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Ecotoxicology and Human Environmental Health

Relative Effect Potency Estimates for Dioxin-like Compounds in Pregnant Women with Gestational Diabetes Mellitus and Blood Glucose Outcomes Based on a Nested Case-control Study Xin Liu, Lei Zhang, Jingguang Li, Jun Wang, Guimin Meng, Min Chi, Yunfeng Zhao, and Yongning Wu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00988 • Publication Date (Web): 31 May 2019 Downloaded from http://pubs.acs.org on June 3, 2019

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Environmental Science & Technology

1

Relative Effect Potency Estimates for Dioxin-like Compounds in Pregnant

2

Women with Gestational Diabetes Mellitus and Blood Glucose Outcomes

3

Based on a Nested Case-control Study

4 5

Xin Liua,b, Lei Zhanga, Jingguang Lia,b,*, Jun Wangc, Guimin Mengd, Min Chia,e, Yunfeng Zhaoa,

6

Yongning Wu a,b

7 8

a NHC

9 10

Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety

Risk Assessment, Beijing, China b

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State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, China

12

c

13

d Beijing

14

e

Shenzhen Center for Chronic Disease Control, Shenzhen, China Fengtai Hospital obstetrics and gynecology, Beijing, China

Taiyuan Center for Disease Control and Prevention, Taiyuan, China

15 16

Corresponding author

17

Jingguang Li (Email: [email protected]; Telephone: +086-10-87720035; Fax: +086-10-

18

52165485). Postal address: Room 205, No.7, Panjiayuan Nanli, Chaoyang District, Beijing

19

100021, China.

20 21

1 ACS Paragon Plus Environment


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Abstract

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To improve the applicability of the toxic equivalents principle for human health risk assessment,

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systemic relative effect potencies (REPs) for dioxin-like compounds (DLCs) deriving from

25

human in vivo data are required. A prospective nested case-control study was performed to

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determine REPs from the human serum concentration of DLCs using gestational diabetes

27

mellitus (GDM) and fasting blood glucose (FBG) as the endpoints of concern.

28

concentration of 29 DLCs from 77 cases and 154 controls were measured.

29

regression were used to estimate the effects of individual congeners on GDM and FBG,

30

respectively.

31

coefficients, βi (DLCs)/βTCDD.

32

and FBG-REP, were established and largely agree with REPs from other human studies.

33

These human-serum REPs show much smaller variation compared to the 4 to 5 orders of

34

magnitude span in REPs database for the present WHO-TEF determination.

35

established REPs fitted well with WHO-TEFs, especially for polychlorinated dibenzo-p-

36

dioxins, furans.

37

could be used to improve health risk assessment of human body burden of DLCs.

38

Keywords: Dioxin-like compounds (DLCs); Relative effect potency (REP); Toxic equivalency

39

factor (TEF); Gestational Diabetes Mellitus (GDM); Fasting blood glucose (FBG)

Serum

Logistic and linear

The REPs based on GDM and FBG were calculated from the ratios of regression Two sets of consistent human serum-based REPs, i.e., GDM-REP

Moreover, the

These REPs reflecting real human exposure scenarios exhibited validity and

40


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41 42 43


TOC art

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1. Introduction

46

Polychlorinated dibenzo-p-dioxins, furans (PCDD/Fs) and dioxin-like polychlorinated

47

biphenyls (DL-PCBs) are a class of structurally related chemicals and commonly occur in the

48

environmental media and food products.1

49

distance transport and bioaccumulation, this notorious class of chemicals continuously generate

50

environmental health issues that capture public attention.

Due to environmental degradation-resistance, long-

51

Evaluating the risks for human health that are associated with PCDD/Fs and DL-PCBs

52

exposure has led to a proposal of using toxic equivalents (TEQs) methodology, in which each

53

compound has been assigned a toxic equivalency factor (TEF) by the World Health

54

Organization (WHO) that reflects its potency to exert an aryl hydrocarbon receptor (AhR)

55

involved biological or toxicological effect compared with the most potent congener, i.e.,

56

2,3,7,8-TCDD.2

57

effect potency (REP) database derived from in vivo rodent experiments and supported by in

58

vitro data.3

59

doses rather than concentrations measured in the blood or tissues (internal dose); however, this

60

has evoked wide concerns on the use of such “intake” TEFs for TEQ determinations in the

61

human body burden of dioxin-like compounds (DLCs).4

62

REP values may also be an important issue to consider.5

63

showed that various types of human cell models, both primary cells and carcinoma cell lines,

64

were much less sensitive to PCB126 than its assigned TEF value of 0.1.

65

expert panel at a recent WHO TEF meeting specified the need to assess whether systemic, as

66

well as human-specific, TEF values would lead to a more accurate human health risk

The present TEFs estimated by the expert meeting of WHO used a relative

It is important to realize that REPs from animal studies are based on oral intake

In addition, species differences in


For example, previous studies

6, 7

Therefore, an

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assessment for DLCs.3

68

project SYSTEQ was initiated, with the main objective of developing a set of in vivo human

69

systemic REPs.

70

from thyroid volume (TV), serum free thyroxine (FT4) concentrations, cytochrome P450 (CYP)

71

1A1 and CYP1B1 mRNA expression endpoints were established from this project.8

72

Additionally, considering the joint toxicological trend in real-life mixed exposure scenarios,

73

Larsson et al.9

74

DLCs at the same time and involved multiple AhR-mediated responses using multivariate

75

statistical analysis, which was also carried out under the EU SYSTEQ project.

76

developed CTF was comparable with the TEF scale and regarded as more valid than studying

77

each DLC congener one by one. Another two in vivo rodent models derived plasma- or tissue-

78

based REPs in EU SYSTEQ project indicated that the systemic REP values of a number of

79

DLC congeners may substantially deviate from the present WHO-TEF values.10,

80

Consequently, a greater number of human studies, involving a variety of health outcomes, are

81

warranted to promote estimating more reliable TEF for human health risk assessment.

In this context, the European Union (EU) 7th Framework Programme

Presently, two studies with four sets of human blood-based REPs derived

developed a consensus toxicity factor (CTF) approach that investigated all

The newly

11

82

In the present study, REPs for the systemic human serum concentration of DLC

83

components were determined in a population of pregnant women using gestational diabetes

84

mellitus (GDM) and fasting blood glucose (FBG) concentration as the outcomes of interest.

85

total of 17 PCDD/Fs and 12 DL-PCBs with halogen substituents on the 2,3,7,8-positions were

86

prospectively detected prior to GDM and FBG measurement to investigate the causal

87

relationship between DLC exposure and outcomes using a nested case-control study.

88

Thereafter, we used a regression-based approach that compared the outcome/concentration


A


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89

regression coefficients of the individual DLC congeners with the outcome/concentration

90

regression coefficient of the TCDD index to calculate REPs.

91

2. Materials and Methods

92

2.1 Study design and population

93

The participants of our study were recruited at Maternal and Child Health Hospital in

94

Xicheng district of Beijing, China.

95

control study which initialed from August 2013 to June 2015.

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enrolled in this study during their initial antenatal care visit in the study hospital.

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were eligible if they were free of pre-diabetes and a family history of diabetes.

98

duration of the study, a total of 439 pregnant women were recruited and agreed to donate a

99

blood sample between 9 and 13 weeks of gestation.

All pregnant women were informed of

100

the purpose of the study and provided signed consent.

The whole procedure of study had the

101

approval from the Ethics Committee of China National Center for Food Safety Risk Assessment.

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All eligible pregnant women in the cohort were routinely screened for GDM at 24–28 weeks of

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gestation.

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GDM across the entire study population.

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from the cohort as paired controls (mathcad by age: ± 2 years).

106

Our research was designed as a prospective nested caseHealthy pregnant women were Participants For the

At the end of the study period, a total of 77 pregnant women were diagnosed with For each case, two healthy women were selected

Maternal age was related to DLC exposure and GDM risk and was identified as a strong

107

confounding variable; consequently, this was used as a pair-matched factor.

108

the other potential confounders such as body mass index (BMI), cigarette and alcohol

109

consumption, parity, occupational exposure, and a family history of diabetes were obtained

110

from medical records or questionnaires.

Information for

All of the study pregnant women were found to be


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primiparas with no cigarette smoking and alcohol consumption during pregnancy, and were

112

free from occupational exposure.

113

as pregnancy body weight (kg) divided by height-squared (m2).

114

2.2 Blood collection and chemical analysis

115

The maternal BMI was calculated at the first prenatal visit

Fasting venous blood samples were collected from the participants by nurses during their

116

first trimester.

117

segregated and preserved at -40oC until further analysis.

118

and 154 pair-matched controls were selected to measure 17 PCDD/Fs and 12 DL-PCBs, as

119

designated by the WHO.

The method for measuring these DLC congeners in serum samples

120

is described elsewhere.12

In brief, each thawed serum sample (about 2 mL) was spiked with

121

13C-labeled

122

Ontario, Canada) and then mixed with 15 g of diatomite.

123

extracted by Accelerated Solvent Extraction (ASE) 350 (ThermoScientific, Sunnyvale,

124

California, USA).

125

and carbon column (CAPE Technologies, South Portland, Maine, USA).

126

were then concentrated to nearly dryness and then resuspended in 20 μL nonane.

127

recovery standards were spiked prior to instrumental analysis.

128

measured by a high-resolution gas chromatography/high resolution mass spectrometry

129

(ThermoFisher Scientific, DFSTM Magnetic sector, Bremen, Germany).

130

then separated on a 60m DB-5 MS capillary column (0.25 mm id, 0.25 μm df;

131

Agilent Technologies, Palo Alto, California, USA).

132

were performed using U.S. EPA isotope dilution methods 1613 and 1668.13,

The whole blood sample was centrifuged immediately, and the serum was Serum samples from 77 GDM cases

internal standards (EPA1613l-LCS and P48-W-ES, Wellington Laboratories Inc., The analytes in mixture were

The extracts were subsequently purified using an acid silica gel column The elution fractions 13C-labeled

PCDD/Fs and DL-PCBs were

The analytes were

Qualitative and quantitative analyses


14

Total


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cholesterol (CHO) and triglycerides (TG) in serum were measured by an automatic

134

biochemistry analyzer (TBA-120FR, Toshiba, Tokyo, Japan) to estimate the total serum lipids

135

by the following formula: Total lipids (mg/dL) = 2.2×cholesterol + triglycerides + 62.3.

136

Lipid-adjusted concentrations of DLC congeners were calculated by diving serum DLCs

137

concentrations by total lipid concentration.

138

15

All samples were analyzed by the laboratory analyst who was blinded to the case-control

139

status.

140

standard reference material (SRM-1957) from the National Institute of Standards and

141

Technology (NIST).

142

system contamination and as a quality control to assess laboratory precision during the entire

143

analytical process.

144

certified reference ranges provided by the manufacturer (Supplementary Table S1).

145

recovery of

146

PCDD/Fs, and from 58 % to 92 % for DL-PCBs, thus meeting the requirements of U.S. EPA

147

1613 and 1668.

148

2.3 Assessment of blood glucose and GDM

149

Each analytical batch consisted of 10 serum samples, one procedure blank and one

Procedure blank and SRM-1957 were analyzed to identify potential

The measured values of each analyte in SRM-1957 samples fell in the

13C-labled

The

internal standards across all samples ranged from 36 % to 108 % for

A “One-Step” approach, involving a 75-g oral glucose tolerance test (OGTT) without prior

150

plasma glucose screening was used to screen for GDM.

151

International Association of Diabetes and Pregnancy Study Groups, and has been adopted in

152

China.16

153

The fasting venous blood sample was collected before 9 a.m. to analyze the fasting glucose

154

concentration.

This strategy is established by the

Pregnant women were tested in the morning after overnight fasting for at least 8 h.

Then, blood samples were taken at intervals of 1 h and 2 h after drinking a


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75-g glucose load to measure the postprandial glucose concentrations.

156

if one or more of the following threshold values were met or were exceeded: 5.1 mmol/L

157

(fasting glucose), 10.0 mmol/L (1 h glucose), and/or 8.5 mmol/L (2 h glucose).

158

2.4 Statistical analysis

159

GDM was diagnosed

Total TEQs were estimated based on multiplying the individual concentrations of

160

PCDD/Fs and DL-PCBs by their respective TEF value, as proposed by the WHO.

161

potencies of each individual congener were estimated based on comparing the magnitude of the

162

regression coefficient (β) for adverse outcomes, which underwent regression analysis with the

163

serum lipid-adjusted mass concentration of the individual DLC congeners.17, 18 Here, we used

164

conditional logistic regression and general linear regression model to estimate the effects of

165

individual DLC congeners on GDM risk and blood glucose, respectively.

166

regression with backward elimination (p > 0.10) was used to query the selection of 2,3,7,8-

167

TCDD as the index (reference) compound in concurrence with other DLCs.

168

regression analysis with all DLC congeners, the cross-tabulation for samples > the maximum

169

limit of detection (MDL) reduced the number of individuals to an insufficient number to

170

construct a regression model; thus, values below the MDL were set to zero in final analysis.

171

The regression analysis of the present study was ultimately conditioned by maternal age and

172

serum lipids, and adjusted only for pregnancy BMI.

173

and DL-PCBs congeners were calculated as the ratio of the β coefficient obtained for the ith

174

congener to β coefficient for 2,3,7,8-TCDD: βi/βTCDD.

175

blood glucose endpoints were compared with human blood-based REPs derived by other

176

investigators,17,

18

The relative

The multiple

In multiple

The REPs of the individual PCDD/Fs

The REPs derived from GDM and

WHO-TEF values,3 WHO-consensus toxicity factors (CTF),9 and with



177 178

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published REP values from an in vivo database 19 using regression or correlation analysis. SPSS Statistical software version 24 (IBM Co., Armonk, NY, USA) and R software

179

version 3.5.2 (R Core Team) were used for the calculation and data visualization.

180

tests were two-tailed, and the statistically significant was set at p < 0.05.

181

3. Results

182

3.1 Participant characteristics

Significance

183

The analyzed population consisted of 77 GDM cases (mean age ± SD: 28.9 ± 4.6 years)

184

and 154 pair-matched controls (28.9 ± 2.8 years); the overall mean age (± SD) of the study

185

population was 28.9 ± 3.8 years.

186

this difference was not statistically significant (22.4 ± 3.0 vs. 21.7 ± 2.8, p =0.11).

187

and control women, the mean (± SD) of fasting blood glucose was 4.8 ± 0.7 mmol/L and 4.4 ±

188

0.4 mmol/L, respectively.

189

were 9.9 (7.3–13.2) and 6.9 (5.8–9.2) pg/g lipids, respectively.

190

rate and median serum concentrations of 29 individual PCDD/Fs and DL-PCBs congeners are

191

shown in Supplementary Table S2.

192

3.2 Index compound identification and REPs for DLC congeners

193

GDM cases showed a slightly higher BMI than controls but For GDM

The median (interquartile range) of total TEQ for GDM and control Data relating to the detection

There is general agreement that the index congener for DLCs should be potent with regard

194

to the expected endpoints.20

195

to the incidence of GDM, 2,3,7,8-TCDD was the most potent congener (Table S3).

196

regression analysis, with FBG as the endpoint of interest, 2,3,7,8-TCDD was also retained in

197

the multiple regression model that showed the positive association.

198

regression analysis did not confirm the potent role for 2,3,7,8-TCDD when 1 h and 2 h

Conditional logistic regression analysis showed that with respect


In linear

However, multiple linear

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postprandial blood glucose was used as the dependent variables (endpoints).

200

coefficient (β), and the REPs for DLC congeners, as derived from GDM and FBG outcomes

201

are presented in Supplementary Table S4.

The regression

202

The regression β of GDM against DLC congener levels were positive for all individuals

203

of the exposure mixture except for 1,2,3,4,7,8-HxCDD, OCDD, 1,2,3,4,7,8-HxCDF,

204

2,3,4,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, OCDF and for DL-PCB congeners 105, 123 and 189.

205

This means that increased exposure to these congeners, including 2,3,7,8-TCDD, was positively

206

associated with GDM risk.

207

congeners were positively associated with FBG concentration.

208

1,2,3,7,8-PeCDD, 1,2,3,4,7,8-HxCDD, 2,3,4,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, OCDF and

209

PCB105 exhibited the opposite association with FBG in comparison to 2,3,7,8-TCDD.

210

order to comply with the TEF methodology assumption that DLCs have a common mode of

211

action, REPs were calculated only for those congeners acting in the same direction as the index

212

compound.

213

human studies

214

allowing easy comparison.

215

3.3 Comparison of REPs calculated from various endpoints in previous human studies

In terms of FBG endpoints, 3 PCDD, 7 PCDF and 11 DL-PCB

All REPs from this study are listed in Table 1. 17, 18

However, the congeners of

In

In addition, REPs from other

are also given in Table 1, along with WHO TEFs2005 and CTFs,9 thus

216

REPs originating from GDM and FBG outcomes were compared with REPs derived from

217

previous human studies involving thyroid volume, serum FT4, CYP1A1 and CYP1B1

218

expression outcomes.

219

in Figure 1.

220

level (asterisk) between row variable and column variable. Within our own study, the

Correlations linking REPs derived from these outcomes are presented

This correlation matrix shows correlation coefficients (number) and significant



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221

conditional logistic regression β coefficient derived GDM-REPs were significantly correlated

222

with linear regression β coefficient derived FBG-REPs (Pearson r = 0.930, p < 0.001).

223

Moreover, GDM-REPs were also significantly correlated with REPs derived from CYP 1A1 (r

224

= 0.898, p < 0.001), thyroid volume (r = 0.744, p = 0.014) and FT4 (r = 0.900, p < 0.001).

225

With regards to FBG-REPs, a significant correlation was observed with CYP 1A1-REPs (r =

226

0.763, p = 0.01) and FT4-REPs (r = 0.777, p = 0.02).

227

TV-REPs, we found that r = 0.617 and p = 0.07, thus showing that the REPs derived from these

228

two outcomes were associated at a marginal level of significance.

229

When comparing of FBG-REPs with

Figure 2 illustrates the distribution of REP values normalized to the respective congener

230

REPs data set.

231

outcomes were relatively smaller than PCDF and PCB congeners.

232

group, CYP 1B1-REPs for 1,2,3,4,7,8-HxCDF and 1,2,3,4,6,7,8-HpCDF were above the

233

normalized median value (0.5) that far from other outcomes derived REPs.

234

was observed for the FBG-REPs of 1,2,3,4,7,8,9-HpCDF which showed a high deviation.

235

Only two REPs (originated from CYP1A1and FT4) were obtained for OCDF, which also

236

exhibited significant deviation from each other.

237

degrees of value distribution were observed, particularly for the REPs of PCB77, 105 and 189.

238

However, almost all of the human serum derived REPs were within or marginally above one

239

order of magnitude.

240

3.4 Comparison of human REPs with WHO-TEFs and CTFs

241 242

The deviation of REPs for PCDD congeners derived from six different In the PCDF congeners

A similar pattern

With regards to DL-PCBs, the high discrete

In order to further compare our REPs with respective WHO-TEFs, the TEFs were set to 1 and the deviation of REPs from TEFs were calculated as REP/TEF (Figure 3).


In the PCDD

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congener group, GDM-REPs and FBG-REPs were comparative to respective WHO-TEFs.

244

Furthermore, the deviations were all within half an order of magnitude of their WHO-TEFs

245

except for the FBG-REP value for 1,2,3,4,6,7,8-HpCDD.

246

REPs of 2,3,7,8-TCDF, 2,3,4,7,8-PeCDF, 1,2,3,4,7,8-HxCDF and 1,2,3,6,7,8-HxCDF derived

247

from GDM or FBG were approximately equal to their respective WHO-TEFs, or the deviations

248

were within the WHO-TEF half-log uncertainty range.

249

approximately one order of magnitude higher than their WHO-TEF values.

250

the DL-PCB congeners group, most congener REP values were much higher than their WHO-

251

TEFs, with deviations spanning one or three orders of magnitude.

252

(based on GDM) and PCB123 (based on FBG) were less than 1 order of magnitude higher than

253

their WHO-TEFs.

254

and FBG endpoints, was close to the WHO-TEF value.

255

to the WHO-TEF value, or the half-log uncertainty border line.

256

was within half an order of magnitude.

257

In the PCDF congener group, the

The REPs for 1,2,3,7,8-PeCDF were With regards to

The REPs for PCB77

It is also worth noting that the REP for PCB126, derived from both GDM The two REPs of PCB169 were close The GDM-REP of PCB118

Figure 4 depicts the combination of PCDD/Fs and DL-PCBs REP values in relation to the

258

WHO TEFs and consensus toxicity factors (CTF).

259

correlated significantly with the WHO-TEFs (GDM, Pearson r = 0.84, p < 0.001; FBG, r =

260

0.79, p < 0.001).

261

GDM-REPs and log FBG-REPs fitted well with the rat log CTF with correlation coefficients

262

of 0.81 (p < 0.001) and 0.80 (p < 0.001), respectively; however, these REPs were not

263

significantly correlated to human log CTFs.

264

above the central axis were higher than TEF or CTF values, and vice versa.

The GDM-REPs and FBG-REPs

In addition, by comparing the REPs with CTFs, we observed that both log

According to the estimates, congener potencies


DLCs were then


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265

divided into PCDD/Fs group and DL-PCBs group to investigate the correlation between the

266

respective subset of REP values and either TEF or CTF.

267

congeners, i.e. PCDD/Fs and DL-PCBs, also correlated well with the TEF and rat CTF subset,

268

respectively, while FBG-REPs for PCDD/Fs did not show a notable correlation with rat CTF

269

(r = 0.561, p = 0.073).

270

3.5 Comparison of human REPs with data from a published in vivo database

Figure 5 shows that the grouped DLC

271

In order to investigate our human REPs in a broader context, we extracted published in

272

vivo experimental REPs from the REP2004 database19 and also identified newly - derived in vivo

273

REPs published between 2005 and 2018.10, 11, 17, 18, 21-24 A violin plot was used to visualize the

274

distribution of the updated in vivo REP values and their probability density (Figure 6A for

275

PCDD/Fs and Fig. 6B for DL-PCBs).

276

predominantly located between the published minimum and maximum values.

277

congener group, the REP for 1,2,3,7,8-PeCDD derived from GDM lies at the 50th percentile of

278

the REP distribution.

279

and CTF values, were close to the maximum value of the REP distribution, while the WHO

280

TEF was above the maximum value.

281

lay between the 50th and 75th percentiles, and the GDM-REP for TCDF was close to the 75th

282

percentile.

283

derived from FBG, were around the 75th percentile.

284

TEF was usually assigned a value within the 50th and 75th percentile of the REPs database

285

distribution, with a general inclination towards the 75th percentile in order to be protective for

286

human health.3

As shown in the violin plot, our REPs were In the PCDD

The 1,2,3,4,6,7,8-HpCDD REP value derived from GDM in our study,

As for the PCDF congener group, the TCDF REP values

REP values for 2,3,4,7,8-PeCDF, 1,2,3,4,7,8-HxCDF and 1,2,3,6,7,8-HxCDF, as As indicated by the WHO expert panel,

With the addition of newly identified in vivo REP values arising from research


Page 15 of 36


287

carried out between 2005 and 2018, the TEF values for OCDD, 1,2,3,4,6,7,8-HpCDF and

288

OCDF fell below the 50th percentile of the REPs distribution range.

289

Figure 6B illustrates the wide variation in REPs for different DL-PCBs, in which the REP

290

values span 4-5 orders of magnitude, depending on the congener.

291

for PCB77 lay within the 50th and 75th percentiles.

292

were greater than the maximum value of REP distribution.

293

CTF rat value and the two REP values in our study were similar to each other and were around

294

the median of the REPs distribution.

295

while the PCB123 FBG-REP value lay within the 25th and 50th percentiles.

296

167 and 189 lay within the 50th and 75th percentiles.

297

ortho PCBs were all below the 50th percentile of the REPs distribution.

298

4. Discussion

299

GDM-REP and FBG-REP

REPs (GDM- and FBG-based) for PCB81 As for PCB126, the TEF value,

REPs for PCB114 and 156 lay above the 75th percentile, REPs for PCB157,

It should be noted that the TEF for mono-

The risk assessment of human exposure to PCDD/Fs and DL-PCBs remains a continuous

300

challenge for regulatory authorities.

301

every few years in order to take into account newly derived in vivo REP values and reduce

302

uncertainties.3

303

recommends deriving systemic TEFs for PCDD/Fs and DL-PCBs for use in human

304

epidemiological studies in the most recent scientific opinion.25

305

REPs become particularly of interest in updating more appropriate and accurate TEF to improve

306

human risk assessment.

307

of PCDD, PCDF and DL-PCBs were established using GDM and FBG as endpoints of interest.

308

Compared with other REPs derived from human studies using four independent endpoints,17, 18

Expert panels from the WHO update the TEF of DLCs

The EFSA Panel on Contaminants in the Food Chain (CONTAM)

Therefore, in vivo systemic

In the present study, two sets of human serum-based systemic REPs



Page 16 of 36

309

we found that the REPs derived from these various outcomes in different human populations

310

environmentally exposed to different levels of DLCs were significantly interrelated.

311

Moreover, the REP values for PCDD/Fs originating from different study populations show

312

relatively small variation compared to the 2 to 4 orders of magnitude span in the REPs database

313

from in vivo or in vitro studies.

314

high correlation, both with the WHO-TEF values, and with CTFs developed as a novel approach

315

to establish toxicity factors for the risk assessment of PCDD/Fs and DL-PCBs.

In addition, these REPs, derived from GDM and FBG, showed

316

The prerequisite for the derivation of human-specific REPs in current study was the pre-

317

identified prospective association between the outcomes, e.g., GDM and FBG), and the

318

concentrations of DLCs in serum. Prospective study characterized by the measurement of risk

319

factors or exposures before the outcome occurs could provide stronger scientific evidence than

320

retrospective studies such as cross-sectional and case-control studies. Thus, the present study

321

examined a prospective data on GDM in pregnant women exposed to a mixture of DLCs to

322

evaluate the plausible causal relationships between DLCs and GDM as well as FBG.

323

TEQ was used to perform a cumulative risk assessment of the DLC mixtures and significantly

324

increased GDM risk, with an estimate odds ratio (OR) of 2.12 (95% CI: 1.57, 2.86)

325

(Supplementary Figure S1).

326

associated with increased FBG.

327

Institute of Environmental Health Sciences (NIEHS)/National Toxicology Program (NTP)

328

workgroup also concluded that the overall evidence from human studies is sufficient for a

329

positive association of DLCs with diabetes despite the heterogeneity of the studies.26

330

Furthermore, accumulating evidence from experimental in vitro and in vivo studies have

Total

The increment in total TEQ concentration was also significantly In addition, a systemic review conducted by the National


Page 17 of 36


331

converged to support the postulate that DLCs are mechanistically implicated in an increased

332

risk of the development of diabetes or its related endpoints observed in human studies.

333

a differentiated adipocytes model, TCDD was found to significantly attenuated insulin-induced

334

glucose uptake.27

335

exposure resulted in an increased basal-insulin release in both islets and β-cell model.28

336

recently, it has been observed that dioxin and dioxin-like PCBs induced activation of the AhR

337

could subsequently affect the expression of gene that related to insulin transport, glucose

338

uptake,29 glucose homeostasis,30 lipid metabolism,31, 32 and inflammation,33, 34 etc., which play

339

a pivotal role in diabetes progression.

340

dysregulation of the link between fatty liver and insulin resistance, correspondently, chemical

341

inhibition of AhR was found that lead to decreased obesity and fatty livers in C57BL/6J mice.32

342

Studies in AhR-deficient mice demonstrate enhanced insulin sensitivity and increased glucose

343

tolerance,36 findings that are consistent with an experiment that found dioxin-exposed mice to

344

have significantly reduced insulin secretion in wild-type mice but not in AhR‐null mice.37

345

cell-based AhR ligand assay conducted in human serum revealed that AhR ligand activity is

346

associated with insulin resistance and resulting T2DM.38

347

that AhR plays a physiological function in glucose metabolism, and supported postulate that

348

sustained activation of the AhR by DLCs could contribute to the development of diabetes.

349

the basis of biological link between DLC-induced AhR activation and diabetes or its related

350

endpoints as well as the identified prospective association between DLCs exposure and GDM

351

in our cohort, estimating REPs based on GDM and FBG was considered reasonable.

352

As in

In an ex-vivo mouse islets and the β-cell-line study, 2,3,7,8-TCDD More

Notably, Lu et al.35 found that AhR activation result in

A

Overall, these findings suggested

On

Most of the previously published REP values for PCDD/Fs and DL-PCBs are based on in



Page 18 of 36

353

vitro or on in vivo animal models.19

354

exposure of humans is more complex; thus human-specific responses and the body burden of

355

DLC concentrations could lead to reliable potency estimates and ultimately improve human

356

risk assessments for DLCs.39

357

concentration was conducted by Trnovec et al.17 under the EU SYSTEQ project.

358

authors applied a novel approach which compared the outcome/concentration regression

359

coefficients of the individual DLC congeners with the outcome/concentration regression

360

coefficient of the TCDD index with which to calculate the REPs.

361

were subsequently complemented and corroborated by Wimmerová et al.18 using more sensitive

362

molecular biomarkers, CYP1A1 and CYP1B1, known to be able to regulated by the aromatic

363

hydrocarbon receptor (AhR).40

364

in eastern Slovakia, an area known to be contaminated by the organochlorine compounds.

365

accumulated concentration of PCDD/Fs and DL-PCBs for these participants was 23.3 pg/g

366

lipid-adjusted TEQ which was approximately 3-fold higher than our present study population

367

(7.7 pg/g lipid TEQ).

368

with background exposure mainly via the diet which could compensate for the low exposure

369

range response for DLCs.

370

FBG in our study were significantly correlated with REPs based on TV, FT4 concentration and

371

CYP1A1 expression outcomes in previous studies of the Slovakian population,17, 18 but also

372

REPs derived from these two different study populations show a relatively small variation, as

373

demonstrated by the normalized REP distribution plot in which the deviations were almost all

374

within or marginally above one order of magnitude.

Compared with laboratory exposures, the environmental

The first human in vivo analysis of REPs based on blood DLCs These

Their thyroid outcome data

These two studies were conducted in a same population living The

The participants in our study were recruited from a general population

It is worth noting that not only the REPs derived from GDM and

Moreover, the REPs for PCDD and


Page 19 of 36


375

PCDF in human blood-based studies using clinical outcomes (GDM, FBG, TV and FT4), or

376

molecular biomarker endpoints (CYP1A1 and CYP1B1), are in good accordance with their

377

assigned WHO-TEF values and the deviations mostly fall within the half-log uncertainty.

378

concordant behavior between REPs derived from human studies and the WHO TEFs imply that

379

the point estimation for TEF value by the WHO expert panel, based on the distribution of REPs

380

between 50th and 75th percentile, is reasonable for human health risk assessment for PCDD and

381

PCDF.

382

concentration of TCDD, 1-PeCDD and 4-PeCDF with an extra-hepatic response (hepatic

383

ethoxyresorufin-O-deethylase activity, and hepatic CYP 1A1, 1A2 and 1B1 expression) were

384

also suggested to provide a more accurate prediction of REPs for humans under environmental

385

exposure conditions.11, 41

386

The

Within the EU SYSTEQ project, systemic REPs based on rat or mice blood

In the current study, the REPs for mono-ortho PCB lie within the published extremes,

387

whereas most of them were seriously deviated from their WHO-TEFs.

388

this might be that mono-ortho PCBs elicit a diverse spectrum of non-AhR-mediated pathways

389

in order to exert diabetogenic or hyperglycemic effects.

390

for PCB169 and 118, based on GDM outcome, were within the +/- half log uncertainty of the

391

WHO-TEF values.

392

Slovakian population-based study, also fell within the half log uncertainty.

393

CYP1B1-REPs for PCB105, 114, 123, 156, 157 and 189 in the previous Slovakian population

394

studies were approximately 1–10 times higher than WHO-TEF values.

395

and FBG-derived REPs for these mono-ortho PCBs were about 10–1000 times greater than

396

WHO-TEF.

One explanation for

It is worth highlighting that the REPs

Consistently, the REPs of these two congeners, based on CYP1B1 in However,

Moreover, our GDM-

The REPs for mono-ortho PCBs were not obtained in Slovakian population based



Page 20 of 36

397

on thyroid volume, FT4 concentration and CYP1A1 expression outcomes due to opposite

398

responses against index compound.

399

from human studies with different endpoints for mono-ortho PCBs.

400

commonly be observed in REPs for the same congener and for similar outcomes in different

401

species or for the same species with different outcomes.

402

REPs within in vivo and in vitro database spans several orders of magnitude.19

403

variation inherent in the REPs database for mono-ortho PCBs has aroused serious concern with

404

regards to the expert WHO TEF panel meeting in 2005.

405

the time, the WHO expert panel expressed low confidence in greater REP values for TEF

406

assignment of certain mono-ortho PCBs.

407

50th percentile of the REP distribution was used to uniformly set the TEFs for all mono-ortho

408

PCBs at 0.00003 in the 2005 WHO TEF expert meeting.

409

data to set scientifically balanced TEF values hinders accurate human risk assessment for mono-

410

ortho PCBs.

411

debate in recent years, because several studies using different human cell models, proved that

412

PCB126 was much less potent than in vivo rodent studies.42-45

413

cell-based assays for PCB126 is 0.0033, which is approximately 30-fold lower than its assigned

414

WHO-TEF.6

415

a REP of 0.017 with CYP1B1 as an endpoint.

416

to the margin-bottom of the half-log uncertainty for the TEF value (0.03).

417

low sensitivity, in terms of human exposure linking to outcomes, and significant variation for

418

PCB126 were also not observed as the REPs for PCB126 were 0.152 (GDM-based) and 0.0745

This scenario reflects the large variation REPs derived Great variation can

The distribution of mono-ortho PCBs The significant

Based upon information available at

Due to the lack of sufficient experimental data, the

Obviously, the lack of sufficient

In terms of non-ortho DL-PCBs, the REP of PCB126 has been the focus of much

The median REP from human

As for the human blood-based REPs of PCB126, Wimmerová et al.18 obtained This human-specific REP for PCB126 is close


In the present study,

Page 21 of 36


419

(FBG-based); these are comparable to its TEF value of 0.1 and the deviations were within +/-

420

half log uncertainty.

421

human risk assessment as toxicokinetic absorption, distribution, metabolism and excretion

422

could be the main issues of concern.6, 41

423

derived systemic TEFs.

424

human-specific PCB126 REP values.

425

are now required to derive appropriate and accurate TEFs for DL-PCBs in human risk

426

assessment.

Human-relevant in vitro cell models may not be best suited for reliable

Human risk assessment may better rely on human-

Consequently, more research and understanding are needed on In particular, more human blood-based investigations

427

One of the limitations of this study is the limited sample size of our study population.

428

Future studies, using a larger population with different exposure ranges and linking to various

429

adverse responses, are now needed to validate concordant behavior in the estimation of human-

430

specific REPs.

431

although several potential confounders were controlled in our study, we cannot exclude other

432

important issues inherent in human studies such as mixture exposure effects, residual

433

confounding factors, the effect of unmeasured AhR-active compounds which may bias the

434

effect estimation with studied outcomes.

435

derived REPs in present study underwent sufficient comparison with established authority TEF,

436

CTF and REP values, and suggested concordance.

437

It is well-known that humans are exposed to a diverse of environmental factors,

While the DLCs exposure and adverse outcome

In conclusion, the estimation of human serum-based REPs for DLCs was reliable and

438

considered to be of high relevance for human risk assessment.

439

values intend to apply weighting factors for different types of studies to the existing REP data

440

set.

Future updates of the TEF

In this context, human systemic REPs are valuable and may be considered to provide a



441

high weighting in order to derive more accurate TEF values for human body burden toxic

442

equivalents (TEQ) determinations. Although the present human data supports the TEF values

443

for PCB 126 and 169, more studies of the REPs for mono-ortho PCBs are warranted due to the

444

inherent significant variation. Additionally, the future research should also focus on what PCB

445

chemicals are actually exposed in people.

446

Acknowledgements

447

The authors gratefully acknowledge all the mothers who collaborated with this study and

448

donated serum samples.

449

Foundation of China [grant numbers 21537001, 21477030 and 21507018], The National Key

450

Research and Development Program of China [grant number 2017YFC1600500], and the

451

Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB

452

14010100].

453

Supporting Information Available

454

Additional information including supplemental tables (Tables S1-S4) and supplemental figure

455

(Figure S1). This information is available free of charge via the internet at http://pubs.acs.org.

456

Disclosures

457

The authors declare no competing financial interest.

This research was supported by the National Natural Science

458


Page 22 of 36

Page 23 of 36


459 460

References:

461

(1) Malisch, R.; Kotz, A., Dioxins and PCBs in feed and food-review from European

462

perspective. Sci. Total Environ. 2014, 491, 2-10.

463

(2) Van den Berg, M.; Birnbaum, L.; Bosveld, A. T.; Brunstrom, B.; Cook, P.; Feeley, M.;

464

Giesy, J. P.; Hanberg, A.; Hasegawa, R.; Kennedy, S. W.; Kubiak, T.; Larsen, J. C.; van

465

Leeuwen, F. X.; Liem, A. K.; Nolt, C.; Peterson, R. E.; Poellinger, L.; Safe, S.; Schrenk, D.;

466

Tillitt, D.; Tysklind, M.; Younes, M.; Waern, F.; Zacharewski, T., Toxic equivalency factors

467

(TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife. Environ. Health Perspect. 1998,

468

106, (12), 775-92.

469

(3) Van den Berg, M.; Birnbaum, L. S.; Denison, M.; De Vito, M.; Farland, W.; Feeley, M.;

470

Fiedler, H.; Hakansson, H.; Hanberg, A.; Haws, L., The 2005 World Health Organization re-

471

evaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like

472

compounds. Toxicol. Sci. 2006, 93, (2), 223-241.

473

(4) van Ede, K. I.; van Duursen, M. B.; van den Berg, M., Evaluation of relative effect potencies

474

(REPs) for dioxin-like compounds to derive systemic or human-specific TEFs to improve

475

human risk assessment. Arch. Toxicol. 2016, 90, (6), 1293-1305.

476

(5) USEPA (U.S. Environmental Protection Agency). Framework for application of the toxicity

477

equivalence methodology for polychlorinated dioxins, furans and biphenyls in ecological risk

478

assessment; EPA/100/R-08/004; Office of the Science Advisor and Risk Assessment Forum:

479

Washington, DC, 2008; www.epa.gov/sites/production/files/2013-09/documents/tefs-draft-

480

052808-0804.pdf.

481

(6) van Duursen, M. B. M.; van Ede, K. I.; van den Berg, M., One TEF concept does not fit all:

482

The case for human risk assessment of polychlorinated biphenyls. Curr Opinion Toxicol 2017,

483

2, 103-108.

484

(7) Farmahin, R.; Jones, S. P.; Crump, D.; Hahn, M. E.; Giesy, J. P.; Zwiernik, M. J.; Bursian,

485

S. J.; Kennedy, S. W., Species-specific relative AHR1 binding affinities of 2,3,4,7,8-

486

pentachlorodibenzofuran explain avian species differences in its relative potency. Com.

487

Biochem. Phys. C. 2014, 161, 21-25.

488

(8) European Commission Website; https://cordis.europa.eu/project/rcn/90542/reporting/en. 23 ACS Paragon Plus Environment


489

[Last update: 21 Jun 2017].

490

(9) Larsson, M.; van den Berg, M.; Brenerová, P.; van Duursen, M. B. M.; van Ede, K. I.; Lohr,

491

C.; Luecke-Johansson, S.; Machala, M.; Neser, S.; Pěnčíková, K.; Poellinger, L.; Schrenk, D.;

492

Strapáčová, S.; Vondráček, J.; Andersson, P. L., Consensus Toxicity Factors for

493

Polychlorinated Dibenzo-p-dioxins, Dibenzofurans, and Biphenyls Combiningin Silico Models

494

and Extensivein Vitro Screening of AhR-Mediated Effects in Human and Rodent Cells. Chem.

495

Res. Toxicol. 2015, 28, (4), 641-650.

496

(10) van Ede, K. I.; Andersson, P. L.; Gaisch, K. P. J.; van den Berg, M.; van Duursen, M. B.

497

M., Comparison of intake and systemic relative effect potencies of dioxin-like compounds in

498

female rats after a single oral dose. Arch. Toxicol. 2014, 88, (3), 637-646.

499

(11) van Ede, K. I.; Andersson, P. L.; Gaisch, K. P. J.; van den Berg, M.; van Duursen, M. B.

500

M., Comparison of Intake and Systemic Relative Effect Potencies of Dioxin-like Compounds

501

in Female Mice after a Single Oral Dose. Environ. Health Perspect. 2013, 121, (7), 847-853.

502

(12) Zhang, L.; Zhong, Y.; Liu, X.; Bao, Y.; Zhao, Y.; Wu, Y.; Cai, Z.; Li, J., Determination of

503

polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans, and dioxin-like

504

polychlorinated biphenyls in human serum using programmable-temperature vaporization gas

505

chromatography with high-resolution mass spectrometry. J. Sep. Sci. 2017, 40, (17), 3453-3461.

506

(13) USEPA (U.S. Environmental Protection Agency). Method 1613: Tetra-through Octa-

507

Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS; EPA 621-B-94-005;

508

United States Environmental Protection Agency Office of Water: Washington, DC, 1994;

509

www.well-labs.com/docs/epa_method_1613b_1994.pdf.

510

(14) USEPA (U.S. Environmental Protection Agency). Method 1668, Revision A: Chlorinated

511

biphenyl congeners in water, soil, sediment, and tissue by HRGC/HRMS; EPA 821-R-00-002;

512

United States Environmental Protection Agency Office of Water: Washington, DC, 1999;

513

www.o2si.com/docs/epa-method-1668A.pdf.

514

(15) Phillips, D. L.; Pirkle, J. L.; Burse, V. W.; Bernert, J. T.; Henderson, L. O.; Needham, L.

515

L., Chlorinated hydrocarbon levels in human serum: effects of fasting and feeding. Arch.

516

Environ. Con. Tox. 1989, 18, (4), 495-500.

517

(16) Yang, H. X., Diagnostic criteria for gestational diabetes mellitus (WS 331-2011). Chin

518

Med J (Engl) 2012, 125, (7), 1212-3. 24 ACS Paragon Plus Environment

Page 24 of 36

Page 25 of 36


519

(17) Trnovec, T.; Jusko, T. A.; Šovcíková, E.; Lancz, K.; Chovancová, J.; Patayová, H.;

520

Palkovicová, L. U.; Drobná, B.; Langer, P.; Van den Berg, M.; Dedik, L.; Wimmerová, S.,

521

Relative effect potency estimates of dioxin-like activity for dioxins, furans, and dioxin-like

522

PCBs in adults based on two thyroid outcomes. Environ. Health Perspect. 2013, 121, (8), 886-

523

892.

524

(18) Wimmerová, S.; van den Berg, M.; Chovancová, J.; Patayová, H.; Jusko, T. A.; van

525

Duursen, M. B. M.; Palkovičová Murínová, Ľ.; Canton, R. F.; van Ede, K. I.; Trnovec, T.,

526

Relative effect potency estimates of dioxin-like activity for dioxins, furans, and dioxin-like

527

PCBs in adults based on cytochrome P450 1A1 and 1B1 gene expression in blood. Environ.

528

Int. 2016, 96, 24-33.

529

(19) Haws, L. C.; Su, S. H.; Harris, M.; DeVito, M. J.; Walker, N. J.; Farland, W. H.; Finley,

530

B.; Birnbaum, L. S., Development of a Refined Database of Mammalian Relative Potency

531

Estimates for Dioxin-like Compounds. Toxicol. Sci. 2006, 89, (1), 4-30.

532

(20) USEPA (U.S. Environmental Protection Agency). Supplementary guidance for conducting

533

health risk assessment of chemical mixtures; EPA/630/R-00/002; United States Environmental

534

Protection Agency Risk Assessment Forum Technical Panel: Washington, DC, 2000;

535

https://ofmpub.epa.gov/eims/eimscomm.getfile?p_download_id=4486.

536

(21) Gray, M. N.; Aylward, L. L.; Keenan, R. E., Relative Cancer Potencies of Selected Dioxin-

537

Like Compounds on a Body-Burden Basis: Comparison to Current Toxic Equivalency Factors

538

(TEFs). J. Toxicol. Env. Heal. A 2006, 69, (10), 907-917.

539

(22) Kim, E.; Suda, T.; Tanabe, S.; Batoev, V. B.; Petrov, E. A.; Iwata, H., Evaluation of

540

Relative Potencies for in Vitro Transactivation of the Baikal Seal Aryl Hydrocarbon Receptor

541

by Dioxin-Like Compounds. Environ. Sci. Technol. 2011, 45, (4), 1652-1658.

542

(23) Strapáčová, S.; Brenerová, P.; Krčmář, P.; Andersson, P.; van Ede, K. I.; van Duursen, M.

543

B. M.; van den Berg, M.; Vondráček, J.; Machala, M., Relative effective potencies of dioxin-

544

like compounds in rodent and human lung cell models. Toxicology 2018, 404-405, 33-41.

545

(24) van Ede, K. I.; van Duursen, M. B. M.; van den Berg, M., Evaluation of relative effect

546

potencies (REPs) for dioxin-like compounds to derive systemic or human-specific TEFs to

547

improve human risk assessment. Arch. Toxicol. 2016, 90, (6), 1293-1305.

548

(25) Knutsen, H. K.; Alexander, J.; Barregård, L.; Bignami, M.; Brüschweiler, B.; Ceccatelli, 25 ACS Paragon Plus Environment


549

S.; Cottrill, B.; Dinovi, M.; Edler, L.; Grasl Kraupp, B.; Hogstrand, C.; Nebbia, C. S.; Oswald,

550

I. P.; Petersen, A.; Rose, M.; Roudot, A. C.; Schwerdtle, T.; Vleminckx, C.; Vollmer, G.;

551

Wallace, H.; Fürst, P.; Håkansson, H.; Halldorsson, T.; Lundebye, A. K.; Pohjanvirta, R.;

552

Rylander, L.; Smith, A.; van Loveren, H.; Waalkens Berendsen, I.; Zeilmaker, M.; Binaglia,

553

M.; Gómez Ruiz, J. Á.; Horváth, Z.; Christoph, E.; Ciccolallo, L.; Ramos Bordajandi, L.;

554

Steinkellner, H.; Hoogenboom, L. R., Risk for animal and human health related to the presence

555

of dioxins and dioxin‐like PCBs in feed and food. EFSA Journal 2018, 16, (11), 5333.

556

(26) Taylor, K. W.; Novak, R. F.; Anderson, H. A.; Birnbaum, L. S.; Blystone, C.; DeVito, M.;

557

Jacobs, D.; Köhrle, J.; Lee, D.; Rylander, L.; Rignell-Hydbom, A.; Tornero-Velez, R.; Turyk,

558

M. E.; Boyles, A. L.; Thayer, K. A.; Lind, L., Evaluation of the association between persistent

559

organic pollutants (POPs) and diabetes in epidemiological studies: A National Toxicology

560

Program Workshop Review. Environ. Health Perspect. 2013, 121, (7), 774-783.

561

(27) Hsu, H.; Tsou, T.; Chao, H.; Kuo, Y.; Tsai, F.; Yeh, S., Effects of 2,3,7,8-

562

tetrachlorodibenzo-p-dioxin on adipogenic differentiation and insulin-induced glucose uptake

563

in 3T3-L1 cells. J. Hazard. Mater. 2010, 182, (1-3), 649-655.

564

(28) Lai, K. P.; Wan, H. T.; Ng, A. H.; Li, J. W.; Chan, T. F.; Wong, C. K., Transcriptomic and

565

Functional Analyses on the Effects of Dioxin on Insulin Secretion of Pancreatic Islets and β-

566

Cells. Environ. Sci. Technol. 2017, 51, (19), 11390-11400.

567

(29) Diani-Moore, S.; Ram, P.; Li, X.; Mondal, P.; Youn, D. Y.; Sauve, A. A.; Rifkind, A. B.,

568

Identification of the aryl hydrocarbon receptor target gene TiPARP as a mediator of suppression

569

of hepatic gluconeogenesis by 2,3,7,8-Tetrachlorodibenzo-p-dioxin and of nicotinamide as a

570

corrective agent for this effect. J. Biol. Chem. 2010, 285, (50), 38801-38810.

571

(30) Zhang, W.; Sargis, R. M.; Volden, P. A.; Carmean, C. M.; Sun, X. J.; Brady, M. J., PCB

572

126 and other dioxin-like PCBs specifically suppress hepatic PEPCK expression via the aryl

573

hydrocarbon receptor. PLoS One 2012, 7, (5), e37103.

574

(31) Sato, S.; Shirakawa, H.; Tomita, S.; Ohsaki, Y.; Haketa, K.; Tooi, O.; Santo, N.; Tohkin,

575

M.; Furukawa, Y.; Gonzalez, F. J., Low-dose dioxins alter gene expression related to

576

cholesterol biosynthesis, lipogenesis, and glucose metabolism through the aryl hydrocarbon

577

receptor-mediated pathway in mouse liver. Toxicol. Appl. Pharm. 2008, 229, (1), 10-19.

578

(32) Moyer, B. J.; Rojas, I. Y.; Kerley-Hamilton, J. S.; Nemani, K. V.; Trask, H. W.; Ringelberg, 26 ACS Paragon Plus Environment

Page 26 of 36

Page 27 of 36


579

C. S.; Gimi, B.; Demidenko, E.; Tomlinson, C. R., Obesity and fatty liver are prevented by

580

inhibition of the aryl hydrocarbon receptor in both female and male mice. Nutr. Res. 2017, 44,

581

38-50.

582

(33) Kim, M. J.; Pelloux, V.; Guyot, E.; Tordjman, J.; Bui, L. C.; Chevallier, A.; Forest, C.;

583

Benelli, C.; Clement, K.; Barouki, R., Inflammatory pathway genes belong to major targets of

584

persistent organic pollutants in adipose cells. Environ. Health Perspect. 2012, 120, (4), 508-14.

585

(34) Vogel, C. F. A.; Khan, E. M.; Leung, P. S. C.; Gershwin, M. E.; Chang, W. L. W.; Wu,

586

D.; Haarmann-Stemmann, T.; Hoffmann, A.; Denison, M. S., Cross-talk between Aryl

587

Hydrocarbon Receptor and the Inflammatory Response. J. Biol. Chem. 2014, 289, (3), 1866-

588

1875.

589

(35) Lu, P.; Yan, J.; Liu, K.; Garbacz, W. G.; Wang, P.; Xu, M.; Ma, X.; Xie, W., Activation

590

of aryl hydrocarbon receptor dissociates fatty liver from insulin resistance by inducing

591

fibroblast growth factor 21. Hepatology 2015, 61, (6), 1908-19.

592

(36) Wang, C.; Xu, C.; Krager, S. L.; Bottum, K. M.; Liao, D.; Tischkau, S. A., Aryl

593

Hydrocarbon Receptor Deficiency Enhances Insulin Sensitivity and Reduces PPAR-α Pathway

594

Activity in Mice. Environ. Health Perspect. 2011, 119, (12), 1739-1744.

595

(37) Kurita, H.; Yoshioka, W.; Nishimura, N.; Kubota, N.; Kadowaki, T.; Tohyama, C., Aryl

596

hydrocarbon receptor-mediated effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin on glucose-

597

stimulated insulin secretion in mice. J. Appl. Toxicol. 2009, 29, (8), 689-694.

598

(38) Roh, E.; Kwak, S. H.; Jung, H. S.; Cho, Y. M.; Pak, Y. K.; Park, K. S.; Kim, S. Y.; Lee,

599

H. K., Serum aryl hydrocarbon receptor ligand activity is associated with insulin resistance and

600

resulting type 2 diabetes. Acta Diabetol. 2015, 52, (3), 489-495.

601

(39) Trnovec, T., Dioxin relative effect potencies calculated from human thyroid data.

602

Endocrine Disruptors 2014, 2, (1), e27904.

603

(40) Nebert, D. W.; Dalton, T. P.; Okey, A. B.; Gonzalez, F. J., Role of aryl hydrocarbon

604

receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer. J. Biol.

605

Chem. 2004, 279, (23), 23847-23850.

606

(41) van Ede, K. I.; Aylward, L. L.; Andersson, P. L.; van den Berg, M.; van Duursen, M. B.

607

M., Tissue distribution of dioxin-like compounds: Potential impacts on systemic relative

608

potency estimates. Toxicol. Lett. 2013, 220, (3), 294-302. 27 ACS Paragon Plus Environment


609

(42) Carlson, E. A.; McCulloch, C.; Koganti, A.; Goodwin, S. B.; Sutter, T. R.; Silkworth, J.

610

B., Divergent Transcriptomic Responses to Aryl Hydrocarbon Receptor Agonists between Rat

611

and Human Primary Hepatocytes. Toxicol. Sci. 2009, 112, (1), 257-272.

612

(43) Silkworth, J. B.; Koganti, A.; Illouz, K.; Possolo, A.; Zhao, M.; Hamilton, S. B.,

613

Comparison of TCDD and PCB CYP1A Induction Sensitivities in Fresh Hepatocytes from

614

Human Donors, Sprague-Dawley Rats, and Rhesus Monkeys and HepG2 Cells. Toxicol. Sci.

615

2005, 87, (2), 508-519.

616

(44) Sutter, C. H.; Bodreddigari, S.; Sutter, T. R.; Carlson, E. A.; Silkworth, J. B., Analysis of

617

the CYP1A1 mRNA Dose-Response in Human Keratinocytes Indicates that Relative Potencies

618

of Dioxins, Furans, and PCBs Are Species and Congener Specific. Toxicol. Sci. 2010, 118, (2),

619

704-715.

620

(45) Westerink, W. M.; Stevenson, J. C.; Schoonen, W. G., Pharmacologic profiling of human

621

and rat cytochrome P450 1A1 and 1A2 induction and competition. Arch. Toxicol. 2008, 82,

622

(12), 909-921.

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Page 28 of 36

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Table 1 The calculated relative effect potencies (REPs) of PCDD/Fs and dl-PCBs with various outcomes from different human studies. REP (βi/βTCDD) calculated from different outcomes WHO CTF Congener TEF GDM FBG TV FT4 CYP1A1 CYP1B1 Human Rat

PCDDs 2378-TCDD

1

1

1

1

1

1

1

1

1

0.517

-

0.432

0.471

0.40371

-

1

1

0.5

123478-HxCDD

-

-

0.257

0.805

0.52356

-

0.1

0.03

0.2

123678-HxCDD

0.0428

0.0765

0.082

0.126

0.01386

-

0.1

0.06

0.06

123789-HxCDD

0.0526

0.0455

-

0.482

-

-

0.1

0.002

0.3

1234678-HpCDD

0.0090

0.0639

0.008

0.029

0.01943

-

0.01

0.2

0.04

-

-

0.003

-

-

-

0.0003

0.005

-

2378-TCDF

0.0805

0.0676

0.828

0.1

0.4105

2.0429

0.1

0.1

0.2

12378-PeCDF

0.441

0.288

0.347

0.03

0.6

0.2

23478-PeCDF

0.099

0.403

0.016

123478-HxCDF

-

0.0463

123678-HxCDF

0.058

0.118

234678-HxCDF

-

123789-HxCDF

12378-PeCDD

OCDD PCDFs

0.61548 0.02

0.00891

0.1657

0.3

1

0.2

-

0.05234

0.74

0.1

1

0.09

0.146

-

0.24078

0.1

0.04

0.07

-

0.78

-

0.59437

1.1857

0.1

0.06

0.07

-

-

-

-

-

-

0.1

0.02

0.3

1234678-HpCDF

0.011

0.0957

0.054

0.053

0.21088

1.1143

0.01

0.01

0.01

1234789-HpCDF

0.015

0.0412

-

-

-

-

0.01

0.3

0.05

-

-

-

0.373

0.01243

-

0.0003

0.2

0.007

PCB77

0.00076

0.0104

-

-

-

-

0.0001

-

0.0004

PCB81

0.0329

0.145

-

-

0.00093

0.2057

0.0003

-

0.0002

PCB126

0.152

0.0745

-

-

-

0.0171

0.1

0.003

0.09

PCB169

0.0362

0.102

-

-

-

0.0371

0.03

-

0.002

PCB105

-

-

-

1.8E-06

-

0.00015

0.00003

-

0.00001

PCB114

0.0114

0.0282

-

-

-

0.00067

0.00003

-

0.00006

PCB118

0.000067

0.000463

-

-

-

0.000043

0.00003

-

0.000009

PCB123

-

0.000178

-

-

-

0.00011

0.00003

-

0.000009

PCB156

0.000355

0.00195

-

-

-

0.000077

0.00003

-

0.00008

PCB157

0.00544

0.0135

-

-

-

0.000497

0.00003

-

0.00003

PCB167

0.00306

0.00579

-

-

-

0.00017

0.00003

-

0.000007

PCB189

-

0.00549

-

-

-

0.00027

0.00003

-

0.000007

OCDF DL-PCBs

627

Note: Relative effect potency (REP) was calculated as the ratio of the regression coefficients (β) of the

628

individual congener to that of TCDD. Gestational diabetes mellitus (GDM) and fasting blood glucose (FBG)



629

were the outcomes investigated in present study; Thyroid volume (TV), free thyroxine (FT4), cytochrome

630

P450 (CYP) 1A1 and 1B1 expression were the outcomes investigated in European Union (EU) SYSTEQ

631

project (Trnovec et al.17 and Wimmerová et al.18). Consensus toxicity factors (CTF) were developed by

632

Larsson et al. 9.

633


Page 30 of 36

Page 31 of 36


634 635

Figure 1. Correlation matrix for human studies derived REPs from GDM, FBG, CYP 1A1, CYP 1B1, thyroid

636

volume and serum FT4 outcomes (Number: Pearson correlation coefficients r; Significance level: *, P

Relative Effect Potency Estimates for Dioxin-like ... - ACS Publications

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