Pesticide exposure and thyroid function in an agricultural population in Brazil

Pesticide exposure and thyroid function in an agricultural population in Brazil

Environmental Research 151 (2016) 389–398 Contents lists available at ScienceDirect Environmental Research journal homepage:

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Environmental Research 151 (2016) 389–398

Contents lists available at ScienceDirect

Environmental Research journal homepage:

Pesticide exposure and thyroid function in an agricultural population in Brazil Camila Piccoli a, Cleber Cremonese b, Rosalina J. Koifman a, Sergio Koifman a,1, Carmen Freire a,c,n a

National School of Public Health, Oswaldo Cruz Foundation, CEP: 21041-210 Rio de Janeiro, Brazil University of Serra Gaúcha, CEP: 95020-472 Caxias do Sul, Brazil c Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Spain b

art ic l e i nf o

a b s t r a c t

Article history: Received 9 May 2016 Received in revised form 8 August 2016 Accepted 9 August 2016 Available online 16 August 2016

Although numerous pesticides may interfere with thyroid function, however, epidemiological evidence supporting this relationship is limited, particularly regarding modern non-persistent pesticides. We sought to evaluate the association of agricultural work practices, use of contemporary-use pesticides, and OC pesticides residue levels in serum with circulating thyroid hormone levels in an agricultural population. A cross-sectional study was conducted with a random sample of 275 male and female farm residents in Farroupilha, South of Brazil. Information on sociodemographics, lifestyle and agricultural work was obtained through questionnaire. Blood samples were collected on all participants and analyzed for cholinesterase activity, serum residues of OC pesticides, and levels of free T4 (FT4), total T3 (TT3) and TSH. Non-persistent pesticides exposure assessment was based on questionnaire information on current use of pesticides, and frequency and duration of use, among others. Associations were explored using multivariate linear regression models. Total lifetime years of use of fungicides, herbicides and dithiocarbamates in men was associated with increased TSH accompanied by decrease in FT4, with evidence of a linear trend. In addition, there was an association between being sampled in the high pesticide-use season and increased TSH levels. Conversely, farm work and lifetime use of all pesticides were related with slight decrease in TSH and increased TT3 and FT4, respectively. In general, pesticide use was not associated with thyroid hormones in women. Subjects with detected serum concentrations of β-hexachlorocyclohexane, endrin, dieldrin, heptachlor epoxide B, γ-chlordane, transnonachlor, heptachlor, p,p ′-dichlorodiphenylethane and endosulfan II experienced slight changes in TT3; however, associations were weak and inconsistent. These findings suggest that both cumulative and recent occupational exposure to agricultural pesticides may affect the thyroid function causing hypothyroid-like effects, particularly in men. & 2016 Elsevier Inc. All rights reserved.

Keywords: Non-persistent pesticides Organochlorine pesticides Thyroid hormones Hypothyroidism Agricultural population

1. Introduction Pesticides are extensively used today in agricultural settings to

Abbreviations: AChE, Acetylcholinesterase; BChE, Butyrylcholinesterase; DDT, Dichlorodiphenyltrichloroethane; DDE, Dichlorodiphenylethane; DDD, Dichlorodiphenyldichloroethane; EBDC, Ethylene-bis-dithiocarbamate; FT4, Free thyroxine; HCH, Hexachlorocyclohexane; HCB, Hexachlorobenzene; OC, Organochlorine; OP, Organophosphate; TSH, Thyroid stimulating hormone; TT3, Total triiodothyronine n Correspondence to: Environment and Public Health Post-graduation Program, National School of Public Health, FIOCRUZ, Rua Leopoldo Bulhões, 1480, CEP: 21041-210 Rio de Janeiro, RJ, Brazil. E-mail addresses: [email protected] (C. Piccoli), [email protected] (C. Cremonese), [email protected] (R.J. Koifman), [email protected] (C. Freire). 1 Dead in March 21st, 2014. 0013-9351/& 2016 Elsevier Inc. All rights reserved.

prevent and control pests. Numerous pesticides, including banned organochlorines (OCs) and modern non-persistent pesticides, have demonstrated thyroid-disrupting activity, affecting the homeostasis of the thyroid system (Boas et al., 2012; Diamanti-Kandarakis et al., 2009). Animal studies have shown that OC pesticides may interfere with the thyroid system through multiple mechanisms of action, as they can affect deiodination of thyroid hormones (i.e., thyroxine –T4– and triiodothyronine –T3–) by inducing or increasing type III deiodinase expression, bind to thyroid hormone-binding proteins, interfere with thyroid hormone binding to hormonal receptors, interfere with TSH receptor function and bind to T3 and T4 receptors, altering thyroid hormone-mediated gene expression (Crofton, 2008; Boas et al., 2012). In humans, several epidemiological studies conducted in the last decade have investigated the association between exposure to OC pesticides and serum levels of


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T4, T3 and thyroid stimulating hormones (TSH) in adult population, reporting negative, positive and null associations with conflicting results (Bloom et al., 2003; Freire et al., 2013; Meeker et al., 2007; Persky et al., 2011; Rathore et al., 2002; Rylander et al., 2006; Sala, 2001; Schell et al., 2009; Turyk et al., 2006, 2007). Experimental studies also suggest that a variety of non-persistent pesticides, including organophosphates (OPs), carbamates, synthetic pyrethroids and dithiocarbamates, may act as thyroid disrupters, affecting the hypothalamic-pituitary-thyroid axis at different levels (Crofton, 2008; Diamanti-Kandarakis et al., 2009). However, current knowledge regarding the impact of modern pesticides on human thyroid function is still limited (Goldner et al., 2010, 2013), although a growing number of human studies has examined levels of circulating T4, T3 and TSH in relation to non-persistent pesticides (Campos and Freire, 2016). A majority of such studies focused on occupational exposure and used urinary pesticide biomarkers. Overall, while existing evidence is insufficient, results from a few studies are consistent with experimental data supporting that exposure to non-persistent pesticides exerts hormonal changes consistent with hypothyroidism (Fortenberry et al., 2012; Lacasaña et al., 2010; Meeker et al., 2006; Steenland et al., 1997; Toft et al., 2006). Brazilian agriculture has grown exponentially in the last few years, and today Brazil is the world's top consumer of pesticides (ANVISA, 2012). Serra Gaúcha is a mountainous region in the South of Brazil settled by German and Italian immigrants characterized by family farms dedicated to fruit growing, especially grapes for wine production. In the present study it was hypothesized that current and/or cumulative lifetime exposure of agricultural workers may be related to thyroid disturbances. This was evaluated by exploring the association of agricultural work practices, current and lifetime use of non-persistent pesticides, and OC pesticides residue levels in serum with circulating levels of thyroid hormones in agricultural population of this region.

2. Materials and methods 2.1. Study design and population A cross-sectional study was conducted between 2012 and 2013 aiming to investigate reproductive and endocrine effects of pesticides in adult agricultural workers in the South of Brazil. A random sample of farmers and farm family members was selected from the agricultural population of Farroupilha, a small town in Serra Gaúcha region. Assuming a participation rate of around 90% and at least 3 adults per household, 90 residences were randomly selected from the list of rural households of the municipal agriculture office to reach the estimated sample size of 220 individuals. All persons aged 18–69 years living in the selected households were personally invited to participate in the study, representing a total of 301 subjects. Among these, 21 (7%) refused to participate. Farm owners working in farm work for less than one year and their respective family members were excluded from the study (5 persons), leaving a final sample of 275 individuals. The study was approved by the Ethics Committee of the National School of Public Health, Oswaldo Cruz Foundation (ENSP/ FIOCRUZ), and written informed consent was obtained from all participants. 2.2. Data collection 2.2.1. Questionnaire Trained interviewers administered a structured questionnaire to participants through face-to-face interviews at farmers’ residences. The questionnaire contained more than 200 questions,

divided into the following sections: demographics, occupation, lifestyle, agricultural work practices, pesticide use, health status, medical and reproductive history. Variables used in the present study were: gender, age (continuous and grouped into 18–30, 31– 45, 46–60, and 460 years), years of education (continuous and categorized as r8, 9–11, and 12 or more years), marital status (married; others), annual household income (categorized as r10, 11–20, 24–50, and 450 thousands of Brazilian reais), place of birth (Farroupilha; other place), smoking habit (never smoked; exsmoker; current smoker), lifetime smoking (categorized as 0, 1–9, and 10 or more years), regular alcohol intake in the last 30 days (no; yes), practiced physical activity regularly in the last 3 months (no; yes), current weight (kg) and height (cm), history of thyroid disease (no; yes), and family history of thyroid disease in firstdegree relatives (no; yes). Body mass index (BMI) was calculated by dividing weight in kg by height in meters squared and categorized as lower than 25 kg/m2 (eutrophic) and equal to or greater than 25 kg/m2 (overweight or obese). The following information on agricultural work practices and pesticide use was obtained from the questionnaire: current occupation (farmer; non farmer), self-reported pesticide exposure (not exposed/rarely exposed; low exposure; high exposure), years of farming activities (categorized as o1, 1–10, 11–25, 26–50, and 450), years mixing or applying pesticides (categorized as r1, 2– 10, and 410), frequency of mixing or applying pesticides (categorized as o5, 5–39, 40–59, and Z60 days per year), season of interview and blood sample collection (low pesticide use season: from March to September; high pesticide use season: from April to August), use of full personal protective equipment (PPE) (yes; no), current use of any pesticide (no; yes), and total number of pesticides currently used (categorized as none, 1, 2 or more). In addition, participants were asked to report on their current and former use of specific pesticides from a list of 18 commercial products. This list was obtained from the Brazilian Entity for Technical Assistance and Rural Extension (EMATER) and included the pesticides most commonly used in the study area (Table S1). Participants were also asked about the use of pesticides not included in this list. Active ingredients in commercial products were then grouped into chemical and functional classes, i.e.: herbicides, insecticides, fungicides, OP insecticides, dithiocarbamate fungicides, carbamates, synthetic pyrethroids, and others chemical classes. Lifetime years of overall pesticide use and for each functional and chemical class were calculated as the difference between starting and finishing dates of use, regardless of simultaneous use of different pesticides of the same class (for example, if mancozeb and carbendazim were used for 10 years, from 2000 to 2010, it was assumed 10 years of fungicide use). Cumulative use of pesticides was categorized as never use, 1–20, and more than 20 years. 2.2.2. Laboratory analysis An intravenous blood sample (15 mL) was collected from each participant by a trained nurse after a 12-h overnight fast at farmers’ residences. Plasma and serum were separated from whole blood by centrifugation. Specimens were stored at 20 °C in vacutainer tubes containing EDTA and delivered to the laboratories responsible for pesticide and biochemical analysis. Levels free T4 (FT4), total T3 (TT3), and TSH were measured in serum samples of 15, 30 and 50 mL, respectively, by electrochemiluminescence immunoassay using Roches kit. Normal laboratory reference range was 0.93–1.70 ng/dL for FT4 and 0.240.37 ng/dL for TT3. Regarding TSH, normal reference range was 0.30–4.30 mUI/mL for subjects aged 18–60 years, and 0.40– 5.80 mUI/mL for those older than 60 years. Samples with hormone values outside reference ranges were run in duplicate. Serum concentrations of total cholesterol and triglycerides (in mg/dL) were determined by colorimetric enzymatic methods. Estimates of

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total serum lipids were calculated by the formula established by Phillips et al. (1989): Total lipids ¼2.27*Total cholesterolþTriglyceridesþ 0.623. For analysis of blood acetylcholinesterase (AChE) activity, the separated cell fractions were thawed, homogenized and centrifuged in a refrigerated centrifuge, with 4000 g for 15 min at 8 °C. The supernatant was removed and 4.5 mL of assay buffer were added and then homogenized and centrifuged under conditions described above. This process was repeated three times in order to eliminate all hemoglobin present in erythrocytes. Measurements of enzyme activities were carried out according to the Ellman's method (Ellman et al., 1961). Briefly, 50 μL of erythrocyte membranes were placed in a test tube, adding 4.0 mL of assay buffer and 1 mL of DTNB solution 0.002 M. To start the reaction, 1 mL of 6.6 mM acetylthiocholine solution was added. Absorbance variation at 412 nm was measured for two min using a spectrophotometer. The same procedure was adopted for determination of plasma butyrylcholinesterase (BChE) activity. In this case, 50 μL of plasma and 9.0 mM butyrylthiocholine solution were used. Laboratory reference cutoff values for enzymatic inhibition were r0.56 μmols/min/mg of protein for AChE, r2.29 μmols/min/mL of plasma for BChE in men, and r1.61 μmols/min/mL for BChE in women. Residues of a total number of 24 OC pesticides in serum were determined by gas chromatography with electron-capture detection following the methodology previously described (Sarcinelli et al., 2003). The following pesticides were analyzed: hexachlorocyclohexane (HCH) (α, β, and γ isomers), hexachlorobenzene (HCB), chlordane (α and γ isomers), heptachlor epoxide A, heptachlor epoxide B, heptachlor, transnonachlor, o,p’dichlorodiphenyltrichloroethane (DDT), p,p’-DDT, o,p′-dichlorodiphenylethane (DDE), p,p′-DDE, o,p′-dichlorodiphenyldichloroethane (DDD), p,p′-DDD, endosulfan I, endosulfan II, aldrin, endrin, dieldrin, methoxychlor, mirex, and pentachloroanisole. Identification of each analyte was based on the mean retention time, established as the mean of retention times in 10 measurements 7three times the standard deviation (SD). According to the IUPAC, limits of detection (LD) were designated as three-fold the SD of the blank, and were the following: 0.05 ng/mL for αHCH; 0.07 ng/mL for β-HCH, HCB, heptachlor epoxide A, and endosulfan I; 0.04 ng/mL for γ-HCH; 0.13 ng/mL for o,p′-DDT; 0.02 ng/mL for p,p′-DDT; 0.12 ng/mL for p,p′-DDD and o,p′-DDD; 0.09 ng/mL for heptachlor epoxide B, transnonachlor, α-chlordane, γ-chlordane, dieldrin, and p,p′-DDE; 0.29 ng/mL for endrin; 0.14 ng/mL for methoxychlor; 0.10 ng/mL for o,p′-DDE, aldrin, and mirex; 0.11 ng/mL for endosulfan II; and 0.06 ng/mL for pentachloroanisole. Recovery in the extraction was determined by fortifying 10 aliquots of 4 mL of blank medium to an intermediate point on the calibration curve. Recovery percentage ranged from 80% to 98%. For quality control, samples were analyzed in batches of 20 samples, with two replicates in each batch. In addition, one blank and one spiked plasma at 1 ng/mL were used. The global coefficient of variation between the replicates was 5.6%. No blinded replicates were made. The coefficient of variation of the spiked samples in all batches varied from 7.2% to 9.8%, indicating a good reproducibility of the analytical method for all OC compounds. Lipid-adjusted serum concentrations of OC pesticides were calculated by dividing wet-weight concentrations (ng/mL) by total lipid serum content (mg/dL) and expressed in ng/g. 2.3. Statistical analysis Frequency distribution, means, SD, and percentiles were used to describe characteristics of the study population. Given the moderate to high proportion of serum samples with not detected OC pesticides, an approach for managing measurements below the


assay's LD was not used. Accordingly, OC concentrations in serum were described through median and percentile values, and were treated as dichotomous variables, categorized into detected and undetected levels, in multivariate analysis. Frequency of abnormal thyroid hormone levels was determined using lower and upper reference limits as cutoff points. The Kolmogorov–Smirnov test was used to check normality of thyroid hormone values. Chi-square and Fisher tests, Spearman correlation, and nonparametric tests were used to examine differences in characteristics of study population between genders and to identify potential confounders in the association between exposure variables and outcomes (FT4, TT3, and TSH). In addition, correlation between detected OC pesticide concentrations and between detected OC values and hormones were calculated using the Spearman test, given that OC and hormone levels did not fit normal distributions. Association between exposure variables and thyroid hormones were performed by multivariate linear regression using naturallogarithm transformed (log-transformed) outcome variables, which fitted normal distributions. Covariates associated with thyroid hormones at a significance level of p r0.20 in the simple regression analysis were included in multivariate models. Following a backward procedure, variables with p-value 4 0.10 were sequentially excluded from the model. Variables were retained in the final model if they were associated with the outcome at a significant level of 0.05 or if they modified the effect of exposure on the outcome by 410%. Regardless of their statistical significance, all models were adjusted for age (continuous, in years), BMI (eutrophic and overweight/obese), smoking habit and alcohol intake, which are variables identified in the literature as potential confounders. Because a significant interaction was identified between gender and some exposure variables on thyroid hormone values, multivariate analysis with agricultural work and pesticide use-related variables was stratified by gender. OC pesticide models were run on the pooled sample of men and women and adjusted for gender. To improve interpretability, regression coefficients (b) and 95% confidence intervals (CI) were transformed back [exp (b)] on the original scale and presented as percent change of dependent variable per unit change in exposure variable. Likelihood ratio test was used to test the significance of linear trend in regression models with ordinal exposure variables. Lastly, sensitivity analysis was performed by excluding subjects with reported history of thyroid disease. A further logistic regression analysis was conducted with subclinical hypothyroidism (i.e. increased TSH in conjunction with normal levels of FT4) as dependent variable, with the whole population and in men and women separately. A significance level of 0.05 was established. Statistical analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL, US) and STATA version 2011.

3. Results Sociodemographics, lifestyle factors, and thyroid disease history of study population are presented in Table 1. More than half of the sample was composed of men (56%). Mean age of participants was 42 years (SD ¼15), while more than one quarter were young adults (18–30 years). One third of women were in the perior postmenopausal phase. Although a high percentage of study subjects had low education (8 or less years), almost 60% reported a moderate to high family income. Only 5% of participants reported current smoking habit, but 65% reported alcohol drinking during the month before the interview. Around two thirds of subjects were overweight or obese and did not practice physical activity regularly. Regarding previous diseases, 17 individuals had a history of thyroid disorder and 49 reported a family history of thyroid


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Table 1 Sociodemographic characteristics, lifestyle factors, and thyroid disease history of study population (N¼ 275).

Table 2 Agricultural work-related characteristics and pesticide use. N (%)

N (%)




Sex Female Male

120 (43.6) – 155 (56.4) –

Age (years) 18–30 31–45 46–60 460

77 67 98 33

(31.0) (21.3) (34.8) (12.9)

29 (24.2) 34 (28.3) 44 (36.7) 13 (10.8)

Years of education r8 9–11 Z12

166 (60.0) 85 (54.8) 79 (29.0) 49 (31.6) 30 (11.0) 21 (13.5)

81 (67.5) 30 (25.0) 9 (7.5)

Marital status Married Single, divorced, or widowed

197 (71.6) 78 (28.4)

98 (63.2) 57 (36.8)

99 (82.5) 21 (17.5)**

Annual family income (thousands of Brazilian reais) r10 39 (14.2) 11–20 77 (28.0) 21–50 96 (34.9) Z51 63 (22.9)

18 (11.6) 40 (25.8) 59 (38.1) 38 (24.5)

21 37 37 25

Place of birth Farroupilha Other town

215 (78.2) 60 (21.8)

137 (88.4) 78 (65.0) 18 (11.6) 42 (35.0)**

Smoking habit Never smoked Ex-smoker Smoker

226 (82.2) 112 (72.3) 34 (12.4) 31 (20.0) 15 (5.5) 12 (7.7)

114 (95.0) 3 (2.5) 3 (2.5)**

Lifetime smoking (years) None 1–10 410

226 (82.0) 112 (72.3) 24 (9.0) 21 (13.5) 25 (9.0) 22 (14.2)

114 (95.0) 3 (2.5) 3 (2.5)**

Alcohol intake in the last 30 days No Yes

95 (34.5) 180 (65.5)

36 (23.2) 119 (76.8)

59 (49.2) 61 (50.8)*

Regular physical activity in the last 3 months No Yes

188 (68.4) 87 (31.6)

103 (66.5) 85 (70.8) 52 (33.5) 35 (29.2)

Body mass index (BMI) Eutrophic ( o25 kg/m2) Overweight or obese ( Z 25 kg/m2)

110 (40.0) 66 (42.6) 165 (60.0) 89 (57.5)

Thyroid disease history No Yes

258 (93.8) 153 (98.7) 105 (87.5) 17 (6.2) 2 (1.3) 15 (12.5)**

(28.0) (24.0) (36.0) (12.0)

48 33 54 20

– –

(17.5) (30.8) (30.8) (20.8)

44 (36.7) 76 (63.3)

Family history of thyroid disease (1st degree) No 226 (82.2) 140 (90.3) 86 (71.7) Yes 49 (17.8) 15 (9.7) 34 (28.3)** Menopausal status (women) Peri- or post-menopausal Pre-menopausal

40 (33.3) 78 (66.7)

– –

40 (33.3) 78 (66.7)

Chi-square between genders. ** *

p-value o 0.001. p-value o0.05.

disease in first degree. Men were more likely to be unmarried, to have been born in Farroupilha, to be a smoker and drink alcohol than women, whereas women were more likely to have a personal and family history of thyroid disease. The majority of study subjects was directly involved in agricultural activities and reported high pesticide exposure (Table 2). Around half of them had been working as a farmer for more than 25 years; 55% had mixed or applied pesticides for more than 10

Occupation Farmer Non farmer

239 (86.9) 36 (13.1)

Self-reported current pesticide exposure High exposure Low exposure Rarely or not exposed

166 (60.4) 84 (30.5) 25 (9.1)

Years of agricultural work o1 1–10 11–25 26–50 450

29 (10.5) 40 (14.5) 60 (21.8) 114 (41.5) 32 (11.6)

Years mixing or applying pesticides o1 1–10 410

62 (22.5) 62 (22.5) 151 (54.9)

Days per year mixing or applying pesticides o5 5–39 40–59 Z 60

73 (26.5) 50 (18.1) 51 (18.5) 101 (36.7)

Sampling season Low pesticide use season (April-August) High pesticide use season (September-March)

140 (50.9) 135 (49.1)

Use of full personal protective equipment (PPE) No Yes

37 (13.5) 238 (86.5)

years; 37% reported mixing or applying pesticides with an average frequency of 60 days per year or higher; and 13% did not use full PPE. Pesticide classes most frequently used by farmers sometime in their lives were herbicides and fungicides, and among chemical families, dithiocarbamate fungicides and OP insecticides (Table 3). Regarding specific compounds, Table S1 shows ever-use of pesticides among farmers in the study. Mancozeb and cooper sulphate were the fungicides most frequently used, while glyphosate and paraquat were the two most frequently reported herbicides (Table S1). One third of participants were using 2 or more pesticide products at the time of the interview, while only 8 of them were using pyrethroids and none of them reported current use of carbamates. Regarding lifetime pesticide use, more than 40% of participants had handled fungicides and dithiocarbamates for 20 or more years, respectively (Table 3). The most prevalent OC pesticide found in sera was γ-HCH (50%), followed by p,p′-DDT, β–HCH, p,p′-DDE, heptachlor, α-HCH and endrin (430%). Endrin and o,p′-DDE showed the highest levels, with mean value of detected concentrations above 90 and 50 ng/g, respectively. Transnonachlor, heptachlor epoxide A, mirex, aldrin, endosulfan II and o,p′-DDD were the least frequently detected compounds, while none of the individuals had detected α-chlordane (Table 4). Positive and statistically significant correlations were observed for almost all OC pesticides. β-HCH was negatively and significantly correlated with α–HCH, γ–HCH, pentachloroanisole, endrin and heptachlor, but did not show significant correlation with HCB and p,p′-DDE (data not shown). One fourth of the population presented inhibited AChE activity (Table 4). Among subjects sampled in the high pesticide use season, 23% showed inhibited AChE compared to 28% in the low pesticide use season (chi-square p-value 40.10). Median values and percentiles of thyroid hormone values are also presented in Table 4. TSH levels exceeded upper reference limit in 27 (23%) women and 23 (15%) men. Regarding FT4 and

C. Piccoli et al. / Environmental Research 151 (2016) 389–398

Table 3 Current and lifetime use of pesticides. Current use of pesticides

Total lifetime years of use

N (%)

N (%)

All pesticides

No Yes

143 (52.0) 132 (48.0)

Never 1–20 420

70 (25.5) 86 (31.3) 119 (43.3)


No Yes

157 (57.1) 118 (42.9)

Never 1–20 420

73 (26.5) 95 (34.5) 107 (38.9)


No Yes

235 (85.5) 40 (14.5)

Never 1–20 420

106 (38.5) 110 (40.0) 59 (21.5)


No Yes

212 (71.1) 63 (22.9)

Never 1–20 420

72 (26.2) 87 (31.6) 116 (42.2)

Organophosphate insecticides

No Yes

238 (86.5) 37 (13.5)

Never 1–20 420

113 (41.1) 104 (37.8) 58 (21.1)

Dithiocarbamate fungicides

No Yes

233 (84.7) 42 (15.3)

Never 1–20 420

71 (25.8) 92 (33.5) 112 (40.7)

Other chemical classesa

No Yes

148 (53.8) 127 (46.2)

Never 1–20 420

185 (67.3) 62 (22.5) 28 (10.2)

143 (52.0) 39 (14.2) 93 (33.8)

– – –

Total number of pesticides None currently used 1 Z2 a

– – –

Synthetic pyrethroids and carbamates included.

TT3, 11 (9%) and 5 (4%) women, respectively, had serum levels below lower reference limits, while 6 (4%) and 2 (1%) male subjects had reduced FT4 and TT3, respectively. Only one woman and one man had reduced TSH. 13% of men and 17% of women had a biochemical profile of subclinical hypothyroidism, while 1% of men and 4% of women presented hypothyroidism, i.e. high TSH and low FT4. Hormonal profiles of subclinical hyperthyroidism (i.e. high FT4 and normal TSH) and clinical hyperthyroidism (i.e. high FT4 and low TSH) were experienced only by one male subject, and one man and one woman, respectively. Correlation analysis between thyroid hormones and OC pesticides revealed significant but weak negative correlations between TSH and HCH isomers, HCB, and endosulfan II, and between FT4 and p,p′-DDT, and weak positive correlations between TT3 and transnonachlor and p,p′-DDE. Multivariate analyses are shown in Tables 5 and 6. In men, TSH showed a significant reduction of 33% in farmers relative to nonfarmers (Table 5). Otherwise, TSH levels were 2.4 times higher in subjects sampled in the high pesticide use season, and working in agriculture for 1–10 years and 26–50 years was associated with 90% and 19% higher levels of TSH, respectively, compared with working less than a year, although no linear pattern was observed. Likewise, frequency of pesticide mixing or applying of 40 days/ year or more was associated with increased TSH, and frequency of 5–39 days/year was associated with lowered FT4 relative to o 5 days/year, but linear trends were not significant. Overall use of pesticides for 1–20 and 420 years was associated with reductions of 29–37% in TSH and increases of 8–14% in FT4, with significant linear trends. Use of fungicides and herbicides for 1–20 and 420 years was associated with increases of 30–55% in TSH and reductions of 11–13% in FT4 levels, with evidence of significant linear trends. In addition, use of dithiocarbamates for 1–20 years was significantly associated with increased TSH and reduced FT4, but linear trends were only marginally significant. Regarding TT3,


levels were slightly increased among farmers and they showed a marginally significant linear trend of decrease with increasing lifetime years of herbicide use. Among women, working in agriculture for 1–10 years was associated with increased FT4 values, while women reporting current use of one pesticide presented increased TSH relative to those not handling pesticides. TT3 in women did not show any significant association with agricultural work and pesticide use (Table 5). No significant associations between thyroid hormones and current pesticide use were noted (data not shown). In general, associations between OC pesticides and thyroid hormone levels were weak and inconsistent (Table 6). Individuals with detected γ–chlordane in serum had increased TSH and TT3 by 25% and 6%, respectively. Detection of β–HCH, heptachlor epoxide B, transnonachlor, p,p′-DDE and endosulfan II was also associated with increases of 3–13% in TT3. In contrast, detected dieldrin was associated with reduction of 8% in FT4, while endrin and heptachlor were associated with a slight reduction in TT3 levels. Cholinesterase inhibition was not associated with thyroid hormone levels (Table 6). Sensitivity analysis revealed no substantial difference between models with and without subjects with history of thyroid disease. Likewise logistic regression analysis for subclinical hypothyroidism did not reveal either significant associations or evidence of linear trend with exposure variables (i.e. agricultural work, nonpersistent pesticides and OC pesticides).

4. Discussion The present data suggest that both acute and chronic occupational exposure to pesticides may increase TSH levels in male rural workers. Lifetime use of fungicides, herbicides and dithiocarbamates was observed to be associated with increased TSH accompanied by decrease in FT4, with evidence of a linear monotonic relationship, These findings further suggest that, in addition to recent exposure, cumulative lifetime exposure to pesticides may be a determinant of possible thyroid disorders, particularly hypothyroid-like effects. However, farm work and total lifetime years of all pesticide use were related with depleted TSH and elevated TT3 and FT4, respectively. Subjects with detected serum concentrations of certain OC pesticides experienced slight alterations in TT3 levels, but associations were not consistent. 4.1. Thyroid hormones and association with pesticide use Overall prevalence of biochemical subclinical hypothyroidism observed here (i.e. 15%) was higher than worldwide prevalence, which is in the range 1–10% and reaches its highest values in women, especially those older than 60 years, whites, and with higher BMI (Cooper and Biondi, 2012; Surks et al., 2004). Accordingly, prevalence of subclinical hypothyroidism in the present study was higher in women, 62% of females who experienced subclinical hypothyroidism were over 50 years of age or had a BMI exceeding 25 kg/m2, and 75% of men with subclinical hypothyroidism were obese or overweight. Subclinical hyperthyroidism is less common than subclinical hypothyroidism, with a population prevalence of approximately 2% (Surks et al., 2004). This is in agreement with our finding, which may be explained by the fact that subclinical hyperthyroidism is found at higher frequency in iodine-deficient populations (Cooper and Biondi, 2012), and the study area is a non-endemic goiter region. The relatively high prevalence of subclinical hypothyroidism suggests that direct contact with pesticides over long periods of time could have contributed to alter thyroid hormone levels. Two previous investigations on over 22,000 male private pesticide


C. Piccoli et al. / Environmental Research 151 (2016) 389–398

Table 4 Serum concentrations of OC pesticides, blood acetilcholinesterase (AChE) and butirilcholinesterase (BChE) activities, and serum levels of thyroid hormones. Men (N ¼155)

Total (N¼ 275)

Women (N¼ 120)

% 4LD




% 4LD




% 4 LD




OC pesticides (ng/g) α–HCH β–HCH γ–HCH Hexachlorobenzene Pentachloroanisole Aldrin Endrin Dieldrin Heptachlor epoxide B Heptachlor epoxide A α–chlordane γ–chlordane Transnonachlor Heptachlor o,p′-DDT p,p′-DDT o,p′-DDE p,p′-DDE o,p′-DDD p,p′-DDD Endosulfan I Endosulfan II Methoxychlor Mirex

30.9 41.3 50.2 28.0 26.6 2.6 30.6 6.6 5.5 1.8 0.0 10.3 1.8 33.2 12.5 42.8 10.3 39.5 3.7 14.4 22.9 2.2 4.4 1.5

oLD oLD 3.71 oLD oLD oLD oLD oLD oLD oLD – oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD

o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD – o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD

21.80 77.87 24.35 31.88 19.96 o LD 179.7 21.39 11.63 o LD – 22.22 o LD 39.89 53.39 84.91 66.33 112.3 o LD 40.38 58.49 o LD o LD o LD

30.9 43.2 51.6 30.9 29.0 1.9 30.9 7.1 7.1 2.6 0.0 9.7 1.3 35.5 13.5 44.5 10.3 36.8 2.6 16.8 22.6 3.2 4.5 1.3

oLD oLD 6.23 oLD oLD oLD oLD oLD oLD oLD – oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD

oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD – oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD oLD

22.86 72.55 24.69 34.06 21.08 o LD 180.4 22.97 18.58 o LD – 22.69 o LD 45.65 58.77 93.16 48.54 93.71 o LD 42.09 63.11 o LD 3.09 o LD

30.0 37.5 46.6 23.3 22.5 3.3 29.2 5.8 3.3 0.8 0.0 10.8 2.5 30.0 10.8 39.2 10.0 41.7 3.3 10.8 22.5 0.8 4.2 1.7

o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD – o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD

o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD – o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD o LD

18.01 115.7 20.78 31.28 17.80 o LD 156.4 19.48 o LD o LD – 19.29 o LD 38.58 40.76 83.74 79.87 132.0 25.68 33.40 53.06 o LD 2.94 o LD

Cholinesterase activity AChE (μmols/min/mg) Inhibition,** N (%) BChE (μmols/min/mL) Inhibition,*** N (%)

– 73 (26.5) – 12 (4.4)

0.65 – 3.34 –

0.48 – 2.06 –

0.92 – 5.03 –













Thyroid hormones Free T4 (ng/dL) Total T3 (ng/dL) TSH (mUI/mL)

– – –

1.16 0.29 2.72

0.89 0.24 0.93

1.45 0.36 6.64

– – –

1.17 0.30 2.67

0.91 0.24 1.10

1.16 0.29 2.50

0.87 0.24 0.92

1.46 0.36 5.98

1.47 0.36 6.77

– – –

LD: limit of detection. ** ***

AChE r 0.56 μmols/min/mg. BChE r2.29 and BChEr 1.61 μmols/min/mL for men and women, respectively.

applicators and 16,000 spouses of applicators from the Agricultural Health Study reported a prevalence of self-reported physician-diagnosed hypothyroidism in males and females of 2% and 7%, respectively (Goldner et al., 2010, 2013), which is slightly higher than prevalence of hypothyroidism in the present study (1% and 4%). Likewise prevalence of hyperthyroidism in the Agricultural Health Study was 1% in male applicators and 2% among spouses, while in our study it was 1% among both men and women. Fungicides were the pesticide class most used by farmers in Farroupilha, followed by herbicides. The ethylene-bis-dithiocarbamate (EBDC) mancozeb, which is widely used for protection of grape vines, peach, and plum trees grown in the study area, was the fungicide most frequently reported (Table S1). The main degradation product of many of the EBDC is ethylene thiourea (ETU), which is an antithyroid compound known to interfere with iodide uptake by inhibiting thyroid peroxidase (TPO) iodide oxidation (Hurley et al., 1998; Marinovich et al., 1997), leading to decreased thyroidal production of T3 and T4 in experimental animals (Axelstad et al., 2011; Mallem et al., 2006). ETU has also been classified as a type IIB carcinogen (IARC, 1991), based on animal data showing that it causes thyroid and other cancers in rodents (Hurley, 1998). Fungicides from the azole family may enhance hepatic catabolism by UDPGT (uridine diphospho-glucuronosyltransferase), increasing biliary elimination of T3 and T4 (Taxvig et al., 2007; Wolf et al., 2006; Ye et al., 2013). Carbendazim, a

widely used, broad-spectrum benzimidazole fungicide, caused histopathological damages in the thyroid and decreased serum T3 levels in rats (Barlas et al., 2002). Carbendazim and methyl thiophanate, a major metabolite of carbendazim, were among pesticides used by study subjects (Table S1). Here-described exposure-response relationships between fungicides and dithiocarbamates lifetime use and increasing TSH and decreasing FT4 in men are consistent with experimental data supporting that exposure to fungicides may exert hypothyroid-like effects (Brucker-Davis et al., 1998; Campos and Freire, 2016). These findings are also in partial agreement with the Agricultural Health Study, which reported associations between increased odds of hypothyroidism and overall use of fungicides and use of maneb/ mancozeb in female spouses of pesticide applicators (Goldner et al., 2010) and a Mexican study that noted increased TSH among backpack sprayers using EBDC fungicides (Steenland et al., 1997). In a subsequent study among banana plantation workers exposed to EBDC fungicides, difference in TSH levels between exposed workers and controls was not statistically significant, but instead a strong correlation was observed between blood ETU levels and size of solitary thyroid nodules (Panganiban et al., 2004). Male farmers may use a larger number of pesticides and may apply higher amounts of pesticides than the female farmers or wives, which could explain the lack of association of fungicide use and overall pesticide exposure with thyroid hormones in women in the present study.

C. Piccoli et al. / Environmental Research 151 (2016) 389–398


Table 5 Adjusteda regression coefficients (95% confidence intervals) for percent change in hormone levels (transformed in natural logarithm) associated with exposure variables stratified by gender. Exposure variables

MEN (N¼ 155)

WOMEN (N¼120)


Free T4

Total T3


Free T4

Total T3

Farmer (ref¼ non  farmer) High pesticide use season (ref¼ low pesticide use)

0.67 (0.49–0.92) 2.43 (2.09–2.86)

1.04 (0.96–1.13) 1.01 (0.97–1.06)

1.06 (1.01–1.12) 0.97 (0.95–1.01)

0.90 (0.69–1.18) 1.02 (0.85–1.23)

0.94 (0.87–1.02) 1.01 (0.95–1.06)

0.95 (0.91–1.00) 0.99 (0.96–1.03)

Years of agricultural work (ref¼ o 1) 1–10 11–25 26–50 450 p for trend

1.90 (1.19–3.03)b 1.46 (0.94–2.29) 1.19 (1.05–1.95) 0.90 (0.46–1.79) 0.42

0.95 (0.84–1.06) 0.94 (0.84–1.06) 0.96 (0.85–1.08) 1.01 (0.85–1.20) 0.59

0.97 (0.90–1.04) 0.98 (0.92–1.05) 1.01 (0.93–1.07) 1.01 (0.91–1.11) 0.94

0.84 (0.52–1.35)b 1.08 (0.76–1.52) 1.26 (0.84–1.88) 1.23 (0.67–2.25) 0.29

1.21 (1.08–1.39) 1.02 (0.93–1.12) 1.02 (0.91–1.13) 1.02 (0.87–1.21) 0.84

1.04 1.03 1.04 1.06 0.18

Years mixing or applying pesticides (ref ¼ o 1) 1–10 410 p for trend

1.21 (0.76–1.92)b 1.30 (0.78–2.16) 0.10

0.91 (0.81–1.02) 0.89 (0.79–1.01) 0.20

0.98 (0.90–1,06) 0.99 (0.91–1.08) 0.67

1.09 (0.84–1.43)b 0.90 (0.72–1.14) 0.51

0.98 (0.90–1.06) 1.01 (0.94–1.08) 0.82

0.99 (0.94–1.04) 1.01 (0.96–1.05) 0.87

Days/year mixing or applying pesticides (ref¼ o 5) 5–39 40–59 Z 60 p for trend

1.19 (0.76–1.88)b 1.36 (1.13–2.11) 1.19 (1.31–1.86) 0.37

0.86 (0.79–0.97) 0.91 (0.82–1.01) 0.93 (0.84–1.03) 0.45

1.06 (0.99–1.15) 1.01 (0.94–1.08) 1.01 (0.94–1.07) 0.10

0.93 (0.73–1.21)b 1.08 (0.75–1.55) 0.94 (0.72–1.24) 0.77

0.97 (0.90–1.05) 1.01 (0.89–1.12) 1.04 (0.96–1.12) 0.39

1.02 (0.97–1.06) 0.98 (0.97–1.05) 0.98 (0.97–1.02) 0.28

Number of pesticides currently used (ref¼none) 1 Z2 p for trend

1.12 (0.84–1.48) 1.03 (0.83–1.27) 0.70

1.01 (0.93–1.08)b 0.99 (0.93–1.05) 0.77

0.99 (0.95–1.04) 0.99 (0.96–1.03) 0.82

1.36 (1.01–1.82) 0.92 (0.72–1.19) 0.90

1.03 (0.94–1.13)b 0.99 (0.92–1.08) 0.66

0.99 (0.94–1.05) 1.01 (0.96–1.05) 0.74

Any pesticide 1–20 420 p-trend

0.71 (0.52–0.97) 0.63 (0.41–0.95) 0.38

1.08 (1.05–1.17) 1.14 (1.02–1.25) 0.02

1.05 (0.95–1.06) 1.08 (0.94–1.08) 0.94

0.83 (0.67–1.05) 1.09 (0.86–1.34) 0.41

0.97 (0.91–1.04) 0.98 (0.91–1.06) 0.18

0.98 (0.95–1.02) 1.02 (0.97–1.06) 0.45

Fungicides 1–20 420 p-trend

1.42 (1.03–1.92) 1.55 (1.03–2.36) 0.03

0.89 (0.84–1.01)b 0.88 (0.79–1.01) 0.02

0.99 (0.9–1.05) 0.99 (0.93–1.06) 0.88

1.11 (0.89–1.39) 0.95 (0.74–1.22) 0.87

1.03 (0.96–1.10)b 1.02 (0.94–1.10) 0.63

1.02 (0.98–1.06) 0.98 (0.94–1.03) 0.70

Insecticides 1–20 420 p-trend

1.01 (0.79–1.28)b 0.99 (0.74–1.34) 0.61

1.01 (0.95–1.07)b 1.03 (0.95–1.11) 0.57

0.99 (0.96–1.03) 1.01 (0.96–1.05) 0.75

1.11 (0.90–1.36)b 0.76 (0.57–1.01) 0.31

0.98 (0.92–1.05)b 1.05 (0.96–1.15) 0.58

1.03 (0.99–1.06) 0.98 (0.93–1.03) 0.95

Herbicides 1–20 420 p-trend

1.30 (1.09–1.82)b 1.40 (1.09–2.16) 0.05

0.88 (0.81–0.96)b 0.87 (0.78–0.96) 0.01

0.96 (0.91–1.02) 0.97 (0.91–1.03) 0.06

1.20 (0.96–1.49)b 0.85 (0.66–1.09) 0.60

1.03 (0.96–1.09)b 1.01 (0.93–1.09) 0.84

1.02 (0.98–1.06) 0.99 (0.94–1.03) 0.99

Organophosphate insecticides 1–20 420 p-trend

1.08 (0.87–1.22) 1.03 (0.78–1.36) 0.57

0.99 (0.93–1.05) 0.99 (0.92–1.07) 0.89

1.01 (0.97–1.04) 1.02 (0.98–1.06) 0.36

1.13 (0.91–1.39) 1.29 (0.58–1.03) 0.33

0.97 (0.90–1.03) 1.04 (0.95–1.14) 0.75

1.02 (0.98–1.05) 1.03 (0.92–1.03) 0.67

Dithiocarbamate fungicides 1–20 420 p-trend

1.42 (1.04–1.93) 1.46 (0.97–2.20) 0.06

0.91 (0.84–1.00)b 0.90 (0.82–1.01) 0.05

0.99 (0.94–1.05) 0.99 (0.93–1.06) 0.98

1.16 (0.93–1.45) 0.96 (0.75–1.25) 0.95

1.01 (0.94–1.08)b 1.03 (0.95–1.12) 0.57

1.01 (0.97–1.05) 0.98 (0.93–1.02) 0.45

Other chemical classesc 1–20 420 p-trend

1.01 (0.84–1.23) 0.86 (0.65–1.15) 0.41

1.01 (0.96–1.06) 1.07 (0.99–1.15) 0.12

1.00 (0.97–1.03) 1.01 (0.97–1.05) 0.69

1.19 (0.88–1.60) 1.01 (0.69–1.45) 0.61

0.99 (0.90–1.07) 1.01 (0.90–1.14) 0.95

1.00 (0.95–1.05) 0.96 (0.90–1.03) 0.35

(0.96–1.13) (1.03–1.08) (0.98–1.10) (0.96–1.18)

Lifetime years of pesticide use (ref ¼ none)

a b c

All models were adjusted for age, BMI, current smoking status, and alcohol intake. Ref: reference category. Models additionally adjusted for years of education. Synthetic pyrethroids and carbamates included.

Regarding cumulative use of herbicides, linear monotonic relationship observed with TSH and FT4 levels in men suggest that herbicides may also lead to changes in thyroid hormones consistent with hypothyroidism. Herbicides are a heterogeneous group of chemical compounds. According to Short and Colborn (1999), more than 60% of herbicides are documented endocrine disruptors, and evidence of thyroid-disrupting potential in laboratory animals exists for several herbicides (Brucker-Davis et al.,

1998; Howdeshell, 2002,; Van den berg et al., 1991). Glyphosate and paraquat were the most commonly used herbicides among study population (Table S1). In vivo studies with amphibians demonstrated that glyphosate-based herbicides interfere with thyroid hormone signaling through elevation of thyroid hormone receptor β (Howe et al., 2004; Navarro-Martín et al., 2014). There are no reports on paraquat and altered thyroid function; however, postmortem analysis of human cases of acute paraquat poisoning


C. Piccoli et al. / Environmental Research 151 (2016) 389–398

Table 6 Adjusted* regression coefficients (95% confidence intervals) for detection of OC pesticide in serum, inhibition of blood AChE and BChE, and levels of TSH, free T4 and total T3 (transformed in natural logarithm) in the pooled sample of men and women (N ¼275). Concentration4LD (ref¼ non detected)


Free T4

Total T3

α–HCH β–HCH γ–HCH Hexachlorobenzene Pentachloroanisole Aldrin Endrin Dieldrin Heptachlor epoxide B Heptachlor epoxide A α–chlordane γ–chlordane Transnonachlor Heptachlor o,p′-DDT p,p′-DDT o,p′-DDE p,p′-DDE o,p′-DDD p,p′-DDD Endosulfan I Endosulfan II Methoxychlor Mirex

1.04 (0.99–1.10) 0.95 (0.82–1.06) 0.97 (0.85–1.09) 0.96 (0.83–1.11) 0.91 (0.79–1.05) 1.09 (0.73–1.62) 0.99 (0.86–1.14) 0.95 (0.73–1.23) 0.92 (0.69–1.23) 1.14 (0.71–1.80) – 1.25 (1.01–1.54) 0.86 (0.54–1.38) 1.01 (0.89–1.16) 1.12 (0.92–1.35) 1.05 (0.92–1.19) 0.99 (0.79–1.22) 1.01 (0.87–1.16) 0.80 (0.58–1.13) 1.01 (0.84–1.21) 0.94 (0.80–1.09) 0.95 (0.62–1.46) 0.92 (0.67–1.24) 1.30 (0.76–2.20)

0.98 (0.97–1.01) 1.01 (0.98–1.05) 0.98 (0.95–1.02) 0.98 (0.94–1.03) 1.03 (0.96–1.04) 0.92 (0.83–1.03) 0.98 (0.94–1.02) 0.92 (0.86–0.99) 1.05 (0.97–1.14) 0.98 (0.86–1.12) – 1.02 (0.96–1.08) 1.09 (0.96–1.26) 0.99 (0.95–1.03) 1.01 (0.95–1.06) 0.98 (0.94–1.02) 0.99 (0.94–1.05) 0.97 (0.96–1.03) 0.99 (0.91–1.09) 1.03 (0.98–1.08) 0.99 (0.95–1.04) 1.03 (0.91–1.16) 1.02 (0.93–1.12) 0.91(0.78–1.05)

0.99 (0.99–1.01) 1.05 (1.02–1.08) 0.98 (0.95–1.01) 0.98 (0.95–1.01) 0.99 (0.96–1.02) 1.08 (0.99–1.17) 0.97 (0.94–0.99) 1.01 (0.95–1.06) 1.09 (1.03–1.16) 1.04 (0.94–1.15) – 1.06 (1.01–1.11) 1.09 (1.00–1.20) 0.97 (0.94–0.97) 1.01 (0.97–1.05) 1.01 (0.97–1.03) 1.04 (0.99–1.08) 1.03 (1.01–1.06) 0.98 (0.91–1.06) 1.03 (0.99–1.07) 1.00 (0.97–1.03) 1.13 (1.03–1.25) 1.00 (0.93–1.07) 1.10 (0.99–1.22)

Cholinesterase activity (ref¼ not inhibited) Inhibited AChE Inhibited BChE

1.07 (0.92–1.23) 0.68 (0.68–1.28)

0.99 (0.95–1.03) 1.03 (0.94–1.13)

1.01 (0.99–1.04) 0.98 (0.93–1.03)

LD: limit of detection. *

Adjusted for sex, age, BMI, current smoking status, and alcohol intake.

revealed detectable concentrations of paraquat in the thyroid (Tsatsakis et al., 1996), suggesting that the thyroid could be affected by this herbicide. Although human studies supporting experimental evidence regarding herbicides are very limited, they somehow point in the same direction than our findings. In the Agricultural Health Study, paraquat use was associated with hypothyroidism in spouses of pesticide applicators (Goldner et al., 2010). In another American study, TSH levels in male herbicide applicators dropped significantly from high to low exposure seasonal shifts (Garry et al., 2003); however, FT4 also dropped from high to low exposure season. Males sampled in the high pesticide use season had higher TSH levels in multivariate analysis. In accordance with this finding, TSH in aerial fungicide applicators in Minnesota was increased in the high compared to the low exposure season (Garry et al., 2003). Conflicting results were obtained, however, in a study with male greenhouse workers exposed to several active ingredients, which showed increased TSH and reduced FT4, FT3 and TT3 in the low compared to the high pesticide use season (Toft et al., 2006). It is not yet well known whether thyroid-disrupting chemical effects are transient or sustained, but present findings suggest that thyroid hormone changes induced by pesticides may be permanent or reversible, depending on the nature of the dose-time relationship. However, both matched longitudinal analysis and cohort studies will be needed to confirm this hypothesis. In addition, other causes than pesticide exposure, such as difference in dietary pattern, may account for the observed shift in TSH values. There were some discrepancies among our results. While lifetime exposure to herbicides and fungicides was related with increased TSH and reduced FT4, farmers had lower TSH levels than non-farmers and lifetime use of all pesticides was associated with increased FT4 and reduced TSH. Family farmers typically use more than one pesticide, hence we cannot determine which chemicals that are responsible for the observed effects nor can we rule out

the possibility that some of the observed associations may have been the result of interaction between pesticides, acting as either agonist or antagonists to thyroid hormone action. In addition, our study did not show significant trends for duration and frequency of overall pesticide use. This is in partial agreement with the study by Goldner et al. (2010), in which the number of years living on a farm, lifetime pesticide application, and days per years mixing or applying pesticides were not associated with thyroid disease in spouses of pesticide applicators. 4.2. OC pesticides and thyroid hormone levels The most prevalent OC pesticides were HCH (α, β and γ isomers) and DDT metabolites p,p′-DDT and p,p-DDE, which is in agreement with data from studies in rural and urban Brazilian population (Delgado et al., 2002; Freire et al., 2013; Paumgartten et al., 1998; Sarcinelli et al., 2003) and elsewhere (Becker et al., 2009; CDC, 2009; Chopra et al., 2010; Zumbado et al., 2005). According to findings from the 2003–2004 NHANES (National Health and Nutrition Examination Survey), levels of β-HCH, γ-HCH, p,p ′-DDT and endrin in adults from the general population of the U.S. were several times lower than those reported here, whereas p,p ′-DDE was much higher in the NHANES (i.e. 268 versus 42.7 ng/g) (CDC, 2009). Overall, exposure to OC pesticides was not associated with thyroid hormones, although presence of certain pesticide residues in blood could have resulted in altered hormone levels, particularly increase in TT3. Our findings are not quite in agreement with data from epidemiological studies addressing the issue of OC pesticides and thyroid function, which in general have shown evidence of null or inverse relationships between internal dose of OC pesticides, particularly DDT metabolites and HCB, and circulating levels of T3 and T4 (Bloom et al., 2003; Hagmar et al., 2001; Persky et al., 2011; Rylander et al., 2006; Sala, 2001, Schell et al.,

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2009; Turyk et al., 2006, 2007). Also, our results are not in agreement with associations reported between use of several OC pesticides and increased risk of hypothyroidism observed in American pesticide applicators and female wives (Goldner et al., 2010, 2013). However, in contrast to our study, Goldner et al. used self-reported OC exposure and thyroid disease outcomes, which could explained the difference in results between studies. Negative association between FT4 and dieldrin observed here is in line with a previous study that found higher concentrations of dieldrin in serum of non-occupationally exposed Indian women with depleted T4 (Rhatore et al., 2002), while the present positive association between p,p′-DDE and TT3 is in agreement with results of a study in adult American men (Meeker et al., 2007). However, p,p′-DDE was associated with increased free T4 and decreased TSH as well (Meeker et al., 2007). One study among 608 adult residents of an area heavily polluted with OC pesticides in Rio de Janeiro, Brazil, found that TT3 was positively associated with serum levels of α-chlordane in men and endosulfan II in women (Freire et al., 2013), as observed in the present study. As other environmental chemicals, OC pesticides may interfere with the thyroid system through multiple mechanisms, producing complex effects on hormonal signaling (Boas et al., 2012). These mechanisms are most likely to manifest as altered T4 and TSH levels due to compensatory production/secretion of T4 or TSH to maintain homeostasis. Thus, mechanisms explaining the positive associations observed between certain OC pesticides and TT3, not necessarily accompanied by an inverse association with TSH, are unclear at this time. OC compounds may affect the deiodination of thyroid hormones by increasing type 3 deiodinase expression leading to increased T3 degradation; or may bind to thyroid hormone binding proteins altering circulating T4 and T3 levels. Findings in the present study may suggest an effect of OC pesticides on one or more of these mechanisms, as well as their interaction may also occur. Nonetheless, because multiple exposures and outcomes were examined and model estimates were not adjusted for multiple comparisons, the possibility that the relationship between OC pesticides and TT3 was due to chance cannot be ruled out. 4.3. Limitations and strengths The present study has some limitations. Firstly, we did not assess associations with specific contemporary pesticides. Because not all pesticides in the same functional or chemical class have similar modes of action, using summary variables for all pesticides in a class can dilute the effects of specific chemicals. However, by grouping pesticides into classes we were able to assess current and cumulative pesticide use and examine exposure-response relationships. Secondly, potential confounding by co-exposure to multiple pesticides and potential interaction between chemicals was not considered. Despite this, given the evidence of exposureresponse relationships between lifetime use of fungicides and herbicides and TSH and FT4 levels, as well as consistency with previous studies, it seems that findings are unlikely to result from unmeasured confounding. Third, there is the possibility of bias due to misclassification of self-reported use of pesticides. However, because it's unlikely that participants recall exposures differently depending on their thyroid status, misclassification might have lead to bias toward the null. Failure of iodine status assessment represents a limitation to the study, as well as the lack of information on other exogenous causes of altered thyroid hormone levels, such as certain drugs. Regarding dietary habits, seasonal variation in fish and dairy products intake may influence iodine status. Nonetheless, fish consumption in the study area is relatively low, while intake of milk, cheese and other dairy products is believed to be stable across seasons, and therefore seasonal


variation in diet is likely to have a minimal influence on thyroid status. Finally, analytical data on free T3 levels would have provided a better understanding of the thyroid status of the study population. The random sampling of the population in the rural area of Farroupilha is the main strength of this study. On the other hand, because OC pesticides are persistent organic pollutants and remain in the environment for many years, the serum measurements likely reflect lifetime cumulative exposure. Also, we measured serum residues of 24 OC pesticides, most of which have been scarcely investigated in relation to human thyroid function. Finally, this is so far the first epidemiological study that has been performed regarding thyroid function and its relationship with pesticide exposure in both male and female agricultural population in Latin America, which, despite producing a large portion of the world's agriculture, is missing in the epidemiological literature on endocrine-disrupting effects of pesticides.

5. Conclusions The present data suggest that both chronic and recent occupational exposure to contemporary pesticides, especially herbicides and dithiocarbamate fungicides, may affect male thyroid function at the peripheral level causing a decrease in circulating levels of thyroid hormones and consequently an increase in TSH. Male farmers might be more likely to experience such thyroid alterations because they may be more intensely and directly exposed to pesticides than female farmers. Evidence from our work is supported by experimental and epidemiological studies that found exposure to modern pesticides to cause hypothyroid-like effects; however, inconsistent results were found for farm work and total lifetime years of all pesticide use, which were related with reduced TSH and elevated TT3 and FT4, respectively. Less certain are the possible combined effect of previously used OC pesticides on thyroid function. Given the intense use of pesticides in agricultural settings in Brazil, future research should evaluate long-term effects of modern pesticides on thyroid function relying on longitudinal studies with repeated measures of exposures and outcomes.

Funding sources This work was partially supported by the CAPES (“Coordenação de Aperfeiçoamento de Pessoal de Nível Superior”). Camila Piccoli had a CAPES master's degree grant and Cleber Cremonese had a CAPES predoctoral grant. Rosalina Jorge Koifman is supported by the CNPq (“Bolsa de Produtividade em Pesquisa”) and Carmen Freire has a “Jovens Talentos” grant from the CAPES/CNPq (“Science Without Borders Program”, grant number A022_2013).

Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at

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