Relative Effect Potency Estimates for Dioxin-like Compounds in

May 31, 2019 - A nested case-control study was conducted to determine REPs from the human serum concentration of DLCs using gestational diabetes ...
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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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Relative Effect Potency Estimates for Dioxin-like Compounds in Pregnant

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Women with Gestational Diabetes Mellitus and Blood Glucose Outcomes

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Based on a Nested Case-control Study

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

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Yongning Wu a,b

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a NHC

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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

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c

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d Beijing

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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

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Corresponding author

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

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

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100021, China.

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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

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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

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

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

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

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respectively.

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coefficients, βi (DLCs)/βTCDD.

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

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

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

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

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dioxins, furans.

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

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

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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

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1. Introduction

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

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

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environmental media and food products.1

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

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environmental health issues that capture public attention.

Due to environmental degradation-resistance, long-

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

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

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

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

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

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2,3,7,8-TCDD.2

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

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vitro data.3

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

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

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

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

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

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

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

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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

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For example, previous studies

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Therefore, an

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assessment for DLCs.3

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

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systemic REPs.

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

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

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

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Larsson et al.9

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

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

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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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regression coefficient of the TCDD index to calculate REPs.

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2. Materials and Methods

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2.1 Study design and population

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

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Xicheng district of Beijing, China.

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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.

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

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blood sample between 9 and 13 weeks of gestation.

All pregnant women were informed of

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the purpose of the study and provided signed consent.

The whole procedure of study had the

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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).

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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

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

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

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

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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

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free from occupational exposure.

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

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2.2 Blood collection and chemical analysis

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The maternal BMI was calculated at the first prenatal visit

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

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first trimester.

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segregated and preserved at -40oC until further analysis.

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

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designated by the WHO.

The method for measuring these DLC congeners in serum samples

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is described elsewhere.12

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

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13C-labeled

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

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

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California, USA).

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

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

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recovery standards were spiked prior to instrumental analysis.

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

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

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

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Agilent Technologies, Palo Alto, California, USA).

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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

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Total

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

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

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

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

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concentrations by total lipid concentration.

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

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status.

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

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Technology (NIST).

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

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analytical process.

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

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recovery of

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

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1613 and 1668.

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2.3 Assessment of blood glucose and GDM

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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

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plasma glucose screening was used to screen for GDM.

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

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China.16

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

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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.

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

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

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2.4 Statistical analysis

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GDM was diagnosed

Total TEQs were estimated based on multiplying the individual concentrations of

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

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

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

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

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

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

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

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

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

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

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

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

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serum lipids, and adjusted only for pregnancy BMI.

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

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

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

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investigators,17,

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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

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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

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

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

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3. Results

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3.1 Participant characteristics

Significance

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

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

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population was 28.9 ± 3.8 years.

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

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

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0.4 mmol/L, respectively.

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

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

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shown in Supplementary Table S2.

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3.2 Index compound identification and REPs for DLC congeners

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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

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to the expected endpoints.20

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

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

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the multiple regression model that showed the positive association.

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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

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In linear

However, multiple linear

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

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

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are presented in Supplementary Table S4.

The regression

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

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

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

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

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associated with GDM risk.

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congeners were positively associated with FBG concentration.

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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

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

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

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

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compound.

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human studies

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allowing easy comparison.

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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

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

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

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expression outcomes.

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in Figure 1.

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

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

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

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

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

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

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

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two outcomes were associated at a marginal level of significance.

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When comparing of FBG-REPs with

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

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REPs data set.

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outcomes were relatively smaller than PCDF and PCB congeners.

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

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

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

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

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exhibited significant deviation from each other.

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

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

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order of magnitude.

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3.4 Comparison of human REPs with WHO-TEFs and CTFs

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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).

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In the PCDD

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

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

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

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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

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

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

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

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

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

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

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their WHO-TEFs.

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

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

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was within half an order of magnitude.

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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

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WHO TEFs and consensus toxicity factors (CTF).

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

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0.79, p < 0.001).

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

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

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significantly correlated to human log CTFs.

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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

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

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respective subset of REP values and either TEF or CTF.

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

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

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(r = 0.561, p = 0.073).

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

Figure 5 shows that the grouped DLC

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

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

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

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

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

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predominantly located between the published minimum and maximum values.

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

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the REP distribution.

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

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TEF was above the maximum value.

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

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percentile.

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derived from FBG, were around the 75th percentile.

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

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

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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

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

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

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

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

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

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were greater than the maximum value of REP distribution.

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

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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

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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

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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

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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

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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

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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

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In the present study,

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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

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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

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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)

29 ACS Paragon Plus Environment

Environmental Science & Technology

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

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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