposures with R2 values above 0.1 (in addition to adjustment for confounders or to confounders and covariates) the association between HCB and “Hypertension” remained significant for both genders together (Table 7) and for women (Table 8), but was only marginally significant (with confounders and covariates) or lost significance (with confounders) for men. Adjustment for covariates did not include BMI for associations between HCB and “Diabetes”, “Diabetes or related condition” or “Hypertension”, as an increase in BMI might be part of the mechanism through which HCB leads to an increase in the risk of diabetes or hypertension. Indeed, HCB showed a highly significant (p < 0.0001) and strong positive correlation with BMI for women (R = 0.31) as well as for men (R = 0.33), whereas no, general positive association was observed between organochlorines and BMI (Supplementary information, Table 1). In our study population, as well for men as for women, a strong and highly significant association was observed between BMI and risk of “Hypertension” or “Diabetes” (Supplementary material, Table 2). Almost all positive associations between HCB and “Diabetes”, “Diabetes or related condition” and “Hypertension” lost significance after additional adjustment for BMI (see Supplementary material, Table 3). Supplementary Tables 1–3 related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijheh.2014. 11.002. p,p -DDE: associations with health status With risk of “Diabetes” and with risk of “Diabetes or related condition” serum concentration of p,p -DDE showed, for men separately (Table 9), a significant and strong positive association, and these associations remained significant after additional adjustment for covariates For women (Table 8) no significant positive association between serum concentration of p,p -DDE and “Diabetes” or “Diabetes or related condition” was observed (p > 0.52). In a model with correlated exposures with R2 values above 0.1 and confounders, or confounders and covariates, the positive association between p,p -DDE and “Diabetes” and “Diabetes or related condition” remained significant for men (Table 9).

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Table 5 Overviewa of the associationsb between internal exposure to pollutants and parameters of self-reported health status. Health status parameter “Weight reported in 2011 minus weight measured in 2004–2005” Felt “In very good health” “Osteoporosis or related condition” “Diabetes”

“Diabetes or related condition”

“Cancer” “Hypertension”

“Atheromata”

Modelc Conf Cov Confcor Covcor Conf Cov Conf Cov. Conf. Cov. Confcor Covcor Conf. Cov. Confcor Covcor Conf. Cov. Conf. Cov. Confcor Covcor Conf. Cov. Confcor Covcor

Dioxin-like activity

Marker PCBs *

n.c.e n.c.e

B↑* W↑ M↑* B↑* W↑* n.c.e n.c.e B↑* W↑* M↑* B↑* W↑* n.c.e n.c.e W↑* W↑* B↑* M↑ B↑* n.c.e n.c.e B↑

B↑ B↑* B↑* B↑*

PCB118

W↑ W↑* W↑* M↑* W↑* M↑*

B↓* W↓* M↓* B↓* W↓* M↓* f

B↓* W↓* M↓* B↓* W↓* M↓* f

B↑* B↑* B↑* B↑* B↑* B↑* B↑* B↑*

W↑ M↑* M↑* M↑* W↑* W↑ W↑* M↑* W↑* M↑ M↑* W↑* W↑* M↑

W↓ B↓* W↓* M↓* B↓* W↓* M↓* f B↓ M↓* M↓*

n.c.d n.c.d

M↑* M↑

HCB

p,p -DDE

Urinary cadmium

W↓* W↓*

M↓ M↓*

n.c.e n.c.e B↓d B↓d W↑ W↑

B↑* B↑* B↑* B↑* B↑* B↑* B↑* B↑*

W↑* M↑* W↑* M↑* f W↑* M↑ W↑* M↑f W↑* M↑* W↑* M↑* f W↑* W↑* f

M↑* M↑* M↑* M↑* M↑* M↑* M↑* M↑*

B↑* B↑* B↑* B↑*

W↑* M↑* W↑ M↑* f W↑* W↑* M↑f

M↑* M↑* M↑* M↑* M↓* M↓* M↓ M↓

n.c.d n.c.d

n.c.e n.c.e

n.c.e n.c.e

B↑* M↑ B↑* W↑* M↑* n.c.e n.c.e

n.c.e n.c.e

a B↑ indicates that higher internal exposure to the pollutant in the corresponding column is associated with a higher risk of the health status parameter in the corresponding row or, for the first parameter, with a higher difference in weight, for both genders considered together. B↓ indicates a lower risk for both genders considered together. W and M refer to women respectively men. b Only associations with p ≤ 0.1 are mentioned in this table. When the association is significant (p < 0.05) B, W or M are printed in bold and marked with *. c Conf indicates a model including confounders; Cov. indicates a model including covariates (as listed in Methods, Table 1) in addition to confounders. Confcor indicates a model including confounders and correlated exposures with R2 (squared correlation coefficient) values above 0.1. Covcor indicates a model including confounders, covariates and correlated exposures with R2 (squared correlation coefficient) values above 0.1. d Persons who felt “In less than good health” had higher urinary cadmium concentrations than persons who felt “In very good health”. This difference was significant, even after adjustment for confounders and covariates, for men and for both genders together, and marginally significant for women (p = 0.055). e n.c. = Not considered in this type of analysis. f In analyses on associations between HCB or marker PCBs with “Diabetes”, “Diabetes or related condition” and “Hypertension”, adjustment for covariates did not include BMI as an effect on BMI might be part of the mechanism through which HCB and marker PCB affect the risk of these conditions (see paragraph HCB, marker PCBs, BMI, diabetes & hypertension in the discussion).

With risk of “Hypertension” serum concentration of p,p -DDE showed, for men separately (Table 9), a significant positive association that remained significant after additional correction for covariates. No significant association with “Hypertension” was observed for women separately (p > 0.63). In a model with correlated exposures with R2 values above 0.1 and confounders, or with confounders and covariates, the positive association between

p,p -DDE and risk of “Hypertension” remained significant and of the same strength for men (Table 9). With risk of “Atheromata”, serum concentration of p,p -DDE showed a significant negative association for men which remained significant after additional correction for covariates (Table 9). In a model with correlated exposures with R2 values above 0.1 and confounders, or confounders and covariates, this negative association

Table 6 Associationsa between exposure and change in weight (Weight reported in November 2011 minus weight measured at sampling in 2004–2005). Gender

Internal exposure

Model

Change in weight (kg) for doubling of exposure (95% CI)

p

Both Both Both Both Women Women Women Women Men Men Men Men Women Women

Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fa Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln p,p -DDE ng/g fat Ln p,p -DDE ng/g fat Ln HCB ng/g fat Ln HCB ng/g fat

Confb Covc Confcord Covcore Confb Covc Confcord Covcore Confcord Covcore Confcord Covcore Confcord Covcore

+0.72 (+0.11/+1.33) +0.79 (+0.18/+1.40) +1.43 (+0.53/+2.34) +1.55 (+0.64/+2.45) +0.83 (−0.02/+1.68) +0.98 (+0.13/+1.83 +1.41 (+0.11/+2.70) +1.72 (+0.42/+3.02) +1.50 (+0.23/+2.78) +1.51 (+0.23/+2.78) −0.43 (−0.87/+0.003) −0.44 (−0.87/−0.005) −0.88 (−1.68/−0.08) −1.12 (−1.93/−0.31)

0.021 0.011 0.0019 0.00082 0.055 0.024 0.033 0.0096 0.020 0.020 0.052 0.048 0.032 0.0070

a b c d e

Only associations which reach p ≤ 0.1 are mentioned in the table. The model includes confounders. The model includes confounders and additional adjustment for covariates. The model includes confounders and correlated exposures with R2 (squared correlation coefficient) values above 0.1. The model includes confounders, covariates and correlated exposures with R2 (squared correlation coefficient) values above 0.1.

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Table 7 Both genders together: odds ratio’s for health conditions in function of internal exposure.a Health condition

Internal exposure

Modelb

Odds ratio for doubling of exposure parameter

Odds ratio for doubling of exposure 95% CI

Odds ratio for exposure = p90 compared to p10

p

Felt “In very good health”

Ln Urinary cadmium, ␮g/g creatinine Ln Urinary cadmium, ␮g/g creatinine Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat

Conf.

0.82

0.65/1.03

0.68

0.088

Cov.

0.80

0.63/1.02

0.65

0.067

Conf.

1.43

1.10/1.87

3.37

0.0073

Cov.

1.45

1.09/1.94

3.20

0.011

Confcor

0.20

0.09/0.47

0.12

0.00023

Covcor-BMI

0.20

0.08/0.48

0.12

0.00029

Conf. Cov-BMI Confcor Covcor-BMI Conf.

2.37 2.39 1.49 2.52 1.78

1.62/3.47 1.62/3.53 1.06/2.10 1.55/4.09 1.28/2.46

5.57 5.66 2.20 6.27 3.19

0.000009 0.000012 0.023 0.00018 0.0006

Cov.

1.70

1.16/2.48

2.90

0.0061

Confcor

2.52

1.47/4.29

6.36

0.00072

Covcor

1.81

1.01/3.22

3.27

0.045

Conf.

1.32

1.10/1.58

3.04

0.0031

Cov.

1.31

1.08/1.60

2.82

0.0073

Confcor

0.28

0.15/0.51

0.19

0.000030

Covcor-BMI

0.28

0.15/0.52

0.19

0.000059

Conf.

1.62

1.23/2.14

2.62

0.0006

Ln HCB, ng/g fat

Cov-BMI

1.66

1.25/2.20

2.73

0.00051

Ln HCB, ng/g fat

Confcor

1.49

1.06/2.10

2.20

0.023

Ln HCB, ng/g fat

Covcor-BMI

1.54

1.07/2.19

2.35

0.018

Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat

Conf.

1.53

1.20/1.96

2.36

0.0006

Cov.

1.47

1.12/1.93

2.17

0.0051

Confcor

2.07

1.42/3.02

4.30

0.00017

Covcor

1.73

1.16/2.59

3.00

0.0078

Conf.

1.16

0.98/1.38

1.60

0.083

Conf.

0.75

0.53/1.05

0.68

0.096

Conf.

1.17

1.03/1.32

1.61

0.014

Cov.

1.15

1.00/1.31

1.52

0.043

Confcor

0.39

0.26/0.58

0.29

0.000004

Covcor-BMI

0.39

0.26/0.59

0.29

0.000006

Conf.

1.42

1.16/1.72

1.99

0.0005

Felt “In very good health” “Diabetes”

“Diabetes”

“Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Atheromata”

“Atheromata” “Hypertension”

“Hypertension”

“Hypertension” “Hypertension” “Hypertension”

N. Van Larebeke et al. / International Journal of Hygiene and Environmental Health 218 (2015) 232–245

239

Table 7 (Continued) Health condition

Internal exposure

Modelb

Odds ratio for doubling of exposure parameter

Odds ratio for doubling of exposure 95% CI

Odds ratio for exposure = p90 compared to p10

p

“Hypertension” “Hypertension” “Hypertension” “Hypertension”

Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln Urinary cadmium, ␮g/g creatinine Ln Urinary cadmium, ␮g/g creatinine

Cov-BMI Confcor Covcor-BMI Conf.

1.46 1.43 1.46 1.26

1.20/1.78 1.12/1.81 1.16/1.92 1.04/1.53

2.13 2.02 2.13 1.57

0.00017 0.0038 0.0017 0.020

Cov.

1.45

1.17/1.79

2.06

0.0006

“Hypertension”

Only associations which reach p ≤ 0.1 are mentioned in the table. The model Conf includes only adjustment for the confounding factors; the model Cov includes additional adjustment for covariates; the model Cov-BMI includes additional adjustment for covariates, but without adjustment for BMI; the model Confcor includes confounders and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor includes confounders, covariates and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor-BMI includes confounders, correlated exposures with R2 (squared correlation coefficient) values above 0.1, and covariates, but without adjustment for BMI. a

b

kept almost the same strength but was only marginally significant (Table 9).

well for both genders combined as for women and men separately (Tables 7–9).

Marker PCBs: associations with health status

PCB118: associations with health status

As only parameter of exposure, marker PCBs showed for men a significant negative association with “Atheromata” but after additional adjustment for correlated exposures with R2 values above 0.1 significance was lost (Supplementary information, Table 4). Supplementary Table 4 related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijheh.2014.11.002. As only parameter of exposure, marker PCBs showed only a weak negative association with “Hypertension”, marginally significant only for women (Table 8). After additional adjustment for the three other organochlorines, showing substantial (R2 > 0.1) correlation with marker PCBs, in a model comprising confounders, this negative association was reinforced and significant for women and became significant also for men (Table 9); significance was lost after additional adjustment for covariates. Detailed analysis revealed that it was adjustment for BMI that annihilated much of the negative association between serum level for marker PCBs and risk of “Hypertension”. Indeed, in a model with confounders, correlated exposures with R2 values above 0.1 and all covariates except BMI, marker PCBs showed a significant and quite strong negative association with “Hypertension” as well in women (Table 8) as in men (Table 9). Serum concentration of marker PCBs showed in our study population a negative association with BMI that was significant for women (R = −0.195, p = 0.00001) but not for men (R = −0.039, p = 0.41), whereas no general negative association was observed between organochlorines and BMI (Supplementary information, Table 1). As only parameter of exposure, marker PCBs showed neither significant association with “Diabetes” nor with “Diabetes or related condition”. However, after correction for confounding factors and additional adjustment for the three other organochlorines showing substantial (R2 > 0.1) correlation with marker PCBs, marker PCBs showed a significant and strong negative association with “Diabetes” and with “Diabetes or related condition”, as well for both genders combined as for women and men separately (Tables 7–9). This negative association lost significance after additional adjustment for covariates; detailed analysis revealed that it was adjustment for BMI that annihilated much of the negative association between serum level for marker PCBs and risk of “Diabetes”, as was the case for “Hypertension” (see above). Indeed, in a model with confounders, correlated exposures and all covariates except BMI, marker PCBs showed a significant and quite strong negative association with “Diabetes” and “Diabetes or related condition” as

For men (Table 9) and both genders together (Table 7), a quite strong and significant positive association with risk of “Diabetes” was observed which was not weakened by additional adjustment for covariates; for women (Table 8) only a positive trend was observed. With “Diabetes or related condition” significant positive associations were observed for both men and women, only marginally so for men after additional adjustment for covariates. After additional adjustment for correlated exposures with R2 values above 0.1 the associations for women and for both genders together were stronger, and for men of about the same strength (but, concerning “Diabetes”, lost significance with additional correction for covariates). In statistical analyses in which a single parameter of internal exposure was considered serum concentration of PCB118 did not show an association with “Hypertension”. However, in a model with confounders and additional adjustment for correlated exposures with R2 values above 0.1, serum concentration of PCB118 showed a significant positive association with “Hypertension” for men separately (Table 9), but no clear trend was observed for women. In a model with additional adjustment for covariates and correlated exposures the association with “Hypertension” was marginally significant for men. Cadmium: associations with health status In logistic regression urinary cadmium showed, for both genders together, a trend towards a negative association with feeling “In very good health” (Table 7). The urinary cadmium levels were significantly higher in subjects (both genders together) who felt “In less than good health” (0.82 ± −0.52 ␮g/g creatinine) compared to subjects who felt “In very good health” (0.68 ± 0.40 ␮g/g creatinine) (p = 0.0014 after adjustment for confounding factors and covariates). For men separately these levels were respectively 0.73 ± 0.46 ␮g/g creatinine and 0.54 ± 0.29 ␮g/g creatinine (p = 0.0086 after adjustment for confounding factors and covariates) and for women separately 0.88 ± 0.43 and 0.80 ± 0.45 ␮g/g creatinine (p = 0.055 after adjustment for confounding factors and covariates). With risk of “Hypertension” urinary cadmium showed a significant positive association for both genders together (Table 7), which was stronger after additional correction for covariates. For women (Table 8) and men (Table 9) separately, the positive association with

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Table 8 Women: odds ratios for health conditions in function of internal exposure.a “Health condition”

Internal exposure

Modelb

Odds ratio for doubling of exposure parameter

Odds ratio for doubling of exposure 95% CI

“Osteoporosis or related condition” “Osteoporosis or related condition” “Cancer” “Cancer” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension”

Ln Urinary cadmium, ␮g/g Creatinine

Conf.

1.26

0.97/1.63

1.56

0.085

Ln Urinary cadmium, ␮g/g Creatinine

Cov.

1.27

0.96/1.68

1.59

0.093

Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat

Conf. Cov. Conf. Cov. Confcor Covcor-BMI Conf. Cov-BMI Confcor Covcor-BMI Conf. Confcor Covcor Conf.

1.34 1.32 1.45 1.50 0.15 0.17 2.23 2.06 3.03 2.76 1.52 3.44 2.19 1.36

1.04/1.72 1.02/1.71 1.00/2.12 1.02/2.22 0.04/0.53 0.05/0.58 1.29/3.86 1.17/3.62 1.54/5.98 1.37/5.54 0.94/2.44 1.45/8.15 0.88/5.43 1.04/1.77

2.46 2.39 3.18 3.48 0.08 0.09 4.46 3.86 7.95 6.66 2.26 11.29 4.64 2.56

Ln Dioxin-like activity, pg TEQ/g fat

Cov.

1.38

1.04/1.82

2.70

0.025

Ln Marker PCBs, ng/g fat

Confcor

0.24

0.10/0.57

0.14

0.0013

Ln Marker PCBs, ng/g fat

Covcor-BMI

0.24

0.10/0.60

0.15

0.0021

Ln HCB, ng/g fat

Conf.

1.61

1.07/2.42

2.43

0.022

Ln HCB, ng/g fat

Cov-BMI

1.67

1.09/2.56

2.62

0.018

Ln HCB, ng/g fat

Confcor

1.71

1.04/2.82

2.72

0.036

Ln HCB, ng/g fat

Covcor-BMI

1.79

1.05/3.03

2.96

0.031

Ln PCB118, ng/g fat

Conf.

1.50

1.04/2.16

2.21

0.031

Ln PCB118, ng/g fat

Cov.

1.52

1.01/2.29

2.27

0.045

Ln PCB118, ng/g fat

Confcor

2.73

1.46/5.11

7.17

0.0017

Ln PCB118, ng/g fat

Covcor

2.20

1.13/4.27

4.68

0.020

Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln Urinary cadmium, ␮g/g creatinine

Conf. Confcor Covcor-BMI Conf. Cov-BMI Confcor Covcor-BMI Cov

0.71 0.37 0.39 1.32 1.30 1.55 1.51 1.38

0.50/1.01 0.21/0.64 0.22/0.69 1.00/1.73 0.98/1.72 1.11/2.17 1.06/2.14 1.03/1.83

0.63 0.26 0.29 1.67 1.63 2.28 2.15 1.86

0.056 0.00042 0.0013 0.046 0.068 0.010 0.021 0.030

Odds ratio for exposure = p90 compared to p10

p

0.023 0.033 0.052 0.045 0.0032 0.0049 0.0043 0.012 0.0013 0.0044 0.086 0.0050 0.092 0.027

Only associations which reach p ≤ 0.1 are mentioned in the table. The model Conf includes only adjustment for the confounding factors; the model Cov includes additional adjustment for covariates; the model Cov-BMI includes additional adjustment for covariates, but without adjustment for BMI; the model Confcor includes confounders and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor includes confounders, covariates and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor-BMI includes confounders, correlated exposures with R2 (squared correlation coefficient) values above 0.1, and covariates, but without adjustment for BMI. a

b

“Hypertension” was only significant after additional adjustment for covariates. With risk of “Osteoporosis or related condition” urinary cadmium showed a trend towards a positive association for women separately (Table 8). Combined exposure to dioxin-like activity, hexachlorobenzene and cadmium: associations with health status As internal exposures in terms of dioxin-like activity (pg TEQ/g fat in blood), HCB (ng/g fat in blood) and urinary cadmium (ng/g creatinine) showed positive associations with risk of several diseases, we assessed the association of the combination of these exposures in terms of the index ITHC (sum of z values) with

parameters of self-reported health status (Table 10). Several significant associations were found. For both sexes combined, this was the case for “Diabetes”, “Diabetes or related condition”, “Hypertension” and “Osteoporosis or related condition” as well after correction for confounders as after additional correction for covariates (Table 10). For men and women separately quite similar positive associations were found (Table 10) with “Diabetes”, “Diabetes or related condition” and “Hypertension”, with however, after additional correction for covariates, only marginal (“Diabetes or related condition”; “Hypertension” for men) or no (“Diabetes”; “Hypertension” for women) statistical significance. For “Osteoporosis or related condition” a significant positive association was observed for women, but not for men.

N. Van Larebeke et al. / International Journal of Hygiene and Environmental Health 218 (2015) 232–245

241

Table 9 Men: odds ratios for health conditions in function of internal exposure.a Health condition

Internal exposure

Modelb

Odds ratio for doubling of exposure

Odds ratio for doubling of exposure 95% CI

Odds ratio for exposure = p90 compared to p10

p

“Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Diabetes or related condition” “Atheromata” “Atheromata” “Atheromata” “Atheromata” “Atheromata” “Atheromata” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension” “Hypertension”

Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln Dioxin-like activity, pg TEQ/g fat Ln Marker PCBs, ng/g fat Ln Marker PCBs, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln HCB, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln p,p -DDE, ng/g fat Ln PCB118, ng/g fat Ln PCB118, ng/g fat Ln Urinary cadmium, ␮g/g creatinine Ln Urinary cadmium, ␮g/g creatinine

Conf Confcor Covcor-BMI Conf Cov-BMI. Confcor Covcor-BMI Conf Cov. Confcor Covcor Conf Cov. Confcor Conf Confcor Covcor-BMI Conf Cov-BMI Conf Cov. Confcor Covcor Conf Cov. Confcor Covcor Conf Cov. Conf Cov. Confcor Covcor Conf Confcor Covcor-BMI Conf Cov-BMI Covcor-BMI Conf Cov. Confcor Covcor Confcor Covcor Conf Cov

1.48 0.21 0.20 2.46 2.67 1.78 1.96 1.67 1.72 1.70 1.66 2.04 1.90 2.39 1.29 0.28 0.27 1.62 1.67 1.42 1.33 1.53 1.42 1.56 1.42 1.87 1.63 0.57 0.57 0.79 0.76 0.79 0.78 1.18 0.42 0.39 1.54 1.60 1.38 1.30 1.23 1.32 1.28 1.76 1.38 1.32 1.49

1.01/2.17 0.06/0.70 0.05/0.70 1.44/4.19 1.52/4.69 0.92/3.45 0.97/3.94 1.25/2.24 1.21/2.42 1.16/2.48 1.09/2.53 1.28/3.25 1.11/3.24 1.22/4.69 1.00/1.67 0.12/0.65 0.11/0.65 1.11/2.37 1.13/2.47 1.15/1.75 1.06/1.68 1.17/2.00 1.07/1.90 1.12/2.18 0.99/2.03 1.16/3.01 0.98/2.72 0.35/0.93 0.34/0.94 0.65/0.95 0.62/0.93 0.62/1.01 0.60/1.01 0.99/1.41 0.23/0.75 0.21/0.72 1.16/2.05 1.20/2.14 0.96/1.99 1.11/1.51 1.04/1.45 1.09/1.60 1.04/1.58 1.25/2.46 0.95/1.99 0.98/1.78 1.08/2.05

3.38 0.14 0.13 5.37 6.32 2.94 3.52 5.50 6.03 5.86 5.39 4.52 3.85 6.28 4.52 0.20 0.19 2.48 2.62 3.22 2.61 4.14 3.24 2.55 2.09 3.73 2.81 0.49 0.49 0.45 0.39 0.46 0.44 1.69 0.33 0.30 2.26 2.41 1.83 2.37 1.97 2.55 2.28 3.27 1.96 1.68 2.08

0.045 0.011 0.012 0.00097 0.00063 0.090 0.061 0.00056 0.0022 0.0060 0.019 0.0026 0.019 0.011 0.049 0.0033 0.0038 0.012 0.0099 0.0011 0.014 0.0017 0.016 0.0092 0.055 0.010 0.060 0.024 0.029 0.013 0.0068 0.056 0.059 0.066 0.0033 0.0028 0.0028 0.0016 0.084 0.00099 0.018 0.0039 0.019 0.0011 0.090 0.066 0.016

Only associations which reach p ≤ 0.1 are mentioned in the table. The model Conf includes only adjustment for the confounding factors; the model Cov includes additional adjustment for covariates; the model Cov-BMI includes additional adjustment for covariates, but without adjustment for BMI; the model Confcor includes confounders and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor includes confounders, covariates and correlated exposures with R2 (squared correlation coefficient) values above 0.1; the model Covcor-BMI includes confounders, correlated exposures with R2 (squared correlation coefficient) values above 0.1, and covariates, but without adjustment for BMI. a

b

For men separately, but not for women, a significant positive association was found for “atheromata” (Table 10); this association was weakened and lost significance after additional correction for covariates. ITHC also showed a positive association with BMI as measured during the biomonitoring campaign in 2005–2006 for both men (p = 0.0002, R2 = 0.030) and women (p = 0.0014, R2 = 0.020). Discussion

efforts such as the REACH (Registration, Evaluation and Authorisation and Restriction of Chemicals) program of the European Commission pertain to effects of single chemicals (Sarigiannis and Hansen, 2012). However, cumulative effects are often synergistic or antagonistic (Crain et al., 2008; Vanhoudt et al., 2012). The U.S. Environmental Protection Agency is working towards multipollutant science and risk assessment approaches (Johns et al., 2012). Here we try to implement an effects-based approach to Cumulative Risk Assessment concerning the combined effects of multiple environmental stressors (Sexton, 2012).

The need to study the effect of concurrent and combined exposures Observed associations and the Bradford Hill criteria for causality In almost all studies on health effects associated with internal exposure to the pollutants considered in our paper statistical analyses relate to the associations of adverse health effects with single pollutants. As well the toxicological data as regulatory

Strength: although certainly small in terms of experimental work, the reported odds ratios are almost all sufficiently large to be of serious concern to public health.

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Table 10 Odds ratio’s for health conditions in function an index, based on z values, of combined exposure to dioxin-like activity, hexachlorobenzene and urinary cadmium. Health condition

Modela

Coefficient in logistic regression

Odds ratio for an increase of 1 in the z value for combined exposureb,c

Odds ratio for an increase of 1 in the z value for combined exposure 95% CI

Odds ratio for the p90 z value compared to p10

p

“Atheromata” (Men) “Diabetes” (both genders) “Diabetes” (both genders) “Diabetes or related condition” (both genders) “Diabetes or related condition” (both genders) “Diabetes” (Men) “Diabetes or related condition” (Men) “Diabetes or related condition” (Men) “Diabetes” (Women) “Diabetes or related condition” (Women) “Diabetes or related condition” (Women) “Hypertension” (both genders) “Hypertension” (both genders) “Hypertension” (Men) “Hypertension” (Men) “Hypertension” (Women) “Osteoporosis or related condition” (both genders) “Osteoporosis or related condition” (both genders) “Osteoporosis or related condition” (women) “Osteoporosis or related condition” (women) Not “in very good health” (women)

Conf Conf Cov Conf Cov Conf Conf Cov Conf Conf Cov Conf Cov Conf Cov Conf Conf Cov Conf Cov Conf

0.191 0.231 0.181 0.178 0.151 0.247 0.214 0.172 0.225 0.153 0.141 0.147 0.116 0.199 0.153 0.110 0.112 0.126 0.125 0.129 0.121

1.21 1.26 1.20 1.20 1.16 1.28 1.24 1.13 1.25 1.17 1.15 1.16 1.12 1.22 1.17 1.12 1.12 1.13 1.13 1.14 1.13

1.02/1.44 1.09/1.45 1.02/1.42 1.07/1.34 1.03/1.32 1.02/1.61 1.04/1.48 0.98/1.30 1.04/1.51 1.01/1.35 0.98/1.366 1.06/1.26 1.02/1.24 1.05/1.42 0.99/1.38 1.00/1.24 1.02/1.22 1.03/1.25 1.02/1.26 1.01/1.28 0.98/1.30

1.83 2.47 2.03 2.00 1.80 2.18 1.97 1.72 2.55 1.89 1.80 1.78 1.57 1.88 1.62 1.58 1.55 1.64 1.69 1.718 1.66

0.031 0.0014 0.032 0.0020 0.019 0.032 0.019 0.093 0.018 0.041 0.091 0.0009 0.016 0.0093 0.072 0.046 0.015 0.009 0.026 0.029 0.095

a

The model “Conf” includes only adjustment for the confounding factors, the model “Cov” includes additional adjustment for covariates. Index of internal exposure on the basis of z-values, calculated as described under Methods for all adults participating in the biomonitoring program, for dioxin-like activity and hexachlorobenzene in blood and urinary cadmium. c The value of this index varied from z = −3.04 to z = 8.35 with p10 z = −1.85 and p90 z = 2.05. b

Consistency: In the discussion of observed associations we mention some literature data to address this point. Specificity: None of our observations can be considered specific. Temporality: The measurements reflecting internal exposures over very long time were made almost seven years before the collection of data concerning self-reported health status. The great majority of participants were not severely ill at the moment of the measurements of internal exposure, as they had to travel to participate. In our study, there is no reversal of cause and effect concerning internal exposures and BMI as there was no general association between serum organochlorine concentrations and BMI (Supplementary information, Table 1). Biological gradient: Is inherently part of this type of study. Plausibility: Several associations (such as the link between dioxinlike activity and cancer and between cadmium and hypertension and cadmium and osteoporosis) are biologically plausible. Other links, such as the negative associations observed with diabetes and hypertension lack a clear mechanistic explanation. Coherence: Several of the observed associations have been described in animal experiments. To our knowledge this is however not (or less) the case for the protective effects suggested for marker PCBs and p,p-DDE. Analogy: Many experimental and observational data indicate that tumour promoting substances such as dioxin increase the risk of cancer, and that endocrine disruption can contribute to the induction of diabetes, hypertension, osteoporosis and atheromatous disease. Cancer Our finding of an association between internal exposure to dioxin-like activity and risk of “Cancer” is not surprising. Indeed, it is well known that dioxins are human carcinogens (Becher et al., 1998; Pesatori et al., 2009; Viel et al., 2008, 2011), classified as such by IARC in 2012. What is less expected is that our findings suggest that even relatively low dioxin-like activity in the blood is associated with a quite important increase in the risk of cancer. Logistic regression indicated that women having an internal exposure equal

to p90 had a more than twofold higher risk on “Cancer” than women having an internal exposure equal to p10. It is however becoming clear that very low concentrations of receptor-binding substances can have important biological effects (Vandenberg et al., 2012). Diabetes That several organochlorines are important endocrine disrupting contaminants and increase the risk of diabetes is well established (Alonso-Magdalena et al., 2011; De Coster et al., 2012). In our study, dioxin-like activity in serum, serum concentration of PCB118 (a marker PCB for dioxin-like PCBs (Park et al., 2007)) and serum concentration of HCB were associated with an important increase in the risk of “Diabetes”. Exposure to dioxins was reported to be associated with type 2 diabetes (Pesatori et al., 1998; Cranmer et al., 2000; Lee et al., 2006; Fierens et al., 2003; Longnecker and Daniels, 2001). According to Lee et al. (2007), the dioxin-like PCBs showed strong positive associations with diabetes. Also HCB was found to be associated with diabetes in several studies (Wu et al., 2013; Gasull et al., 2012; Codru et al., 2007; Glynn et al., 2000). Whereas we found a strong positive association between PCB118 and “Diabetes”, we did not find such an association between marker PCBs and “Diabetes”. This is consistent with the findings and views of Everett et al. (2011). As to p,p -DDE, a positive association between internal exposure and diabetes was reported in the literature (Lee et al., 2006; Codru et al., 2007; Son et al., 2010; Arrebola et al., 2013). Interestingly, in our study serum concentration of p,p -DDE, known to have antiandrogenic activity (Sonnenschein and Soto, 1998), was associated with increased risk of “Diabetes” in men, but not in women. Rylander et al. (2005), however, found the positive association between p,p -DDE and diabetes to be stronger in women than in men. The median serum p,p -DDE concentration was higher in the Rylander study (about 580 ng/g fat) than in our study (486 ng/g fat). Hypertension We found positive associations between serum dioxin-like activity, serum p,p -DDE concentration and urinary cadmium

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concentration and the risk of “Hypertension”. Lee et al. (2007) and, for women, Ha et al. (2009) found a positive association between serum levels of PCDDs and PCDFs and high blood pressure. According to Kopf and Walker (2009), animal research has confirmed human epidemiology studies that dioxin exposure in adulthood is associated with hypertension and cardiovascular disease. A systematic review by Humblet et al. (2008) suggests that dioxin exposure is associated with mortality from both ischemic heart disease and all cardiovascular disease. Positive associations between p,p -DDE and hypertension were described by Valera et al. (2013) and Lind et al. (2014). Moreover La Merrill et al. (2013) found a positive association between prenatal exposure to DDT and hypertension. We found a gender difference with respect to the association between p,p -DDE and “Hypertension”, similar to the gender difference found concerning “Diabetes”, with only in men a significant positive association. Next, it is well known that exposure to cadmium can increase the risk on hypertension (Gallagher and Meliker, 2010). We found a positive association serum HCB concentration and the risk of “Hypertension”. We are not aware of published data reporting a positive association between HCB and hypertension, although an association has been described between left ventricle hypertrophy and internal exposure to HCB (Sjoberg et al., 2013). Hypertension is often accompanied by left ventricle hypertrophy (see medical textbooks). Osteoporosis We found that persons having higher urinary cadmium concentrations tended to have a higher risk of osteoporotic symptoms. This is in line with other epidemiological evidence (Nawrot et al., 2010). In addition, experimental studies strongly support the epidemiologic evidence for a direct osteotoxic effect of cadmium. In animals exposed to cadmium, bone demineralization begins early after the start of cadmium exposure and well before the onset of kidney damage (Wang and Bhattacharyya, 1993; Wilson and Bhattacharyya, 1997). Presence or absence of association unveiled by statistical analysis comprising several pollutants In function of additional adjustment for correlated exposures we found differences in the association of marker PCBs, p,p -DDE, HCB and PCB118 with self-reported health status. So, it is only after adjustment for other exposures that we found significant negative associations between serum concentration of marker PCBs and risk of “Diabetes” and of “Hypertension”. The absence of associations in analyses with only marker PCBs as exposure parameter might be due to the co-exposure with PCB118, with p,p -DDE and with HCB which were rather associated with an increase in the risk of “Diabetes” and/or “Hypertension”. The negative association of marker PCB with risk of “Atheromata” on the contrary disappeared after adjustment of other exposures and might well be due to co-exposure with p,p -DDE. Also, the positive association between serum concentration of PCB118 and “Hypertension” in men was only detected after adjustment for other exposures. The absence of association in analyses with only PCB118 as exposure parameter might be due to co-exposure with marker PCBs which are rather associated with a decrease in the risk of “Hypertension”. Adjustment for other exposures unveiled a negative association between weight gain from 2005 to 2011 with serum concentration of p,p -DDE for men and with HCB for women. These findings are illustrations of the fact that, in analyses comprising one single pollutant there is always the possibility that another pollutant, the concentration of which is correlated with the pollutant under study, co-determines the association or lack thereof observed for the pollutant under study. A possible drawback of the inclusion

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of several parameters of exposure, showing similar associations with the parameter of effect and showing a certain level of correlation, resides in the fact that the statistical significance of individual associations can decrease (Hannigan and Lynch, 2013), as observed here for the association, for men, between HCB and “Diabetes” and HCB and “Hypertension”. Observations stemming from multiple regression analysis with correlated independent variables should be confirmed by other approaches. Protective effects Remarkable is that several significant and sometimes strong negative associations were observed between internal exposure and risk of disease. We observed a negative association of marker PCBs with risk of “Diabetes” and “Hypertension”, this after additional adjustment for p,p -DDE, HCB and PCB118. We found many publications describing a positive association between PCB concentrations and risk of “Hypertension” or “Diabetes”, but no publication on a negative association with marker PCBs. Tanaka et al. (2011) report differences between PCB congeners and risk of diabetes. A possible explanation for differences between our findings and published literature is that we found the protective effects of marker PCBs only after adjustment for other organochlorines. For men, a significant and quite strong negative associations between p,p -DDE and “Atheromata” was found. These findings are not concordant with published data. Lee et al. (2012) found a positive association between internal exposure to p,p -DDE and risk of stroke. Min et al. (2011) found a positive association, in obese subjects, between internal exposure to p,p -DDE and prevalence of peripheral arterial disease. Protective effects suggested by our observations certainly need confirmation by other approaches, including assessment of biological plausibility. HCB, marker PCBs, BMI, diabetes & hypertension The positive associations between HCB and “Diabetes” and “Hypertension” might in part be due to an increase in BMI induced by HCB. HCB is associated with a higher BMI, and a higher BMI is associated both with diabetes and with hypertension, as well in general (see medical textbooks) as in our study population (Supplementary information, Table 2). In our study population it is unlikely that higher HCB serum concentrations result from an increase in BMI as there was no general positive association between serum organochlorine concentrations and BMI: we observed a significant negative association with marker PCBs and no significant association with dioxin-like activity (Supplementary information, Table 1). So it is possible that HCB contributes to an increase in BMI, and that an increase in BMI might be part of the mechanism through which HCB contributes to diabetes and hypertension. Similarly, it is not excluded that a decrease in BMI might be part of the mechanism, especially for women, through which marker PCBs might contribute to a decrease in the risk of diabetes and hypertension. The BMI-subsequent weight gain paradox in our study In our study population, in women, serum levels of HCB and marker PCBs were respectively associated with a significant increase and a significant decrease in BMI (Supplementary information, Table 1), but also respectively with a significant decrease and a significant increase (from 2005 to 2011) in weight (Table 6). For men, p,p -DDE was associated with a marginally significant increase in BMI (Supplementary information, Table 1), but also with a significant decrease (from 2005 to 2011) in weight (Table 6). This is paradoxical, but not contradictory. In our study population, BMI

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(measured in 2004–2005) showed itself a negative association with weight gain between 2004–2005 and November 2011 as well for men (R = −0.177, p = 0.0001) as for women (R = −0.209, p < 0.00001). In the Nurses’ Health Study subsequent weight gains during a first 4 year follow up period were positively associated with BMI, but negatively in the second 4 year period (Colditz et al., 1990). Subjective health status We found higher urinary cadmium concentrations for persons reporting to feel in less than good health. This is consistent with observations concerning the importance of toxic effects of cadmium in humans (Thevenod and Lee, 2013; Akesson et al., 2014). Gender-specific effects Important differences were observed between men and women. The differences concerning the associations between dioxin-like activity with risk of “Cancer” and between internal exposure to cadmium and risk of “Osteoporosis or related condition” could, at least in part, be explained by the higher incidence of this conditions among the female participants compared to the male participants (Table 4). Interestingly, in our study serum concentration of p,p -DDE, known to have antiandrogenic activity (Sonnenschein and Soto, 1998), was associated with increased risk of “Diabetes” and “Hypertension” in men, but not in women. p,p -DDE showed also a negative association, for men but not for women, with weight gain from 2005 to 2011. HCB, reported to inhibit aromatase activity in Flemish adolescents (Dhooge et al., 2011) and to lower oestradiol levels in female rats (Alvarez et al., 2000) and in monkeys (Foster et al., 1995), appeared to have, as to “Diabetes” and “Hypertension” in our study population, rather a stronger influence on women than on men. HCB showed also a negative association, for women but not for men, with weight gain from 2005 to 2011. PCB118 showed a quite strong positive association with “Diabetes” and “Hypertension” for men, whereas for women only with “Diabetes”. That we observed differences between men and women in association with internal exposure to organochlorine pollutants is consistent with the finding, in the same population, that genes were predominantly regulated in opposite directions in males and females (De Coster et al., 2013). Combined exposure to dioxin-like substances, hexachlorobenzene and cadmium Our data suggest that, at least in the Flemish population, combined exposure to these pollutants is associated with an increase in the risk of more diseases than exposure to only one of these pollutants. Possibly dioxin-like substances, hexachlorobenzene and cadmium are in Flanders among the most important pollutants in terms of adverse health effects. Strengths and limitations It is evident that a study like this cannot provide information on causal relations. Further limitations of the study comprise reliance on self-reported data (not limited to doctor-diagnosed diseases) collected through a questionnaire that was not validated. Strengths of the study comprise information on different pathologies, a quite high sample size and response rate, the fact that the population whose participation was requested was representative of the eight Flemish regions included in the study, and the fact that much information was available on the non-participants in the follow-up

study, showing that characteristics and exposures of participants and non-participants were quite similar. As to the “multipollutant science” aspect of our study as presented in this paper a main limitation is the fact that we used classical statistical methods. Probably the use of more advanced complex statistical methods (as reviewed by Billionnet et al., 2012) could yield even more interesting results. Conclusion Our findings suggest that exposure to pollutants can lead to an important increase in the risk of diseases such as cancer, diabetes and hypertension, but that some pollutants may possibly also decrease the risk of some health problems. In view of the fact that our observations of protective effects of marker PCBs and p,p DDE are in contradiction with some published data and are as yet not confirmed by other studies, it is prudent not to take them for granted at this stage. Acknowledgement The studies of the Flemish Centre of Expertise on Environment and Health are commissioned, financed and steered by the Ministry of the Flemish Community (Department of Economics, Science and Innovation; Flemish Agency for Care and Health; and Department of Environment, Nature and Energy). Isabelle Sioen is financially supported by the Research Foundation – Flanders (Grant n◦ : 1.2.683.11.N.00). The authors thank Prof Dr Marc Elskens for statistical advice. References Akesson, A., Barregard, L., Bergdahl, I.A., Nordberg, G.F., Nordberg, M., Skerfving, S., 2014. Non-renal effects and the risk assessment of environmental cadmium exposure. Environ. Health Perspect. 122, 431–438. Alonso-Magdalena, P., Quesada, I., Nadal, A., 2011. Endocrine disruptors in the etiology of type 2 diabetes mellitus. Nat. Rev. Endocrinol. 7, 346–353. Alvarez, L., Randi, A., Alvarez, P., Piroli, G., Chamson-Reig, A., Lux-Lantos, V., Kleiman de Pisarev,.D., 2000. Reproductive effects of hexachlorobenzene in female rats. J. Appl. Toxicol. 20, 81–87. Arrebola, J.P., Pumarega, J., Gasull, M., Fernandez, M.F., Martin-Olmedo, P., MolinaMolina, J.M., Fernandez-Rodriguez, M., Porta, M., Olea, N., 2013. Adipose tissue concentrations of persistent organic pollutants and prevalence of type 2 diabetes in adults from Southern Spain. Environ. Res. 122, 31–37. Becher, H., Steindorf, K., Flesch-Janys, D., 1998. Quantitative cancer risk assessment for dioxins using an occupational cohort. Environ. Health Perspect. 106 (2), 663–670. Billionnet, C., Sherrill, D., Annesi-Maesano, I., 2012. Estimating the health effects of exposure to multi-pollutant mixture. Ann. Epidemiol. 22, 126–141. Codru, N., Schymura, M.J., Negoita, S., Rej, R., Carpenter, D.O., 2007. Diabetes in relation to serum levels of polychlorinated biphenyls and chlorinated pesticides in adult Native Americans. Environ. Health Perspect. 115, 1442–1447. Colditz, G.A., Willett, W.C., Stampfer, M.J., London, S.J., Segal, M.R., Speizer, F.E., 1990. Patterns of weight change and their relation to diet in a cohort of healthy women. Am. J. Clin. Nutr. 51, 1100–1105. Covaci, A., Schepens, P., 2001. Simplified method for determination of organochlorine pollutants in human serum by solid-phase disk extraction and gas chromatography. Chemosphere 43, 439–447. Crain, C.M., Kroeker, K., Halpern, B.S., 2008. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315. Cranmer, M., Louie, S., Kennedy, R.H., Kern, P.A., Fonseca, V.A., 2000. Exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is associated with hyperinsulinemia and insulin resistance. Toxicol. Sci. 56, 431–436. De Coster, S., Koppen, G., Bracke, M., Schroijen, C., Den, H.E., Nelen, V., Van De Mieroop,.E., Bruckers, L., Bilau, M., Baeyens, W., Schoeters, G., Van Larebeke, N., 2008. Pollutant effects on genotoxic parameters and tumor-associated protein levels in adults: a cross sectional study. Environ. Health 7, 26. De Coster, S., Van Larebeke, N., 2012. Endocrine-disrupting chemicals: associated disorders and mechanisms of action. J. Environ. Public Health 2012, 713696. De Coster, S., van Leeuwen, D.M., Jennen, D.G., Koppen, G., Den Hond, E., Nelen, V., Schoeters, G., Baeyens, W., van Delft, J.H., Kleinjans, J.C., van Larebeke, N., 2013. Gender-specific transcriptomic response to environmental exposure in Flemish adults. Environ. Mol. Mutagen. 54, 574–588. Dhooge, W., Den Hond, E., Koppen, G., Bruckers, L., Nelen, V., Van de Mieroop, E., Bilau, M., Croes, K., Baeyens, W., Schoeters, G., van Larebeke, N., 2011. Internal exposure to pollutants and sex hormone levels in Flemish male adolescents in

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