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Is dietary macronutrient intake associated with serum concentrations of organochlorine pesticides in humans?
Environmental Pollution 259 (2020) 113819
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Is dietary macronutrient intake associated with serum concentrations of organochlorine pesticides in humans?* Yu-Mi Lee a, Somi Heo a, Se-A Kim b, c, Duk-Hee Lee a, c, * a
Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea Department of Biomedical Science, Graduate School, Kyungpook National University, Daegu, Republic of Korea c BK21 Plus KNU Biomedical Convergence Program, Department of Biomedical Science, Kyungpook National University, Daegu, Republic of Korea b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 July 2019 Received in revised form 3 December 2019 Accepted 13 December 2019 Available online 16 December 2019
In the general population, chronic exposure to low-dose persistent organic pollutants (POPs), particularly organochlorine pesticides (OCPs), has been recently linked to many chronic diseases. Widespread contamination of the food chain and human adipose tissue has made avoiding exposure to these chemicals impossible; thus, alternative strategies for decreasing the chemical burden must be investigated. Recently, macronutrient intake was found to significantly modify the toxicokinetics of POPs in animal experimental studies. Thus, we evaluated whether macronutrient intake was related to serum concentrations of OCPs in healthy adults without cardio-metabolic diseases. Subjects included 1,764 adults, aged 20 years or above, who participated in the National Health and Nutrition Examination Survey 1999e2004. Macronutrient intake was assessed based on a 24-h dietary recall interview. Six individual OCPs commonly detected among the general population were evaluated as markers of OCPs and other coexisting lipophilic chemicals stored in adipose tissue and released into circulation. High fat intake was associated with lower concentrations of OCPs, while high carbohydrate intake showed the opposite result. When three types of fats were individually evaluated, both saturated fatty acids and monounsaturated fatty acids, but not polyunsaturated fatty acids, were inversely associated with serum concentrations of OCPs. Adjustment for possible confounders did not change the results. When stratified by age, gender, body mass index, and physical activity, these associations were similar in most subgroups. Thus, similar to the findings observed in animal experimental studies, a moderate-fat diet with low carbohydrate intake was related to low serum concentrations of OCPs in humans. Although these findings need to be replicated, changing dietary macronutrient intake can be investigated as a practical strategy for dealing with unavoidable lipophilic chemical mixtures such as OCPs in modern society. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Adipose tissue Low carbohydrate high fat diet Macronutrient Organochlorine pesticide Persistent organic pollutant
1. Introduction Recently, serum concentrations of chlorinated persistent organic pollutants (POPs), particularly organochlorine pesticides (OCPs), have been linked to various chronic diseases (Evangelou et al., 2016; Lee et al., 2014, 2017; Lind and Lind, 2012; Ruzzin et al., 2012). Many chlorinated POPs were banned in the 1970s and 1980s but are still widely detected in the environment and
* This paper has been recommended for acceptance by Wen Chen. * Corresponding author. Department of Preventive Medicine, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea. E-mail addresses: [email protected] (Y.-M. Lee), [email protected] (S. Heo), [email protected] (S.-A. Kim), [email protected] (D.-H. Lee).
https://doi.org/10.1016/j.envpol.2019.113819 0269-7491/© 2019 Elsevier Ltd. All rights reserved.
human body because of their persistency. As strong lipophilic chemicals, POPs are predominantly stored in the adipose tissue of living organisms and are slowly released into circulation for metabolism (Birnbaum, 1985; Needham et al., 1990). Because of the toxicokinetics of POPs, serum concentrations of POPs measured in the general population can be considered as markers of various lipophilic chemical mixtures in the adipose tissue, which include both POPs and other chemicals coexisting with POPs (Lee et al., 2018). When certain environmental chemicals are suspected to be harmful, the reduction of exposure to external exposure sources is generally considered as a preventive strategy (Hutter et al., 2016). However, in the case of POPs, which have widely contaminated the food chain and human adipose tissue, neither further regulation nor exposure avoidance may be effective for protecting humans
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Table 1 General characteristics of the study participants. Total
Mean ± standard deviation Age (years) BMI (kg/m2) Weight change over the past year (kg) Leisure-time physical activity (MET-minutes/day) Dietary intake of Total calories (kcal) Total protein (g) Total carbohydrate (g) Total fat (g) Total saturated fatty acids (g) Total monounsaturated fatty acids (g) Total polyunsaturated fatty acids (g) Serum total cholesterol (mg/dL) Serum triglycerides (mg/dL) Proportion [n (%)] Male Non-Hispanic white BMI 30 kg/m2 No leisure-time physical activity
Ptrenda
Calories from dietary fat intake (%) Q1 <25.8
Q2 25.8e31.0
Q3 31.1e35.1
Q4 35.1e40.4
Q5 40.4e75.1
(n ¼ 1,764)
(n ¼ 352)
(n ¼ 353)
(n ¼ 353)
(n ¼ 353)
(n ¼ 353)
39.4 ± 15.8 27.3 ± 5.8 1.8 ± 8.3 153.0 ± 369.4
39.5 ± 16.3 27.5 ± 5.7 0.9 ± 8.7 135.1 ± 289.2
37.6 ± 14.1 27.2 ± 5.8 2.3 ± 8.7 168.3 ± 421.8
38.6 ± 16.1 26.9 ± 5.6 1.9 ± 7.6 125.6 ± 237.7
39.7 ± 16.5 27.4 ± 5.8 2.5 ± 7.8 177.4 ± 482.5
41.4 ± 15.5 27.7 ± 6.2 1.5 ± 8.7 158.6 ± 361.7
0.026 0.544 0.296 0.367
2281.4 ± 1065.4 84.3 ± 43.6 285.1 ± 138.2 84.3 ± 48.4 27.9 ± 17.5 31.7 ± 19.2 17.2 ± 11.8 197.4 ± 43.3 129.2 ± 113.5
1893.7 ± 866.1 68.1 ± 35.8 298.4 ± 140.4 43.4 ± 22.2 14.2 ± 8.6 15.6 ± 8.5 9.2 ± 5.6 194.1 ± 41.3 132.3 ± 110.4
2161.5 ± 956.8 76.0 ± 35.6 303.1 ± 143.8 67.8 ± 30.3 22.2 ± 11.1 25.5 ± 12.5 14.0 ± 7.9 195.0 ± 40.0 120.8 ± 72.5
2352.7 ± 1039.6 85.6 ± 41.5 299.6 ± 131.6 84.9 ± 35.7 28.1 ± 12.7 31.8 ± 14.2 17.5 ± 10.1 200.5 ± 43.4 134.2 ± 83.2
2548.8 ± 1111.1 97.1 ± 48.8 293.9 ± 132.0 104.7 ± 45.4 35.4 ± 17.4 39.6 ± 18.1 20.5 ± 11.0 198.0 ± 50.3 139.0 ± 163.8
2449.4 ± 1198.5 94.6 ± 47.8 230.7 ± 129.9 120.5 ± 57.5 39.5 ± 21.5 45.9 ± 23.0 24.6 ± 15.4 199.3 ± 40.5 119.9 ± 114.3
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.063 0.733
835 799 464 711
174 (49.4) 124 (35.2) 93 (26.4) 166 (47.2)
145 (41.1) 157 (44.5) 91 (25.8) 149 (42.2)
167 (47.3) 168 (47.6) 82 (23.2) 138 (39.1)
189 (53.5) 176 (49.9) 94 (26.6) 139 (39.4)
160 174 104 119
0.612 <0.001 0.349 <0.001
(47.3) (45.3) (26.3) (40.3)
(45.3) (49.3) (29.5) (33.7)
Notes: BMI, body mass index; Q1eQ5, 1st quintilee5th quintile. a A generalized linear model was used for continuous variables, and a Cochran-Armitage trend analysis was used for categorical variables.
(Lee and Jacobs, 2019). Therefore, alternative approaches for decreasing POPs-related health risks in humans are needed. Interestingly, several animal experimental studies have suggested that diet can be used as an intervention to decrease serum concentrations of lipophilic chemicals in humans. For example, a high-fat diet resulted in increased elimination of POPs in the feces of mice compared to that of mice fed a low-fat diet (Kania-Korwel et al., 2008). In another animal study, the accumulation of POPs in the adipose tissue and liver was affected by the composition of macronutrient intake, not by the total intake of POPs (Myrmel et al., 2016); a high-fat and high-protein diet resulted in lower deposition of POPs in the adipose tissue and liver than did a low-fat and highcarbohydrate diet. In this study, high-fat and high-protein diet increased hepatic expression of genes involved in metabolism and elimination of xenobiotics. These studies suggest that the elimination of POPs can be modified by dietary macronutrient intake. To the best of our knowledge, the association between dietary macronutrient intake and serum concentrations of POPs has never been investigated in humans. As fatty animal food is the main external exposure source of POPs in the current general population (Schafer and Kegley, 2002), the general belief is that fatty animal food should be avoided to reduce the exposure to POPs. However, if the results of the animal experimental studies mentioned above (Kania-Korwel et al., 2008; Myrmel et al., 2016) are relevant to humans, a high-fat diet may be related to low serum concentrations of POPs in humans. Although randomized clinical trials are generally considered as the best study design to evaluate the effects of diet, a crosssectional design has the advantage of enabling investigation of the long-term effect of diet on serum concentrations of POPs. The dynamic nature of POPs between blood and adipose tissue, which function to reach equilibrium (Jackson et al., 2017; La Merrill et al., 2013; Levitt, 2010), can mask short-term effects of diet intervention. This study was performed to evaluate if macronutrient intake is related to serum concentrations of OCPs in healthy adults without any cardio-metabolic diseases. In this study, serum concentrations
of OCPs were selected as markers of OCPs and other coexisting lipophilic chemicals that are released from adipocytes to the circulation because human adipose tissue is widely contaminated with a tremendous number of environmental chemicals including OCPs (Kim et al., 2014; Moon et al., 2012), and serum concentrations of OCPs are mainly determined by the release of OCPs from adipose tissue to circulation (Birnbaum, 1985; Lee et al., 2018; Needham et al., 1990), In fact, serum concentrations of many lipophilic compounds belonging to POPs are positively correlated among the general population (Kang et al., 2008; Porta et al., 2010). 2. Materials and methods 2.1. Study subjects This study was performed based on the dataset from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004 conducted by the Centers for Disease Control and Prevention (CDC) in the U.S. Study subjects were adults aged 20 years and had information on their serum concentrations of OCPs and dietary intake of macronutrients. We excluded patients with diabetes, dyslipidemia, hypertension, coronary heart disease, stroke, and cancer, which were diagnosed by physicians or based on medication administration, as these diseases are related to increased serum concentration of OCPs and the diet can be influenced by the status of disease. The final sample size was 1,764 subjects. 2.2. Data collection and measurements The survey questionnaires included information on demographic characteristics, health behaviors, and dietary habits. For nutritional assessment, a 24-h dietary recall interview was conducted by trained dietary interviewers using a standard set of measuring guides. Venous blood was sampled and frozen at 20 C. OCP concentrations in the serum were measured by highresolution gas chromatography/mass spectrometry using isotope
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P P Fig. 1. The sum of OCP concentrations ( OCPs, geometric mean ± standard error, ng/g of lipid) according to the calories from dietary intake of selected nutrients. Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st P quintilee5th quintile of each nutrient. Ptrend and Pquadratic were used to test whether the trends of the OCPs according to the quintiles of macronutrient intake have a statistically significant linear or quadratic shape.
dilution for quantification. The procedure used for OCP measurement is described in detail in the CDC laboratory procedure manual (CDC/National Center for Health Statistics, 2006). We selected six individual OCPs (b-hexachlorocyclohexane, p,p0 -DDE, p,p0 -DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide), which are commonly detected among the general population. The detection rates (the values at or above the limit of detection) of OCPs among study participants were as follows: b-hexachlorocyclohexane, 66.3%; p,p0 -DDE, 100%; p,p0 -DDT, 46.5%; oxychlordane, 74.9%; transnonachlor, 86.2%; and heptachlor epoxide, 46.3%. NHANES provides
the detection limit divided by the square root of 2 for values that are below the limit of detection (CDC/National Center for Health Statistics, 2008). Although we attempted to use both lipid-standardized concentrations and wet concentrations with lipid adjustment in our analyses, only the results of lipid-standardized concentrations are presented, as they revealed similar associations. Results of wet concentrations are presented as supplementary results. The lipidstandardized concentration was calculated by dividing the wet weight concentrations by the total lipids [total lipids (mg/
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Table 2 P Adjusted sum OCP concentrations ( OCPs, geometric mean ± standard error, ng/g of lipid) according to the calories from dietary intake of selected nutrients. Nutrients
Modela
Total fat % 1 2 Total saturated fatty acids % 1 2 Total monounsaturated fatty acids % 1 2 Total polyunsaturated fatty acids % 1 2 Total protein % 1 2 Total carbohydrate % 1 2
Calories from dietary intake of selected nutrients (%)
Ptrend
Q1
Q2
Q3
Q4
Q5
n ¼ 352 <25.8 481.3 ± 23.4 462.9 ± 22.6 n ¼ 352 <7.7 459.6 ± 22.5 439.6 ± 21.7 n ¼ 352 <9.2 490.3 ± 23.8 472.5 ± 23.1 n ¼ 352 <4.3 416.1 ± 20.3 406.0 ± 19.6 n ¼ 352 <11.4 357.1 ± 17.4 352.2 ± 16.9 n ¼ 352 <43.3 357.9 ± 17.4 362.5 ± 17.4
n ¼ 353 25.8e31.0 387.8 ± 18.8 385.2 ± 18.4 n ¼ 353 7.7e9.8 401.3 ± 19.5 400.6 ± 19.1 n ¼ 353 9.2e11.3 400.4 ± 19.4 394.5 ± 18.9 n ¼ 353 4.3e5.7 400.6 ± 19.5 398.8 ± 19.1 n ¼ 353 11.4e13.6 381.7 ± 18.6 385.6 ± 18.5 n ¼ 353 43.3e49.0 383.3 ± 18.6 392.5 ± 18.8
n ¼ 353 31.1e35.1 375.2 ± 18.1 374.2 ± 17.9 n ¼ 353 9.9e11.7 376.9 ± 18.3 385.3 ± 18.5 n ¼ 353 11.4e13.3 388.9 ± 18.8 391.0 ± 18.7 n ¼ 353 5.7e7.0 372.2 ± 18.1 374.0 ± 17.9 n ¼ 353 13.6e16.1 426.4 ± 20.7 428.2 ± 20.5 n ¼ 353 49.1e54.3 367.9 ± 17.9 369.6 ± 17.7
n ¼ 353 35.1e40.4 371.5 ± 18.0 380.2 ± 18.3 n ¼ 353 11.7e13.8 355.5 ± 17.3 358.7 ± 17.2 n ¼ 353 13.3e15.4 338.3 ± 16.3 345.4 ± 16.6 n ¼ 353 7.0e9.0 363.8 ± 17.7 364.7 ± 17.5 n ¼ 353 16.1e19.0 369.5 ± 18.0 373.7 ± 17.9 n ¼ 353 54.3e60.0 395.6 ± 19.2 395.2 ± 18.9
n ¼ 353 40.4e75.1 342.5 ± 16.6 351.2 ± 16.9 n ¼ 353 13.8e39.0 360.5 ± 17.6 366.0 ± 17.6 n ¼ 353 15.4e34.5 344.9 ± 16.6 353.9 ± 16.9 n ¼ 353 9.0e28.5 394.5 ± 19.2 403.3 ± 19.3 n ¼ 353 19.0e47.1 414.5 ± 20.2 409.6 ± 19.7 n ¼ 353 60.0e99.5 445.9 ± 21.7 428.4 ± 20.7
<0.001 <0.001
<0.001 0.003
<0.001 <0.001
0.189 0.507
0.085 0.074
0.002 0.027
P Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. a Model 1, adjusted for age, sex, and race/ethnicity; Model 2, further adjusted for BMI, changes in weight over the past year, physical activity, and total calorie intake.
dL) ¼ 2.27 ✕ total cholesterol þ triglycerides þ 62.3] (Phillips et al., 1989).
2.3. Statistical analysis P The sum OCP concentrations ( OCPs) were calculated by summing the absolute concentration of the 6 individual OCPs. Macronutrient intake (i.e., fat, protein, carbohydrate) was analyzed as a percentage of the total energy intake. Caloric values of macronutrients were calculated using the Atwater general factor system and were 9.0 kcal/g for fat, 4.0 kcal/g for protein, and 9.0 kcal/g for carbohydrate (Maclean et al., 2003). For each participant, the total energy intake was calculated by multiplying each macronutrient intake (g) by the Atwater factors (kcal/g) corresponding to each macronutrient. Afterwards, the contribution (%) of each macronutrient to the total energy intake was calculated. Because the concentration of OCPs was skewed to the right, P unadjusted or adjusted geometric means of the OCPs (both OCPs and 6 individual OCPs) according to the quintiles of macronutrients were calculated using a generalized linear model. Additionally, we separately evaluated the associations of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). Covariates were selected based on prior knowledge of factors that can affect serum concentrations of OCPs and included age, sex, race/ethnicity, body mass index (BMI, body weight ÷ squared height, kg/m2), weight change over the past year (current body weight e body weight 1 year ago, kg), leisure-time physical activity (MET-minutes per day), and total energy intake. Additionally, stratified analyses were performed to evaluate the association between OCPs and nutrients among various subgroups categorized by age (<40, 40e59, and 60), sex (male and female), obesity (BMI <30 kg/m2 and 30 kg/m2) (US Centers for Disease Control and Prevention, 2017), and leisure-time physical activity (no and yes).
The main results were estimated accounting for the NHANES stratification and clustering, with values adjusted for age, gender, and race/ethnicity rather than using sample weights (Korn and Graubard, 1991). This adjustment was considered as a good compromise between efficiency and bias (Graubard and Korn, 1999). All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). P-values less than 0.05 were considered to indicate statistical significance. 3. Results 3.1. General characteristics of study participants Table 1 shows the distribution of the general characteristics of the study participants by quintiles of dietary fat intake. High fat intake was associated with a young age, white race, and higher physical activity. Subjects who consumed more fat also consumed more protein, but less carbohydrate. Total cholesterol and triglyceride according to dietary fat intake did not show any significant linear trend. 3.2. Macronutrient intake and serum concentrations of OCPs Fig. 1 shows the distribution of the sum OCP concentrations by the quintiles of macronutrient intake. Among the 3 macronutrients, high fat intake was associated with low serum concentrations of OCPs (Ptrend < 0.001), while subjects who consumed both high protein and carbohydrate diets showed relatively higher serum concentrations of OCPs (Ptrend ¼ 0.012, Ptrend ¼ 0.023, respectively). The geometric mean of the sum OCP concentrations among subjects in the lowest quintile of total fat intake was 526.6 ng/g of lipid, while this value was 356.9 ng/g of lipid among those in the highest quintile. The results for total protein and total carbohydrate intake were nearly opposite those of total fat intake. When 3 fatty acids were separately evaluated, both SFAs and MUFAs were found
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P Fig. 2. Stratified analyses of the associations between calories from dietary fat intake and the sum OCP concentrations ( OCPs, geometric mean, ng/g of lipid). (A) Stratified by age (sample sizes: 1,033 for age <40 years; 498 for age 40e59 years; 233 for age 60 years). (B) Stratified by sex (sample sizes: 835 for men; 929 for women). (C) Stratified by BMI (sample sizes: 1,300 for BMI <30 kg/m2; 464 for BMI 30 kg/m2). (D) Stratified by physical activity (sample sizes: 711 for no leisure-time physical activity; 1053 for doing leisureP time physical activity). Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. These analyses were adjusted for age, sex, race/ethnicity, BMI, changes in weight over the past year, physical activity, and total calorie intake. Among all covariates mentioned above, the variable used for stratification was excluded from adjustment in each analysis. Ptrend was used P to test whether the trends of the OCPs according to the quintiles of macronutrient intake have a statistically significant linear shape in each stratum.
to be inversely associated with serum concentrations of OCPs (Ptrend < 0.001 for both), while PUFAs showed no association. The adjusted geometric means of the sum OCP concentrations by the quintiles of macronutrient intake are shown in Table 2. Adjustment for possible confounders did not influence the results. Serum concentrations of OCPs substantially declined when dietary fat intake was about 25% or more of total calorie intake, while serum concentrations of OCPs were rather high when dietary carbohydrate intake was about 55% or more of total calorie intake. The adjusted geometric means of the sum OCP concentrations were 462.9, 385.2, 374.2, 380.2, and 351.2 ng/g of lipids according to the quintiles of total fat intake (Ptrend < 0.001). These values according to the quintiles of total carbohydrate intake were 362.5, 392.5, 369.6, 395.2, and 428.4 ng/g of lipids (Ptrend ¼ 0.027). Results of wet concentrations of OCPs and dietary macronutrient intake showed similar associations with those of lipid-standardized concentrations (Supplementary Table 1). When six individual OCPs were separately analyzed, serum concentrations of b-hexachlorocyclohexane, p,p’-DDE, and p,p’-DDT were similar to those of the sum OCP concentrations (Supplementary Table 2).
3.3. Subgroup analysis: macronutrient intake and serum OCP concentrations Fig. 2 shows the association between the total fat intake and serum concentrations of OCPs stratified by age, gender, BMI, and physical activity. When stratified by age, an inverse association was mostly observed among persons less than 40 years of age (Ptrend ¼ 0.003, Fig. 2A). However, all subgroups stratified by gender, BMI, and physical activity showed significant inverse associations between total fat intake and serum concentrations of OCPs (Fig. 2BeD), although men, subjects with obesity, and those who were physically inactive tended to show clearer inverse patterns than their counterparts. The results of stratified analyses of total carbohydrate intake were generally similar to those of total fat intake (Fig. 3AeD). 4. Discussion In this study, subjects with low fat intake showed high serum concentrations of OCPs compared to those with high fat intake. This association was similarly observed in many subgroups stratified by
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P Fig. 3. Stratified analyses of the associations between calories from dietary carbohydrate intake and the sum OCP concentrations ( OCPs, geometric mean, ng/g of lipid). (A) Stratified by age (sample sizes: 1,033 for age <40 years; 498 for age 40e59 years; 233 for age 60 years). (B) Stratified by sex (sample sizes: 835 for men; 929 for women) (C) Stratified by BMI (sample sizes: 1,300 for BMI <30 kg/m2; 464 for BMI 30 kg/m2). (D) Stratified by physical activity (sample sizes: 711 for no leisure-time physical activity; 1053 for P doing leisure-time physical activity). Notes: OCPs, sum of 6 OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. These analyses were adjusted for age, sex, race/ethnicity, BMI, changes in weight over the past year, physical activity, and total calorie intake. Among all covariates mentioned above, the variable used for stratification was excluded from adjustment in each analysis. Ptrend was used to test whether the trends of the P OCPs according to the quintiles of macronutrient intake have a statistically significant linear shape in each stratum.
age, gender, BMI, and physical activity. The largest difference was observed among subjects in the 1st and 2nd quintiles of total fat intake; the highest concentration of OCPs was observed among subjects in whom fats accounted for less than 25% of the total energy intake. When fat intake was more than 25%, the decreasing trend in serum concentrations of OCPs was weak. In contrast, carbohydrate intake was positively associated with serum concentrations of OCPs. Serum concentrations of OCPs were similar among subjects in the 1st to 3rd quintiles of carbohydrate intake in which the carbohydrate intake percentage among the total energy intake was less than about 55% (Table 2), but began to rise in the 4th quintile and reached a peak in the 5th quintile. From the perspective of serum concentration of OCPs, these results indicate that the substitution of fat intake with carbohydrate intake might be harmful. At first glance, these findings may appear unexpected because fatty animal food is the main source of exposure to OCPs in the current general population (Dougherty et al., 2000). However, although the absolute amount of exposure to OCPs from the environment is higher among subjects with high fat intake than in subjects with low fat intake, it is important to note that serum concentrations of OCPs are not determined by the amount of exposure from the environment. In fact, serum concentrations of OCPs are primarily determined by the net result of release from
adipose tissue into circulation and elimination from circulation (Birnbaum, 1985; Needham et al., 1990). Therefore, serum concentrations of OCPs in the general population should be considered as markers of OCPs and other coexisting lipophilic chemicals released from the adipose tissue into circulation (Lee et al., 2018). Biliary excretion is an important elimination route of OCPs (Minh et al., 2001). As bile acids are released into the intestine to absorb dietary fat during a meal, sufficient fat intake is necessary to eliminate OCPs through bile. In rats, bile salt synthesis was reduced when a fat-free diet was consumed (Bertolotti et al., 1995), but stimulated by fat feeding (Botham and Boyd, 1983). In a randomized crossover human study with eucaloric diets and extreme differences in dietary fat intake, the synthesis and turnover rates of primary salts were 30e50% lower during low-fat diet feeding (0% fat) than during standard-fat diet feeding (41% fat) (Bisschop et al., 2004). Therefore, high fat intake can effectively lower serum concentrations of OCPs by increasing the elimination of OCPs. Another important finding was that serum concentrations of OCPs showed strong inverse associations with SFA and MUFA intake but not with PUFA intake. The results of MUFAs were similar to those of total fat intake; lower serum concentrations of OCPs were evident in the comparison of the 1st and 2nd quintiles compared to the other quintiles of MUFA intake. Comparatively, SFAs showed a more linear trend; the serum concentration of OCPs
Y.-M. Lee et al. / Environmental Pollution 259 (2020) 113819
was lowered in the 1st to 4th quintiles of SFA intake. When SFA intake was approximately 14% of the total energy intake, serum concentrations of OCPs were similar to the concentrations in those who consumed more than 14% SFAs (Table 2). We speculated that the lack of association with PUFA intake may be related to at least two factors: toxicokinetics of OCPs released from adipose tissue and the relatively higher contamination of OCPs in marine food than in terrestrial food (Dougherty et al., 2000). It has been reported that mobilization of fatty acids from adipose tissue during periods of energy deficit is selective rather than a random process; fatty acids with more double bonds and shorter chain lengths lead to higher relative mobilization from adipose tissue during lipolysis (Price et al., 2008; Raclot and Groscolas, 1993, 1995). Thus, for a given chain length, PUFAs are preferentially released from triacylglycerols in adipose tissues compared to SFAs or MUFAs. The fatty acid composition of adipose tissue reflects that of the diet over the past months to years (Katan et al., 1997), and marine food is the main dietary source of both PUFAs and contamination by OCPs (Dougherty et al., 2000). Therefore, the amount of OCPs released from adipose tissue may be higher among those who consume more PUFAs than in those who consume SFAs or MUFAs. Interestingly, our findings agree with recent findings regarding the effects of dietary macronutrient intake on health. For example, in a prospective study in 18 countries on five continents, higher carbohydrate intake was associated with an increased risk of total mortality, while total fat intake was associated with a lower risk (Dehghan et al., 2017). Total fat and the types of fat were not associated with cardiovascular diseases or myocardial infarction, whereas SFAs showed an inverse association with stroke (Dehghan et al., 2017). This study concluded that removing current restrictions on fat intake but limiting carbohydrate intake may improve health, calling into question current dietary guidelines. This study had several limitations. First, this cross-sectional study was not able to establish temporality. However, reverse causality was unlikely because the current study subjects were apparently healthy without cardio-metabolic diseases. Healthy persons with high serum OCP concentrations do not require a drastic change in their diets from a high-fat and low-carbohydrate diet to a low-fat and high-carbohydrate diet. Moreover, the crosssectional design can have merit in evaluating the effects of usual diet patterns on serum concentrations of OCPs. Second, a one-time 24-h dietary recall is not sufficient for estimating the effects of a long-term diet. Therefore, caution should be taken when interpreting the current results. However, inter-day variations in macronutrient intake may lead to non-differential misclassification and typically bias the result to show no effect (Hornell et al., 2017). Thus, the true associations of macronutrient intake with serum concentrations of OCPs may be stronger than those observed in this study. Third, there may be residual confounders, although we extensively adjusted for covariates, including weight changes. However, the consistent associations observed in most subgroups may not easily be explained by residual confounders. 5. Conclusions Supporting the findings from animal experimental studies (Kania-Korwel et al., 2008; Myrmel et al., 2016), our findings suggest that dietary macronutrient intake may be one of the practical methods for reducing serum concentrations of lipophilic chemical mixtures such as OCPs, which are released into circulation from adipose tissue. Although these findings need to be replicated in other populations, changing dietary macronutrient intake can be a practical method to manage unavoidable lipophilic chemical
7
mixtures such as OCPs in modern society. Funding This work was supported by the Environmental Health Action Program [grant number 2016001370002] funded by the Ministry of Environment of the Republic of Korea. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. CRediT authorship contribution statement Yu-Mi Lee: Writing - original draft. Somi Heo: Formal analysis. Se-A Kim: Formal analysis. Duk-Hee Lee: Conceptualization, Supervision, Writing - review & editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2019.113819. References Bertolotti, M., Spady, D.K., Dietschy, J.M., 1995. Regulation of hepatic cholesterol metabolism in the rat in vivo: effect of a synthetic fat-free diet on sterol synthesis and low-density lipoprotein transport. Biochim. Biophys. Acta 1255, 293e300. Birnbaum, L.S., 1985. The role of structure in the disposition of halogenated aromatic xenobiotics. Environ. Health Perspect. 61, 11e20. Bisschop, P.H., Bandsma, R.H., Stellaard, F., ter Harmsel, A., Meijer, A.J., Sauerwein, H.P., Kuipers, F., Romijn, J.A., 2004. Low-fat, high-carbohydrate and high-fat, low-carbohydrate diets decrease primary bile acid synthesis in humans. Am. J. Clin. Nutr. 79, 570e576. Botham, K.M., Boyd, G.S., 1983. The effect of dietary fat on bile salt synthesis in rat liver. Biochim. Biophys. Acta 752, 307e314. CDC/National Center for Health Statistics, 2006. Laboratory Procedure Manual. PCBs and Persistent Pesticides. https://wwwn.cdc.gov/nchs/data/nhanes/2003-2004/ labmethods/l28_c_met_-pcbs_and_persistent_pesticides.pdf. CDC/National Center for Health Statistics, 2008. National Health and Nutrition Examination Surveys (NHANES) 2003-2004 Laboratory Data. 2003-2004 Data Documentation, Codebook, and Frequencies. Pesticides - Organochlorine Metabolites - Serum (Surplus). https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/ L28OCP_C.htm. Dehghan, M., Mente, A., Zhang, X., Swaminathan, S., Li, W., Mohan, V., Iqbal, R., Kumar, R., Wentzel-Viljoen, E., Rosengren, A., Amma, L.I., Avezum, A., Chifamba, J., Diaz, R., Khatib, R., Lear, S., Lopez-Jaramillo, P., Liu, X., Gupta, R., Mohammadifard, N., Gao, N., Oguz, A., Ramli, A.S., Seron, P., Sun, Y., Szuba, A., Tsolekile, L., Wielgosz, A., Yusuf, R., Hussein Yusufali, A., Teo, K.K., Rangarajan, S., Dagenais, G., Bangdiwala, S.I., Islam, S., Anand, S.S., Yusuf, S., Prospective Urban Rural Epidemiology study, i, 2017. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 390, 2050e2062. Dougherty, C.P., Henricks Holtz, S., Reinert, J.C., Panyacosit, L., Axelrad, D.A., Woodruff, T.J., 2000. Dietary exposures to food contaminants across the United States. Environ. Res. 84, 170e185. Evangelou, E., Ntritsos, G., Chondrogiorgi, M., Kavvoura, F.K., Hernandez, A.F., Ntzani, E.E., Tzoulaki, I., 2016. Exposure to pesticides and diabetes: a systematic review and meta-analysis. Environ. Int. 91, 60e68. Graubard, B.I., Korn, E.L., 1999. Analyzing health surveys for cancer-related objectives. J. Natl. Cancer Inst. 91, 1005e1016. Hornell, A., Berg, C., Forsum, E., Larsson, C., Sonestedt, E., Akesson, A., Lachat, C., Hawwash, D., Kolsteren, P., Byrnes, G., De Keyzer, W., Van Camp, J., Cade, J.E., Greenwood, D.C., Slimani, N., Cevallos, M., Egger, M., Huybrechts, I., Wirfalt, E., 2017. Perspective: an extension of the STROBE statement for observational studies in nutritional epidemiology (STROBE-nut): explanation and elaboration. Adv. Nutr. 8, 652e678. Hutter, H.P., Kundi, M., Hohenblum, P., Scharf, S., Shelton, J.F., Piegler, K., Wallner, P., 2016. Life without plastic: a family experiment and biomonitoring study. Environ. Res. 150, 639e644. Jackson, E., Shoemaker, R., Larian, N., Cassis, L., 2017. Adipose tissue as a site of toxin accumulation. Comp. Physiol. 7, 1085e1135. Kang, J.H., Park, H., Chang, Y.S., Choi, J.W., 2008. Distribution of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in human serum from urban areas in Korea. Chemosphere 73, 1625e1631. Kania-Korwel, I., Hornbuckle, K.C., Robertson, L.W., Lehmler, H.J., 2008. Influence of dietary fat on the enantioselective disposition of 2,2’,3,3’,6,6’-hexachlorobiphenyl (PCB 136) in female mice. Food Chem. Toxicol. 46, 637e644. Katan, M.B., Deslypere, J.P., van Birgelen, A.P., Penders, M., Zegwaard, M., 1997.
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Kinetics of the incorporation of dietary fatty acids into serum cholesteryl esters, erythrocyte membranes, and adipose tissue: an 18-month controlled study. J. Lipid Res. 38, 2012e2022. Kim, K.S., Lee, Y.M., Kim, S.G., Lee, I.K., Lee, H.J., Kim, J.H., Kim, J., Moon, H.B., Jacobs Jr., D.R., Lee, D.H., 2014. Associations of organochlorine pesticides and polychlorinated biphenyls in visceral vs. subcutaneous adipose tissue with type 2 diabetes and insulin resistance. Chemosphere 94, 151e157. Korn, E.L., Graubard, B.I., 1991. Epidemiologic studies utilizing surveys: accounting for the sampling design. Am. J. Public Health 81, 1166e1173. La Merrill, M., Emond, C., Kim, M.J., Antignac, J.P., Le Bizec, B., Clement, K., Birnbaum, L.S., Barouki, R., 2013. Toxicological function of adipose tissue: focus on persistent organic pollutants. Environ. Health Perspect. 121, 162e169. Lee, D.H., Jacobs, D.R., 2019. New approaches to cope with possible harms of lowdose environmental chemicals. J. Epidemiol. Community Health 73, 193e197. Lee, D.H., Jacobs Jr., D.R., Park, H.Y., Carpenter, D.O., 2017. A role of low dose chemical mixtures in adipose tissue in carcinogenesis. Environ. Int. 108, 170e175. Lee, D.H., Porta, M., Jacobs Jr., D.R., Vandenberg, L.N., 2014. Chlorinated persistent organic pollutants, obesity, and type 2 diabetes. Endocr. Rev. 35, 557e601. Lee, Y.M., Jacobs, D.R., Lee, D.H., 2018. Persistent organic pollutants and type 2 diabetes: a critical review of review articles. Front Endocrinol. (Lausanne) 9, 712. Levitt, D.G., 2010. Quantitative relationship between the octanol/water partition coefficient and the diffusion limitation of the exchange between adipose and blood. BMC Clin. Pharmacol. 10, 1. Lind, L., Lind, P.M., 2012. Can persistent organic pollutants and plastic-associated chemicals cause cardiovascular disease? J. Intern. Med. 271, 537e553. Maclean, W., Harnly, J., Chen, J., Chevassus-Agnes, S., Gilani, G., Livesey, G., Warwick, P., 2003. Food EnergyeMethods of Analysis and Conversion Factors, Food and Agriculture Organization of the United Nations Technical Workshop Report. Minh, T.B., Watanabe, M., Tanabe, S., Yamada, T., Hata, J., Watanabe, S., 2001. Specific accumulation and elimination kinetics of tris(4-chlorophenyl)methane, tris(4-
chlorophenyl)methanol, and other persistent organochlorines in humans from Japan. Environ. Health Perspect. 109, 927e935. Moon, H.B., Lee, D.H., Lee, Y.S., Kannan, K., 2012. Occurrence and accumulation patterns of polycyclic aromatic hydrocarbons and synthetic musk compounds in adipose tissues of Korean females. Chemosphere 86, 485e490. Myrmel, L.S., Fjaere, E., Midtbo, L.K., Bernhard, A., Petersen, R.K., Sonne, S.B., Mortensen, A., Hao, Q., Brattelid, T., Liaset, B., Kristiansen, K., Madsen, L., 2016. Macronutrient composition determines accumulation of persistent organic pollutants from dietary exposure in adipose tissue of mice. J. Nutr. Biochem. 27, 307e316. Needham, L., Burse, V., Head, S., Korver, M., McClure, P., Andrews Jr., J., Rowley, D., Sung, J., Kahn, S., 1990. Adipose tissue/serum partitioning of chlorinated hydrocarbon pesticides in humans. Chemosphere 20, 975e980. Phillips, D.L., Pirkle, J.L., Burse, V.W., Bernert Jr., J.T., Henderson, L.O., Needham, L.L., 1989. Chlorinated hydrocarbon levels in human serum: effects of fasting and feeding. Arch. Environ. Contam. Toxicol. 18, 495e500. Porta, M., Gasull, M., Puigdomenech, E., Gari, M., Bosch de Basea, M., Guillen, M., Lopez, T., Bigas, E., Pumarega, J., Llebaria, X., Grimalt, J.O., Tresserras, R., 2010. Distribution of blood concentrations of persistent organic pollutants in a representative sample of the population of Catalonia. Environ. Int. 36, 655e664. Price, E.R., Krokfors, A., Guglielmo, C.G., 2008. Selective mobilization of fatty acids from adipose tissue in migratory birds. J. Exp. Biol. 211, 29e34. Raclot, T., Groscolas, R., 1993. Differential mobilization of white adipose tissue fatty acids according to chain length, unsaturation, and positional isomerism. J. Lipid Res. 34, 1515e1526. Raclot, T., Groscolas, R., 1995. Selective mobilization of adipose tissue fatty acids during energy depletion in the rat. J. Lipid Res. 36, 2164e2173. Ruzzin, J., Lee, D.H., Carpenter, D.O., Jacobs Jr., D.R., 2012. Reconsidering metabolic diseases: the impacts of persistent organic pollutants. Atherosclerosis 224, 1e3. Schafer, K.S., Kegley, S.E., 2002. Persistent toxic chemicals in the US food supply. J. Epidemiol. Community Health 56, 813e817. US Centers for Disease Control and Prevention, 2017. Defining Adult Overweight and Obesity. https://www.cdc.gov/obesity/adult/defining.html.
Contents lists available at ScienceDirect
Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Is dietary macronutrient intake associated with serum concentrations of organochlorine pesticides in humans?* Yu-Mi Lee a, Somi Heo a, Se-A Kim b, c, Duk-Hee Lee a, c, * a
Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea Department of Biomedical Science, Graduate School, Kyungpook National University, Daegu, Republic of Korea c BK21 Plus KNU Biomedical Convergence Program, Department of Biomedical Science, Kyungpook National University, Daegu, Republic of Korea b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 July 2019 Received in revised form 3 December 2019 Accepted 13 December 2019 Available online 16 December 2019
In the general population, chronic exposure to low-dose persistent organic pollutants (POPs), particularly organochlorine pesticides (OCPs), has been recently linked to many chronic diseases. Widespread contamination of the food chain and human adipose tissue has made avoiding exposure to these chemicals impossible; thus, alternative strategies for decreasing the chemical burden must be investigated. Recently, macronutrient intake was found to significantly modify the toxicokinetics of POPs in animal experimental studies. Thus, we evaluated whether macronutrient intake was related to serum concentrations of OCPs in healthy adults without cardio-metabolic diseases. Subjects included 1,764 adults, aged 20 years or above, who participated in the National Health and Nutrition Examination Survey 1999e2004. Macronutrient intake was assessed based on a 24-h dietary recall interview. Six individual OCPs commonly detected among the general population were evaluated as markers of OCPs and other coexisting lipophilic chemicals stored in adipose tissue and released into circulation. High fat intake was associated with lower concentrations of OCPs, while high carbohydrate intake showed the opposite result. When three types of fats were individually evaluated, both saturated fatty acids and monounsaturated fatty acids, but not polyunsaturated fatty acids, were inversely associated with serum concentrations of OCPs. Adjustment for possible confounders did not change the results. When stratified by age, gender, body mass index, and physical activity, these associations were similar in most subgroups. Thus, similar to the findings observed in animal experimental studies, a moderate-fat diet with low carbohydrate intake was related to low serum concentrations of OCPs in humans. Although these findings need to be replicated, changing dietary macronutrient intake can be investigated as a practical strategy for dealing with unavoidable lipophilic chemical mixtures such as OCPs in modern society. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Adipose tissue Low carbohydrate high fat diet Macronutrient Organochlorine pesticide Persistent organic pollutant
1. Introduction Recently, serum concentrations of chlorinated persistent organic pollutants (POPs), particularly organochlorine pesticides (OCPs), have been linked to various chronic diseases (Evangelou et al., 2016; Lee et al., 2014, 2017; Lind and Lind, 2012; Ruzzin et al., 2012). Many chlorinated POPs were banned in the 1970s and 1980s but are still widely detected in the environment and
* This paper has been recommended for acceptance by Wen Chen. * Corresponding author. Department of Preventive Medicine, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea. E-mail addresses: [email protected] (Y.-M. Lee), [email protected] (S. Heo), [email protected] (S.-A. Kim), [email protected] (D.-H. Lee).
https://doi.org/10.1016/j.envpol.2019.113819 0269-7491/© 2019 Elsevier Ltd. All rights reserved.
human body because of their persistency. As strong lipophilic chemicals, POPs are predominantly stored in the adipose tissue of living organisms and are slowly released into circulation for metabolism (Birnbaum, 1985; Needham et al., 1990). Because of the toxicokinetics of POPs, serum concentrations of POPs measured in the general population can be considered as markers of various lipophilic chemical mixtures in the adipose tissue, which include both POPs and other chemicals coexisting with POPs (Lee et al., 2018). When certain environmental chemicals are suspected to be harmful, the reduction of exposure to external exposure sources is generally considered as a preventive strategy (Hutter et al., 2016). However, in the case of POPs, which have widely contaminated the food chain and human adipose tissue, neither further regulation nor exposure avoidance may be effective for protecting humans
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Table 1 General characteristics of the study participants. Total
Mean ± standard deviation Age (years) BMI (kg/m2) Weight change over the past year (kg) Leisure-time physical activity (MET-minutes/day) Dietary intake of Total calories (kcal) Total protein (g) Total carbohydrate (g) Total fat (g) Total saturated fatty acids (g) Total monounsaturated fatty acids (g) Total polyunsaturated fatty acids (g) Serum total cholesterol (mg/dL) Serum triglycerides (mg/dL) Proportion [n (%)] Male Non-Hispanic white BMI 30 kg/m2 No leisure-time physical activity
Ptrenda
Calories from dietary fat intake (%) Q1 <25.8
Q2 25.8e31.0
Q3 31.1e35.1
Q4 35.1e40.4
Q5 40.4e75.1
(n ¼ 1,764)
(n ¼ 352)
(n ¼ 353)
(n ¼ 353)
(n ¼ 353)
(n ¼ 353)
39.4 ± 15.8 27.3 ± 5.8 1.8 ± 8.3 153.0 ± 369.4
39.5 ± 16.3 27.5 ± 5.7 0.9 ± 8.7 135.1 ± 289.2
37.6 ± 14.1 27.2 ± 5.8 2.3 ± 8.7 168.3 ± 421.8
38.6 ± 16.1 26.9 ± 5.6 1.9 ± 7.6 125.6 ± 237.7
39.7 ± 16.5 27.4 ± 5.8 2.5 ± 7.8 177.4 ± 482.5
41.4 ± 15.5 27.7 ± 6.2 1.5 ± 8.7 158.6 ± 361.7
0.026 0.544 0.296 0.367
2281.4 ± 1065.4 84.3 ± 43.6 285.1 ± 138.2 84.3 ± 48.4 27.9 ± 17.5 31.7 ± 19.2 17.2 ± 11.8 197.4 ± 43.3 129.2 ± 113.5
1893.7 ± 866.1 68.1 ± 35.8 298.4 ± 140.4 43.4 ± 22.2 14.2 ± 8.6 15.6 ± 8.5 9.2 ± 5.6 194.1 ± 41.3 132.3 ± 110.4
2161.5 ± 956.8 76.0 ± 35.6 303.1 ± 143.8 67.8 ± 30.3 22.2 ± 11.1 25.5 ± 12.5 14.0 ± 7.9 195.0 ± 40.0 120.8 ± 72.5
2352.7 ± 1039.6 85.6 ± 41.5 299.6 ± 131.6 84.9 ± 35.7 28.1 ± 12.7 31.8 ± 14.2 17.5 ± 10.1 200.5 ± 43.4 134.2 ± 83.2
2548.8 ± 1111.1 97.1 ± 48.8 293.9 ± 132.0 104.7 ± 45.4 35.4 ± 17.4 39.6 ± 18.1 20.5 ± 11.0 198.0 ± 50.3 139.0 ± 163.8
2449.4 ± 1198.5 94.6 ± 47.8 230.7 ± 129.9 120.5 ± 57.5 39.5 ± 21.5 45.9 ± 23.0 24.6 ± 15.4 199.3 ± 40.5 119.9 ± 114.3
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.063 0.733
835 799 464 711
174 (49.4) 124 (35.2) 93 (26.4) 166 (47.2)
145 (41.1) 157 (44.5) 91 (25.8) 149 (42.2)
167 (47.3) 168 (47.6) 82 (23.2) 138 (39.1)
189 (53.5) 176 (49.9) 94 (26.6) 139 (39.4)
160 174 104 119
0.612 <0.001 0.349 <0.001
(47.3) (45.3) (26.3) (40.3)
(45.3) (49.3) (29.5) (33.7)
Notes: BMI, body mass index; Q1eQ5, 1st quintilee5th quintile. a A generalized linear model was used for continuous variables, and a Cochran-Armitage trend analysis was used for categorical variables.
(Lee and Jacobs, 2019). Therefore, alternative approaches for decreasing POPs-related health risks in humans are needed. Interestingly, several animal experimental studies have suggested that diet can be used as an intervention to decrease serum concentrations of lipophilic chemicals in humans. For example, a high-fat diet resulted in increased elimination of POPs in the feces of mice compared to that of mice fed a low-fat diet (Kania-Korwel et al., 2008). In another animal study, the accumulation of POPs in the adipose tissue and liver was affected by the composition of macronutrient intake, not by the total intake of POPs (Myrmel et al., 2016); a high-fat and high-protein diet resulted in lower deposition of POPs in the adipose tissue and liver than did a low-fat and highcarbohydrate diet. In this study, high-fat and high-protein diet increased hepatic expression of genes involved in metabolism and elimination of xenobiotics. These studies suggest that the elimination of POPs can be modified by dietary macronutrient intake. To the best of our knowledge, the association between dietary macronutrient intake and serum concentrations of POPs has never been investigated in humans. As fatty animal food is the main external exposure source of POPs in the current general population (Schafer and Kegley, 2002), the general belief is that fatty animal food should be avoided to reduce the exposure to POPs. However, if the results of the animal experimental studies mentioned above (Kania-Korwel et al., 2008; Myrmel et al., 2016) are relevant to humans, a high-fat diet may be related to low serum concentrations of POPs in humans. Although randomized clinical trials are generally considered as the best study design to evaluate the effects of diet, a crosssectional design has the advantage of enabling investigation of the long-term effect of diet on serum concentrations of POPs. The dynamic nature of POPs between blood and adipose tissue, which function to reach equilibrium (Jackson et al., 2017; La Merrill et al., 2013; Levitt, 2010), can mask short-term effects of diet intervention. This study was performed to evaluate if macronutrient intake is related to serum concentrations of OCPs in healthy adults without any cardio-metabolic diseases. In this study, serum concentrations
of OCPs were selected as markers of OCPs and other coexisting lipophilic chemicals that are released from adipocytes to the circulation because human adipose tissue is widely contaminated with a tremendous number of environmental chemicals including OCPs (Kim et al., 2014; Moon et al., 2012), and serum concentrations of OCPs are mainly determined by the release of OCPs from adipose tissue to circulation (Birnbaum, 1985; Lee et al., 2018; Needham et al., 1990), In fact, serum concentrations of many lipophilic compounds belonging to POPs are positively correlated among the general population (Kang et al., 2008; Porta et al., 2010). 2. Materials and methods 2.1. Study subjects This study was performed based on the dataset from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004 conducted by the Centers for Disease Control and Prevention (CDC) in the U.S. Study subjects were adults aged 20 years and had information on their serum concentrations of OCPs and dietary intake of macronutrients. We excluded patients with diabetes, dyslipidemia, hypertension, coronary heart disease, stroke, and cancer, which were diagnosed by physicians or based on medication administration, as these diseases are related to increased serum concentration of OCPs and the diet can be influenced by the status of disease. The final sample size was 1,764 subjects. 2.2. Data collection and measurements The survey questionnaires included information on demographic characteristics, health behaviors, and dietary habits. For nutritional assessment, a 24-h dietary recall interview was conducted by trained dietary interviewers using a standard set of measuring guides. Venous blood was sampled and frozen at 20 C. OCP concentrations in the serum were measured by highresolution gas chromatography/mass spectrometry using isotope
Y.-M. Lee et al. / Environmental Pollution 259 (2020) 113819
3
P P Fig. 1. The sum of OCP concentrations ( OCPs, geometric mean ± standard error, ng/g of lipid) according to the calories from dietary intake of selected nutrients. Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st P quintilee5th quintile of each nutrient. Ptrend and Pquadratic were used to test whether the trends of the OCPs according to the quintiles of macronutrient intake have a statistically significant linear or quadratic shape.
dilution for quantification. The procedure used for OCP measurement is described in detail in the CDC laboratory procedure manual (CDC/National Center for Health Statistics, 2006). We selected six individual OCPs (b-hexachlorocyclohexane, p,p0 -DDE, p,p0 -DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide), which are commonly detected among the general population. The detection rates (the values at or above the limit of detection) of OCPs among study participants were as follows: b-hexachlorocyclohexane, 66.3%; p,p0 -DDE, 100%; p,p0 -DDT, 46.5%; oxychlordane, 74.9%; transnonachlor, 86.2%; and heptachlor epoxide, 46.3%. NHANES provides
the detection limit divided by the square root of 2 for values that are below the limit of detection (CDC/National Center for Health Statistics, 2008). Although we attempted to use both lipid-standardized concentrations and wet concentrations with lipid adjustment in our analyses, only the results of lipid-standardized concentrations are presented, as they revealed similar associations. Results of wet concentrations are presented as supplementary results. The lipidstandardized concentration was calculated by dividing the wet weight concentrations by the total lipids [total lipids (mg/
4
Y.-M. Lee et al. / Environmental Pollution 259 (2020) 113819
Table 2 P Adjusted sum OCP concentrations ( OCPs, geometric mean ± standard error, ng/g of lipid) according to the calories from dietary intake of selected nutrients. Nutrients
Modela
Total fat % 1 2 Total saturated fatty acids % 1 2 Total monounsaturated fatty acids % 1 2 Total polyunsaturated fatty acids % 1 2 Total protein % 1 2 Total carbohydrate % 1 2
Calories from dietary intake of selected nutrients (%)
Ptrend
Q1
Q2
Q3
Q4
Q5
n ¼ 352 <25.8 481.3 ± 23.4 462.9 ± 22.6 n ¼ 352 <7.7 459.6 ± 22.5 439.6 ± 21.7 n ¼ 352 <9.2 490.3 ± 23.8 472.5 ± 23.1 n ¼ 352 <4.3 416.1 ± 20.3 406.0 ± 19.6 n ¼ 352 <11.4 357.1 ± 17.4 352.2 ± 16.9 n ¼ 352 <43.3 357.9 ± 17.4 362.5 ± 17.4
n ¼ 353 25.8e31.0 387.8 ± 18.8 385.2 ± 18.4 n ¼ 353 7.7e9.8 401.3 ± 19.5 400.6 ± 19.1 n ¼ 353 9.2e11.3 400.4 ± 19.4 394.5 ± 18.9 n ¼ 353 4.3e5.7 400.6 ± 19.5 398.8 ± 19.1 n ¼ 353 11.4e13.6 381.7 ± 18.6 385.6 ± 18.5 n ¼ 353 43.3e49.0 383.3 ± 18.6 392.5 ± 18.8
n ¼ 353 31.1e35.1 375.2 ± 18.1 374.2 ± 17.9 n ¼ 353 9.9e11.7 376.9 ± 18.3 385.3 ± 18.5 n ¼ 353 11.4e13.3 388.9 ± 18.8 391.0 ± 18.7 n ¼ 353 5.7e7.0 372.2 ± 18.1 374.0 ± 17.9 n ¼ 353 13.6e16.1 426.4 ± 20.7 428.2 ± 20.5 n ¼ 353 49.1e54.3 367.9 ± 17.9 369.6 ± 17.7
n ¼ 353 35.1e40.4 371.5 ± 18.0 380.2 ± 18.3 n ¼ 353 11.7e13.8 355.5 ± 17.3 358.7 ± 17.2 n ¼ 353 13.3e15.4 338.3 ± 16.3 345.4 ± 16.6 n ¼ 353 7.0e9.0 363.8 ± 17.7 364.7 ± 17.5 n ¼ 353 16.1e19.0 369.5 ± 18.0 373.7 ± 17.9 n ¼ 353 54.3e60.0 395.6 ± 19.2 395.2 ± 18.9
n ¼ 353 40.4e75.1 342.5 ± 16.6 351.2 ± 16.9 n ¼ 353 13.8e39.0 360.5 ± 17.6 366.0 ± 17.6 n ¼ 353 15.4e34.5 344.9 ± 16.6 353.9 ± 16.9 n ¼ 353 9.0e28.5 394.5 ± 19.2 403.3 ± 19.3 n ¼ 353 19.0e47.1 414.5 ± 20.2 409.6 ± 19.7 n ¼ 353 60.0e99.5 445.9 ± 21.7 428.4 ± 20.7
<0.001 <0.001
<0.001 0.003
<0.001 <0.001
0.189 0.507
0.085 0.074
0.002 0.027
P Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. a Model 1, adjusted for age, sex, and race/ethnicity; Model 2, further adjusted for BMI, changes in weight over the past year, physical activity, and total calorie intake.
dL) ¼ 2.27 ✕ total cholesterol þ triglycerides þ 62.3] (Phillips et al., 1989).
2.3. Statistical analysis P The sum OCP concentrations ( OCPs) were calculated by summing the absolute concentration of the 6 individual OCPs. Macronutrient intake (i.e., fat, protein, carbohydrate) was analyzed as a percentage of the total energy intake. Caloric values of macronutrients were calculated using the Atwater general factor system and were 9.0 kcal/g for fat, 4.0 kcal/g for protein, and 9.0 kcal/g for carbohydrate (Maclean et al., 2003). For each participant, the total energy intake was calculated by multiplying each macronutrient intake (g) by the Atwater factors (kcal/g) corresponding to each macronutrient. Afterwards, the contribution (%) of each macronutrient to the total energy intake was calculated. Because the concentration of OCPs was skewed to the right, P unadjusted or adjusted geometric means of the OCPs (both OCPs and 6 individual OCPs) according to the quintiles of macronutrients were calculated using a generalized linear model. Additionally, we separately evaluated the associations of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). Covariates were selected based on prior knowledge of factors that can affect serum concentrations of OCPs and included age, sex, race/ethnicity, body mass index (BMI, body weight ÷ squared height, kg/m2), weight change over the past year (current body weight e body weight 1 year ago, kg), leisure-time physical activity (MET-minutes per day), and total energy intake. Additionally, stratified analyses were performed to evaluate the association between OCPs and nutrients among various subgroups categorized by age (<40, 40e59, and 60), sex (male and female), obesity (BMI <30 kg/m2 and 30 kg/m2) (US Centers for Disease Control and Prevention, 2017), and leisure-time physical activity (no and yes).
The main results were estimated accounting for the NHANES stratification and clustering, with values adjusted for age, gender, and race/ethnicity rather than using sample weights (Korn and Graubard, 1991). This adjustment was considered as a good compromise between efficiency and bias (Graubard and Korn, 1999). All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). P-values less than 0.05 were considered to indicate statistical significance. 3. Results 3.1. General characteristics of study participants Table 1 shows the distribution of the general characteristics of the study participants by quintiles of dietary fat intake. High fat intake was associated with a young age, white race, and higher physical activity. Subjects who consumed more fat also consumed more protein, but less carbohydrate. Total cholesterol and triglyceride according to dietary fat intake did not show any significant linear trend. 3.2. Macronutrient intake and serum concentrations of OCPs Fig. 1 shows the distribution of the sum OCP concentrations by the quintiles of macronutrient intake. Among the 3 macronutrients, high fat intake was associated with low serum concentrations of OCPs (Ptrend < 0.001), while subjects who consumed both high protein and carbohydrate diets showed relatively higher serum concentrations of OCPs (Ptrend ¼ 0.012, Ptrend ¼ 0.023, respectively). The geometric mean of the sum OCP concentrations among subjects in the lowest quintile of total fat intake was 526.6 ng/g of lipid, while this value was 356.9 ng/g of lipid among those in the highest quintile. The results for total protein and total carbohydrate intake were nearly opposite those of total fat intake. When 3 fatty acids were separately evaluated, both SFAs and MUFAs were found
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P Fig. 2. Stratified analyses of the associations between calories from dietary fat intake and the sum OCP concentrations ( OCPs, geometric mean, ng/g of lipid). (A) Stratified by age (sample sizes: 1,033 for age <40 years; 498 for age 40e59 years; 233 for age 60 years). (B) Stratified by sex (sample sizes: 835 for men; 929 for women). (C) Stratified by BMI (sample sizes: 1,300 for BMI <30 kg/m2; 464 for BMI 30 kg/m2). (D) Stratified by physical activity (sample sizes: 711 for no leisure-time physical activity; 1053 for doing leisureP time physical activity). Notes: OCPs, the sum of absolute serum concentrations of 6 individual OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. These analyses were adjusted for age, sex, race/ethnicity, BMI, changes in weight over the past year, physical activity, and total calorie intake. Among all covariates mentioned above, the variable used for stratification was excluded from adjustment in each analysis. Ptrend was used P to test whether the trends of the OCPs according to the quintiles of macronutrient intake have a statistically significant linear shape in each stratum.
to be inversely associated with serum concentrations of OCPs (Ptrend < 0.001 for both), while PUFAs showed no association. The adjusted geometric means of the sum OCP concentrations by the quintiles of macronutrient intake are shown in Table 2. Adjustment for possible confounders did not influence the results. Serum concentrations of OCPs substantially declined when dietary fat intake was about 25% or more of total calorie intake, while serum concentrations of OCPs were rather high when dietary carbohydrate intake was about 55% or more of total calorie intake. The adjusted geometric means of the sum OCP concentrations were 462.9, 385.2, 374.2, 380.2, and 351.2 ng/g of lipids according to the quintiles of total fat intake (Ptrend < 0.001). These values according to the quintiles of total carbohydrate intake were 362.5, 392.5, 369.6, 395.2, and 428.4 ng/g of lipids (Ptrend ¼ 0.027). Results of wet concentrations of OCPs and dietary macronutrient intake showed similar associations with those of lipid-standardized concentrations (Supplementary Table 1). When six individual OCPs were separately analyzed, serum concentrations of b-hexachlorocyclohexane, p,p’-DDE, and p,p’-DDT were similar to those of the sum OCP concentrations (Supplementary Table 2).
3.3. Subgroup analysis: macronutrient intake and serum OCP concentrations Fig. 2 shows the association between the total fat intake and serum concentrations of OCPs stratified by age, gender, BMI, and physical activity. When stratified by age, an inverse association was mostly observed among persons less than 40 years of age (Ptrend ¼ 0.003, Fig. 2A). However, all subgroups stratified by gender, BMI, and physical activity showed significant inverse associations between total fat intake and serum concentrations of OCPs (Fig. 2BeD), although men, subjects with obesity, and those who were physically inactive tended to show clearer inverse patterns than their counterparts. The results of stratified analyses of total carbohydrate intake were generally similar to those of total fat intake (Fig. 3AeD). 4. Discussion In this study, subjects with low fat intake showed high serum concentrations of OCPs compared to those with high fat intake. This association was similarly observed in many subgroups stratified by
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P Fig. 3. Stratified analyses of the associations between calories from dietary carbohydrate intake and the sum OCP concentrations ( OCPs, geometric mean, ng/g of lipid). (A) Stratified by age (sample sizes: 1,033 for age <40 years; 498 for age 40e59 years; 233 for age 60 years). (B) Stratified by sex (sample sizes: 835 for men; 929 for women) (C) Stratified by BMI (sample sizes: 1,300 for BMI <30 kg/m2; 464 for BMI 30 kg/m2). (D) Stratified by physical activity (sample sizes: 711 for no leisure-time physical activity; 1053 for P doing leisure-time physical activity). Notes: OCPs, sum of 6 OCPs (b-hexachlorocyclohexane, p,p’-DDE, p,p’-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide); Q1eQ5, 1st quintilee5th quintile of each nutrient. These analyses were adjusted for age, sex, race/ethnicity, BMI, changes in weight over the past year, physical activity, and total calorie intake. Among all covariates mentioned above, the variable used for stratification was excluded from adjustment in each analysis. Ptrend was used to test whether the trends of the P OCPs according to the quintiles of macronutrient intake have a statistically significant linear shape in each stratum.
age, gender, BMI, and physical activity. The largest difference was observed among subjects in the 1st and 2nd quintiles of total fat intake; the highest concentration of OCPs was observed among subjects in whom fats accounted for less than 25% of the total energy intake. When fat intake was more than 25%, the decreasing trend in serum concentrations of OCPs was weak. In contrast, carbohydrate intake was positively associated with serum concentrations of OCPs. Serum concentrations of OCPs were similar among subjects in the 1st to 3rd quintiles of carbohydrate intake in which the carbohydrate intake percentage among the total energy intake was less than about 55% (Table 2), but began to rise in the 4th quintile and reached a peak in the 5th quintile. From the perspective of serum concentration of OCPs, these results indicate that the substitution of fat intake with carbohydrate intake might be harmful. At first glance, these findings may appear unexpected because fatty animal food is the main source of exposure to OCPs in the current general population (Dougherty et al., 2000). However, although the absolute amount of exposure to OCPs from the environment is higher among subjects with high fat intake than in subjects with low fat intake, it is important to note that serum concentrations of OCPs are not determined by the amount of exposure from the environment. In fact, serum concentrations of OCPs are primarily determined by the net result of release from
adipose tissue into circulation and elimination from circulation (Birnbaum, 1985; Needham et al., 1990). Therefore, serum concentrations of OCPs in the general population should be considered as markers of OCPs and other coexisting lipophilic chemicals released from the adipose tissue into circulation (Lee et al., 2018). Biliary excretion is an important elimination route of OCPs (Minh et al., 2001). As bile acids are released into the intestine to absorb dietary fat during a meal, sufficient fat intake is necessary to eliminate OCPs through bile. In rats, bile salt synthesis was reduced when a fat-free diet was consumed (Bertolotti et al., 1995), but stimulated by fat feeding (Botham and Boyd, 1983). In a randomized crossover human study with eucaloric diets and extreme differences in dietary fat intake, the synthesis and turnover rates of primary salts were 30e50% lower during low-fat diet feeding (0% fat) than during standard-fat diet feeding (41% fat) (Bisschop et al., 2004). Therefore, high fat intake can effectively lower serum concentrations of OCPs by increasing the elimination of OCPs. Another important finding was that serum concentrations of OCPs showed strong inverse associations with SFA and MUFA intake but not with PUFA intake. The results of MUFAs were similar to those of total fat intake; lower serum concentrations of OCPs were evident in the comparison of the 1st and 2nd quintiles compared to the other quintiles of MUFA intake. Comparatively, SFAs showed a more linear trend; the serum concentration of OCPs
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was lowered in the 1st to 4th quintiles of SFA intake. When SFA intake was approximately 14% of the total energy intake, serum concentrations of OCPs were similar to the concentrations in those who consumed more than 14% SFAs (Table 2). We speculated that the lack of association with PUFA intake may be related to at least two factors: toxicokinetics of OCPs released from adipose tissue and the relatively higher contamination of OCPs in marine food than in terrestrial food (Dougherty et al., 2000). It has been reported that mobilization of fatty acids from adipose tissue during periods of energy deficit is selective rather than a random process; fatty acids with more double bonds and shorter chain lengths lead to higher relative mobilization from adipose tissue during lipolysis (Price et al., 2008; Raclot and Groscolas, 1993, 1995). Thus, for a given chain length, PUFAs are preferentially released from triacylglycerols in adipose tissues compared to SFAs or MUFAs. The fatty acid composition of adipose tissue reflects that of the diet over the past months to years (Katan et al., 1997), and marine food is the main dietary source of both PUFAs and contamination by OCPs (Dougherty et al., 2000). Therefore, the amount of OCPs released from adipose tissue may be higher among those who consume more PUFAs than in those who consume SFAs or MUFAs. Interestingly, our findings agree with recent findings regarding the effects of dietary macronutrient intake on health. For example, in a prospective study in 18 countries on five continents, higher carbohydrate intake was associated with an increased risk of total mortality, while total fat intake was associated with a lower risk (Dehghan et al., 2017). Total fat and the types of fat were not associated with cardiovascular diseases or myocardial infarction, whereas SFAs showed an inverse association with stroke (Dehghan et al., 2017). This study concluded that removing current restrictions on fat intake but limiting carbohydrate intake may improve health, calling into question current dietary guidelines. This study had several limitations. First, this cross-sectional study was not able to establish temporality. However, reverse causality was unlikely because the current study subjects were apparently healthy without cardio-metabolic diseases. Healthy persons with high serum OCP concentrations do not require a drastic change in their diets from a high-fat and low-carbohydrate diet to a low-fat and high-carbohydrate diet. Moreover, the crosssectional design can have merit in evaluating the effects of usual diet patterns on serum concentrations of OCPs. Second, a one-time 24-h dietary recall is not sufficient for estimating the effects of a long-term diet. Therefore, caution should be taken when interpreting the current results. However, inter-day variations in macronutrient intake may lead to non-differential misclassification and typically bias the result to show no effect (Hornell et al., 2017). Thus, the true associations of macronutrient intake with serum concentrations of OCPs may be stronger than those observed in this study. Third, there may be residual confounders, although we extensively adjusted for covariates, including weight changes. However, the consistent associations observed in most subgroups may not easily be explained by residual confounders. 5. Conclusions Supporting the findings from animal experimental studies (Kania-Korwel et al., 2008; Myrmel et al., 2016), our findings suggest that dietary macronutrient intake may be one of the practical methods for reducing serum concentrations of lipophilic chemical mixtures such as OCPs, which are released into circulation from adipose tissue. Although these findings need to be replicated in other populations, changing dietary macronutrient intake can be a practical method to manage unavoidable lipophilic chemical
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mixtures such as OCPs in modern society. Funding This work was supported by the Environmental Health Action Program [grant number 2016001370002] funded by the Ministry of Environment of the Republic of Korea. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. CRediT authorship contribution statement Yu-Mi Lee: Writing - original draft. Somi Heo: Formal analysis. Se-A Kim: Formal analysis. Duk-Hee Lee: Conceptualization, Supervision, Writing - review & editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2019.113819. References Bertolotti, M., Spady, D.K., Dietschy, J.M., 1995. Regulation of hepatic cholesterol metabolism in the rat in vivo: effect of a synthetic fat-free diet on sterol synthesis and low-density lipoprotein transport. Biochim. Biophys. Acta 1255, 293e300. Birnbaum, L.S., 1985. The role of structure in the disposition of halogenated aromatic xenobiotics. Environ. Health Perspect. 61, 11e20. Bisschop, P.H., Bandsma, R.H., Stellaard, F., ter Harmsel, A., Meijer, A.J., Sauerwein, H.P., Kuipers, F., Romijn, J.A., 2004. Low-fat, high-carbohydrate and high-fat, low-carbohydrate diets decrease primary bile acid synthesis in humans. Am. J. Clin. Nutr. 79, 570e576. Botham, K.M., Boyd, G.S., 1983. The effect of dietary fat on bile salt synthesis in rat liver. Biochim. Biophys. Acta 752, 307e314. CDC/National Center for Health Statistics, 2006. Laboratory Procedure Manual. PCBs and Persistent Pesticides. https://wwwn.cdc.gov/nchs/data/nhanes/2003-2004/ labmethods/l28_c_met_-pcbs_and_persistent_pesticides.pdf. CDC/National Center for Health Statistics, 2008. National Health and Nutrition Examination Surveys (NHANES) 2003-2004 Laboratory Data. 2003-2004 Data Documentation, Codebook, and Frequencies. Pesticides - Organochlorine Metabolites - Serum (Surplus). https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/ L28OCP_C.htm. Dehghan, M., Mente, A., Zhang, X., Swaminathan, S., Li, W., Mohan, V., Iqbal, R., Kumar, R., Wentzel-Viljoen, E., Rosengren, A., Amma, L.I., Avezum, A., Chifamba, J., Diaz, R., Khatib, R., Lear, S., Lopez-Jaramillo, P., Liu, X., Gupta, R., Mohammadifard, N., Gao, N., Oguz, A., Ramli, A.S., Seron, P., Sun, Y., Szuba, A., Tsolekile, L., Wielgosz, A., Yusuf, R., Hussein Yusufali, A., Teo, K.K., Rangarajan, S., Dagenais, G., Bangdiwala, S.I., Islam, S., Anand, S.S., Yusuf, S., Prospective Urban Rural Epidemiology study, i, 2017. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 390, 2050e2062. Dougherty, C.P., Henricks Holtz, S., Reinert, J.C., Panyacosit, L., Axelrad, D.A., Woodruff, T.J., 2000. Dietary exposures to food contaminants across the United States. Environ. Res. 84, 170e185. Evangelou, E., Ntritsos, G., Chondrogiorgi, M., Kavvoura, F.K., Hernandez, A.F., Ntzani, E.E., Tzoulaki, I., 2016. Exposure to pesticides and diabetes: a systematic review and meta-analysis. Environ. Int. 91, 60e68. Graubard, B.I., Korn, E.L., 1999. Analyzing health surveys for cancer-related objectives. J. Natl. Cancer Inst. 91, 1005e1016. Hornell, A., Berg, C., Forsum, E., Larsson, C., Sonestedt, E., Akesson, A., Lachat, C., Hawwash, D., Kolsteren, P., Byrnes, G., De Keyzer, W., Van Camp, J., Cade, J.E., Greenwood, D.C., Slimani, N., Cevallos, M., Egger, M., Huybrechts, I., Wirfalt, E., 2017. Perspective: an extension of the STROBE statement for observational studies in nutritional epidemiology (STROBE-nut): explanation and elaboration. Adv. Nutr. 8, 652e678. Hutter, H.P., Kundi, M., Hohenblum, P., Scharf, S., Shelton, J.F., Piegler, K., Wallner, P., 2016. Life without plastic: a family experiment and biomonitoring study. Environ. Res. 150, 639e644. Jackson, E., Shoemaker, R., Larian, N., Cassis, L., 2017. Adipose tissue as a site of toxin accumulation. Comp. Physiol. 7, 1085e1135. Kang, J.H., Park, H., Chang, Y.S., Choi, J.W., 2008. Distribution of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in human serum from urban areas in Korea. Chemosphere 73, 1625e1631. Kania-Korwel, I., Hornbuckle, K.C., Robertson, L.W., Lehmler, H.J., 2008. Influence of dietary fat on the enantioselective disposition of 2,2’,3,3’,6,6’-hexachlorobiphenyl (PCB 136) in female mice. Food Chem. Toxicol. 46, 637e644. Katan, M.B., Deslypere, J.P., van Birgelen, A.P., Penders, M., Zegwaard, M., 1997.
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