International Journal of Environmental Health Research

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Association between arsenic exposure and thyroid function: data from NHANES 2007–2010 Ram B. Jain To cite this article: Ram B. Jain (2015): Association between arsenic exposure and thyroid function: data from NHANES 2007–2010, International Journal of Environmental Health Research, DOI: 10.1080/09603123.2015.1061111 To link to this article: http://dx.doi.org/10.1080/09603123.2015.1061111

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Date: 06 November 2015, At: 14:51

International Journal of Environmental Health Research, 2015 http://dx.doi.org/10.1080/09603123.2015.1061111

Association between arsenic exposure and thyroid function: data from NHANES 2007–2010 Ram B. Jain* Private Consultant, Sanford, NC, USA

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(Received 17 November 2014; final version received 5 May 2015) The association of arsenic variables in urine, total arsenic (UAS), arsenobetaine (UAB), dimethylarsinic acid (UDMA), and arsenic adjusted for arsenobetaine (UAAS) with thyroid-stimulating hormone (TSH), free and total serum thyroxine (FT4, TT4), free and total triiodothyronine (FT3, TT3), and thyroglobulin (TGN) was evaluated by analyzing data from 2007–2010 National Health and Nutrition Examination Survey. For iodine deficient males, there was a positive association between TSH and UDMA (p < 0.01) and a negative association between the levels of TT4 and UDMA (p < 0.01). Levels of UAAS were inversely associated with the levels of TT4 for both iodine-deficient (p = 0.054) and iodine-replete females (p < 0.01). For iodine-replete females, levels of both TSH and TGN increased with decrease in the levels of both UAB (p < 0.01) and UAS (p < 0.01). There was also a negative association between TSH and UAB as well as UAS (p < 0.01). For iodinereplete males, increased levels of UDMA were associated with decreasing levels of FT4 (p = 0.03). Keywords: arsenobetaine; dimethylarsinic acid; smoking; iodine sufficiency

Introduction Arsenic is widespread in the environment and earth’s crust (http://www.atsdr. cdc.gov/substances/toxsubstance.asp?toxid=3). It occurs in both organic and inorganic forms (Wei et al. 2014). Some of the organic forms include arsenobetaine and arsenosugars, which are often found in fish and other seafood (Wei et al. 2014). Organic arsenic compounds are also used as pesticides (http://www.atsdr.cdc.gov/substances/tox substance.asp?toxid=3). Exposure to organic forms of arsenic can also be from consumption of seafood among others. Inorganic forms include arsenites and arsenates (Wei et al. 2014). Humans are exposed to inorganic arsenic through the consumption of arsenic-contaminated drinking water (Amster et al. 2011) and certain foods such as fruits, fruit juices, rice (Wei et al. 2014), and other grains. Routes of exposure to arsenic include occupational, environmental, and medical sources. Exposure to arsenic has been shown to be associated with urinary cancer (Chen et al. 2010), lung function/cancer (Putila & Guo 2011; Ferreccio et al. 2013; Parvez et al. 2013; Sawada et al. 2013), bladder cancer (Ferreccio et al. 2013), head and neck cancer (Khlifi et al. 2014), peripheral vascular disease (Tseng et al. 1996), and respiratory diseases (Parvez et al. 2010; Dauphine et al. 2011). Cardiovascular effects related to the exposure of arsenic have also been documented (Mumford et al. 2007; Moon et al. 2013). Exposure to arsenic *Email: [email protected] © 2015 Taylor & Francis

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R.B. Jain

from drinking water has shown to be associated with excess mortality from cardiovascular diseases (Chen et al. 2011). Association between the levels of arsenic exposure and Type 2 diabetes has also been shown to exist (Pan et al. 2013). Even a low-level prolonged exposure to arsenic has been found to be associated with renal damage and progression to an existing chronic kidney disease (Palaneeswari et al. 2013). Exposure to arsenic has also been studied in relation to its effect on neurological functions and reproductive outcomes (Watanabe et al. 2003, 2007; Wasserman et al. 2004, 2007). An excellent review of the epidemiology of exposure to arsenic has been provided by Hughes et al. (2011). Thyroid gland is one of the largest endocrine glands in the human body. Principal hormones produced by thyroid are triiodothyronine (T3) and thyroxine (T4). According to a document by the American Thyroid Association (http://thyroid.org/wp-content/ uploads/patients/brochures/CAM_brochure.pdf), thyroid hormones help the body use energy, stay warm, and keep the brain, heart, muscles, and other organs working as they should. These two hormones, T3 and T4, regulate the rate of metabolism. Tyrosines and iodine are two principle “raw materials” necessary to synthesize T3 and T4 (http:// www.vivo.colostate.edu/hbooks/pathphys/endocrine/thyroid/synthesis.html). Thus, inadequate iodine uptake may perturb thyroid homeostasis which is maintained by a multiloop feedback system called the hypothalamic–pituitary–thyroid axis. In a review article, Miller et al. (2009) have identified eight classes of chemicals that can disrupt thyroid homeostasis. These include iodine transport chemicals, transport disruptors, chemicals which enhance hepatic catabolism, enhanced cellular, sulfotransferases, deiodinases, and finally thyroid receptor agonists and antagonists. Miller et al. (2009) provide comprehensive references to the mechanism of how these chemicals affect thyroid homeostasis. There have been very few studies that we can determine that have evaluated the relationship between arsenic exposure and thyroid function variables. In a study by Ciarrocca et al. (2012), values of the levels of thyroid-stimulating hormones (TSH), free thyroxine (FT4), free triiodothyronine (FT3) and thyroglobulin (TGN) were compared between traffic policemen in urban areas, who were exposed to arsenic in the amount of 2.9 μg/m3 and roadmen in rural areas, who were exposed to arsenic in the amount of 0.1 μg/m3. Traffic policemen were found to have statistically significantly higher levels of TSH and TGN than roadmen and statistically significantly lower levels of both FT3 and FT4 than roadmen. Thus, a positive correlation between arsenic levels and TSH and TGN, and a negative correlation between arsenic levels and FT3 and FT4 were shown to exist. Arsenic has been shown to disrupt retinoid acid and thyroid hormone receptors (Davey et al. 2008), estrogen receptors (Davey et al. 2007), and steroid hormone receptors (Bodwell et al. 2006). To the best of our knowledge, no study has evaluated the association between arsenic exposure and thyroid function by analyzing a nationally representative sample whose results are generalizable for the entire population. We selected National Health and Nutrition Examination Survey (NHANES) conducted by the US Centers for Disease Control and Prevention (www.cdc.gov/nchs/nhanes.htm) to study the association between thyroid function and arsenic exposure. NHANES is a cross-sectional, ongoing, continuous survey since 1999, designed to assess the health and nutritional status of the adults and children in the USA. Each year, the survey examines a nationally representative sample of about 5000 individuals residing across 15 different counties of the USA. In the first part of the survey, individuals selected for participation are interviewed at their homes and are administered a series of questions about demographics, socioeconomic status, health status, dietary habits, etc. If found eligible, each of these individuals is asked to continue

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his/her participation in the survey by visiting a mobile examination center (MEC) 2–4 weeks after the home interview. Those who decide to continue participating in the survey and visit MEC, go through a series of examinations which include dental, medical, and physiological measurements. In addition, additional questionnaires addressing health-related issues, for example smoking habits, are also administered at MEC. Finally, each participant visiting MEC is asked to provide urine and blood samples for conducting various tests in laboratory. While data are collected on an ongoing basis every year, data are released in the public domain every two years. All survey activities are done with the approval of the Institutional Review Board of the Centers for Disease Control and Prevention. NHANES has publically released data on arsenic exposure since 2003–2004 for a representative sample of non-institutionalized US population. Starting from NHANES cycle 2007–2008, comprehensive data on the thyroid hormones have also been made available. Consequently, the focus of this study was to evaluate the relationship between arsenic exposure and thyroid function by analyzing data from NHANES for the years 2007–2010. Materials and methods Study population Study population for this research was drawn from 2007 to 2010 NHANES conducted by the US Centers for Disease Control. The sampling plan for NHANES is a complex, stratified, multistage, probability cluster, designed to be representative of the civilian, non-institutionalized US population. Sampling weights are created in NHANES to account for the complex survey design, including oversampling, survey non-response, and post-stratification. The study was restricted to those who were aged 12 years and older. Data for a total of 4855 participants were available for analysis. However, after removing 215 participants for whom race/ethnicity could not be classified, those female participants who were pregnant at the time of participation in NHANES (N = 49), those who self-reported having current thyroid problems (N = 196), those who were taking prescription thyroid treatment drugs (N = 29), and those who had thyroid peroxidase antibodies (TPOAb) ≥ 35 IU/mL or thyroglobulin antibodies (TgAB) ≥ 20 IU/mL (N = 410) were also removed from the analysis, leaving a total of 4171 participants available for analysis. Finally, there were a total of 45 participants who had a weight of zero in NHANES data files and as such, they were removed from the database, leaving a total of 4126 participants for analyses. Details are given in Table 1. In summary, database for iodine-deficient males had 584 participants, database for iodine-replete males had 1465 participants, database for iodine-deficient females had 638 participants, and database for iodine-replete females had 1156 participants. However, actual sample sizes used for each analysis were smaller and varied depending up on the number of missing values for different independent variables, for example smoking status. Laboratory methods Description of the laboratory methodology used to detect and measure various thyroid profile variables is given elsewhere (www.cdc.gov/nchs/nhanes/nhanes2007-2008/thy rod_e.htm#Description_of_Laboratory_Methodology). Laboratory methods used to detect

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Table 1. Un-weighted sample sizes (N) by age, gender, race/ethnicity, iodine sufficiency, and smoking status. Gender Male

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Iodine deficient

Female Iodine deficient

Iodine replete

Iodine replete

N

%

N

%

N

%

N

%

Total 12–19 years 20–64 years 65+ years

584 95 395 94

100.0 16.3 67.6 16.1

1465 307 871 287

100.0 21.0 59.5 19.6

638 119 430 89

100.0 18.7 67.4 13.9

1156 227 707 222

100.0 19.6 61.2 19.2

Non-Hispanic White Non-Hispanic Black Hispanic

261 158 165

44.7 27.1 28.3

679 295 491

46.3 20.1 33.5

291 155 192

45.6 24.3 30.1

480 268 408

41.5 23.2 35.3

Non-smokers Smoker Missing

379 172 33

64.9 29.5 5.7

1004 374 87

68.5 25.5 5.9

483 120 35

75.7 18.8 5.5

882 184 90

76.3 15.9 7.8

Note: Data from National Health and Nutrition Examination Survey, 2007–2010.

and measure various arsenic variables are available at http://www.cdc.gov/nchs/nhanes/ nhanes2007-2008/UAS_E.htm#Description_of_Laboratory_Methodology. Exposure This study as previously mentioned was conducted to evaluate the impact of arsenic exposure on thyroid function. For arsenic, data for eight variables were available in NHANES, namely, urinary total arsenic (UAS), arsenous acid, arsenic acid, arsenobetaine (UAB), arsenocholine, dimethylarsinic acid (UDMA), monomethyl-arsonic acid, and trimethylarsine oxide. Percent weighted observations at or above the limit of detection (LOD) for UAS, UAB, and UDMA were 99.5, 61.8, and 81.3 %, respectively. For other five arsenic variables, percent observations at or above the LOD varied from 0.4 to 34.6 %. Since percent observations at or above LOD below 60 % may not be able to provide reliable results, these five variables were not considered for analyses. All observations below the LOD for UAS, UAB, and UDMA were set at LOD/Sqrt(2). In addition, estimate of the total arsenic adjusted for arsenobetaine (UAAS) was obtained by subtracting the values of UAB from UAS as recommended by Steinmaus et al. (2009) and used by Amster et al. (2011). Outcome variables Outcome variables of interest were the thyroid function variables. Starting with the 2007–2008 cycle (www.cdc.gov/nchs/nhamnes/nhanes2007-2008/nhanes07_08.htm) of NHANES, comprehensive data on thyroid profile have become available. Specifically, the variables available on thyroid profile were: thyroglobulin antibodies (TgAb), free triiodothyronine (FT3), free thyroxine (FT4), thyroglobulin (TGN), TSH, thyroid peroxidase antibodies (TPOAb), total triiodothyronine (TT3), and TT4.

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Covariates Self-reported age, gender (male and female), and race/ethnicity were the three categorical variables that were used as covariates. Three age groups, namely those who were between 12 and 19 years old, those who were between 20 and 64 years old, and those who were aged 65 years and above were created. The rationale for using these age categories was to be able to draw conclusions for adolescents (12–19 years old), adults who were not yet senior citizens (20–64 years), and senior citizens (65 + years old). These categories have previously been used by Jain (2013a) in a study that evaluated the impact of exposure to perfluoroalkyl acids on thyroid function. Further, based on the results of a preliminary analysis, a decision was made to analyze data separately for males and females. Starting with the NHANES cycle 2007–2008, in addition to Mexican Americans, other Hispanics were also oversampled (http://www.cdc.gov/nchs/data/nhanes/analyticnote_2007-2010. pdf), making it possible to have large enough sample size to generate an ethnic category of all Hispanics including Mexican Americans. As such, instead of using four racial/ethnic categories as is normally reported in the papers analyzing NHANES data, namely, NHW, NHB, Mexican Americans, and Others, three racial/ethnic categories, namely, NHW, NHB, and HISP were used in this study because, in our opinion, it was not possible to have meaningful results/discussion about those in other racial/ethnic category. In addition, smoking status was also used as a categorical variable. A variable for smoking status was created by splitting serum cotinine data in two different levels. Those whose serum cotinine levels were < 10 ng/mL were defined as non-smokers and those whose serum cotinine levels were ≥ 10 ng/mL were defined as smokers. Those with serum cotinine levels of ≥ 10 ng/mL have been defined as smokers among others by Rokadia and Agarwal (2012), Jain and Wang (2011), and Jain (2013b). In addition, because iodine sufficiency status affects thyroid profile, separate analyses were done for iodine-deficient participants, defined as those whose urinary iodine levels were < 100 ng/mL, and iodinereplete participants, defined as those for whom urinary iodine levels were ≥ 100 ng/mL. This scheme based on urinary iodine concentration levels to classify those who are iodine deficient has also been used among others by Blount et al. (2006). WHO (2007) also defines iodine deficients as those who had urinary iodine levels below 100 ng/mL. Continuous variables used as covariates included C-reactive protein (CRP), body mass index (BMI), fasting time (FTIME) before the blood was drawn for analysis, urine creatinine, and arsenic variables, namely, UAB, UAS, UDMA, and UAAS. Kelly (2000), in a review article, found aging and fasting to be factors that may affect the levels of TT3 and as such, FTIME was also included as a covariate. For females, estrogen use and menopausal status were also used as covariates. Also, as has been considered among others by Blount et al. (2006) and Turyk et al. (2007), a consideration was given to the possibility of the use of drugs other than thyroid treatment drugs having association with thyroid variables. In order to evaluate that, all those who were using one or more prescription drugs such as non-steroidal anti-inflammatory drugs, beta blockers, blood glucose regulators, and others such as interferon, lithium, phenytoin, etc. were identified. An indicator variable, use or non-use of these drugs, was created and used in all models as a covariate. Statistical analysis There were four data-sets that were available for analysis, namely, for iodine-deficient males, iodine-replete males, iodine-deficient females, and iodine-replete females. Each

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of these data-sets were analyzed using SAS version 9.2 (www.sas.com, SAS, Cary, North Carolina, USA) and SUDAAN version 11.0 (www.rti.org/SUDAAN, Research Triangle Institute International, Research Triangle Park, North Carolina, USA). All analyses used appropriate weights as provided in the data files. Specifically, the variable WTSA2YR provided weights for all data. SUDAAN Proc DESCRIPT was used to do all univariate analyses. SUDAAN Proc REGRESS was used to fit linear regression models to do multivariate analysis. Because of the positively skewed distributions, all thyroid variables were log10 transformed before being used as independent variables in the regression models. Also, all variables on thyroid profile were multiplied by 1000 before analysis so as to avoid working with very small values and as such cause unstable estimations of statistical parameters. This change in unit should be carefully considered in interpreting the magnitude of regression slopes. All analyses used appropriate sampling weights and incorporated sampling design variables SDMVSTRA and SDMVPSU as provided in NHANES in the univariate as well as multivariate analyses. At first, an evaluation was made if the interaction between age and race/ethnicity was statistically significant at α = 0.05. If the answer was yes, the interaction term was included in the final models for analyses. Only one arsenic variable at a time was considered for the analysis in each model. Since there were a total of six thyroid variables, namely, FT3, FT4, TT3, TT4, TSH, and TGN, and four arsenic variables, there were a total of 24 regression models that were fitted for each of these four databases. However, since both linear and non-linear associations between arsenic and thyroid variables were possible, at first, each arsenic variable was used as a continuous variable in the models. If the association between the pair of thyroid and arsenic variables under consideration was not found to be statistically significant at α = 0.05, arsenic variable under consideration was used as a categorical variable in the model. For this purpose, all three arsenic variables were categorized as tertiles. For UAB, first tertile was established at < 0.40 μg/ L, second tertile between 0.40 and 2.73 μg/L, and third tertile at > 2.73 μg/L. For UAS, first tertile was established at < 5.39 μg/L, second tertile between 5.39 and 12.3 μg/L, and third tertile at > 12.3 μg/L. For UDMA, first tertile was established at < 2.61 μg/L, second tertile between 2.61 and 5.14 μg/L, and third tertile at > 5.14 μg/L. For UAAS, first tertile was established at < 4.35 μg/L, second tertile between 4.35 and 8.9 μg/L, and third tertile at > 8.9 μg/L. Results Tremendous amount of data from 96 regression models were generated in this study. In addition, another 50 or so regression models that evaluated non-linear association between arsenic variables and thyroid variables also generated data. Reproducing all these data in this manuscript will be confusing and counterproductive. Consequently, only the regression coefficients generated when evaluating linear association between arsenic and thyroid variables are presented. In addition, adjusted geometric means are presented for only one arsenic variable. Interactions between age and race/ethnicity were not found to be statistically significant for iodine-deficient males, iodine-replete males, and iodine-deficient females. However, for iodine-replete females, there was a statistically significant interaction (p < 0.01) between age and race/ethnicity. While the results of these interactions are described in sections below, the results are also displayed in Supplemental Figures 1–3.

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Iodine-deficient females For females, in the presence of iodine deficiency, except for the negative association of UAS and UDMA with TT4 (p < 0.01, Table 2), none of the four arsenic variables had a statistically significant linear association with any of the other thyroid variables (Table 2). However, low levels of UDMA as compared to high levels of UDMA were associated with relatively higher levels of FT3 (3.18 pg/ml vs. 3.01 pg/ml, p = 0.03, Table 3). For iodine-deficient females, levels of FT3, TT3, and TT4 increased with increase in BMI when UAAS was in the model. In fact, TT3 levels increased with increase in BMI (p < 0.001, Table S1) irrespective of the arsenic variable in the model. FTIME was positively associated with the levels of TSH irrespective of arsenic variable in the model (p ≤ 0.035, Table S1). When UAAS was in the model, FTIME was also associated with increased levels of FT3. Use of prescription drugs other than the thyroid treatment drugs did not affect the levels of any thyroid variable. Estrogen drug use was associated with increased TT3 levels (p ≤ 0.024, Table S1). The same was true for TT4 when UAAS was in the model. Being in menopause was associated with increased levels of TT4 (p < 0.01) and TGN (p < 0.01). For FT3 and TT3, the order of levels by age was A12–19 > A20–64 > A65 +, but only the levels for A12–19 were found to be statistically significantly higher than those for A20–64 and A65+ (p ≤ 0.03, Table 3). NHW had statistically significantly higher levels of TSH, FT3, and TT3 than NHB (p ≤ 0.03, Table 3), but NHW had statistically significantly lower levels of TGN than NHB (11.51 ng/ml vs. 15.44 ng/ml, p < 0.01). Both NHW and NHB had statistically significantly lower levels of FT3 and TT4 than HISP (p < 0.01). Iodine-replete females There was a statistically significant inverse linear relationship for both UAB and UAS with both TSH and TGN (p ≤ 0.02, Table 2). There was also a statistically significant negative relationship of UAAS with TT4 (p < 0.01, Table 2). Statistically significantly higher levels of FT4, TT3, and TT4 were associated with low levels of UDMA (p ≤ 0.02, Table 3). Similarly, statistically significantly higher levels of FT3 were associated with low levels of UAB (p < 0.01, Table 3). Also, statistically significantly higher levels of FT3 were associated with medium levels of UAAS (p = 0.02, Table 3). In addition, statistically significantly higher levels of TT3 were associated with low levels of UAAS (p = 0.01, Table 3). For iodine-replete females, CRP was positively associated with the levels of TSH and TT4 (Table S1, p ≤ 0.038). Urine creatinine did not affect the levels of any of the six thyroid variables, except when UAAS was in the model, in which case, levels of FT3 increased with increase in urine creatinine. BMI was positively associated with the levels of TT4 (p < 0.01, Table S1) irrespective of the arsenic variable in the model. The same was true when UAAS was in the model. Increased FTIME was associated with increase in levels of TSH (p = 0.033, Table S1). The same was true for FT3 when UAAS was in the model. Use of prescription drugs other than thyroid treatment drugs was associated with decreased levels of TT3 (p ≤ 0.012). The same was true for FT3 when UAAS was in the model. Being in menopause was associated with higher levels of TSH (p ≤ 0.017). Use of estrogen drugs was associated with elevated levels of TT4 (p ≤ 0.001). The levels (or adjusted geometric means) of FT3 and TT3 varied inversely with age, i.e. the levels of FT3 and TT3 were in the order: A12–19 > A20–64 > A65+ (p < 0.01,

Females

Females

Females

Females

Females

Females

Log TSH

Log FT4

Log FT3

Log TT4

Log TT3

Log TGN

Deficient

Deficient Replete

Deficient Replete

Deficient Replete

Deficient Replete

Deficient Replete

Iodine status

0.0006 −0.0020

0.0001 0.0001 0.252 0.008

0.642 0.471

0.062 0.492

−0.0002 0.0001

0.200 0.492

−0.0001 0.0001 0.693 0.720

0.486 0.001

−0.0002 −0.0008

0.0000 0.0000

p

β

Note: Data from National Health and Nutrition Examination Survey, 2007–2010.

Gender

Urine arsenobetaine (UAB)

0.0002 −0.0014

0.0000 0.0000

0.411 0.019

0.959 0.777

0.001 0.719

0.293 0.752

−0.0001 0.0000 −0.0002 0.0000

0.175 0.719

0.139 0.001

p

−0.0001 0.0000

−0.0003 −0.0005

β

Urine total arsenic (UAS)

0.008 0.067 0.218 0.554 0.388 0.189

−0.0030 −0.0002 −0.0043 −0.0043

0.053 0.800

0.930 0.067

0.768 0.947

p

−0.0024 −0.0007

−0.0022 0.0001

0.0001 −0.0007

−0.0012 −0.0001

β

Dimethylarsinic acid (UDMA)

Regression slopes of arsenic variables with thyroid function variables for females by iodine sufficiency status.

Thyroid variable

Table 2.

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0.0599 −0.0905

−0.0255 −0.0175

−0.0214 −0.0320

−0.0140 −0.0078

0.0053 −0.0905

−0.0123 −0.0002

β

0.212 0.135

0.092 0.102

0.054 0.002

0.105 0.186

0.689 0.135

0.739 0.995

p

Adjusted arsenic (UAAS)

8 R.B. Jain

Gender

Females

Females

Females

Thyroid variable

TSH in UIU/ml

TSH in UIU/ml

FT4 in ng/dl

Deficient

Replete

Deficient

Iodine status

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAB (Low) Medium UAB (Medium) High UAB (High)

Variable

0.8 (0.76–0.85) 0.77 (0.75–0.79) 0.81 (0.76–0.86) 0.78 (0.76–0.8) 0.77 (0.75–0.8) 0.78 (0.75–0.81) 0.78 (0.76–0.8)

1.42 (1.11–1.81) 1.42 (1.33–1.52) 1.65 (1.43–1.9) 1.54 (1.42–1.67) 1.1 (0.97–1.24) 1.44 (1.32–1.58) 1.47 (1.36–1.58) 1.4 (1.24–1.58)

1.51 (1.24–1.83) 1.42 (1.3–1.54) 1.61 (1.33–1.95) 1.53 (1.4–1.67) 1.19 (1.02–1.37) 1.34 (1.18–1.53) 1.49 (1.4–1.58) 1.3 (1.1–1.55) 1.47 (1.32–1.64) 1.36 (1.19–1.56) 1.51 (1.32–1.73)

AGM

NHW > NHB (p < 0.001) NHB < HISP (p < 0.001)

NHW > NHB (p = 0.01)

Statistically significant differences

(Continued)

Actual N = 536, R2 = 2.1 %, Variable in Model: Categorical UAS

Actual N = 896, R2 = 11.8 %, Variable in Model: Continuous UAB and UAS, βUAB = −0.0008, pUAB = 0.001, βUAS = −0.0005, pUAS = 0.001

Actual N = 535, R2 = 7.5 %, Variable in model: Categorical UAB

Comments

Table 3. Adjusted geometric means with 95 % confidence intervals for thyroid function variables by age, race/ethnicity, smoking status, levels of urine arsenic variables, and iodine sufficiency status for females aged 12 years and higher.

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Gender

Females

Females

FT4 in ng/dl

FT3 in pg/ml

(Continued).

Thyroid variable

Table 3.

Deficient

Replete

Iodine status

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

Variable (0.75–0.81) (0.76–0.79) (0.76–0.82) (0.74–0.83)

3.42 (3.33–3.51) 3.11 (3.07–3.16) 3.03 (2.89–3.17) 3.14 (3.09–3.19) 3.04 (2.97–3.12) 3.3 (3.23–3.36) 3.14 (3.1–3.19) 3.15 (3.07–3.25) 3.18 (3.13–3.22) 3.11 (3.03–3.19) 3.01 (2.86–3.16)

0.79 (0.75–0.83) 0.75 (0.74–0.77) 0.78 (0.75–0.81) 0.76 (0.75–0.77) 0.78 (0.75–0.81) 0.76 (0.75–0.78) 0.76 (0.75–0.78) 0.76 (0.74–0.78) 0.79 (0.77–0.8) 0.76 (0.74–0.78) 0.75 (0.73–0.76)

0.78 0.77 0.79 0.78

AGM

Low > High (p = 0.03)

NHW > NHB (p = 0.046) NHB < HISP (p < 0.001) NHW < HISP (p < 0.001)

A12–19 > A20–64 (p < 0.001), A12–19 > A65+ (p = 0.001)

Low > Medium (p = 0.02), Low > High (p = 0.02)

A12–19 > A20–64 (p = 0.04)

Statistically significant differences

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Actual N = 535, R2 = 23.9 %, Variable in Model: Categorical UDMA

Actual N = 896, R2 = 5.5 %, Variable in Model: Categorical UDMA, also βUAAS = −0.0165, p = 0.02.

Comments

10 R.B. Jain

Females

Females

Females

FT3 in pg/ml

TT4 in ug/ml

TT4 in ug/ml

Replete

Deficient

Replete

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM)

(7.16–7.99) (7.76–8.06) (7.33–8.37) (7.52–7.95) (7.79–8.26) (7.98–8.71) (7.72–8.02) (7.41–8.2)

8.08 (7.72–8.47) 7.78 (7.62–7.95) 8.04 (7.66–8.43) 7.73 (7.58–7.88) 8.06 (7.83–8.29) 8.33 (8.09–8.58) 7.87 (7.73–8.01) 7.79 (7.45–8.15) 8.25 (7.93–8.59) 7.79 (7.6–7.99)

7.57 7.91 7.84 7.74 8.02 8.34 7.87 7.8

3.12 3.17 3.18 3.09 3.11

Non-smoker (NSM) Smoker (SM) Low UAB (Low) Medium UAB (Medium) High UAB (High)

(3.09–3.16) (3.13–3.22) (3.13–3.23) (3.02–3.15) (3.06–3.16)

3.48 (3.37–3.58) 3.1 (3.06–3.14) 2.96 (2.89–3.03) 3.12 (3.08–3.17) 3.11 (3.03–3.19) 3.18 (3.12–3.24)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP)

Low > Medium (p = 0.01), Low < High (p = 0.01)

NHW < NHB (p = 0.01) NHB < HISP (p = 0.02) NHW < HISP (p = 0.01)

NHB < HISP (p < 0.001)

Low > High (p < 0.01)

A12–19 > A20–64 > A65+ (p < 0.001)

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(Continued)

Actual N = 893, R2 = 13.6 %, Variable in Model: Categorical UDMA. Also, βUIAS = −0.032, p < 0.01.

Actual N = 534, R2 = 11.5 %, Variables in Model: UAS and UDMA, βUAS = −0.002, pUAS = 0.001, βUDMA = −0.0024, pUDMA = 0.008

Actual N = 894, R2 = 21 %, Variable in Model: Categorical UAB. Also when UAAS was in the model, levels of FT3 were higher when UAAS was medium than when it was high, p = 0.02.

International Journal of Environmental Health Research 11

Gender

Females

Females

TT3 in ng/dl

TT3 in ng/dl

(Continued).

Thyroid variable

Table 3.

Replete

Deficient

Iodine status

130.87 (124.54–137.51) 112.89 (110.07–115.77) 105.82 (101.12–110.74) 114.46 (111.45–117.56) 113.02 (108.4–117.83) 113.92 (110.63–117.3)

113.9 (111–116.88) 114.11 (110.42–117.93) 117.75 (113.49–122.17) 113.71 (109.78–117.78) 111.12 (107.85–114.48)

Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

119.84 (115.27–124.58) 114.34 (111.44–117.31) 110.26 (102.82–118.24) 114.31 (111.29–117.41) 111.99 (107.32–116.88) 119.75 (115.8–123.83) 115.36 (112.86–117.92) 111.95 (107.01–117.1) 116.02 (112.42–119.73) 113.59 (110.14–117.14) 111.02 (105.35–117)

7.61 (7.39–7.84)

AGM

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

High UDMA (High)

Variable

Low > High (p = 0.02)

A12–19 > A20–64 > A65+ (p = 0.01)

NHW < HISP (p = 0.01)

NHW > NHB (p = 0.02)

A12–19 > 20–64 (p = 0.03), A12–19 > 65+ (p = 0.02)

Statistically significant differences

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Actual N = 894, R2 = 13.5 %, Variable in Model: Categorical UDMA. Also when UIAS was in the model, levels of TT3 were higher when UIAS was low compared to when UIAS was high (p = 0.01)

Actual N = 836, R2 = 8.7 %, Variable in Model: Categorical UAS

Comments

12 R.B. Jain

Females

TGN in ng/ml

Replete

Deficient

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM)

Age: 12–19 years (A12–19) Age: 20–64 years (A20–64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

Note: Data from National Health and Nutrition Examination Survey, 2007–2010.

Females

TGN in ng/ml

8.95 (7.85–10.21) 10.92 (9.78–12.19) 12.99 (10.97–15.38) 10.57 (9.59–11.65) 16.71 (14.53–19.22) 8.52 (7.29–9.96) 10.31 (9.42–11.28) 13.82 (11.96–15.96)

9.7 (7.35–12.81) 11.93 (10.56–13.48) 11.86 (8.92–15.78) 11.51 (10.05–13.17) 15.44 (13.54–17.6) 8.72 (7.4–10.29) 11 (9.83–12.32) 14.2 (11.11–18.15) 11.03 (9.65–12.6) 12.42 (10.43–14.8) 12.63 (9.41–16.96)

NHW < NHB (p < 0.001) NHB > HISP (p < 0.001) NHW > HISP (p=0.02) Non-smoker < Smoker (p < 0.001)

A12–19 > A20–64 > A65+ (p = 0.01)

NHW < NHB (p < 0.01) NHB < HISP (p < 0.01) NHW < HISP (p = 0.01)

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Actual N = 893, R2 = 9.4 %, Variables in Model: Continuous UAB/UAS, βUAB = −0.002, pUAB = 0.008, βUAS = −0.0014, pUAS = 0.009

Actual N = 534, R2 = 11.4 %, Variable in Model: Categorical UAS

International Journal of Environmental Health Research 13

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14

R.B. Jain

Table 3). Levels of TGN were in the order: A12–19 < A20–64 < A65+ (p = 0.01, Table 3). For FT4 also, those who were in the age group A12–19 had higher levels than those in the age group A20–64 (p < 0.01). Levels of TT4 were in the order: HISP > NHB > NHW and all three pairwise differences were statistically significant (p ≤ 0.02, Table 3). Levels of TGN were in the order: NHB > NHW > HISP and all three pairwise differences were statistically significant (p ≤ 0.02, Table 3). For TSH, NHW had statistically significantly higher levels than NHB (1.54 μIU/l vs. 1.10 μIU/l, p < 0.01, Table 3), and NHB had statistically significantly lower levels than HISP (1.10 μIU/l vs. 1.44 μIU/l, p < 0.01, Table 3). When interactions between age and race/ethnicity were taken into account, (i) NHB had statistically significantly lower TSH levels than both NHW and HISP (p < 0.01, Figure S1, Panel A) for both 60-–64- and 65+-year-old participants, (ii) 20–64 years old NHB had statistically significantly higher FT4 levels than 20–64 years old NHW (p = 0.03, Figure S1, Panel B), (iii) for 12–29 years old, levels of FT3 by race/ethnicity were in the order: NHB > HISP > NHW, for 20–64 years old, in the order HISP > NHB > NHW, and for 65+ years old, in the order NHB > NHW > HISP, and all pairwise differences were statistically significant (p = 0.01, Figure S2, Panel A), (iv) for TT4 for NHW, 12–19 years old had statistically significantly higher levels than 20–64 years old (p < 0.2, Figure S2, Panel B), and for 20–64 years old only, the order of TT4 levels by race/ethnicity was NHW < NHB < HISP (p < 0.01, Figure S2, Panel B), (v) for NHW, the levels of TT3 by age were in the order: A12–19 > A20–64 > A65+ and for A12–19 only, the order of the levels of TT3 by race/ethnicity was: NHW > NHB > HISP (Figure S3, Panel A), and (vi) for NHB, both A12–19 and A20–64 had statistically significantly lower levels of TGN than for A65+ (Figure S3, Panel B). Non-smokers had statistically significantly lower levels of TGN than smokers (10.31 ng/ml vs. 13.82 ng/ml, p < 0.001, Table 2). Iodine-deficient males Levels of TSH increased with increase in the levels of UDMA (β = 0.009, p = 0.004, Table 4). Levels of TT4 decreased with increase in the levels of UDMA (β = −0.0039, p = 0.004, Table 4). Low UAS as compared to medium (p = 0.04) and high UAS (p = 0.03) was found to be associated with relatively high FT3 (Table 5). Similarly, low UDMA as compared to medium UDMA was associated with higher levels of TT3 (119.05 ng/dl vs. 113.29 ng/dl, p = 0.03, Table 5). CRP was found to be positively associated with the levels of TSH (p ≤ 0.02, Supplementary Table S2) and FT3 and TGN (p < 0.01, Table S2). Except for the barely statistically significant association between BMI and TT4 (p = 0.049, Table S2) when UAB was in the model, BMI did not affect the levels of thyroid variables. When UAS (p = 0.04) and UDMA (p = 0.01) were in the model, there was a positive association between urine creatinine and FT4. The same was true for FT3 when UAS was in the model (p = 0.02) and when UAAS was in the model (p = 0.03, Table S2). FTIME was positively associated with the levels of FT3 (p < 0.01) and TT3 (p ≤ 0.033, Table S2) irrespective of which arsenic variable was in the model. Levels of FT3 and TT3 by age were in the order: A12–19 > A20–64 > A65+ (p < 0.01, Table 5). For FT4, NHB had lower levels than HISP (0.74 ng/dl vs. 0.81 ng/dl, p < 0.01, Table 5). For FT3, both NHW and NHB had statistically significantly lower levels than HISP (3.33 pg/ml and 3.29 pg/ml vs. 3.46 pg/ml, p < 0.01, Table 4). The same was true for TT4 and TT3 (p ≤ 0.02, Table 5). NHB had higher TGN levels than HISP

Males

Males

Males

Males

Males

Males

Log TSH

Log FT4

Log FT3

Log TT4

Log TT3

Log TGN

Deficient Replete

Deficient Replete

Deficient Replete

Deficient Replete

Deficient Replete

Deficient Replete

Iodine status

0.441 0.540

0.429 0.294

−0.0002 −0.0001 0.0003 0.0002

0.625 0.508

0.22400 0.068

−0.00015 −0.00009 0.0000 −0.0001

0.147 0.257

0.417 0.618

p

0.0002 −0.00005

0.0003 0.0001

β

Note: Data from National Health and Nutrition Examination Survey, 2007–2010.

Gender

Urine arsenobetaine (UAB)

0.0004 0.0001

−0.0002 0.0000

0.0000 −0.0001

−0.00013 −0.00004

0.0002 −0.00004

0.0003 0.0000

β

0.326 0.589

0.391 0.306

0.924 0.386

0.20700 0.224

0.140 0.180

0.302 0.841

p

Urine total arsenic (UAS)

0.0031 −0.0003

0.548 0.721

0.064 0.252

0.003 0.120

−0.0039 −0.0007 −0.0022 −0.0002

0.75400 0.814

0.448 0.033

−0.0010 −0.00054 −0.00024 0.00004

0.004 0.718

p

0.0093 −0.0003

β

Dimethylarsinic acid (UDMA)

Regression slopes of arsenic variables with thyroid function variables for males by iodine sufficiency status.

Thyroid variable

Table 4.

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0.0207 0.0285

−0.0160 −0.0172

−0.0151 −0.0265

−0.0130 −0.0072

0.0106 −0.0068

0.0000 0.0138

β

0.658 0.315

0.176 0.016

0.154 0.001

0.054 0.138

0.378 0.355

1.000 0.583

p

Adjusted arsenic (UAAS)

International Journal of Environmental Health Research 15

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

TSH in UIU/ml Males Replete

Variable

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM)

Iodine Gender status

TSH in UIU/ml Males Deficient

Thyroid variable

1.34 (1.25–1.43) 1.56 (1.47–1.66) 1.41 (1.34–1.5) 1.39 (1.26–1.53) 1.57 (1.46–1.68) 1.55 (1.46–1.63)

Low < Medium (p = 0.03)

HISP < NHW (p < 0.01)

A12–19 < A65+ (p = 0.04) NHW > NHB (p < 0.01)

1.75 (1.62–1.89) 1.6 (1.53–1.68) 1.27 (1.16–1.39)

A20–64 < A65+ (p < 0.01)

Statistically significant differences

1.47 (1.39–1.56)

1.54 (1.4–1.68)

1.35 (1.22–1.5) 1.47 (1.36–1.58) 1.42 (1.27–1.59)

1.33 (1.17–1.51)

1.45 (1.21–1.74) 1.5 (1.37–1.64)

1.47 (1.37–1.57)

1.34 (1.07–1.68)

AGM

Actual N = 1319, R2 = 5.7 %

Actual N = 531, R2=4.5 %, βUDMA=0.0093, pUDMA = .004

Comments

Table 5. Adjusted geometric means with 95 % confidence intervals for thyroid function variables by age, race/ethnicity, smoking status, levels of urine arsenic variables, and iodine sufficiency status for males aged 12 years and higher.

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16 R.B. Jain

Males Deficient

Males Replete

FT4 in ng/dl

FT4 in ng/dl

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM)

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

NHB < HISP (p < 0.01)

0.75 (0.73–0.77) 0.79 (0.77–0.81) 0.78 (0.76–0.8) 0.78 (0.76–0.8)

NHW > NHB (p < 0.01)

A12–19 < A20–64 (p = 0.03)

NHB < HISP (p < 0.01)

0.79 (0.76–0.82) 0.78 (0.76–0.8)

0.78 (0.76–0.79)

0.79 (0.78–0.81)

0.81 (0.78–0.84) 0.78 (0.76–0.8) 0.78 (0.76–0.81) 0.79 (0.77–0.81) 0.76 (0.74–0.79) 0.77 (0.74–0.81)

0.74 (0.71–0.78)

0.8 (0.77–0.83) 0.78 (0.77–0.8)

0.78 (0.76–0.79)

0.8 (0.75–0.85)

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(Continued)

Actual N = 1319, R2 = 3.4 %, βUDMA = −.0005, pUDMA = 0.033

Actual N = 531, R2 = 5.3 %

International Journal of Environmental Health Research 17

Iodine Gender status

Males Deficient

Males Replete

FT3 in pg/ml

FT3 in pg/ml

(Continued).

Thyroid variable

Table 5.

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

Variable

HISP > NHW (p < 0.01)

3.42 3.32 3.37 3.39 3.33 3.32

(3.37–3.47) (3.29–3.35) (3.31–3.44) (3.32–3.46) (3.29–3.38) (3.27–3.36)

NHB < HISP (p < 0.01)

A12–19 > A20–64 > A65+ (p < 0.01)

3.28 (3.22–3.34)

3.03 (2.97–3.08) 3.33 (3.29–3.36)

3.32 (3.29–3.36)

3.71 (3.63–3.79)

High < Low (p = 0.03)

Low > Medium (p = 0.04)

HISP > NHW (p < 0.01)

3.46 3.33 3.36 3.38 3.31 3.29

(3.38–3.54) (3.28–3.39) (3.29–3.42) (3.34–3.43) (3.24–3.37) (3.21–3.36)

NHB < HISP (p < 0.01)

A12–19 > A20–64 > A65+ (p < 0.01)

Statistically significant differences

3.29 (3.23–3.36)

3.04 (2.96–3.12) 3.33 (3.28–3.37)

3.32 (3.29–3.36)

3.78 (3.66–3.89)

AGM

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Actual N = 1334, R2 = 28.1 %

Actual N = 538, R2=33.2 %

Comments

18 R.B. Jain

Males Deficient

Males Replete

TT4 in ug/ml

TT4 in ug/ml

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) HISP > NHW (p < 0.0)

7.82 (7.56–8.09) 7.42 (7.27–7.58) 7.37 (7.11–7.64)

HISP > NHW (p < 0.01)

7.81 (7.66–7.96) 7.49 (7.37–7.61) 7.48 (7.27–7.69) 7.76 (7.56–7.96) 7.46 (7.33–7.6) 7.37 (7.23–7.52)

High < Low (p < 0.01)

Low > Medium (p = 0.03)

NHB < HISP (p < 0.01)

7.39 (7.15–7.64)

7.56 (7.29–7.84) 7.42 (7.31–7.54)

7.5 (7.38–7.61)

7.38 (7.12–7.64)

NHB < HISP (p < 0.01)

7.25 (6.96–7.55)

7.66 (7.38–7.96) 7.36 (7.17–7.55)

7.38 (7.21–7.55)

7.36 (6.99–7.74)

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(Continued)

Actaul N = 1306, R2=5.1 %. Also βUAAS = −0.027, p < 0.01.

Actual N = 518, R2 = 6.4 %, βUDMA = −0.0039, pUDMA = .003

International Journal of Environmental Health Research 19

Iodine Gender status

Males Deficient

Males Replete

TT3 in ng/dl

TT3 in ng/dl

(Continued).

Thyroid variable

Table 5. AGM

Non-Hispanic White (NHW)

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+)

Low > Medium (p = 0.03)

HISP > NHW (p = 0.02)

NHB < HISP (p < 0.01)

A12–19 > A20–64 > A65+ (p < 0.01)

Statistically significant differences

132.18 (127.61– A12–19 > A20–64 > A65+ 136.92) (p < 0.01) 115.59 (113.18– 118.06) 104.39 (101.92– 106.92) 116.04 (113.3–118.84)

141.54 (135.02– 148.37) 115.84 (113.03– 118.71) 105.09 (101.1–109.25) 116.75 (113.79– 119.79) 113.32 (109.59– 117.18) 124.33 (119.38– 129.48) Non-smoker (NSM) 115.93 (112.12– 119.87) Smoker (SM) 119.81 (114.41– 125.46) Low UDMA (Low) 119.05 (116.71– 121.44) Medium UDMA (Medium) 113.29 (108.87– 117.89) High UDMA (High) 117.83 (113.04– 122.81)

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP)

Variable

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Actual N = 1319, R2 = 18.9 %. Also, βUAAS = −0.017, p = 0.02.

Actual N = 530, R2= 18.5 %

Comments

20 R.B. Jain

TGN in ng/ml Males Deficient

Thyroid variable

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UDMA (Low) Medium UDMA (Medium) High UDMA (High)

8.82 (7.41–10.49) 9.39 (7.97–11.07) Non-smoker < Smoker (p = 0.02) 12.16 (10.05–14.71) 10.05 (8.7–11.61) 10.53 (8.03–13.8) 10.74 (8.57–13.46)

13.23 (11.52–15.19) NHB > HISP (p < 0.01)

9 (7.02–11.54) 10.08 (8.28–12.26)

10.39 (8.85–12.19)

10.75 (8.48–13.63)

Iodine status Variable 112.88 (109.36– NHB < HISP (p < 0.01) 116.52) 119.93 (117.83– HISP > NHW (p = 0.02) 122.06) Non-smoker (NSM) 116.08 (114.08– 118.11) Smoker (SM) 117.04 (112.23– 122.06) Low UDMA (Low) 118.95 (115.03– 123.01) Medium UDMA (Medium) 116.73 (114.32– 119.18) High UDMA (High) 114.66 (111.6–117.81)

Gender Non-Hispanic Black (NHB) Hispanic (HISP)

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Actual N = 530, R2= 9.2 %

AGM

(Continued)

International Journal of Environmental Health Research 21

Iodine Gender status

(Continued).

Age: 12–19 years (A12– 19) Age: 20–64 years (A20– 64) Age: 65+ years (A65+) Non-Hispanic White (NHW) Non-Hispanic Black (NHB) Hispanic (HISP) Non-smoker (NSM) Smoker (SM) Low UAS (Low) Medium UAS (Medium) High UAS (High)

Variable

AGM

NHW < NHB (p < 0.01)

Statistically significant differences

7.77 (7.02–8.61) 8.39 (7.8–9.03) 10.57 (9.65–11.57) 8.31 (7.51–9.19) 8.54 (7.82–9.32) 9.7 (9.05–10.41) Low < High (p < 0.01) Medium < High (p < 0.01)

HISP < NHW (p = 0.03) Non-smoker < Smoker (p < 0.01)

11.96 (10.67–13.41) NHB > HISP (p < 0.01)

8.99 (7.8–10.36) 8.9 (8.23–9.62)

8.99 (8.23–9.82)

8.88 (7.62–10.35)

Note: Data from National Health and Nutrition Examination Survey, 2007–2010.

TGN in ng/ml Males Replete

Thyroid variable

Table 5.

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Actual N = 1337, R2 = 5.6 %

Comments

22 R.B. Jain

International Journal of Environmental Health Research

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(13.23 ng/ml vs. 8.82 ng/ml, p < 0.01, Table 5). Smokers had statistically significantly higher TGN levels than non-smokers (12.16 ng/ml vs. 9.39 ng/ml, p = 0.02, Table 5). Iodine-replete males Increased levels of UDMA were associated with decreasing levels of FT4 (β = −0.0005, p = 0.03, Table 4). There was a negative association between UAAS levels and TT3 and TT4 (Table 4, p = 0.02). Low levels of TSH were associated with lower levels of UAS as compared to when the level of UAS was medium (1.39 μIU/ml vs. 1.57 μIU/ ml, p = 0.03, Table 5). Low levels of TT4 were associated with higher levels of UDMA as compared to when the level of UDMA was medium (7.76 ng/ml vs. 7.46 ng/ml, p = 0.03, Table 5) or high (7.76 ng/ml vs. 7.37 ng/ml, p < 0.01, Table 4). Low levels of TGN were associated with low and medium levels of UAS as compared to high levels of UAS (8.31 and 8.54 ng/ml vs. 9.70 ng/ml, p < 0.01, Table 5). CRP was negatively associated with TSH (p < 0.02, Table S2), FT3 (p < 0.01, Table S2), and TT3 (p < 0.01, Table S2), but positively associated with TT4 (p < 0.01, Table S2). BMI was negatively associated with FT4 (p < 0.04, Table S2), but positively associated with TT4 (p < 0.02, Table S2) irrespective of the arsenic variable in the model. Urine creatinine had a positive association with FT3 (p = 0.017) when UAS and UAAS were in the model and with TT3 (p < 0.02) irrespective of the arsenic variable in the model. Levels of FT3, FT4, TT3, and TT4 increased with increase in FTIME (p < 0.02, Table S2). Use of prescription drugs other than the thyroid treatment drugs was associated with decreased levels of FT3 and TT3 (p < 0.03, Table S2). Levels of both FT3 and TT3 by age were in the order: A12–19 > A20–64 > A65+ (p < 0.01, Table 5). Levels of TSH were statistically significantly lower for both A12–19 (1.54 μIU/ml, Table 5) and A20–64 (1.47 μIU/ml, Table 4) as compared to the levels for A65+ (1.75 μIU/ml, Table 5). Both HISP and NHB had statistically significantly lower levels of TSH than NHW (1.34 and 1.27 μIU/ml vs. 1.60 μIU/ml, p < 0.01, Table 5). Both HISP and NHW had statistically significantly higher levels of FT4 than NHB (0.79 and 0.78 ng/dl vs. 0.75 ng/dl, p < 0.01, Table 5). The same was true for FT3. HISP had statistically significantly higher levels of TT4 than both NHB and NHW (7.81 ng/ml vs. 7.39 and 7.42 ug/ml, p < 0.01, Table 5). The same was true for TT3 (119.93 ng/dl for HISP vs. 112.88 ng/dl for NHB and 116.04 ng/dl for NHW, p < 0.02, Table 5). The levels of TGN by race/ethnicity were: NHB (11.96 ng/ml) > NHW (8.90 ng/ml) > HISP (7.77 ng/ml) and all three pairwise differences were statistically significant (p ≤ 0.03, Table 5). Smokers had statistically significantly higher levels of TGN than non-smokers (10.57 ng/ml vs. 8.39 ng/ml, p < 0.01, Table 5). Discussion Association between arsenic exposure and thyroid function: iodine-deficient males and females Levels of FT4 below the lower limit of the normal reference range (0.8–2.7 ng/dL, Kratz et al. 2004), when accompanied by levels of TSH above the upper limit of the reference range or above 4.5 μIU/ml as used by Hollowell et al. (2002), may have the potential for developing hypothyroidism. Relatively high levels of TSH even when within the normal range have been shown to be positively associated with blood glucose levels, serum triglycerides, HDL-cholesterol, and hypertension in females (Boggio et al. 2014). TSH levels were also found to be positively associated with vascular dementia (Forti et al.

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2012) and heart failure events (Gencer et al. 2012). Hence, exposure to any environmental contaminant that may be associated with increased levels of TSH should be of concern, even if the positive association with TSH is not accompanied by a negative association with FT4. For iodine-deficient males, higher levels of UDMA were associated with higher levels of TSH (p < 0.01, Table 4) and lower levels of TT3 (p = 0.03, Table 5) and TT4 (p < 0.01, Table 4), meaning higher levels of UDMA do have the potential to disrupt thyroid homeostasis, at least in the presence of iodine deficiency among males. For iodinedeficient females, while levels of UDMA (or UAB and UAS) were not found to be related to the levels of TSH, lower levels of UDMA were associated with higher levels of FT3 (Table 3, p = 0.03) and TT4 (Table 2, p < 0.01). Thus, while the effect of higher levels of arsenic is similar among iodine-deficient males and females in influencing the levels of triiodothryonine (T3) and thyroxine (T4), the influence on TSH seems to be limited to iodine-deficient males. In fact, even though statistical significance was not observed, the association of UAB, UAS, UDMA as well as UAAS was weekly negative with TSH for iodine-deficient females (Table 2). On the other hand, even though it was only between UDMA and TSH that a statistical significant association was observed, the association between TSH and UAB, UAS, and UAAS was also weekly positive without statistical significance being reached (Table 4). The reason why the impact of higher levels of arsenic on the levels of TSH differs in males and females is not known, and to the best of our knowledge has neither been identified nor discussed before. However, there are certain possibilities. First, it is possible that there are certain enzymatic reactions in females only that counteract and nullify the impact that high levels of arsenic may have on TSH levels in females. Second, when hypothalamus senses low-circulating levels of T3 or T4, it releases thyrotropin-releasing hormone which stimulates the pituitary to produce more TSH. TSH, in turn stimulates thyroid to produce more thyroid hormones until thyroid homeostasis is reached. It is possible that high levels of arsenic may not be associated with as large a decrease in the levels of TT3 and TT4 as they are in males. If so, additional TSH may be produced in males but not in females because of this negative feedback loop mechanism. In fact, this may be happening because for males the negative regression slope between UDMA and TT4 was 0.0039, but it was 0.0024 for females. It should be noted that while a high correlation exists between the levels of FT3 and TT3 and between FT4 and TT4, FT3 and FT4 are considered to be more physiologically relevant, whereas TT3 and TT4 are representative of peripheral thyroidal status (Eales & Shostak 1985). Diagnosis of clinical hypothyroidism is generally made when TSH levels are above the upper limit of its reference range and FT4 levels are below the lower limit of its reference range or alternatively when TT4 levels are below the lower limit of its reference range (Hollowell et al. 2002). The comparative associations between UDMA with TSH and TT4 for iodine-deficient males observed in this study were indicative that UDMA has the potential to be associated with clinical hypothyroidism in iodine-deficient males. No attempt was made in this study to find out what levels of UDMA were associated with TSH above the upper limit of its reference range and with TT4 below the lower limit of its reference range. Association between arsenic exposure and thyroid function: iodine-replete males and females For iodine-replete males, there was an inverse association between the levels of UDMA and FT4 (Table 4, p < 0.01), between UAAS and TT3 (p = 0.01, Table 4) and TT4 (p < 0.01, Table 4), and between UDMA and TT4 (p ≤ 0.03, Table 5), and low levels

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of UAS were associated with low levels of TSH (Table 5, p < 0.03). Thus, for iodine-replete males, higher levels of arsenic are undesirable because they may be associated with higher levels of TSH and lower levels of TT3, TT4, and FT4. Consequently, arsenic exposure among replete males has the potential to lead to hypothyroidism. For iodine-replete females, there was an inverse association for UDMA with FT4 (p = 0.02, Table 3), TT3 (p = 0.02, Table 3), and TT4 (p = 0.01, Table 3), as well as between FT3 and UAB (p = 0.01, Table 3), and between UAAS and TT4 (p < 0.01, Table 2). However, increased levels of both UAB (p < 0.01, Table 2) and UAS (p < 0.01, Table 2) were associated with decreased levels of TSH. Thus, for iodine-replete females, high levels of arsenic may be both desirable and undesirable: desirable because they are associated with low levels of TSH and undesirable because they are associated with low levels of FT4, TT3, and TT4. These results indicating movement of the levels of thyroid hormones and TSH in the same direction are quite contrary to what would be expected according to the negative feedback loop mechanism of the hypothalamus–pituitary–thyroid (HPT) axis. The sample sizes were not small enough to cause a statistical artifact. The only unlikely explanation that can be given is that higher levels of arsenic may be negatively affecting the mechanism of HPT axis among iodine-replete females. It is not known if there may be some kind of interplay between the levels of iodine, arsenic metabolism, and sex hormones. More work will need to be done in this area. Probably, an independent statistical analysis – probably using a different statistical methodology – is needed. Date for NHANES 2011–2012 should soon be available for analysis. May be the conclusions here will need to verified by analyzing additional data. Conclusion UDMA, a metabolite of arsenic, was primarily responsible for the statistically significantly observed association with the levels of FT3, TT3, or TT4 irrespective of gender and iodine sufficiency status. High levels of UDMA or arsenic were found to be associated with low levels of these hormones. Consequently, exposure to high levels of arsenic should be a cause for concern since it may disrupt thyroid homeostasis and lead to low levels of FT3, TT3, or TT4. Positive association between UDMA and TSH and negative association between UDMA and FT3, TT3, and/or FT4 for iodine-deficient and iodine-replete males observed in this study parallel the results observed by Ciarrocca et al. (2012). The main focus of this communication was to determine the association between thyroid variables and arsenic exposure adjusted for the differential contribution of different age, race/ethnicity, smoking status, and other variables such as CRP, BMI, and FTIMEs. In this process, geometric means adjusted for other variables in the model for different thyroid variables were also generated. These adjusted means have been generated previously also by quite a few authors, for example by Jain (2013a) and Jain (2014). Results generated here are quite similar to the results generated in these publications, in spite of the differential focus of these previous publications. Cross-sectional nature of the data used in this study limits the confidence that can be placed in the results obtained from this analysis. Neither the magnitude nor the source of exposure to arsenic was available. The timing of the exposure was also not available. While limited data on arsenic levels in water by the US geographical locations may be available, the residential location of the NHANES participants is not identified in the publically released NHAES data-sets. Otherwise, it could have been possible to add residential location of the participants coupled with corresponding arsenic levels as independent

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variables in the models. A relatively long-term follow-up study that can keep track of the ongoing arsenic exposure and observed urinary arsenic levels may better be able to evaluate the association between arsenic exposure and thyroid function. However, a longitudinal study of the size and scale of NHANES will be prohibitively expensive and may not even be possible. It should also be noted that there are other variables that have the potential to affect thyroid function but were not included in the analysis. Some of these variables include diabetes, physical activity, and even certain dietary habits. In addition, there are variables that affect the levels of arsenic that were not included in the analysis. Some of these variables include selenium intake, dietary intake of certain kinds of food, for example sea food, and residential location, for example urban vs. rural. In what way non-inclusion of the variables that directly affect thyroid function and that directly affect arsenic levels in the analyses is not known. In summary, the main findings of this communication were: (i) UDMA was associated with higher levels of TSH and lower levels of TT3 and TT4 among iodine-deficient and iodine-replete males, (ii) UDMA was associated with lower levels of TT4 irrespective of gender and iodine sufficiency status, and (iii) UAAS was associated with low levels of TT4 for females irrespective of iodine sufficiency status and for iodine-deficient males. List of abbreviations BMI CDC CRP FT3 FT4 FTIME HISP HPT MEC NHB NHW NHANES TgAB TGN TPOAb TSH TT3 TT4 UAAS UAB UAS UDMA WHO

Body mass index the US Centers for Disease Control and Prevention C-reactive protein Free triiodothyronine Free thyroxine Fasting time in hours Hispanics Hypothalamus–pituitary–thyroid Mobile examination center Non-Hispanic blacks Non-Hispanic whites National Health and Nutrition Examination Survey Thyroglobulin antibodies Thyroglobulin Thyroid peroxidase antibodies Thyroid-stimulating hormone Total triiodothyronine Total thyroxine Total sum of urinary arsenic adjusted for arsenobetaine Urinary arsenobetaine Total sum of urinary arsenic Uirnary dimethylarsinic acid World Health Organization

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Supplementary material The supplementary material for this article is available online at http://dx.doi.10.1080/ 09603123.2015.1061111. Disclosure statement No potential conflict of interest was reported by the author.

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Association between arsenic exposure and thyroid function: data from NHANES 2007-2010.

The association of arsenic variables in urine, total arsenic (UAS), arsenobetaine (UAB), dimethylarsinic acid (UDMA), and arsenic adjusted for arsenob...
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