**7. Epidemiological data linking EDCs exposure to T2DM**

Male mice treated orally with 0.5-50 µg/kg bw TBT for 45 days demonstrated hepatic steatosis, hyperinsulinemia, hyperleptinemia and a reduction in hepatic adiponectin levels, in a dose-

Similar results were obtained in adult Sprague-Dawley rats exposed for 28 days to crude salmon oil containing POPs [107]. The animals developed insulin resistance syndrome, abdominal obesity and hepatosteatosis, the contribution of POPs to insulin resistance being confirmed also by the same authors in cultured adipocytes. These findings are important since POPs are accumulating in the lipid fraction of fish, and fish consumption represents the main

Also coplanar PCBs (e.g. PCB-77 and PCB-126), at dosage of 50 mg/kg orally, impaired glucose homeostasis in lean C57BL/6 mice and mitigate beneficial effects of weight loss on glucose homeostasis in obese mice [66], while inorganic arsenic (III) administered in the drinking water for 20 weeks, at doses of 25 or 50 ppm As/kg bw/day, impaired glucose tolerance in C57BL/6

In the animal studies mentioned before, not just blood glucose levels were investigated, but other markers of insulin regulation, such as HOMA-IR, pancreatic production of NO, SOD and CAT activity, in order to reflect the magnitude of the global disturbance. While most studies have demonstrated perturbations in insulin action, some of them have shown improved glucose tolerance or even hypoglycaemia. Acute exposure of adult male mice to high dosage of BPA (100 µg/kg bw/day) produced a rapid hyperinsulinemia based on significant increase in β-cell insulin content, as a direct result of BPA estrogenic properties [109], while sustained exposure to lower dosage (10 µg /kg bw/day) impaired glucose tolerance and reduced the hypoglycaemic effect of insulin, through a compensatory peripheral insulin resistance [54]. These results are easily correlated with non-monotonic dose response curve exhibited *in*

Taking into account that EDCs alter glucose homeostasis and endocrine pancreatic function not just in adult animals but also during pregnancy or in offspring, these effects were also investigated in pregnant animals. For example, prenatal exposure to high dosage of diisobutyl phthalate (600 mg/kg bw/day) from gestation Day 7 to Day 21 reduced plasma leptin and insulin levels in male and female offspring, complementary to sexual distrurbance [110]. Maternal glucose intolerance was observed in pregnant mice exposed to inorganic arsenic (V) at dosage of 9.6 mg/kg bw, this explaining the neural tube defects induced by arsenate [111].

In conclusion, all these examples regarding *in vivo* effects in animal models are highly suggestive, taking into account that the experimental exposure is very close or even similar with environmental human exposure. However, data on co-exposure are lacking, therefore new studies should focus on this issue, in order to reveal possible additive, synergistic or

dependent fashion, confirming PPARγ stimulation observed *in vitro* [106].

source of POP exposure to humans.

232 Treatment of Type 2 Diabetes

mice in a dose-dependent manner [108].

antagonistic effects exhibited by the mixtures.

*vitro* by BPA.

There is growing concern in the scientific community that EDCs may be contributing to the high incidence of diabetes, particularly in young people.

Epidemiological studies (as occupational or population-based studies) but also disasters tried to link, at least partially, the environmental exposure to EDCs with the development of T2DM.

We collected and compiled from a comprehensive scientific literature the most relevant epidemiological studies concerning the T2DM and exposure to EDCs like TCDD, arsenic, phthalates or BPA.

Disasters such as Seveso accident or exposure of military personnel during the Vietnam War and follow-up studies have suggested a link between TCDD exposure and a higher incidence of diabetes [112, 113, 114]. Other cross-sectional studies [115, 116] did not revealed such correlation, while longitudinal studies that have been conducted are inconsistent [117].

Some poisoning cases reported during late 1970s have involved contaminated rice oil with PCBs. PCB exposure was associated with an increased prevalence of diabetes in women [118]. Other prospective studies on PCB153 showed a positive association with T2DM, but taking into account the variation across studies, it did not allow a metaanalysis. For example, five studies used different diagnostic strategies and several approaches to address serum lipid levels [119]. In addition, the age varied between cohorts from 18 to 30 years [119] to 70 years [120] while gender was also inconsistent, exclusively female in one study [121], exclusively male in another [119] and mixed in the remaining studies [120; 122, 123]. The temporal and geographic variation among the studies induced significant differences in the exposure assessment especially on duration of exposures or on the composition of the mixtures. However, other variables must be considered in the interpretation of PCBs studies, such as the use of PCB153 as a surrogate for total PCBs or the lack of data regarding kinetics of different PCBs (especially on accumulation) that influence their current serum levels.

A closer evaluation of the cohorts described before revealed the non-monotonic exposureresponse relationships exhibited by PCBs: the risk of diabetes was significantly increased with small increases within the lower ranges of PCBs concentrations, but only slightly increased with significant increases in concentrations of PCBs. This non-monotonic relationship exhib‐ ited by PCBs in cohorts was also observed in brominated flame retardants studies, like those conducted on PBDE-153 [124], but not in BPA cohorts, where BPA urinary levels were associated with diabetes incidence in a dose-dependent manner [125].

Evaluation of studies conducted on EDCs (PCBs or TCDD) reveal that many of them focused specific populations (e.g. occupational studies or exposure through industrial accidents or disasters), so they might not reflect the actual risk of the general population. However, recent investigations were done on representative sampling of the US population, using data from the National Health and Nutrition Examination Survey (NHANES). For example, Lee et al. [126] reported strong and highly significant associations, among participants in the NAHNES study, between serum concentrations of POPs and the HOMA-IR insulin resistance values, after correction for age, sex, BMI, and waist circumference.


The Involvement of Environmental Endocrine-Disrupting Chemicals in Type 2 Diabetes Mellitus Development http://dx.doi.org/10.5772/59110 235


Abbreviations: 95% CI – confidence interval 95%; adjOR-adjusted odds ratio; adjPR-adjusted prevalence ratio; As-ar‐ senic; CEI, cumulative exposure index; OGTT-oral glucose tolerance test; Q-quintile; RR-relative risk; SMR-standar‐ dized mortality ratios; n.r. – not reported

#### **Table 3.** Association between arsenic and diabetes

**Study design Diagnosis Findings**

Self-report

Self-report prior to baseline

> Fasting blood glucose, OGTT

Death certificate

Death certificate

[n=1,185] Self-report adjOR=1.02

Fasting blood glucose, self-report, medication

Fasting blood glucose, self-report, medication

Fasting blood glucose, self-report, medication

**Study design Diagnosis Findings**

Self-report prior to baseline

[n=87] Not reported

[n=235] Hospital records

[n=117] Not reported

Cross-sectional [male 225 nonsmokers 209 smokers]

234 Treatment of Type 2 Diabetes

Cross-sectional [n=11,319]

Case–control [n=144 female]

Retrospective [41,282male 38,722 female]

Case–control

Retrospective [n=1,074 deaths]

Cross-sectional

Case–control

Cross-sectional

Cross-sectional [n=788]

Cross-sectional [n=1,279]

Cross-sectional [n=795]

Cross-sectional [n=11,319]

**(95% CI)**

Increased urinary As in nonsmoking diabetics

> adjOR=1.24 (0.82- 1.87)

Increased As in urine from diabetics

Male SMR =1.28 (1.18-1.37) Female SMR =1.27 (1.19- 1.35)

RR=0.87

RR=1.6

RR=1.098 (0.98- 1.231)

RR=1.09

(0.49 - 2.15)

adjOR=3.58

adjOR=2.60

adjOR=1.15

**(95% CI)**

adjOR=1.11 (0.73- 1.69)

(0.53 - 2.50) -

**High exposure (≥ 150 µg/L drinking water)**

**Low-to-moderate exposures (< 150 µg/L drinking water)**

**As in drinking**

n.r.

n.r.

16–272

**water (µg/L) Exposure Ref.**

0.1–864 41–92 (Q3) vs. 0.1–8 (Q1) µg

1.27–11.98 6 counties vs. state µg As/L

(0.5- 1.53) n.r. 75th vs. 25th percentile µg

(0.36- 7.16) n.r. Residence time within 1.6 km

(0.79- 1.49) n.r. 75th vs. 25th percentile µg

(1.18- 10.83) - 18 (≥ 80th) vs. 3.5 (≤ 20th

(1.12 - 6.03) - 7.4 (80th) vs. 1.6 (20th

**As in drinking**

Nonsmokers: 5.59 (diabetics) vs. 4.7 (nondiabetics) µg As/L Smokers: 7.27 (diabetics) vs. 5.41 (nondiabetics) µg As/L (urine)

As/L drinking water, CEI

4.13 (diabetics) vs. 1.48 (nondiabetics) µg As/L in urine

(drinking water)

As/L (urine)

(1 mi): ≥ 10 years vs. < 1 year

21–272 (range) vs. 16–38 (range) µg As/L (drinking water)

As/mL (plasma)

water)

percentile) µg As/L (urine)

percentile) µg As/L (urine)

12 (≥ 80th) vs. 2.7 ( ≤ 20th percentile) µg As/L (urine, not adjusted for creatinine)

µg As/L drinking water, CEI

**water (µg/L) Exposure Ref.**

0.1–864 176.2–864 (Q5) vs. 0.1–8 (Q1)

0–2,389 > 10 vs. < 2 µg As/L (well-

127

128

129

130

131

132

133

134

135

136

137

138

134

The correlation between the level of arsenic in drinking water and the incidence of T2DM was extensively investigated. The published cohorts were categorized based on the level of exposure (table 3) in order to identify the correlation between exposure and critical endpoints. In addition to diabetes, epidemiological studies have associated exposure to arsenic with other measures of disturbed glucose homeostasis, such as glucose tolerance or metabolic syndrome.

Preliminary analysis on the existing human data provide limited support for an association between arsenic and diabetes in populations exposed to relatively high levels (≥ 150 µg As/L in drinking water), but the evidence is insufficient to conclude that exposure to low to moderate level is associated with diabetes. However, a major gap is obvious. The measurement of arsenic in drinking water supplies, which was often used to assess arsenic exposure, is not appropriate to calculate the internal dose, taking into account individual variation in arsenic uptake and metabolism. Also, individual information on the duration and timing of exposure, which is critical, especially for estimating cumulative exposure, are missing.

Regarding phthalates, cohort studies were mainly focused on correlation between exposure and obesity and less on T2DM. However, those found were done on representative sampling of the US population, using data from NHANES. For example, Stahlhut et al. [145] investigated 1,292 adult US male participants in the NHANES 1999–2002 and revealed that urinary concentrations of three phthalate metabolites (mono-n-butyl phthalate, monobenzyl phthalate and monoethylphthalate) were associated with increased insulin resistance, assessed by HOMA-IR. In addition, phthalates levels were associated with increased waist circumference. A similar association between urinary phthalate metabolite concentrations, body mass index and waist circumference was found in another cross-sectional study of NHANES data [146]. However, considering the methodological limitations of the existing data, there is no sufficient evidence to conclude there is a correlation between phthalates and diabetes or obesity.

The epidemiological data on BPA and T2DM is less consistent compared with POPs, but is growing. There are two cross-sectional analyses of NHANES data 2003-2008 that reported a positive associations of BPA exposure (median 2.5 and 1.8 µg/l) with self-reported diagnosis of diabetes [125, 147].However,these analyseshave animportantweakness thatlimits theirvalue: the use of a single spot urine sample collected concurrent with the information on diagnosis of diabetes. The single spot sample reflects only recent BPA exposure, so cannot be extrapolated to longerperiod(like years ordecades) which is relevantforthedevelopment ofdiabetes.Other large cross-sectional studies on BPA in China provide conflicting data [148,149].

A closer evaluation of all epidemiological studies on EDCs reveals some weaknesses, such as the assessment of one compound as a surrogate for total mixture (in case of PCBs), the lack of data regarding kinetics, especially on accumulation in lipid-rich tissues (in case of POPs), limited type of biological material used for direct measurement EDCs (serum or urine) or environmental measurement which is not appropriate to calculate the internal dose (in case of As). Other caveats must be considered in the interpretation of studies, such as heterogeneity in the definition of diabetes or insulin resistance.
