obesity | VOLUME 19 NUMBER 4 | apRiL 2011 709
nature publishing group
In recent years, the prevalence of obesity, defined as a BMI of
>30 kg/m2, has reached alarming proportions with 30–80%
of the European adult population currently being over-
weight (BMI >25 kg/m2) and obesity affecting up to a third
of the population (1). Obesity and overweight are known to
adversely affect health and to impact the risks and prognosis
for a number of serious medical conditions such as type 2
diabetes and coronary heart disease (1,2). Traditionally, the
increase in obesity is attributed to an increased calorie intake
and a concomitant significant reduction in physical activity
and energy expenditure (3). However, evidence has emerged
recently that other mechanisms might be involved. Among
different hypotheses—such as gut bacterial overgrowth (4),
shortness of sleep (5), and viral theories (6)— environmental
factors, the so-called endocrine-disrupting chemicals, might
also affect or cause changes in fat mass and subsequent
endocrine-disrupting chemicals. They include polychlori-
nated biphenyls (PCBs) and organochlorine pesticides, such
as pp-dichloro-diphenyltrichloroethane and its major metab-
olite dichloro-diphenyl-dichloroethylene (pp-DDE), and
β-hexachlorocyclohexane (βHCH). Organochlorine pesti-
cides were abundantly used pesticides until the 1960s, whereas
PCBs were used since the 1930s on a worldwide scale for vari-
ous industrial purposes, such as dielectric fluids in electrical
capacitors, transformers, and hydraulic systems. More than
100 individual PCB congeners have been identified in com-
mercial mixtures, whose chemical and toxicological properties
are related to the number and position of the chlorine atoms.
Despite the ban on their use in the United States and Europe
since the 1970s, their stability, resistance to degradation, and
lipophilicity has lead to significant bioaccumulation in most
compartments of the ecosystem and human tissues (9–12).
This bioaccumulation leads to an ongoing human exposure to
organic pollutants (POPs) are known
Obesity and persistent Organic pollutants:
possible Obesogenic Effect of Organochlorine
pesticides and polychlorinated Biphenyls
Eveline Dirinck1, Philippe G. Jorens1, Adrian Covaci2, Tinne Geens2, Laurence Roosens2, Hugo Neels2,
Ilse Mertens1 and Luc Van Gaal1
Persistent organic pollutants (POPs) are endocrine-disrupting chemicals associated with the development of the
metabolic syndrome and type 2 diabetes. In humans, little is known about their role in the potential origin of obesity.
This study aims to assess the associations between serum levels of POPs and the prevalence of obesity in a cohort
of obese and lean adult men and women. POP serum samples were investigated cross-sectionally in 98 obese and 47
lean participants, aged ≥18 years. Serum samples were analyzed for the presence of polychlorinated biphenyl (PCB)
congeners 153, 138, 180, and 170 and for the organochlorine pesticides, dichloro-diphenyl-dichloroethylene (pp-DDE),
and β-hexachlorocyclohexane (βHCH). We established a significant negative correlation between BMI, waist, fat
mass percentage, total and subcutaneous abdominal adipose tissue, and serum levels of PCB 153, 180, 170, and the
sumPCBs. For βHCH, we demonstrated a positive correlation with BMI, waist, fat mass percentage, and total and
subcutaneous abdominal adipose tissue. PCBs 180, 170, and the sum of PCBs correlated significantly negative with
homeostasis model assessment for insulin resistance (HOMAIR). βHCH correlated significantly positively with HOMAIR.
A strong correlation was established between all POP serum levels and age. We established a positive relationship
between high serum levels of βHCH and BMI and HOMAIR, whereas serum PCB levels were inversely correlated with
BMI and HOMAIR. Combined, these results suggest that the diabetogenic effect of low-dose exposure to POPs might
be more complicated than a simple obesogenic effect.
Obesity (2011) 19, 709–714. doi:10.1038/oby.2010.133
1Department of Endocrinology, Diabetology and Clinical Pharmacology, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium; 2Toxicology Centre,
University of Antwerp, Antwerp, Belgium. Correspondence: Eveline Dirinck (email@example.com)
Received 3 March 2010; accepted 10 May 2010; published online 17 June 2010. doi:10.1038/oby.2010.133
VOLUME 19 NUMBER 4 | apRiL 2011 | www.obesityjournal.org
POPs through a variety of pathways, but the most important is
dietary intake (13). So the present concentration in serum of
POPs reflects both a release from fat storage compartments as
well as an uptake from present exposure.
Recent epidemiological data suggest an association between
POPs burden, diabetes, and the metabolic syndrome (14–16).
The metabolic syndrome, characterized by a cluster of meta-
bolic disorders including central obesity, glucose intolerance,
dyslipidemia, and hypertension, is a known risk factor for the
development of diabetes and cardiovascular disease. A sig-
nificant association between the presence of POPs and the
metabolic syndrome has previously been established (15,17).
Moreover, obesity is a key factor in this metabolic syndrome,
as it is a known risk factor for the development of insulin resist-
ance and diabetes (18).
PCBs are hormonally active substances, mimicking the
action of natural hormones such as the thyroid hormone
and estrogens (19). Low levels of PCB 77 increase adipocyte
differentiation, promote the expression of proinflammatory
cytokines, and augment the expression of the peroxisome
proliferator–activated receptor γ, a key promotor in regulat-
ing cell energy homeostasis (20,21). These data also indicate
that even low-level exposure to PCBs, as observed today in
the human population, might contribute to the development
of obesity (19–21). Mullerova et al. showed a negative correla-
tion between PCB 153 and adiponectin in obese women, thus
suggesting a possible suppression of adiponectin release or
production by PCB 153 (ref. 22).
The aim of this study was to assess the associations between
serum levels of POPs and the prevalence of obesity in a Belgian
cohort of lean and obese men and women.
A cohort of 98 obese men and women were retrospectively selected
from the database of patients visiting the weight management clinic of
the Department of Endocrinology, Diabetology and Metabolism of the
Antwerp University Hospital between 1998 and 2007. To be included,
BMI had to be ≥30 kg/m2. All subjects were ≥18 years. In the obese
population, 12 subjects (12.2%) were type 2 diabetics.
A control group of 46 men and women, matched by age and sex, was
recruited from hospital staff and volunteers during the same time period.
Their BMI was in the normal range between 18 and 25 kg/m2. In the
control population, one subject was diagnosed having type 2 diabetes.
The subject characteristics are described in Table 1.
This study was approved by the ethical committee of the Antwerp
University Hospital and all participants gave their informed consent.
All anthropometric measurements were performed in the morning with
patients in fasting conditions and undressed. Height was measured to the
nearest 0.5 cm and body weight was measured with a digital scale to the
nearest 0.1 kg. Obesity was defined as a BMI ≥30 kg/m2. Waist circum-
ference was measured at the mid-level between the lower rib margin and
the iliac crest. Hip circumference was measured at the level of the tro-
chanter major and the waist-to-hip ratio was calculated. Body composi-
tion was determined by bioimpedance analysis as described by Lukaski
and Bolonchuk (23), and fat mass % was calculated using the formula
of Deurenberg et al. (24). A computed tomography-scan at the L4–L5
level was performed to measure the amount of total abdominal adipose
tissue, visceral abdominal adipose tissue, and subcutaneous abdominal
adipose tissue according to previously described methods (25).
Venous blood samples were obtained from fasting subjects from
an anticubital vein between 08.00 am and 01.00 pm into sterile BD
Vacutainer tubes (Plymouth, UK). Serum was centrifuged within 15 min
at 2,500–3,000 r.p.m. and stored in Eppendorf Safe lock tubes at −80 °C
during the study period. An oral glucose tolerance test was performed
with 75 g of glucose, with blood samples taken to determine glucose and
insulin in the fasting state and 2 h after the glucose load. Analysis was
performed at the Antwerp University Hospital laboratory. Glucose was
measured using dry chemistry and reflectometry on a Vitros Fusion.
Insulin was determined with the Cobas method on a Modular 170 by
Roche. Diabetes was classified according to World Health Organization
criteria 1998 (26). The homeostasis model assessment (HOMA) was
used to calculate insulin resistance as described previously (27).
determination of PoPs
Analyses of POPs were performed at the Toxicology Centre (University
of Antwerp). The samples were analyzed for the PCB congeners CB 153,
CB 138, CB 180, CB 170, together with pp-DDE and βHCH. These POPs
are very persistent and are indicators for background (dietary) expo-
sure (28). The analytical method was based on the method described by
Covaci and Schepens (29) and used with minor modifications for low
serum volumes (up to 1 ml). Serum samples were extracted with solid-
phase extraction and analyzed by gas chromatography–mass spectro-
metry. POP serum levels are expressed in ng/g lipids. Lipid-normalized
concentrations were calculated using the formula proposed by Bernert
et al. (30).The limit of quantifications for the analyzed POPs ranged
between 4 and 12 ng/g lipids.
Statistical calculations were performed using SPSS, version 16.0 (SPSS,
Chicago, IL). Levels below the limit of quantification were entered in
the database as 50% of limit of quantification. Normality of distribution
was verified using the Kolmogorov–Smirnov test. Age, BMI, fasting and
2-h postprandial glucose and insulin, and the concentrations of POPs
were not normally distributed within the entire, obese, and lean groups.
Therefore, Mann–Whitney U test and Spearman rank correlation were
used and results are presented as median, with minimal and maximal
values. Transformation with the natural logarithm, log10, or square root
did not alter the distribution (data not shown). Because PCBs were
always used as a mixture of different congeners, we summed the con-
centrations of all four PCBs (sumPCB). Other variables were normally
distributed so results are presented as mean ± s.d. with minimal and
maximal values. Results were considered significant at P < 0.05.
The characteristics of both study groups are summarized in
Table 1. Age and sex distribution are identical between obese
and lean individuals, with a mean age of 40 years. The lowest
BMI was 18.8 kg/m2, so no underweight participants were
included. Abdominal adiposity is more prevalent among obese
participants, as indicated by the higher waist circumference
and the waist-to-hip ratio.
POP serum levels of the obese and control population
revealed a significantly different serum concentration for PCB
153, 180, 170, sumPCB, and βHCH (Table 2).
Analyzing the correlation between weight and POP serum
levels, a significant inverse relationship with PCB 153, 138, 180,
170, and sumPCB was found (Table 3). BMI and βHCH serum
levels correlated significantly positive, whereas a distinctly
obesity | VOLUME 19 NUMBER 4 | apRiL 2011 711
negative correlation was seen for PCB 153, 180, 170, and
sumPCB. Because females and males differ in body fat mass
percentage and body fat mass distribution, the correlation
between waist and fat mass percentage and serum POPs levels
was calculated. In the entire group, this analysis revealed a sig-
nificant positive correlation between waist and βHCH, whereas
a significant negative correlation was detected between waist
and PCB 153, 180, 170, and sumPCB. The same pattern was
established for the correlation between fat mass percentage
and POP levels in the entire group. After dividing the entire
group according to sex, βHCH correlates significantly positive
and PCB 180 and 170 correlate significantly negative with waist
and fat mass percentage in the male subgroup. In the female
subgroup, fat mass percentage correlated significantly inverse
with all four PCBs and their sum, but not with βHCH. Waist in
females correlated significantly with βHCH, and inversely with
PCB 153, 180, 170, and the sumPCB (Table 4). When analyz-
ing the abdominal fat distribution in more detail, it is notewor-
thy that the significant negative correlation between the PCBs
and abdominal fat was almost solely due to the subcutaneous
fat mass. In contrast, βHCH correlated significantly positive
with both subcutaneous and visceral abdominal fat. We could
not establish a significant correlation between fasting glucose
and any of the POPs levels. PCB 180, 170, and sumPCB did
correlate in a significantly inverse manner with fasting insulin
and HOMA for insulin resistance (HOMAIR), whereas βHCH
correlated positively with HOMAIR, fasting insulin, and 2 h
postprandial glucose and insulin.
We established a strong positive correlation between all POPs
serum levels and age (Table 2). Our data even indicated a six-
fold increase of the sum of PCB serum concentration between
the youngest and oldest subjects in our population (Figure 1).
In the group with individuals aged <25 years, the mean sum of
PCB serum concentration was 48.5 ng/g lipids, which increased
to 315.8 ng/g lipids for individuals aged >50 years.
Repetition of these analysis with the levels of POPs below
limit of quantification as zero, revealed statistically identical
table 1 subjects characteristics
All subjects (n = 144)Obese subjects (n = 98)Lean subjects (n = 46)
Diabetes 13/14412/98 1/46
Age (years)40 (21–60)40 (21–52) 39 (23–60)
BMI (kg/m2) 37 (19–55)40 (35–55) 23 (19–25)
Waist (cm) 108 ± 22 (63–150)122 ±11 (99–150)79 ± 6.7 (63–97)
WHR0.94 ± 0.13 (n = 142) (0.7–1.4)0.99 ± 0.11 (n = 97) (0.8–1.4)0.81 ± 0.06 (n = 45) (0.7–1.0)
FM%41 ± 14 (n = 140) (9–61)49 ± 7 (n = 95) (30–61)24 ± 8 (n = 45) (9–41)
TAT (cm2)646 ± 304 (n = 142) (56–1,310)830 ± 139 (578–1,310) 234 ± 89 (n = 44) (56–384)
VAT (cm2)149 ± 90 (11–519) 189 ± 78 (58–519)60 ± 29 (n = 44) (11–138)
SAT (cm2)496 ± 242 (n = 142) (24–1,026)641 ± 117 (415–1,026) 174 ± 80 (n = 44) (24–336)
Gluc 0 (mg/dl)84 (n = 140) (64–137)84 (n = 95) (64–137)83 (n = 45) (73–98)
Gluc 120 (mg/dl)113 (n = 137) (52–275)135 (n = 95) (53–275) 88 (n = 42) (52–232)
Ins 0 (µU/ml)14 (n = 141) (0.2–134)17 (n = 96) (0.3–134)6 (n = 45) (0.2–42.0)
Ins 120 (µU/ml) 61 (n = 134) (2.6–653) 94 (n = 93) (2.6–653) 25 (n = 41) (2.9–230))
Age, BMI, fasting and 2-h postprandial glucose and insulin are presented as median (minimum − maximum), other variables are presented as mean ± s.d.
(minimum − maximum).
FM, fat mass; Gluc 0, initial glucose; Gluc 120, glucose after 2 h; Ins 0, initial insulin; Ins 120, insulin after 2 h; SAT, subcutaneous abdominal adipose tissue; TAT, total
abdominal adipose tissue; VAT, visceral abdominal adipose tissue; WHR, waist-to-hip ratio.
table 2 concentrations of investigated PoPs (ng/g lipid) presented as median (minimum − maximum)
Entire group (n = 144) Obese group (n = 98)Control group (n = 46)
PCB 153 80 (9–331)73 (9–165)95 (24–331)
P < 0.05; r = −0.26
PCB 13842 (2–217)37 (2–108) 49 (12–217)
P = 0.065; r = −0.15
PCB 18057 (2–218)44 (2–103) 75 (14–218)
P < 0.001; r = −0.41
PCB 17013 (2–73.8)11 (2–39)22 (2–74)
P < 0.001; r = −0.32
SumPCB191 (24–827)176 (24.0–412.2)246 (63.1–826.8)
P < 0.001; r = −0.30
pp-DDE205 (30.2–1,073.2)209 (30.2–1,010.8)205 (43–1,073)
P = 0.97; r = −0.002
βHCH 19 (2–200)24 (1.9–200)12 (2–85)
P < 0.001; r = −0.30
Mann–Whitney U test was used to detect differences in serum POP levels between the obese and control group.
βHCH, β-hexachlorocyclohexane; DDE, dichloro-diphenyl-dichloroethylene; PCB, polychlorinated biphenyl; POP, persistent organic pollutants.
VOLUME 19 NUMBER 4 | apRiL 2011 | www.obesityjournal.org
In recent years, endocrine-disrupting chemicals have emerged
as a novel cause contributing to the worldwide epidemic of dia-
betes. A strong link between POPs and disorders of glucose
metabolism has been established in several populations
(16,31–34). Obesity is a known risk factor for the development
of type 2 diabetes. In this study, the relationship between POP
serum levels and obesity was assessed.
We established a significant difference in BMI according to
serum levels of PCB 153, 180, 170, sumPCB, and βHCH. As BMI
increased, levels of PCB 153, 180, and 170 declined. The inverse
was seen for βHCH. Hue et al. (35) did not detect a correla-
tion between the serum levels of PCB 170 and BMI, although a
positive relation was seen for PCB 180. In a Japanese study, the
BMI was not associated with POPs, but a positive association
was described between BMI and the dioxin-like PCB group.
Unfortunately, PCB 170, 180, and βHCH were not included in
their study (17). Interestingly, we found a negative association
between BMI and all PCBs, except PCB 138. This inverse rela-
tionship between BMI and serum concentrations of PCBs has
been established previously by Agudo et al. (10) and Wolff et al.
(36). In a Spanish population, Agudo et al. (10) found that obese
individuals (BMI >30 kg/m2) had lower PCB serum concentra-
tion compared to the lean group. In contrast, the group with
a BMI between 25 and 30 kg/m2 showed higher PCB concen-
trations. A potential explanation may be found in the dilution
capabilities of the PCBs: as these lipophilic contaminants are
preferably stored in adipose tissue, a higher percentage of body
fat will lead to fast and efficient storage, with lower serum levels
as a consequence. Indeed, a significant negative correlation
between serum levels of PCB 153, 170, 180, and sumPCB and
table 4 spearman rank correlations between PoPs and waist
and fat mass percentage according to gender
Males (n = 73) Females (n = 71)
Waist FM% WaistFM%
PCB 153 −0.044−0.118−0.265** −0.430**
PCB 1380.025−0.031 −0.208−0.337**
PCB 180−0.244* −0.3433**−0.315** −0.506**
PCB 170−0.261* −0.245* −0.237*−0.388**
pp-DDE0.033 0.067 0.039−0.113
βHCH 0.433**0.388**0.361** 0.030
βHCH, β-hexachlorocyclohexane; DDE, dichloro-diphenyl-dichloroethylene;
FM, fat mass; PCB, polychlorinated biphenyl.
n = 46
y = 9.9x − 108
n = 98
y = 7.7x − 127
r = 0.599
Fit line control
Fit line total
Fit line obese
r = 0.588
r = 0.738
y = 8.4x − 121
Serum PCB (ng/g lipids)
20 30 40
Figure 1 Relationship between age and sumPCB serum levels in the
entire group (n = 144).
table 3 spearman rank correlations between PoPs and measures of obesity and glucose metabolism in the entire group (n = 144)
PCB 153PCB 138PCB 180PCB 170SumPCBspp-DDE
Gluc 1200.0400.059−0.0030.011 0.0330.150 0.280**
Ins 0 −0.185* −0.142−0.307** −0.235** −0.223**−0.067 0.205*
Ins 120 −0.122−0.085 −0.165−0.113−0.130−0.0310.230**
βHCH, β-hexachlorocyclohexane; DDE, dichloro-diphenyl-dichloroethylene; FM, fat mass; Gluc 0, initial glucose; Gluc 120, glucose after 2 h; HOMAIR, homeostasis
model assessment for insulin resistance; Ins 0, initial insulin; Ins 120, insulin after 2 h; PCB, polychlorinated biphenyl; POP, persistent organic pollutants; SAT, subcutane-
ous abdominal adipose tissue; TAT, total abdominal adipose tissue; VAT, visceral abdominal adipose tissue; WHR, waist-to-hip ratio.
*Correlation significant with P < 0.05; **correlation significant with P < 0.001.
obesity | VOLUME 19 NUMBER 4 | apRiL 2011 713
fat mass percentage was detected, which supports this hypothe-
sis. A significant negative association between PCB serum levels
and the amount of abdominal, subcutaneous abdominal fat in
particular, seems to suggest that PCBs stored in subcutaneous
fat, are less easily diluted into the blood stream. A confound-
ing factor may be the fact that the elimination time of PCBs in
obese individuals might be different from that observed in lean
individuals. Flesch-Janys et al. (37) indeed showed that individ-
uals with higher BMI have reduced dioxin clearance, although
this has not been clearly demonstrated for PCBs.
For βHCH, a positive relation was observed with BMI.
Jakszyn et al. (38) also found a positive relationship between
BMI and serum βHCH concentration in a Spanish population,
which was not confirmed by others (35). Because βHCH is the
most hydrophilic of all substances analyzed in this study, it is
expected that it will be more readily detectable in serum. Jung
et al. (39) established that βHCH is eliminated more slowly in
individuals with a higher percentage of body fat. A significantly
positive correlation was observed between fat mass percent-
age and βHCH in our study. In the current population, we did
not detect any difference in BMI according to pp-DDE serum
levels. Others found a significant difference in BMI, which
might be due to the older age of the participants (35). Recently,
Karmaus et al. reported a positive relationship between mater-
nal DDE levels and BMI in adult female offspring (40).
In our study, no data were collected on other known obesogenic
factors, such as sedentary lifestyle, diet, family history of obesity,
or obesogenic medication. Moreover, the design of the study is
cross-sectional. Therefore, a causal relationship between serum
POPs and obesity is difficult to determine.
Our data confirm the influence of POPs on glucose metabo-
lism, with a statistically significant higher insulin resistance (as
assessed by HOMAIR) with higher serum βHCH levels. PCB
180, 170, and sumPCB are negatively correlated with HOMAIR.
As previously discussed, higher PCB serum levels were found
in the group with a lower BMI, thus indicating that the
endocrine-disrupting effect of PCBs might involve a different
pathway than the classical insulin resistance inducing effects of
obesity. Henriksen et al. (41) described an increased fasting glu-
cose with higher levels of dioxin in a large cohort of US veterans
exposed to Agent Orange. We could not demonstrate a signifi-
cant effect of serum levels of the sum of PCBs on fasting glucose.
Our results are comparable with those of Lee et al., who found
a strong association between organochlorine pesticides (such
as βHCH) and HOMAIR in the National Health and Nutrition
Examination Survey study (42). We did not find any statisti-
cally significant differences in fasting glucose or HOMAIR for
pp-DDE. In our study sample, 13 participants were diagnosed
with diabetes, of which one was a participant in the lean group.
We could not detect a difference in serum POP levels between
obese participants with and without diabetes (data not shown).
Given the small number of diabetics, we estimate the group to
be too small to detect such difference.
Our data confirm the life-long accumulation of POP in the
human body. Previous studies have clearly demonstrated the
capability of POPs to accumulate in the human body throughout
lifespan (34,35,43). This positive relationship between age and
serum levels of POPs was previously observed in lean, obese,
and severely obese patients (35,44). Nichols et al. (44) even pre-
sented age-specific reference ranges for PCBs, based on the US
National Health and Nutrition Examination Survey 2001–2002
data. In their analysis, the mean summed PCB serum level in a
20- to 29-year-old population was 30 ng/g lipid weight, whereas
it was 163 ng/g lipid for 40- to 49-year-old participants, and up
to 302 ng/g lipid for 60- to 69-year-old individuals. In a recent
Spanish study (10), a mean serum concentration of 431 ng/g
lipid in an age group of 35–44 years of age was observed. In
the age group of 55–64, the mean serum concentration rose
to 498 ng/g lipid. Our data, as shown in Figure 1, also indi-
cate a sixfold increase of cumulative PCB burden between the
youngest and oldest subjects in our population. Exposure to
POPs has decreased in recent decades, due to the cessation of
production and/or use of several of the investigated products.
Together with the shorter duration of exposure, this can also
account for the significant difference in burden of endocrine
disruptors in younger vs. older obese and lean subjects.
There are some shortcomings associated with our study. Data
were collected over a large period of time, spanning almost
10 years. Environmental PCB burden is known to have dropped
substantially over a decade. We did not register information
about possible professional exposure. It would be very inter-
esting to investigate the relationship between concentration
of POPs in serum and fat. Given the data from the literature
(19–22), in particular the data on the influence via peroxisome
proliferator–activated receptor γ, an effect on fat cell signal-
ing by POPs seems indeed possible. In the present population,
however, no fat samples were collected. It is therefore impossi-
ble to make an assumption on the concentration of POPs in fat
and their effect on energy homeostasis in the fat cell. None of
our groups displayed a normal distribution of POP levels, thus
making the use of nonparametric tests necessary.
In conclusion, we were able to show a positive relationship
between the serum concentration of the less lipophilic endocrine
disruptor βHCH and BMI, whereas we found a negative relation-
ship between the serum level of more lipophilic PCBs and BMI.
We could not find a statistically significant relationship between
serum levels of pp-DDE and BMI. Our study is concordant with
the previous reports describing a positive relationship between
serum levels of βHCH and insulin resistance, whereas we addi-
tionally found a negative relationship between serum levels of
PCBs and insulin resistance. Combined, these results suggest
that the diabetogenic effect of low-dose exposure to POPs might
be more complex than a simple obesogenic effect. The exact
mechanisms of influence of POPs on body energy homeostasis
remain largely unknown. Given the current worldwide epidemic
of obesity, the possible effects of endocrine disruptors on body
weight are an imperative field of future research.
T.G. and a.C. acknowledge the Funds for Scientific Research Flanders for
a phD fellowship and a postdoctoral fellowship, respectively. The study
was supported by a GOa project (Fa020000/2/3565) of the University of
VOLUME 19 NUMBER 4 | apRiL 2011 | www.obesityjournal.org
The authors declared no conflict of interest.
© 2010 The Obesity Society
1. Mokdad AH, Ford ES, Bowman BA et al. Prevalence of obesity, diabetes,
and obesity-related health risk factors, 2001. JAMA 2003;289:76–79.
2. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with
cardiovascular disease. Nature 2006;444:875–880.
3. Prentice AM. Overeating: the health risks. Obes Res 2001;9 Suppl 4:
4. Turnbaugh PJ, Hamady M, Yatsunenko T et al. A core gut microbiome in
obese and lean twins. Nature 2009;457:480–484.
5. Cappuccio FP, Taggart FM, Kandala NB et al. Meta-analysis of short sleep
duration and obesity in children and adults. Sleep 2008;31:619–626.
6. Vasilakopoulou A, le Roux CW. Could a virus contribute to weight gain?
Int J Obes (Lond) 2007;31:1350–1356.
7. Baillie-Hamilton PF. Chemical toxins: a hypothesis to explain the global
obesity epidemic. J Altern Complement Med 2002;8:185–192.
8. Grün F, Blumberg B. Endocrine disrupters as obesogens. Mol Cell
9. Patterson DG Jr, Turner WE, Caudill SP, Needham LL. Total TEQ reference
range (PCDDs, PCDFs, cPCBs, mono-PCBs) for the US population 2001-
2002. Chemosphere 2008;73:S261–S277.
10. Agudo A, Goñi F, Etxeandia A et al. Polychlorinated biphenyls in Spanish
adults: determinants of serum concentrations. Environ Res 2009;109:
11. Den Hond E, Govarts E, Bruckers L, Schoeters G. Determinants of
polychlorinated aromatic hydrocarbons in serum in three age classes–
Methodological implications for human biomonitoring. Environ Res
12. La Rocca C, Alivernini S, Badiali M et al. TEQ(S) and body burden
for PCDDs, PCDFs, and dioxin-like PCBs in human adipose tissue.
13. Bilau M, Matthys C, Baeyens W et al.; Flemish Center of Expertise for
Environment and Health. Dietary exposure to dioxin-like compounds in
three age groups: results from the Flemish environment and health study.
14. Rylander L, Rignell-Hydbom A, Hagmar L. A cross-sectional study of the
association between persistent organochlorine pollutants and diabetes.
Environ Health 2005;4:28.
15. Lee DH, Lee IK, Porta M, Steffes M, Jacobs DR Jr. Relationship between
serum concentrations of persistent organic pollutants and the prevalence
of metabolic syndrome among non-diabetic adults: results from the
National Health and Nutrition Examination Survey 1999-2002. Diabetologia
16. Fierens S, Mairesse H, Heilier JF et al. Dioxin/polychlorinated biphenyl body
burden, diabetes and endometriosis: findings in a population-based study in
Belgium. Biomarkers 2003;8:529–534.
17. Uemura H, Arisawa K, Hiyoshi M et al. Prevalence of metabolic
syndrome associated with body burden levels of dioxin and related
compounds among Japan’s general population. Environ Health Perspect
18. Adler A. Obesity and target organ damage: diabetes. Int J Obes Relat Metab
Disord 2002;26 Suppl 4:S11–S14.
19. McKinney JD, Waller CL. Polychlorinated biphenyls as hormonally active
structural analogues. Environ Health Perspect 1994;102:290–297.
20. Arsenescu V, Arsenescu RI, King V, Swanson H, Cassis LA. Polychlorinated
biphenyl-77 induces adipocyte differentiation and proinflammatory
adipokines and promotes obesity and atherosclerosis. Environ Health
21. Staels B. PPAR agonists and the metabolic syndrome. Therapie
22. Mullerova D, Kopecky J, Matejkova D et al. Negative association
between plasma levels of adiponectin and polychlorinated biphenyl 153
in obese women under non-energy-restrictive regime. Int J Obes (Lond)
23. Lukaski HC, Bolonchuk WW. Estimation of body fluid volumes using
tetrapolar bioelectrical impedance measurements. Aviat Space Environ Med
24. Deurenberg P, Weststrate JA, Hautvast JG. Changes in fat-free mass during
weight loss measured by bioelectrical impedance and by densitometry.
Am J Clin Nutr 1989;49:33–36.
25. van der Kooy K, Seidell JC. Techniques for the measurement of visceral fat:
a practical guide. Int J Obes Relat Metab Disord 1993;17:187–196.
26. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes
mellitus and its complications. Part 1: diagnosis and classification of
diabetes mellitus provisional report of a WHO consultation. Diabet Med
27. Matthews DR, Hosker JP, Rudenski AS et al. Homeostasis model
assessment: insulin resistance and beta-cell function from fasting plasma
glucose and insulin concentrations in man. Diabetologia 1985;28:412–419.
28. Wingfors H, Seldén 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.
29. Covaci A, Schepens P. Simplified method for determination of
organochlorine pollutants in human serum by solid-phase disk extraction
and gas chromatography. Chemosphere 2001;43:439–447.
30. Bernert JT, Turner WE, Patterson DG Jr, Needham LL. Calculation
of serum “total lipid” concentrations for the adjustment of persistent
organohalogen toxicant measurements in human samples. Chemosphere
31. Jørgensen ME, Borch-Johnsen K, Bjerregaard P. A cross-sectional study
of the association between persistent organic pollutants and glucose
intolerance among Greenland Inuit. Diabetologia 2008;51:1416–1422.
32. Wang SL, Tsai PC, Yang CY, Leon Guo Y. Increased risk of diabetes and
polychlorinated biphenyls and dioxins: a 24-year follow-up study of the
Yucheng cohort. Diabetes Care 2008;31:1574–1579.
33. Codru N, Schymura MJ, Negoita S, Rej R, Carpenter DO; Akwesasne Task
Force on Environment. Diabetes in relation to serum levels of polychlorinated
biphenyls and chlorinated pesticides in adult Native Americans. Environ
Health Perspect 2007;115:1442–1447.
34. Lee DH, Lee IK, Jin SH, Steffes M, Jacobs DR Jr. Association between
serum concentrations of persistent organic pollutants and insulin resistance
among nondiabetic adults: results from the National Health and Nutrition
Examination Survey 1999-2002. Diabetes Care 2007;30:622–628.
35. Hue O, Marcotte J, Berrigan F et al. Plasma concentration of organochlorine
compounds is associated with age and not obesity. Chemosphere
36. Wolff MS, Britton JA, Teitelbaum SL et al. Improving organochlorine
biomarker models for cancer research. Cancer Epidemiol Biomarkers Prev
37. Flesch-Janys D, Becher H, Gurn P et al. Elimination of polychlorinated
dibenzo-p-dioxins and dibenzofurans in occupationally exposed persons.
J Toxicol Environ Health 1996;47:363–378.
38. Jakszyn P, Goñi F, Etxeandia A et al. Serum levels of organochlorine
pesticides in healthy adults from five regions of Spain. Chemosphere
39. Jung D, Becher H, Edler L et al. Elimination of beta-hexachlorocyclohexane
in occupationally exposed persons. J Toxicol Environ Health 1997;51:23–34.
40. Karmaus W, Osuch JR, Eneli I et al. Maternal levels of dichlorodiphenyl-
dichloroethylene (DDE) may increase weight and body mass index in adult
female offspring. Occup Environ Med 2009;66:143–149.
41. Henriksen GL, Ketchum NS, Michalek JE, Swaby JA. Serum dioxin and
diabetes mellitus in veterans of Operation Ranch Hand. Epidemiology
42. Lee DH, Lee IK, Steffes M, Jacobs DR Jr. Extended analyses of the
association between serum concentrations of persistent organic pollutants
and diabetes. Diabetes Care 2007;30:1596–1598.
43. Voorspoels S, Covaci A, Maervoet J, Schepens P. Relationship between
age and levels of organochlorine contaminants in human serum of a belgian
population. Bull Environ Contam Toxicol 2002;69:22–29.
44. 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 Part A 2007;70:1873–1877.