Associations between the exposure to persistent organic pollutants and type 2 diabetes in East China: A case-control study

Chemosphere 241 (2020) 125030

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Associations between the exposure to persistent organic pollutants and type 2 diabetes in East China: A case-control study Xu Han a, b, 1, Lingling Meng c, 1, Yingming Li a, *, An Li d, Mary E. Turyk d, Ruiqiang Yang a, Pu Wang a, Ke Xiao a, Junpeng Zhao a, b, Jianqing Zhang e, Qinghua Zhang a, b, Guibin Jiang a, b a

State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China University of Chinese Academy of Sciences, Beijing, 100049, China c Shandong Provincial Qianfoshan Hospital, The First Hospital Affiliated with Shandong First Medical University, Jinan, 250014, China d School of Public Health, University of Illinois at Chicago, Chicago, IL, 60612, USA e POPs Lab, Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, 518055, China b

h i g h l i g h t s
Exposure to PCBs and PBDEs were positively associated with type 2 diabetes.
POPs showed stronger associations with diabetes in males than in females.
Adiposity did not modify the associations between POPs and diabetes.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 June 2019 Received in revised form 30 September 2019 Accepted 30 September 2019 Available online 1 October 2019

Persistent organic pollutants (POPs) have been associated with a high risk of type 2 diabetes in different regions, although few studies from China have been published. We aimed to investigate the associations between POP exposure and type 2 diabetes in Chinese population. A total of 158 participants diagnosed with type 2 diabetes and 158 participants without the disorder from Shandong Province were enrolled in this case-control study during 2016e2017. Nine polychlorinated biphenyl congeners (PCBs) and 2 polybrominated diphenyl ethers with detectable levels in 75% of the participants were selected for data analysis. The results showed that POP exposure was significantly and positively associated with the risk of diabetes after adjusting for age, sex, BMI, triglycerides and total cholesterol. However, we did not observe an obvious modified effect of adiposity on the associations between POP exposure and diabetes in the present study, as strong associations between POPs and diabetes were observed in both the higher-BMI (BMI25 kg/m2) and the lower-BMI (BMI<25 kg/m2) groups. POPs showed stronger associP ations with diabetes in males than in females. The odds ratio (OR) for the highest quartile of POPs was 6.97 for males, nearly two times higher than that for females (OR ¼ 3.58). All these findings suggest that POP exposure may impact the risk of diabetes in Chinese population. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Myrto Petreas Keywords: PCBs PBDEs Type 2 diabetes Case-control

1. Introduction As a serious chronic disease, diabetes has become a growing health concern in recent years (WHO, 2016). From 1980 to 2014, the

* Corresponding author. E-mail address: [email protected] (Y. Li). 1 Equal contribution. https://doi.org/10.1016/j.chemosphere.2019.125030 0045-6535/© 2019 Elsevier Ltd. All rights reserved.

worldwide prevalence of diabetes in adults increased from 4.7% to 8.5% (WHO, 2016). In 2015, the International Diabetes Federation (IDF) estimated that there were 415 million adults aged between 20 and 79 years with diabetes worldwide and an additional 318 million adults with impaired glucose tolerance, a condition associated with a high risk of developing diabetes in the future (IDF, 2015). In addition, the prevalence of diabetes in children, adolescents and younger adults is increasing (IDF, 2015; Pinhas-Hamiel and Zeitler, 2005; Viner et al., 2017). Diabetes is accompanied by

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X. Han et al. / Chemosphere 241 (2020) 125030

serious complications, such as cardiovascular disease, blindness and kidney failure. Cardiovascular disease, a dominant global public health challenge, is the main cause of mortality in people with diabetes. In 2017, there were approximately 4 million deaths (people aged between 20 and 79 years) caused by diabetes (Chatterjee et al., 2017; IDF, 2017). Type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus are the three main types of diabetes, and T2D accounts for approximately 90% of all cases (IDF, 2017). The development of T2D is influenced by multiple factors, among which genetics, environment and lifestyle are currently considered the dominant factors (Zimmet et al., 2001). Specifically, experimental and epidemiologic studies indicated that exposure to persistent organic pollutants (POPs), a group of lipophilic and bioaccumulative chemicals, may be one of the diabetogenic factors. Studies reported that 2,3,7,8-tetrachlorodibenzo-p-dioxin can bind to the aryl hydrocarbon receptor (AhR) resulting in changes in the translational and transcriptional mechanisms. Such changes further inhibit the expression of glucose transporters (Enan et al., 1992a, 1992b; Enan and Matsumura, 1994). Thus, the glucose uptake activities in adipose, liver and pancreas tissues are limited, and accompanied by a decrease in insulin production and secretion by the pancreatic beta cells (Enan et al., 1992a; Hectors et al., 2011; Lee et al., 2018). However, the mechanism of binding to the AhR is not the only explanation for the associations between POP exposure and diabetes (Wang et al., 2010). The mechanisms linking POP exposure to diabetes remain inconclusive. An increasing number of epidemiologic studies worldwide suggest that POP exposure may play an important role in the development of T2D. However, there are limited epidemiologic studies focused on the associations between POP exposure and the prevalence of type 2 diabetes in China. China was estimated to have 110 million people (aged between 20 and 79 years) with diabetes in 2017, making it the country with the largest number of people with diabetes, followed by India with 72.9 million people and the United States with 30.2 million people with diabetes (IDF, 2017; Ma, 2018). Additionally, there are differences in the characteristics of patients with diabetes in China compared with those of Western populations, such as younger age and lower body mass index (BMI) (Chan et al., 2014; Kong et al., 2013; Ma, 2018), as well as differences in the classes of POPs to which they are exposed due to regional agricultural and industrial pollution; and these factors may impact the associations of POPs with diabetes. The aim of the present case-control study was to (1) evaluate the associations between the serum concentrations of PCBs and polybrominated diphenyl ether (PBDEs) and the prevalence of T2D in East China; (2) assess the potential modification of the associations of POPs with the prevalence of T2D by BMI and sex; and (3) investigate the differences in the diabetes prevalence and exposure to POPs mixtures across populations. 2. Materials and methods 2.1. Study population Initially, 507 participants including 265 participants in the case group and 242 participants in the control group from Shandong Province in East China were recruited in the case-control study during 2016e2017. Participants in the case group were recruited from patients who were under medical treatment for diabetes at Shandong Provincial Qianfoshan Hospital in Jinan. Fifteen participants unable to stand on their own, 91 participants with missing data on height or weight and 1 participant with T1D were excluded, for a final enrollment of 158 patients. Participants in the control group were randomly selected from people who were undergoing

physical examinations in the same hospital. Controls were matched with patients for people of similar age (±5 years) and residential area. Participants were excluded from the control group if they had a fasting plasma glucose (FPG) level more than 7.0 mmol/L or had a history of physician-diagnosed diabetes. Out of 242 eligible control subjects, 158 controls were enrolled in the study. All participants in the study provided fasting blood samples. In addition, information on age, sex, height and weight was obtained from medical records. 2.2. Materials The organic solvents, including dichloromethane (DCM), nhexane and methanol, were pesticide grade and were obtained from J.T. Baker Chemical Company (Phillipsburg, NJ, USA). 2Propanol (99.9%) was gradient grade for liquid chromatography and was purchased from Merck (Darmstadt, Germany). Formic acid (99%) and nonane (chromatographic grade) were purchased from Acros Organics (Belgium) and Sigma-Aldrich (USA), respectively. The Oasis® HLB cartridge (6 cm3/500 mg) was obtained from Waters (Milford, MA, USA). Silica gel 60 (0.063e0.100 mm) and anhydrous sodium sulfate (Na2SO4) were purchased from Merck (Darmstadt, Germany). Prior to use, the silica gel was baked in a muffle furnace at 550  C for 12 h, and the anhydrous Na2SO4 was baked at 660  C for 6 h. The 13C-labeled standard of PCBs (surrogate standards: 68C-LCS, and injection standards: 68C-IS) and PBDEs (surrogate standards: MBDE-MXG-LCS) were obtained from Wellington Laboratories (Ontario, Canada). 2.3. POPs analysis The method for sample preparation was based on previous studies (Lee et al., 2011a; Salihovic et al., 2012). Briefly, the serum samples were kept at room temperature for 1 h to attain equilibrium. For each of the serum samples, 1 mL of formic acid was added to 0.5 mL of the sample, and the mixture was sonicated to denature the protein. Then, 13C-labeled surrogate standards of PCBs (68CLCS) and PBDEs (MBDE-MXG-LCS) were spiked into the sample with an additional 1 mL of 3% isopropanol in water, followed by another sonication. Next, solid phase extraction (SPE) was performed on an Oasis® HLB cartridge (6 cm3/500 mg, Waters, Milford, MA, USA) that was washed with methanol, dichloromethane, methanol/dichloromethane (1/1, v/v), methanol and Milli-Q water prior to extraction. After the sample was conditioned and loaded, the cartridge was rinsed with 3% isopropanol in water and then dried under vacuum and nitrogen. Afterwards, the cartridge was eluted with 10 mL of dichloromethane/hexane (1/1, v/v). The cleaned extract was then concentrated. Next, a small multilayer column, which was packed with 1 g 33% H2SO4 silica gel, 0.2 g silica gel and 2 g activated anhydrous sodium sulfate (Na2SO4) was used for further cleaning. Then, 10 mL hexane was added to the column for elution. Finally, the eluent was evaporated and transferred to a GC vial together with the 13C labeled recovery standard (68C-IS). The methods of instrumental analysis for PCBs and PBDEs were described elsewhere (Wang et al., 2017). The PCBs and PBDEs were determined on a high-resolution gas chromatogram coupled with a high-resolution mass spectrometer (HRGC/HRMS, AutoSpec Premier, Waters, USA) with an electron impact (EIþ) ion source. A 60 m DB-5MS fused silica capillary column (J&W, Scientific, 0.25 mm i.d.  0.25 mm film thickness) and a 30 m DB-5MS column (J&W, Scientific, 0.25 mm i.d.  0.10 mm film thickness) were used to separate the PCBs and PBDEs, respectively. The electron emission energy was set to 35 eV. The source temperature was 270  C for the PCBs and 280  C for the PBDEs.

X. Han et al. / Chemosphere 241 (2020) 125030

2.4. Clinical chemistry parameters analysis The hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), total cholesterol and triglycerides were measured in Shandong Provincial Qianfoshan Hospital. The fasting plasma glucose levels were enzymatically quantified. The total lipids (TLs) were calculated using Phillip’s formula: TL ¼ 2.27  total cholesterol þ triglyceridesþ0.623 (Phillips et al., 1989). 2.5. Quality assurance/quality control For each batch of 11 serum samples, a procedural blank was added. Most analytes were undetected in the blank samples, except for PCB-118, 138 and 153, which were detected in the blank samples at less than 10% of the concentration in the participant samples. Therefore, the analyte concentrations reported in the present study were not corrected for blanks. The recoveries of the spiked serum samples were 47e120% for the PCBs and 50e110% for the PBDEs. The limit of detection (LOD) was defined by a signal-to-noise ratio of three. In the current study, the LODs were 0.03e1.76 pg/mL for the PCBs and 0.07e4.09 pg/mL for the PBDEs. 2.6. Statistical analysis Among the 44 POP congeners (including PCB-77, 81, 105, 114, 118, 123, 126, 156, 157, 167, 169, 189, 28, 52, 101, 138, 153, 180, 209, 202, 205 and 208, and BDE-3, 7, 15, 17, 28, 47, 49, 66, 71, 77, 85, 99, 100, 119, 126, 138, 153, 154, 156, 183, 184 and 191) analyzed, 11 POPs that were detectable in 75% of the participants were selected for the present study, including nine PCBs (PCB-105, 114, 118, 156, 157, 167, 138, 153, and 180) and two PBDEs (BDE-47, 153). Levels of POP congeners below the LOD were imputed as ½*LOD. Student’s ttests, Mann-Whitney U-tests and Chi-square tests were employed to compare the differences between the patient group and the control group. Spearman correlation coefficients (r) were used to evaluate the relationships among the POP concentrations, ages, BMIs (weight in kilograms/height in meters2), levels of HbA1c and fasting plasma glucose levels. We used logistic regression models to assess the associations between POP concentrations in serum and the risk of type 2 diabetes. POP concentrations were treated as both log-transformed continuous variables and categorical variables to fit the regression analyses. When serum POP concentrations expressed as categorical variables, they were categorized into quartiles based on the distribution among controls, with the lowest quartile using as the reference group. The linear trend for the p-values was based on the median value of each category of POPs. Confounding variables were retained in the regression model if their inclusion changed the risk estimates by >10%. In the present study, age, sex, BMI, triglycerides and total cholesterol remained in the final model as confounders. Since serum lipids and BMI may be in the causal pathway (Lee et al., 2011b; Schisterman et al., 2005), we first modeled the POPs wetweight concentrations adjusted for age and sex. Subsequently, we adjusted for BMI, triglycerides and total cholesterol. For comparison, lipid-standardized concentrations were also analyzed. In addition, we stratified the study population by BMI (BMI<25 and BMI25 kg/m2) and sex to evaluate effect modification. Multiple linear regression models were run to estimate the relationships between the POP exposure and the fasting plasma glucose and Hb1Ac among the participants in the control group. To fit the multiple regression, the levels of fasting plasma glucose and Hb1Ac were log-transformed, and the wet-weight serum POP concentrations were log-transformed or categorized into quartiles. Age, sex, BMI, triglycerides and total cholesterol were selected as covariates in the final linear regression model for consistency with

3

the logistical regression model. Multicollinearity was assessed by variance inflation factors (VIF). We considered a two-tailed p-value less than 0.05 to be significant for all tests. Statistical analysis was performed by SPSS 25.0 (IBM, Chicago, IL). 3. Results The study included 158 participants diagnosed with diabetes in the patient group and 158 participants in the control group. The main characteristics of the participants are provided in Table 1. The mean age was 51.2 years for the patients, and 50.2 years for the controls. In comparison to the participants in the control group, the participants diagnosed with T2D were more likely to have a higher BMI, with geometric means (GM) of 26.2 kg/m2 and 24.8 kg/m2 for the patients and controls, respectively. In addition, overweight participants (BMI25 kg/m2) accounted for 65.2% (n ¼ 103) of the patient group, and 45.6% (n ¼ 72) of the control group. The geometric mean concentrations of total cholesterol were significantly higher in the controls than in the patients, while the levels of triglycerides and total lipids showed no significant differences across the groups. Among the people in the patient group, the geometric mean level of fasting plasma glucose was 8.82 mmol/L, which was significantly higher than that of the people in the control group (GM±SD, 5.23 ± 1.15 mmol/L). Additionally, there was no data on HbA1c for 23 people in the control group and 5 people in the patient group. The GM concentrations of HbA1c were 5.68 ± 1.10% for the nondiabetic people and 8.61 ± 1.24% for the diabetic participants. PCB-153 and BDE-153 were the most abundant congeners for the PCBs and PBDEs, respectively (Table S1). The GM concentration was 53.67 ± 2.09 pg/mL for PCB-153 and 9.16 ± 2.16 pg/mL for BDE153. The serum concentrations of all the POPs were significantly higher in the patients than in the controls. The median concenP tration of the sum of all 11 POP congeners ( POPs) in the patient group was 0.19 ng/mL, and 0.13 ng/mL in the control group. The PCBs showed strongly positive correlations with each other (r ¼ 0.53e0.95, p < 0.01) (Table S2), while the PBDEs showed relatively weaker positive associations with the other POP congeners, with the Spearman’s r ranging from 0.16 to 0.51 (p < 0.01). Table 2 shows the associations between the serum POP concentrations in wet-weight and the risk of type 2 diabetes. The serum concentrations of PCBs and PBDEs were significantly and positively associated with an elevated risk of diabetes after adjusting for age and sex (model 1). Since associations between POP exposure and diabetes were not altered after further adjustment for BMI, total cholesterol and triglycerides, we used the final model to discuss in the present study. While most POPs showed the highest risk for diabetes in the fourth quartile, PCB-157 showed the strongest association with diabetes in the third quartile. However, all the p-values for the linear trend were less than 0.05, which indicated that the associations between POP exposure and diabetes were consistent with a linear dose-response relationship. Compared with the reference group, odds ratios for the highest quartile ranged from 2.36 to 8.98 for the PCBs, and from 2.50 to 2.79 for the PBDEs. Among all 11 POPs, PCB-114 showed the strongest association with diabetes, and the OR of the fourth quartile was 8.98 (95% CI: 3.72e21.70), followed by PCB-167 (OR ¼ 6.81, 95% CI: 2.81e16.49) and PCB-153 (OR ¼ 5.35, 95% CI: 2.20e13.01). Additionally, results remained unchanged when POPs expressed as continuous variables in the regression (Table S3). The associations between diabetes and the lipid-standardized POP concentrations are presented in Table S4. Most lipid-standardized PCBs and PBDEs had strong associations with diabetes, which is similar to models based on wet-weight. For example, for PCB-118, the OR of the fourth

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X. Han et al. / Chemosphere 241 (2020) 125030 Table 1 Baseline characteristics and clinical variables of the study participants.

Sex (n) Male Female Age (years) a BMI (kg/m2) b Triglycerides (mmol/L) b Total cholesterol (mmol/L) b Total lipid (g/L) b Fasting plasma glucose (mmol/L) HbA1c (%) b, c a b c d

Control (n ¼ 158)

Case (n ¼ 158)

52 106 50.16 ± 10.27 24.78 ± 1.14 1.48 ± 1.67 5.16 ± 1.23 6.61 ± 1.24 5.23 ± 1.15 5.68 ± 1.10

99 59 51.16 ± 11.89 26.21 ± 1.18 1.68 ± 1.88 4.89 ± 1.26 6.64 ± 1.33 8.82 ± 1.37 8.61 ± 1.24

p-value

d

0.000

b

0.247 0.000 0.145 0.016 0.660 0.000 0.000

Mean ± Standard Deviation. Geometric mean ± Geometric Standard Deviation. 23 people in the control group and 5 people in the case group lacked the data on HbA1c. p values are based on Student’s-test and Mann-Whitney U test for continuous variables, and Chi-square test for categorical variables.

quartile was 4.50 (95% CI: 1.93e10.53) for wet-weight adjusted for age, sex, BMI, total cholesterol and triglycerides, and it was 4.07 (95% CI: 1.85e8.95) for the lipid-standardized concentration adjusted for age, sex and BMI. The associations between mixtures of POPs and diabetes are presented in Table 2 and Table S5. For PCBs, the sum of the PCBs P P ( PCBs), the sum of the dioxin-like PCBs ( DL-PCBs) and the sum P of the nondioxin-like PCBs ( NDL-PCBs) were all positively associated with the risk of diabetes (all p-value<0.05). Additionally, PCBs were grouped based on the number of chlorine substitutions (PCB with 5 chlorines, PCB with 6 chlorines and PCB with 7 chlorines), and the Wolff’s method (Group 2A, Group 2B and Group 3) (Wolff et al., 1997). All groups of PCBs showed significant associations with elevated risk of type 2 diabetes. In addition, the sum of P P the PBDEs ( PBDEs) and POPs ( POPs) also showed positive associations with diabetes, and all the mixtures of POPs showed the highest risk of diabetes in the fourth quartile. In the BMI-stratified analysis, no obvious differences were observed in the associations between POP exposure and the risk of diabetes. Most PCBs and PBDEs were positively associated with the prevalence of diabetes in both the higher-BMI group (BMI 25 kg/ m2) and the lower-BMI group (BMI <25 kg/m2) after adjusting for age, sex, BMI, cholesterol and total triglycerides (Table 3). PCB-105, 118, 157, 153 and BDE-47 and 153 showed slightly stronger associations with high risk of diabetes in the lower-BMI group than in higher-BMI group, while other POPs showed weaker associations in the lower-BMI group than in higher-BMI group. Table 4 presents the sex-stratified results. POP exposure was positively associated with a high risk of diabetes in both males and females. Most PCBs and PBDEs showed stronger associations with the risk of type 2 diabetes in males than in females. For example, P the OR for the highest quartile of POPs was 6.97 (95% CI: 2.03e23.88) for males, nearly two times higher than that for females (OR ¼ 3.58, 95% CI: 1.02e12.57). The associations between POP exposure and fasting plasma glucose in the control group estimated by multiple linear regression are presented in Table S6. With POPs expressed as logtransformed continuous variables, PCB-105, 118, 156, 167 and 180 showed significantly positive associations with fasting plasma glucose, adjusted for age, sex, BMI, triglyceride and total cholesterol. For example, a 10-fold increase in PCB-105 was associated with a 0.04 mmol/L (95% CI: 0.002e0.07) increase in the concentration of lgFPG. When POPs were expressed as quartiles in the linear regression, the positive associations between POP exposure and fasting plasma glucose were not substantially altered. Additionally, POP concentrations in the highest quartile showed the strongest associations with the levels of fasting plasma glucose, and

most associations failed to reach statistical significance in the lower quartiles. Additionally, no PCBs or PBDEs showed significant associations with HbA1c (Table S7). 4. Discussion The present study showed significantly positive associations of PCBs, PBDEs, and their mixtures with the risk of type 2 diabetes in the study population after adjusting for age, sex, BMI, triglycerides and total cholesterol. All the POP congeners showed a linear doseresponse relationship with diabetes. In the stratified analysis, strong associations between POPs and diabetes were observed in both the higher-BMI and the lower-BMI groups, while slightly stronger associations between POPs and diabetes were observed in males than in females. In addition, some individual POPs and POP mixtures were associated with higher levels of fasting plasma glucose among the controls. These findings indicated that exposure to PCBs and PBDEs may be a diabetogenic factor. 4.1. Persistent organic pollutants and diabetes PCBs were found to be strongly associated with diabetes in our study. The results in the current study are consistent with previous findings from other countries. In the general population of Catalonia, Gasull et al. found PCB-118, 138, 153 and 180, and HCB were significantly associated with diabetes in a dose-dependent manner (Gasull et al., 2012). Similarly, a number of studies were consistent with ours in finding linear dose-responses for the relation between POPs and diabetes (Lee et al., 2006, 2007b, 2011a; Zong et al., 2018), while others showed nonlinear relationships, such as an inverted U-shape (Lee et al., 2007a, 2010). In a biological system, receptormediated responses may initially increase with increases in the dose and then decrease with further increases in the dose (Welshons et al., 2003). As a kind of endocrine disruptor, POPs may exhibit different dose-response curves under different exposure distributions in epidemiological studies. Therefore, a wide range of POP concentrations might exhibit an inverted U-shaped association with diabetes, while a narrow range might show positive, inverse and null associations with diabetes across the study population (Lee et al., 2010, 2014; Lee, 2012). P BDE-47, 153 and PBDEs also showed significant positive associations with diabetes in our study. There are limited epidemiological studies of the associations between PBDE exposure and diabetes in humans. Using data from the National Health and Nutrition Examination Survey (NHANEs), Lim et al. found that BDE153 showed a nonlinear association with diabetes, while other PBDE congeners (such as BDE-28, 47, 99 and 100) and diabetes

X. Han et al. / Chemosphere 241 (2020) 125030

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Table 2 ORs and 95% CI for the associations between serum concentrations of POP in wet-weight (pg/mL) and type 2 diabetes. POPs

PCB-105

PCB-114

PCB-118

PCB-156

PCB-157

PCB-167

PCB-138

PCB-153

PCB-180

BDE-47

BDE-153

∑PCBs

∑PBDEs

∑POPs

Quartiles

Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model Case-Control Adjusted Model Adjusted Model Adjusted Model

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

p

Q1

Q2

16/40 Reference Reference Reference 8/51 Reference Reference Reference 15/40 Reference Reference Reference 21/40 Reference Reference Reference 11/40 Reference Reference Reference 11/40 Reference Reference Reference 13/40 Reference Reference Reference 12/40 Reference Reference Reference 18/40 Reference Reference Reference 25/40 Reference Reference Reference 24/40 Reference Reference Reference 13/40 Reference Reference Reference 19/39 Reference Reference Reference 14/40 Reference Reference Reference

33/39 1.91 (0.87, 1.80 (0.82, 2.16 (0.97, 26/28 5.86 (2.29, 5.59 (2.17, 5.62 (2.15, 29/39 1.60 (0.71, 1.54 (0.68, 1.64 (0.72, 22/39 0.79 (0.36, 0.72 (0.32, 0.81 (0.36, 29/39 2.42 (1.04, 2.32 (0.99, 2.36 (1.00, 26/39 1.76 (0.74, 1.85 (0.77, 2.06 (0.86, 20/39 1.33 (0.56, 1.36 (0.57, 1.53 (0.63, 29/39 2.04 (0.88, 1.86 (0.80, 2.23 (0.93, 34/39 1.71 (0.81, 1.74 (0.81, 1.80 (0.83, 17/39 0.69 (0.31, 0.71 (0.32, 0.76 (0.34, 23/39 0.72 (0.34, 0.80 (0.37, 0.89 (0.41, 30/39 1.93 (0.84, 1.94 (0.84, 2.20 (0.95, 26/40 1.05 (0.49, 1.16 (0.53, 1.35 (0.61, 32/39 1.92 (0.86, 1.92 (0.86, 2.22 (0.97,

Q3 4.17) 3.96) 4.81) 15.00) 14.40) 14.69) 3.59) 3.46) 3.70) 1.73) 1.58) 1.81) 5.64) 5.44) 5.54) 4.19) 4.44) 4.98) 3.18) 3.28) 3.71) 4.73) 4.36) 5.34) 3.65) 3.74) 3.90) 1.50) 1.57) 1.68) 1.55) 1.73) 1.95) 4.40) 4.44) 5.12) 2.27) 2.52) 2.99) 4.29) 4.31) 5.08)

44/40 2.72 (1.27, 2.38 (1.09, 2.83 (1.27, 58/40 7.18 (3.02, 6.91 (2.89, 7.41 (3.07, 41/40 2.51 (1.13, 2.28 (1.02, 2.52 (1.09, 52/40 1.83 (0.88, 1.66 (0.79, 1.94 (0.90, 63/40 4.40 (1.96, 4.04 (1.79, 4.68 (2.03, 37/40 2.56 (1.10, 2.47 (1.06, 2.75 (1.16, 57/40 3.52 (1.56, 3.17 (1.39, 3.46 (1.49, 43/39 3.29 (1.41, 2.97 (1.26, 3.32 (1.39, 39/40 1.77 (0.80, 1.64 (0.73, 1.69 (0.74, 38/40 1.35 (0.67, 1.26 (0.62, 1.26 (0.62, 42/40 1.07 (0.52, 1.13 (0.54, 1.29 (0.61, 44/40 2.86 (1.26, 2.57 (1.12, 2.89 (1.22, 35/40 1.15 (0.54, 1.22 (0.56, 1.44 (0.66, 45/39 2.56 (1.14, 2.26 (0.99, 2.59 (1.11,

a

Q4 5.84) 5.19) 6.31) 17.12) 16.52) 17.91) 5.58) 5.12) 5.79) 3.78) 3.48) 4.19) 9.89) 9.13) 10.78) 5.95) 5.79) 6.53) 7.95) 7.23) 8.04) 7.66) 6.99) 7.98) 3.94) 3.68) 3.87) 2.71) 2.55) 2.55) 2.21) 2.34) 2.73) 6.49) 5.92) 6.83) 2.47) 2.64) 3.19) 5.75) 5.15) 6.08)

65/39 3.71 (1.75, 3.19 (1.47, 4.02 (1.76, 66/39 8.68 (3.66, 8.31 (3.48, 8.98 (3.72, 73/39 4.58 (2.07, 4.00 (1.78, 4.50 (1.93, 63/39 2.05 (0.96, 2.03 (0.94, 2.36 (1.03, 55/39 3.64 (1.59, 3.43 (1.49, 3.82 (1.62, 84/39 5.84 (2.54, 5.59 (2.41, 6.81 (2.81, 68/39 4.21 (1.85, 4.11 (1.79, 4.68 (1.97, 74/40 4.89 (2.11, 4.50 (1.92, 5.35 (2.20, 67/39 2.78 (1.24, 2.81 (1.25, 3.05 (1.29, 78/39 2.60 (1.35, 2.49 (1.28, 2.50 (1.27, 69/39 1.82 (0.91, 1.95 (0.97, 2.79 (1.28, 71/39 4.33 (1.89, 4.20 (1.81, 4.91 (2.04, 78/39 2.64 (1.29, 2.80 (1.35, 4.26 (1.89, 67/40 3.48 (1.55, 3.38 (1.49, 4.09 (1.72,

7.89) 6.94) 9.19)

0.001 0.006 0.003

20.59) 19.80) 21.70)

0.000 0.000 0.000

10.11) 9.00) 10.53)

0.000 0.000 0.000

4.36) 4.37) 5.40)

0.018 0.014 0.009

8.32) 7.90) 9.05)

0.018 0.010 0.006

13.46) 12.94) 16.49)

0.000 0.000 0.000

9.60) 9.45) 11.14)

0.000 0.000 0.000

11.34) 10.51) 13.01)

0.000 0.000 0.000

6.21) 6.35) 7.18)

0.018 0.018 0.016

5.01) 4.83) 4.89)

0.000 0.001 0.001

3.62) 3.93) 6.07)

0.014 0.011 0.001

9.96) 9.72) 11.86)

0.001 0.001 0.001

5.39) 5.77) 9.57)

0.001 0.001 0.000

7.84) 7.65) 9.76)

0.004 0.006 0.003

OR: Odds ratio. CI: Confidence interval. Adjusted Model 1: adjusted for age and sex. Adjusted Model 2: adjusted for age, sex and BMI. Adjusted Model 3: adjusted for age, sex, BMI, total cholesterol and triglycerides. a p for trend based on the median value of variable for each quartile.

failed to reach statistical significance (Lim et al., 2008). A Chinese study focusing on the associations between PBDEs and diabetes found positive associations between BDE-47 and the prevalence of human diabetes (Zhang et al., 2016). To our knowledge, no statistical associations between PBDE exposure and diabetes have been reported in other studies, except the two studies above. For example, Turyk et al. found that exposure to p,p’-diphenyldichloroethene (p,p’-DDE) and dioxin-like mono-ortho PCBs was

significantly associated with diabetes in a cohort of sport fish consumers, while PBDEs were not (Turyk et al., 2009). Our results on the associations between PBDE exposure and diabetes were not consistent with most studies, which might have been affected by population characteristics, lifestyle, limited studies and other potential confounding factors. However, some studies suggested that the associations between diabetes and chlorinated POPs (such as PCBs) were different than the associations between diabetes and

6

X. Han et al. / Chemosphere 241 (2020) 125030

Table 3 Adjusted ORs

a

POPs

and 95% CI for the associations between serum concentrations of POP in wet-weight (pg/mL) and type 2 diabetes stratified by BMI. Quartiles BMI<25 kg/m2 (n ¼ 175)

PCB-105 PCB-114 PCB-118 PCB-156 PCB-157 PCB-167 PCB-138 PCB-153 PCB-180 BDE-47 BDE-153 P PCBs P PBDEs P POPs a

Q1

Q2

Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference

2.74 7.33 4.13 0.72 6.95 1.76 2.55 3.81 1.60 0.59 1.13 3.62 2.55 3.76

BMI25 kg/m2 (n ¼ 141) Q3

(0.75, (1.60, (1.00, (0.22, (1.30, (0.50, (0.62, (0.96, (0.48, (0.14, (0.33, (0.90, (0.71, (0.95,

10.10) 33.60) 16.95) 2.44) 37.11) 6.16) 10.5) 15.05) 5.30) 2.56) 3.80) 14.49) 9.17) 14.94)

7.26 7.99 4.53 1.49 9.53 1.77 3.81 2.85 1.13 2.76 1.62 3.61 2.26 3.45

Q4 (1.78, (1.96, (1.00, (0.44, (1.70, (0.48, (0.91, (0.67, (0.29, (0.89, (0.47, (0.86, (0.60, (0.80,

29.69) 32.53) 20.51) 4.98) 53.44) 6.49) 15.98) 12.22) 4.41) 8.60) 5.63) 15.08) 8.57) 14.79)

8.98 6.11 7.20 1.62 8.70 3.72 3.50 4.10 1.08 5.07 3.23 3.84 5.92 3.91

(2.09, (1.49, (1.52, (0.48, (1.57, (1.00, (0.79, (0.98, (0.27, (1.63, (0.92, (0.85, (1.55, (0.88,

38.53) 25.10) 34.06) 5.44) 48.35) 13.87) 15.50) 17.19) 4.38) 15.81) 11.33) 17.31) 22.57) 17.49)

Q2 2.63 1.41 0.71 0.89 1.23 1.46 0.88 0.87 1.17 0.90 0.88 1.29 0.94 1.09

Q3 (0.93, (0.35, (0.23, (0.26, (0.40, (0.41, (0.24, (0.29, (0.39, (0.32, (0.30, (0.39, (0.33, (0.33,

7.46) 5.72) 2.23) 3.00) 3.84) 5.22) 3.27) 2.65) 3.52) 2.53) 2.53) 4.26) 2.72) 3.53)

2.27 7.18 2.70 3.39 3.09 2.64 2.64 1.80 2.32 0.95 1.37 2.07 1.55 1.55

Q4 (0.78, (2.20, (0.89, (1.08, (1.05, (0.78, (0.86, (0.61, (0.77, (0.36, (0.51, (0.64, (0.58, (0.49,

6.65) 23.47) 8.16) 10.64) 9.13) 8.98) 8.07) 5.34) 7.05) 2.53) 3.70) 6.69) 4.18) 4.93)

3.17 (1.08, 9.28) 9.95 (3.07, 32.30) 4.75 (1.60, 14.13) 4.83 (1.44, 16.21) 3.90 (1.28, 11.87) 10.12 (2.98, 34.37) 8.36 (2.56, 27.29) 3.71 (1.28, 10.77) 4.36 (1.35, 14.07) 2.19 (0.87, 5.55) 1.92 (0.68, 5.47) 7.40 (2.25, 24.31) 2.18 (0.75, 6.31) 5.98 (1.88, 19.03)

Adjusted ORs: adjusted for age, sex, BMI, total cholesterol and triglycerides.

Table 4 Adjusted ORs POPs

a

and 95% CI for the associations between serum concentrations of POP in wet-weight (pg/mL) and type 2 diabetes stratified by sex. Quartiles Female (n ¼ 165)

PCB-105 PCB-114 PCB-118 PCB-156 PCB-157 PCB-167 PCB-138 PCB-153 PCB-180 BDE-47 BDE-153 P PCBs P PBDEs P POPs a

Q1 Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference

Q1

Q2

Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference

3.73 5.21 1.43 0.56 1.61 2.35 1.56 1.90 1.33 0.48 0.82 1.87 0.98 2.14

Male (n ¼ 151) Q3 (1.06, (1.22, (0.43, (0.18, (0.51, (0.71, (0.48, (0.55, (0.46, (0.14, (0.29, (0.60, (0.34, (0.64,

13.21) 22.24) 4.74) 1.76) 5.11) 7.76) 5.04) 6.58) 3.87) 1.60) 2.34) 5.82) 2.85) 7.19)

2.79 8.40 2.34 0.89 2.63 2.30 1.90 2.82 1.46 1.28 0.82 2.07 1.26 2.72

Q4 (0.74, (2.46, (0.72, (0.31, (0.86, (0.70, (0.59, (0.84, (0.48, (0.50, (0.29, (0.64, (0.44, (0.77,

10.55) 28.69) 7.57) 2.61) 8.05) 7.58) 6.15) 9.54) 4.50) 3.30) 2.31) 6.68) 3.59) 9.67)

4.12 9.87 3.08 1.58 2.88 4.79 2.88 3.69 1.64 2.09 2.14 2.52 2.70 3.58

(1.04, (3.00, (0.92, (0.54, (0.93, (1.42, (0.90, (1.08, (0.52, (0.85, (0.80, (0.77, (0.97, (1.02,

16.23) 32.47) 10.35) 4.63) 8.90) 16.19) 9.22) 12.66) 5.15) 5.14) 5.76) 8.28) 7.57) 12.57)

Q1

Q2

Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference

1.64 6.79 1.09 2.88 2.21 1.49 2.10 0.75 1.41 0.75 0.70 1.54 0.46 2.41

Q3 (0.53, (1.79, (0.35, (0.93, (0.74, (0.44, (0.63, (0.23, (0.46, (0.24, (0.22, (0.48, (0.13, (0.76,

5.05) 25.72) 3.37) 8.89) 6.62) 5.03) 7.07) 2.43) 4.31) 2.31) 2.24) 4.89) 1.55) 7.65)

2.30 7.37 1.04 4.32 2.88 3.38 4.48 1.98 1.81 1.33 1.49 2.07 1.95 2.81

Q4 (0.80, (1.91, (0.34, (1.24, (0.96, (1.01, (1.39, (0.65, (0.56, (0.47, (0.52, (0.66, (0.67, (0.87,

6.60) 28.38) 3.20) 15.04) 8.63) 11.36) 14.44) 6.02) 5.87) 3.74) 4.26) 6.47) 5.64) 9.10)

3.74 (1.22, 11.43) 10.59 (2.73, 41.00) 4.62 (1.52, 14.06) 7.29 (2.01, 26.41) 4.58 (1.48, 14.18) 10.28 (2.99, 35.31) 7.35 (2.12, 25.43) 3.91 (1.26, 12.18) 4.16 (1.22, 14.25) 2.91 (1.06, 8.00) 4.31 (1.48, 12.53) 4.60 (1.39, 15.20) 4.83 (1.62, 14.46) 6.97 (2.03, 23.88)

Adjusted ORs: adjusted forage, BMI, total cholesterol and triglycerides.

PBDEs. Generally, the effects of POPs on the disruption of glucose homeostasis require long-term exposure (Lim et al., 2008). Unlike those of PCBs, PBDE biomarkers are affected by both the indoor environment and dietary exposure (Schecter et al., 2006; Wu et al., 2007); therefore, PBDE levels in the human body might not reflect the long-term exposure as completely as do PCB levels (Lim et al., 2008). The weakly positive correlations between PBDEs and PCBs and the nonsignificant correlations between PBDEs and age observed in our study also support the differences in exposure routes for PBDEs and PCBs. Additionally, no significant associations were found between the PBDE congeners and fasting plasma glucose, suggesting that the effect of PBDEs on diabetes might not occur through disruption of glucose homeostasis. 4.2. Potential factors influencing the associations between POPs and diabetes Being overweight or obese has been recognized as an important modifiable risk factor for type 2 diabetes (Zaccardi et al., 2017). However, strong associations between POPs and diabetes were observed in both the higher-BMI and lower-BMI groups, which was inconsistent with some, but not all, studies. A previous study found

positive associations between POP exposure and diabetes only among overweight participants with high POP exposure, not among normal-weight participants with high POP exposure or overweight participants with low POP exposure (Airaksinen et al., 2011). Another study found the OR of diabetes for the highest quartile of PCBs was 9.1 for obese participants, nearly 9 times higher than the OR for the lowest quartile in normal-weight participants (Gasull et al., 2012). These results suggested that the coexistence of obesity and POP exposure might have a synergistic effect on the diabetes risk (Airaksinen et al., 2011). However, we did not observe an obvious effect of obesity on the associations between PCB and PBDE exposure and diabetes in the present study. The inconsistent results might be affected by differences in race, with Asian diabetics having a lower BMI than Western people. Therefore, the range of BMIs may be lower in our study, and, along with the relatively small sample size, may have limited our ability to detect modification by BMI. A weak sex difference was noted in the present study. PCB and PBDE concentrations in males showed stronger associations with diabetes than that in females. However, the results differed from some other studies (Lee et al., 2006; Song et al., 2016; Turyk et al., 2009). Song et al. found stronger associations between PCBs and

X. Han et al. / Chemosphere 241 (2020) 125030

diabetes for females than for males (Song et al., 2016). Some studies showed no significant association for both males and females (Lee et al., 2006; Turyk et al., 2009). Previous studies indicated that endogenous sex hormones might modulate glycemic status and risk of type 2 diabetes in males and females differently (Ding et al., 2006). As a group of well-known endocrine-disrupting chemicals, some POPs might disturb estrogen and/or androgen receptor signaling pathways and alter metabolic regulatory mechanisms in a sex-dependent manner (Song et al., 2016). Additionally, higher levels of POPs were observed in males than in females in this study (data not shown). The stronger associations between POPs and diabetes in males might be due to the higher POP concentrations, which is consistent with the linear dose-response relationship between POP exposure and diabetes in this study. Limited by the relatively small number of participants in the current study, most ORs in the sex-stratified analysis showed wide confidence intervals, which may also cause the weak difference of POP exposure and diabetes between males and females. More studies are needed to explain it. In the current study, we used lipid-standardized concentrations, wet-weight concentrations and wet-weight concentrations adjusted with total cholesterol and triglycerides for PCBs and PBDEs to investigate associations between POP exposure and diabetes. As highly lipophilic substances, POPs showed significantly positive relationships with triglycerides and total lipids in our study (data not shown). Previous studies indicated that some POPs might disturb lipid metabolism and promote dyslipidemia (La Merrill et al., 2013; Lee et al., 2010, 2014). As dyslipidemia is closely involved in the pathogenesis of T2D, lipid concentrations might play an intermediate role in the associations of POPs and T2D (Taylor et al., 2013; Zhang et al., 2016). Therefore, using lipidstandardized or wet-weight POP concentrations adjusted with total cholesterol and triglycerides may underestimate the true associations between POPs and diabetes (Lee et al., 2010; Taylor et al., 2013). Lee et al. recognized that the true associations of POPs and type 2 diabetes might be between the estimates with and without the lipid adjustment (Lee et al., 2014). 4.3. Comparison to other regions Compared with many Western countries, there were relatively stronger associations between POP exposure and diabetes in China. For example, a strong association was observed between PCBs and diabetes in this study. The adjusted OR in the highest quartile of PCB-153 was 5.35 in our study, compared to 1.6 in Finnish people (lipid-standardized value) (Airaksinen et al., 2011), 3.0 and 1.4 in Native Americans (wet-weight value and lipid-standardized value) (Codru et al., 2007) and 2.9 in the general population of Catalonia (wet-weight value) (Gasull et al., 2012). However, people in China had a relatively lower concentration of PCBs than many western countries. The geometrical mean concentration of PCB-153 was 53.7 pg/mL (lipid-standardized value: 8.1 ng/g lipid) in this study, whereas the mean value of PCB-153 was 310 ± 3.4 ng/g lipid in Finnish people (Airaksinen et al., 2011), 0.70 ± 0.61 ppb in Native Americans (Codru et al., 2007), and 0.564 ng/mL in people from Catalonia, Spain (Gasull et al., 2012). Different races might show different susceptibility to POPs (Son et al., 2010). The associations between relatively lower PCBs exposure and higher risk of diabetes in this study might suggest that people in China were more susceptible to the adverse effects of PCBs than people in some other regions. Since few studies reported associations between POPs exposure and diabetes in the Chinese population, more future epidemiological studies are needed to investigate the differences in the susceptibility to POPs across populations. Additionally, different designs of different studies also limited the comparisons from

7

regions. 4.4. Limitations There were some limitations to our study. First, the possibility of reverse-causality cannot be excluded in case-control studies. Type 2 diabetes is often accompanied by dyslipidemia, which may in turn affect the levels of POPs in human serum. Since serum samples were collected at the time of diagnosis or treatment of type 2 diabetes in this study, the treatment of diabetes may also influence POPs levels. However, several prospective studies have also found positive associations between POP exposure and prevalence of type 2 diabetes, indicating the effect of POPs on diabetes might not be due to the reverse-causality (Wu et al., 2013; Zong et al., 2018). Secondly, the sample size of our study was small, limiting our ability to examine stratified associations and resulting in relatively wide confidence intervals. Thirdly, diet and family history of diabetes were not obtained in this study. As a complex disease, type 2 diabetes is affected by both genetic and environmental factors. Dietary factors are significantly associated with the development of diabetes (Hu et al., 2001; Hu, 2011), and diet was also the main route of exposure for PCBs. In addition, family history of diabetes has also been recognized as a traditional risk factor for type 2 diabetes. Consequently, there may be uncontrolled confounding effects in our estimates without adjustment for these factors or for other environmental exposures that have been associated with diabetes. 5. Conclusion In this case-control study, serum concentrations of PCBs and PBDEs showed significantly positive linear associations with the risk of type 2 diabetes in Chinese population. The results in our study are consistent with previous studies and indicate that POPs might be a diabetogenic factor. Potential modifiable factors between POPs and diabetes such as BMI and sex were also assessed in this study, although no obviously synergistic effect of BMI and POPs was found. Additionally, the sex-difference between POPs and diabetes observed in this study remained unclear due to the limited sample size. Further research is needed to clarify the coeffects of POPs, BMI and sex on the risk of diabetes in Chinese population. Declaration of competing interest All authors declare they have no actual or potential competing financial interest. Acknowledgements This work was supported by National Natural Science Foundation of China (91743206, 21777186, 41676183 and 21621064), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB14010100) and the Sanming Project of Medicine in Shenzhen (No. SZSM201811070). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2019.125030. References Airaksinen, R., Rantakokko, P., Eriksson, J.G., Blomstedt, P., Kajantie, E., Kiviranta, H., 2011. Association between type 2 diabetes and exposure to persistent organic pollutants. Diabetes Care 34, 1972e1979.

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