Skip to main content
Download PDF
Download ePub

Occupational exposure to pesticides and endometrial cancer in the Screenwide case-control study

Environmental Health volume 22, Article number: 77 (2023) Cite this article

Abstract

Background

Endometrial cancer is the most common gynaecological tumour in developed countries and disease burden is expected to increase over the years. Identifying modifiable risk factors may help developing strategies to reduce the expected increasing incidence of these neoplasms.

Objective

This study evaluates the association between occupational exposure to pesticides and endometrial cancer using data from a recent case-control study in Spain.

Methods

The analyses included data from 174 consecutive incident endometrial cancer cases and 216 hospital controls frequency-matched by age. Data were collected through structured epidemiological questionnaires and exposure to pesticides was assessed using a Spanish job-exposure matrix (MatEmESp).

Results

Overall, 12% of controls and 18% of cases were occupationally exposed to pesticides. We observed a positive association between occupational exposure to pesticides and endometrial cancer (OR = 2.08; 95% CI = 1.13–3.88 compared to non-exposed). In general, exposures that occurred farther in the past were significantly associated with endometrial cancer. Exposure to insecticides, fungicides and herbicides were positively associated with endometrial cancer (OR = 2.08; 95% CI = 1.13–3.88, OR = 4.40; 95% CI = 1.65–13.33, and OR = 5.25; 95% CI = 1.84–17.67, respectively). The agricultural, poultry and livestock activities scenario was associated with endometrial cancer (OR = 4.16; 95% CI = 1.59–12.32), while the cleaning exposure scenario was not (OR = 1.22; 95% CI = 0.55–2.67).

Conclusions

Assessment of occupational exposure to pesticides assessed using a Spanish job-exposure matrix revealed a positive association with endometrial cancer. The elucidation of the role of pesticide compounds on endometrial cancer should shed a light on the aetiology of this tumour.

Peer Review reports

Introduction

Endometrial cancer is the most common gynaecological tumour in developed countries and disease burden is expected to increase over the years [1]. Among women, endometrial carcinoma has been the cancer most consistently associated with body mass index (BMI) [2]. Each 5 kg/m² increase in BMI is linked to a 54% elevated risk of developing this cancer [3]. Genetic susceptibility for endometrial cancer include Lynch syndrome, which is caused by pathogenic germline variants in one of the mismatch repair (MMR) genes that maintain genomic stability. Lynch syndrome is the most common hereditary cause of endometrial cancer, and it is associated with 3% of all endometrial cancer cases [4, 5]. Other risk factors include diabetes [6], and hormonal-related factors, such as nulliparity [7, 8], postmenopausal estrogen-only hormone use [9], age at last birth [10], age at menarche [11], and oral contraceptive use [12]. Identifying other modifiable risk factors may help developing strategies to reduce the expected increasing incidence of these neoplasms.

Pesticides are a heterogeneous group of chemicals, including insecticides, fungicides and herbicides. These compounds are mainly used in agriculture for increasing food-production productivity and decreasing food-borne and vector-borne diseases [13]. Historically, cereals, olive trees, and vineyards dominated land use in Spain. In recent decades, intensive vegetable farming has grown significantly. Pesticide use has been led by insecticides and fungicides, followed by herbicides until the mid-70s of the last century [14]. In the last decades, European Union has been implementing measures on use and distribution of pesticides aimed to reduce environmental and health risks while maintaining crop productivity and improving controls [15]. Nevertheless, there has been controversy on the safety of certain pesticides as a consequence of diverging results from various assessments in their potential carcinogenicity [15, 16].

Oxidative stress, disruption of methyltransferases activity and epigenetic alterations are some mechanisms related to pesticide exposure that may lead to cancer development and other chronic diseases [16, 17]. Certain pesticides are also considered endocrine disruptors as certain compounds can interact with estrogenic and androgenic pathways [18, 19]. Pesticides residues are found in air, water and soil [20]; exposure can unintentionally occur through consuming foods or liquids with pesticide residues or occupationally, during their manufacture and manipulation [17, 20]. Few studies have assessed the possible relationship between exposure to pesticides and endometrial cancer with different assessment methods, such as measuring levels of pesticides in serum [21, 22] and adipose tissue [23], with no positive results. However, measurements of short half-life pesticide levels in biological samples may only indicate recent exposures, and those of long half-life pesticide levels or their derivatives may not accurately reflect the actual exposure to these substances [24, 25], which are relevant limitations when evaluating its association cancer and other diseases [26].

Assessing longer exposure periods through occupational exposures can help to overcome limitations of studies measuring pesticides levels in biological samples [26]. Several epidemiologic studies have evaluated the relationship between occupational exposure to pesticides and the risk of hematologic, bladder, breast, and prostate cancers [27, 28]. However, to the best of our knowledge, no previous epidemiological studies have specifically evaluated the association between occupational exposure to pesticides and endometrial cancer. In this study, we evaluated the association between occupational exposure to pesticides and endometrial cancer using a job-exposure matrix (JEM) in the Screenwide case-control study.

Materials and methods

Study design

The current study utilized data from the Screenwide study, a case-control study conducted in Spain [29]. Consecutive cases of endometrial cancer were recruited from 2017 to 2021, with no age limit restrictions, as well as hospital controls frequency matched to cases by age. Hospital controls comprised patients both with and without benign gynecologic conditions. Gynecologic benign conditions included endometriosis, fibroids, benign cysts, prolapse, and polyps. Hospital controls without gynecologic conditions were enrolled in the study during their preoperative anesthesia evaluations for surgical procedures related to ophthalmic, traumatologic, or other non-gynecologic diseases. The response rates were 89.6% among cases, 80.5% for controls with benign gynecological pathology, and 76.8% for asymptomatic women attending hospital for non-gynecological diseases. Participants were excluded from the study if they were pregnant, had given birth within the past 8 weeks, received chemotherapy or radiotherapy treatment in the preceding 6 months, or had communication difficulties that prevented them from understanding the informed consent or answering the questionnaire, such as not being fluent in Spanish or having an intellectual disability. In our study we considered the epidemiologic data collected for the 180 consecutive incident endometrial cancer cases, as well as 218 hospital controls; controls included 146 women with benign gynaecological pathology and 72 women attending hospital for non-gynaecological diseases. Occupational data was missing in 8 participants, yielding a sample size of 174 cases and 216 controls for the present analyses (Supplemental Fig. 1).

Data collection and exposure assessment

Data were collected through structured epidemiological questionnaires administered by trained personnel in personal interviews [29]. The questionnaire included basic epidemiologic information such as demographic factors, tobacco consumption, lifetime occupational history (including jobs held for at least 1 year), coffee and tea consumption, physical activity, family history of cancer, anthropometric factors, reproductive factors and exogenous hormone use, sun exposure, sleeping habits, and chronotype information (individual preference for morning or evening activity). Each occupation was independently coded by two industrial hygienists according to the Spanish National Classification of Occupations (CNO-94), the Spanish version of the International Standard Classification of Occupations 1988 (ISCO-88). The coding process was carried out blinded to the case-control status of the participants. An agreement was reached by consensus when discrepancies occurred between the two coders. Workplace exposures were then evaluated through MatEmESp, a JEM developed in 2009 and designed for Spanish working conditions that covered the period 1996–2005 [30]. A JEM is a tool used to assess exposure to potential health hazards in occupational epidemiological studies. It comprises a list of levels of exposure to a variety of potentially harmful agents for selected occupational titles. In large population-based epidemiological studies, JEMs may be used as a quick and systematic means of converting coded occupational data (job titles) into a matrix of possible exposures, eliminating the need to assess each individual’s exposure in detail. The JEM exposure scores reflect the likelihood that a person’s exposure from their occupation is a significant contributor to their overall exposure compared to other sources. MatEmESp includes occupational exposure estimates in five categories based on job titles coded according to the CNO-94 coding system [30]: safety, ergonomics, hygiene, work conditions and psychosocial factors. Related to work conditions, identification of potentially exposed to pesticides occupations in MatEmESp was based on those occupations considered in the Finnish Job-Exposure Matrix (FINJEM) [31] and was extensively extended and adapted to Spanish working conditions by local experts [30]. Exposure to pesticide active compounds in the MatEmESp was based on use, toxicological relevance, legal status of the use in Spain, and existence of professional exposure limits. In particular, ten different active compounds were selected: four insecticides (endosulfan, methomyl, pyrethrin, and chlorpyrifos), four herbicides (2,4D, atrazine, diquat, and diuron), and two fungicides (captan and thiram) [32].

Supplemental Table 1 shows those job titles exposed to pesticides according to MatEmESp in Screenwide study. MatEmESp included quantitative indicators of probability (proportion of workers exposed by chemical and job title) and intensity of exposure (annual average environmental levels by chemical and job title). Duration, age at first exposure, time since first exposure and time since last exposure to pesticides were calculated based on the years at start and stop reported for each job and/or the date of interview. As MatEmESp covered the 1996–2005 period, similar exposures scores than those for the 1996–2005 period were assigned for exposures occurring outside the 1996–2005 timeframe. We adjusted the duration of exposure when participants reported holding multiple concurrent occupations that involved exposure to the same active substance simultaneously. This adjustment aimed to avoid overestimating the duration of exposure and was determined by the number of simultaneous jobs during the overlapping period by inversely weighting duration by the number of overlapping jobs during the corresponding period. Cumulative exposure scores (CES) were calculated for each compound as the result of the product of probability, intensity and duration (in years) of exposure. Continuous variables were categorized using median as the cut-off point based on the distribution among exposed controls. We grouped the job titles potentially exposed to pesticides into three different scenarios of exposure: (a) agricultural, poultry and livestock activities, (b) cleaning staff, (c) manufacturing and lumber industries. The latter group includes factory workers in the manufacture of pesticides and in wood production, as timber is often treated for pest control. Participants that reported no employment history (housewives, N = 28) were classified as never occupationally exposed to pesticides.

Statistical analyses

The distribution of potential risk factors between cases and controls was compared using the Pearson’s chi-squared test. Calculated CES were normalized and calculated per 1 standard deviation (SD) increase. Correlation among exposed was calculated comparing each pesticide application group using Pearson’s correlation coefficients. Multivariate unconditional logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for the association between occupational exposure to pesticides and endometrial cancer. The variables considered for inclusion in the multivariable models are shown in the Directed Acyclic Graph (Supplemental Fig. 2). Basic adjusted models included age at interview (< 60, 60–69, ≥ 70) and educational level (primary or less, secondary, higher). Variables for multivariate models were selected using the stepwise selection method, which in addition to basic adjustments, included body mass index (BMI; <25, 25–29.9 or ≥ 30), hormonal contraceptives use (ever, never) and menopause status (premenopause, postmenopause). The statistical significance level (alpha) was set at 0.05. For all variables, missing data was < 10% of subjects. Missing values were introduced in models as independent categories.

We conducted sensitivity analyses excluding housewives and stratified analyses by BMI and by type of control. Certain solvents have been previously associated to gynaecological tumours [21, 33]. Therefore, sensitivity analysis excluding participants who reported any occupational exposure to solvent compounds were performed to ensure that exposure to solvents was not influencing the association between pesticides and endometrial cancer. We assumed that earlier exposures were at least similar or higher than the ones estimated by MatEmESp in 1996–2005. However, exposures occurring after 2005 could be lower than estimated by the MatEmESp, as exposures are expected to decrease over time. Therefore, we performed sensitivity analyses excluding exposures after 2005, (4 registries from 2 cases and 2 controls).

We determined that, in order to estimate odds ratios of at least 2.3 with 80% power and assuming a 10% prevalence of exposure in controls, a sample size of 171 cases and 214 controls, maintaining a case-to-control ratio of 1.25, was required. Similarly, for odds ratios of at least 2.0 with the same power but assuming a 20% prevalence of exposure in controls, a sample size of 156 cases and 195 controls was deemed necessary. All analyses were conducted using R version 4.2.2.

Ethical approval

The Screenwide study followed the national and international directives on ethics and data protection (Declaration of Helsinki and subsequent amendments; EU Reglament 2016/679) and the Spanish laws on data protection (Organic Law 3/2018; Law 14/2007 biomedical research). Participation in the study was voluntary, and all eligible subjects signed an informed consent form after receiving information about the study, before participating in any intervention. Study protocol was approved by the Ethics Committee for Clinical Research from the Bellvitge University Hospital.

Results

Demographic features of participants

Baseline demographic characteristics are shown in Table 1. The median age of cases was 67 (IQR 59–74), while controls had a median age of 66 (IQR 55–73). Cases were more likely to have a higher BMI (p-value < 0.001), to be diabetic (p-value = 0.014) and hypertense (p-value = 0.010). The proportion of postmenopausal participants was higher among cases than controls (p-value < 0.001). Controls were more likely to have used hormonal contraceptives (p-value = 0.003) compared to cases. Among the control group, there were no associations between occupationally exposed to pesticides and never exposed. (Table 2).

Table 1 Descriptive characteristics of the study population
Full size table
Table 2 Descriptive characteristics among controls by occupational exposure to pesticides
Full size table

Associations between endometrial cancer and exposure to pesticides

Overall, 12% of controls and 18% of cases were occupationally exposed to pesticides (Table 3). Occupational exposure to pesticides was associated with endometrial cancer (OR = 2.08; 95% CI = 1.13–3.88 compared to non-exposed). We observed positive associations with earlier exposures in time. In particular, associations were observed for exposures that occurred before 2004 (OR = 2.35; 95% CI = 1.08–5.30). Similarly, associations were observed among those who started exposure at age 32 or more (OR = 2.69; 95% CI = 1.24–6.06), whose first exposure started ≥ 32 years or finished ≥ 13 years before the interview (OR = 2.39; 95% CI = 1.06–5.51 and OR = 2.35; 95% CI = 1.08–5.30, respectively). An exposure duration of less than 16 years was associated with endometrial cancer (OR = 2.43; 95% CI = 1.14–5.38), while an OR of 1.11 (95% CI = 0.38–3.04) was observed for a longer duration. Normalized cumulative exposure score showed no significant associations (OR = 1.25; 95% CI = 0.70–2.38).

Table 3 Associations between endometrial cancer and occupational exposure to pesticides
Full size table

Occupational exposure to each pesticide application group, including insecticides (OR = 2.08; 95% CI = 1.13–3.88), fungicides (OR = 4.40; 95% CI = 1.65–13.33) and herbicides (OR = 5.25; 95% CI = 1.84–17.67), were positively associated with endometrial cancer (Table 3; Fig. 1). However, independent effects for each pesticide application group were difficult to assess due to the correlation between exposures (Supplemental Table 2). Working as cleaning staff was not associated with endometrial cancer (OR = 1.22; 95% CI = 0.55–2.67). Contrarily, pesticide exposure related to agricultural, poultry and livestock activities revealed a positive association (OR = 4.16; 95% CI = 1.59–12.32).

Fig. 1
figure 1

Forest plot of associations on pesticides by scenario and type of pesticide. * Adjusted for age, educational level, body mass index, hormonal contraceptives and menopausal status

Full size image

Sensitivity analyses

In general, analyses restricted to participants unexposed to solvents yielded similar patterns on the association between pesticide exposure and endometrial cancer (Supplemental Tables 3, ORever exposed = 2.44; 95% CI = 1.15–5.30). Similarly, excluding housewives from the analyses yielded similar patterns of associations (ORever exposed = 2.08, 95% CI = 1.12–3.91, data not shown). Stratified analyses by type of control yielded positive estimates among controls with benign gynaecologic pathology (Supplemental Tables 4, ORgynaecologic = 2.08, 95% CI = 1.04–4.36; ORnon−gynaecological = 1.86, 95% CI = 0.83–4.44). Stratified analyses by BMI did not reveal clear patterns (Supplemental Table 5). Results were virtually identical excluding exposures occurring after 2005 (data not shown).

Discussion

Main findings

We observed a positive association between occupational exposure to pesticides and endometrial cancer using data from a recent case-control study in Spain. The three application groups (insecticides, fungicides and herbicides) were positively associated with endometrial cancer, although independent effects were difficult to assess due to correlation between exposures. Exposure to pesticides in the agricultural, poultry and livestock activities scenario was positively associated with endometrial cancer. On the contrary, the cleaning staff scenario did not reveal associations. These latter null results could be the result of a lower intensity and probability of exposure to pesticides compared with those in farmer and related occupations. Cumulative exposure scores did not show clear patterns, while exposures that occurred farther in the past were significantly associated with endometrial cancer. A short duration of the exposure was also associated with endometrial cancer, although further studies are required to untangle the relationship between the timing of exposure and its impact on endometrial cancer.

The results of our study suggest a positive association between occupational exposure to pesticides and endometrial cancer. Current evidence suggests that multiple mechanisms are involved in toxicity of pesticides [17, 28]. Pesticides can cause cellular and molecular alterations, such as oxidative stress, interference with methyltransferase activity and genotoxic effects, which may increase the risk of cancer [16, 17, 34]. Pesticides such as DDT (insecticide), glyphosate (herbicide) and mancozeb (fungicide) have recently been shown to have in vitro effects on endometrial cells [35,36,37]. Despite these disclosing insights on the potential carcinogenicity on endometrial tissue, the effects of environmental pesticide on human health have yet to be well defined. Additionally, some pesticides are considered endocrine disruptors due to their ability to interact with estrogenic and androgenic pathways, inhibit or induct aromatase activity, and disrupt the hypothalamic control of hormone levels, among other mechanisms [18]. The authors from a recent review suggested that there may be a potential association between exposure to endocrine disrupting chemicals and endometrial cancer, but the specific molecular pathways are yet unclear [19]. Factors such as frequency of exposure, specific types of pesticides, their metabolites and persistence in the organism could play a role in the potential carcinogenic effect of pesticides [19, 27, 38].

Previous results

Assessing the association between pesticide exposure and long-latency diseases is still a challenge due to the complexity of the pathways involved and limitations in exposure assessments [37, 38]. Only three studies have evaluated pesticides and endometrial cancer with heterogenous methodologies [21,22,23]. In particular, pesticides levels were evaluated in serum [21, 22] and adipose tissue [23] and yielded negative associations. However, two of them had limited sample sizes (below 100 cases and/or below 40 controls) [22, 23]. In addition, they evaluated compounds with long half-lives, such as DDT. However, little is known regarding the rest of pesticides with shorter half-lives, such as glyphosate [24], and evaluating its levels in biosamples may reflect recent exposures rather than long-term exposure to these compounds [25]. In this regard, occupational exposure assessments using JEMs have been proposed in evaluating lifelong exposure in population-based studies of diseases with long-latency periods, such as cancer [39].

Pesticide exposure in the agricultural activities scenario was positively associated with endometrial cancer in this study. In the last decade, many studies have assessed risk of cancers other than endometrial and exposure to pesticides among these workers [27, 28]. There is increasing evidence that occupational pesticide exposure influences the risk of certain cancers in agricultural workers [27], although with inconsistencies as some studies suffer from confounding [28, 40, 41]. Participants in our study who had exposure to pesticides farther in the past showed associations with endometrial cancer. The changing regulations of these compounds and the increasing use of personal protective equipment over time may explain these associations. Nowadays, many farm workers and employers overlook the importance of personal protective equipment and adequate pesticide handling training [42]. Thus, future investigations should consider the possible uneven use of personal protective equipment to accurately estimate pesticide exposure in both agricultural and other occupational settings.

Strengths and limitations

To the best of our knowledge, our study is the first to assess the potential association between occupational exposure to pesticides and endometrial cancer. We counted with detailed data that allowed us to potentially control for confounding for several factors. We did not observe clear evidence of confounding, although residual confounding cannot be completely discarded in explaining some of our results. In addition, those working in agriculture scenario might be exposed to other common exposures beyond pesticides, including infectious agents, that could potentially act as confounding factors in the observed association. We used a JEM to assess pesticides exposure, which may overcome previous exposure assessment limitations, and JEMs perform better than self-reported occupational exposures [43]. They represent an efficient method to estimate a wide range of exposures, although they can lead to substantial exposure misclassification [44]. Dosemeci et al., showed that several strategies improved JEM exposure assessment, which included considering the industry sectors of economic activities involved in specific occupations, accounting for differences in exposures over time considering periods of predominant use; and including both intensity and frequency of exposures in the assessments. Our JEM considers intensity and frequency of exposures, but it does not consider the industry sectors of economic activities, nor does it account for differences in exposures over time. In addition, a different use of personal protective equipment may occur within a same occupation, which can also contribute to exposure misclassification [45]. However, as exposure assessment was blind to the case-control status, misclassification would result in the attenuation for exposure estimates [46], which would reinforce our conclusions. The JEM exposure scores reflect the likelihood that a person’s exposure from their occupation is a significant contributor to their overall exposure compared to other sources. In this regard, we could not assess non-occupational potential sources of exposure to pesticides, including dietary factors, residential pesticide use, personal care or household cleaning products. Combining both direct and indirect exposure assessment methods will facilitate a thorough evaluation of occupational exposures and reduce the likelihood of exposure misclassification. Additionally, controls with benign gynaecological conditions were also included, which could lead to selection bias. Controls with benign gynaecological conditions may have a different distribution of risk factors compared to the general population, particularly with regards to hormonal exposures. However, the estimates were also almost two-fold excluding these controls, suggesting that this bias may not drive the associations. The lower response rate of controls compared with cases might have introduced selection bias. Controls of lower socioeconomic level may be less likely to participate, and socioeconomic level may be associated to exposure to pesticides. However, we did not observe significant differences in education level between cases and controls. This is the largest study on pesticides and endometrial cancer. However, our results should be cautiously interpreted given that sample size was limited for subgroup analyses, and the scenario that showed a greater risk (agriculture) was small. Additional evaluations are needed to confirm this association and to assess the impact of the various compounds and exposure scenarios.

Conclusions

Assessment of occupational exposure to pesticides assessed using a Spanish JEM revealed a positive association with endometrial cancer. Additional large population-based studies and detailed exposure assessments are needed to confirm our results. The elucidation of the role of pesticide compounds on endometrial cancer should shed a light on the aetiology of this tumour and help the implementation of appropriate public health policies to mitigate its expected increasing burden.

Data Availability

Data are available from the corresponding author at lcostas@iconcologia.net on reasonable request.

References

  1. Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399(10333):1412–28. https://doi.org/10.1016/S0140-6736(22)00323-3

    Article  Google Scholar 

  2. Reeves GK, Pirie K, Beral V, et al. Cancer incidence and mortality in relation to body mass index in the million women study: cohort study. BMJ. 2007;335(7630):1134. https://doi.org/10.1136/bmj.39367.495995.AE

    Article  Google Scholar 

  3. Aune D, Navarro Rosenblatt DA, Chan DS, et al. Anthropometric factors and endometrial cancer risk: a systematic review and dose-response meta-analysis of prospective studies. Ann Oncol. 2015;26(8):1635–48. https://doi.org/10.1093/annonc/mdv142

  4. Lynch HT, Snyder CL, Shaw TG, Heinen CD, Hitchins MP. Milestones of Lynch syndrome: 1895–2015. Nat Rev Cancer. 2015;15(3):181–94. https://doi.org/10.1038/nrc3878

  5. Ryan NAJ, Glaire MA, Blake D, Cabrera-Dandy M, Evans DG, Crosbie EJ. The proportion of endometrial cancers associated with Lynch syndrome: a systematic review of the literature and meta-analysis. Genet Med. 2019;21(10):2167–80. https://doi.org/10.1038/s41436-019-0536-8

    Article  CAS  Google Scholar 

  6. Pearson-Stuttard J, Papadimitriou N, Markozannes G, et al. Type 2 diabetes and cancer: an umbrella review of observational and mendelian randomization studies. Cancer Epidemiol Biomarkers Prev. 2021;30(6):1218–28. https://doi.org/10.1158/1055-9965.EPI-20-1245

    Article  CAS  Google Scholar 

  7. Wu QJ, Li YY, Tu C, et al. Parity and endometrial cancer risk: a meta-analysis of epidemiological studies. Sci Rep. 2015;5:14243. https://doi.org/10.1038/srep14243

    Article  CAS  Google Scholar 

  8. Raglan O, Kalliala I, Markozannes G, et al. Risk factors for endometrial cancer: an umbrella review of the literature. Int J Cancer. 2019;145(7):1719–30. https://doi.org/10.1002/ijc.31961

    Article  CAS  Google Scholar 

  9. Sjögren LL, Mørch LS, Løkkegaard E. Hormone replacement therapy and the risk of endometrial cancer: a systematic review. Maturitas. 2016;91:25–35. https://doi.org/10.1016/j.maturitas.2016.05.013

    Article  CAS  Google Scholar 

  10. Setiawan VW, Pike MC, Karageorgi S, et al. Age at last birth in relation to risk of endometrial cancer: pooled analysis in the epidemiology of endometrial cancer consortium. Am J Epidemiol. 2012;176(4):269–78. https://doi.org/10.1093/aje/kws129

    Article  Google Scholar 

  11. Gong TT, Wang YL, Ma XX. Age at menarche and endometrial cancer risk: a dose-response meta-analysis of prospective studies. Sci Rep. 2015;5:14051. https://doi.org/10.1038/srep14051

    Article  Google Scholar 

  12. Mueck AO, Seeger H, Rabe T. Hormonal contraception and risk of endometrial cancer: a systematic review. Endocr Relat Cancer. 2010;17(4):R263–71. Published 2010 Sep 23. https://doi.org/10.1677/ERC-10-0076

    Article  CAS  Google Scholar 

  13. Kim KH, Kabir E, Jahan SA. Exposure to pesticides and the associated human health effects. Sci Total Environ. 2017;575:525–35. https://doi.org/10.1016/j.scitotenv.2016.09.009

    Article  CAS  Google Scholar 

  14. Cano-Manuel JRM. Evolution of the consumption of phytosanitary products in Spain. Bol Serv Plagas. 1979;5:203–13.

    Google Scholar 

  15. Chemicals and Pesticides - Fact Sheets on the European Union. European Union. European Parliament. 2022. Available online: https://www.europarl.europa.eu/factsheets/en/sheet/78/chemicals-and-pesticides.

  16. Guyton KZ, Loomis D, Grosse Y, et al. Carcinogenicity of tetrachlorvinphos, parathion, malathion, diazinon, and glyphosate. Lancet Oncol. 2015;16(5):490–1. https://doi.org/10.1016/S1470-2045(15)70134-8

  17. Mostafalou S, Abdollahi M. Pesticides and human chronic diseases: evidences, mechanisms, and perspectives. Toxicol Appl Pharmacol. 2013;268(2):157–77. https://doi.org/10.1016/j.taap.2013.01.025

    Article  CAS  Google Scholar 

  18. Mnif W, Hassine AI, Bouaziz A, Bartegi A, Thomas O, Roig B. Effect of endocrine disruptor pesticides: a review. Int J Environ Res Public Health. 2011;8(6):2265–303. https://doi.org/10.3390/ijerph8062265

    Article  CAS  Google Scholar 

  19. Mallozzi M, Leone C, Manurita F, Bellati F, Caserta D. Endocrine disrupting chemicals and endometrial cancer: an overview of recent laboratory evidence and epidemiological studies. Int J Environ Res Public Health. 2017;14(3):334. https://doi.org/10.3390/ijerph14030334

  20. Tudi M, Daniel Ruan H, Wang L, et al. Agriculture development, pesticide application and its impact on the environment. Int J Environ Res Public Health. 2021;18(3):1112. https://doi.org/10.3390/ijerph18031112

  21. Weiderpass E, Adami HO, Baron JA, et al. Organochlorines and endometrial cancer risk. Cancer Epidemiol Biomarkers Prev. 2000;9(5):487–93.

    CAS  Google Scholar 

  22. Sturgeon SR, Brock JW, Potischman N, et al. Serum concentrations of organochlorine compounds and endometrial cancer risk (United States). Cancer Causes Control. 1998;9(4):417–24. https://doi.org/10.1023/a:1008823802393

    Article  CAS  Google Scholar 

  23. Hardell L, van Bavel B, Lindström G, et al. Adipose tissue concentrations of p,p'-DDE and the risk for endometrial cancer. Gynecol Oncol. 2004;95(3):706–11. https://doi.org/10.1016/j.ygyno.2004.08.022

    Article  CAS  Google Scholar 

  24. WHO/FAO Data Sheet on Pesticides. No. 91, Glyphosate. World Health Organization. 1996. Available online: https://iris.who.int/handle/10665/63290.

  25. LaKind JS, Barraj L, Tran N, Aylward LL. Environmental chemicals in people: challenges in interpreting biomonitoring information. J Environ Health. 2008;70(9):61–4.

    CAS  Google Scholar 

  26. Martín-Bustamante M, Oliete-Canela A, Diéguez-Rodríguez M, et al. Job-exposure matrix for the assessment of alkylphenolic compounds. Occup Environ Med. 2017;74(1):52–8. https://doi.org/10.1136/oemed-2016-103614

  27. Pedroso TMA, Benvindo-Souza M, de Araújo Nascimento F, Woch J, Dos Reis FG, de Melo E Silva D. Cancer and occupational exposure to pesticides: a bibliometric study of the past 10 years. Environ Sci Pollut Res Int. 2022;29(12):17464–75. https://doi.org/10.1007/s11356-021-17031-2

  28. Burns CJ, Juberg DR. Cancer and occupational exposure to pesticides: an umbrella review. Int Arch Occup Environ Health. 2021;94(5):945–57. https://doi.org/10.1007/s00420-020-01638-y

    Article  Google Scholar 

  29. Peremiquel-Trillas P, Paytubi S, Pelegrina B, et al. An integrated approach for the early detection of endometrial and ovarian cancers (screenwide study): rationale, study design and pilot study. J Pers Med. 2022;12(7):1074. https://doi.org/10.3390/jpm12071074

  30. García AM, González-Galarzo MC, Kauppinen T, Delclos GL, Benavides FG. A job-exposure matrix for research and surveillance of occupational health and safety in Spanish workers: MatEmESp. Am J Ind Med. 2013;56(10):1226–38. https://doi.org/10.1002/ajim.22213

    Article  Google Scholar 

  31. Kauppinen T, Toikkanen J, Pukkala E. From cross-tabulations to multipurpose exposure information systems: a new job-exposure matrix. Am J Ind Med. 1998;33(4):409–17. https://doi.org/10.1002/(sici)1097-0274(199804)33:43.0.co;2-2

    Article  CAS  Google Scholar 

  32. Vila J, van der Haar R, García AM. Evaluación de la exposición laboral a plaguicidas en España mediante una matriz empleo-exposición (MatEmESp, 1996–2005). Med Segur Trab. 2014;60(237):645–59. https://doi.org/10.4321/S0465-546X2014000400005

  33. Xiao W, Huang J, Wang J, Chen Y, Hu N, Cao S. Occupational exposure to organic solvents and breast cancer risk: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2022;29(2):1605–18. https://doi.org/10.1007/s11356-021-17100-6

    Article  CAS  Google Scholar 

  34. Morice P, Leary A, Creutzberg C, Abu-Rustum N, Darai E. Endometrial cancer. Lancet. 2016;387(10023):1094–108. https://doi.org/10.1016/S0140-6736(15)00130-0

    Article  Google Scholar 

  35. Wrobel M, Mlynarczuk J, Kotwica J. The adverse effect of dichlorodiphenyltrichloroethane (DDT) and its metabolite (DDE) on the secretion of prostaglandins and oxytocin in bovine cultured ovarian and endometrial cells. Reprod Toxicol. 2009;27(1):72–8. https://doi.org/10.1016/j.reprotox.2008.10.008

  36. Wang Z, Kottawatta KSA, Kodithuwakku SP, et al. The fungicide Mancozeb reduces spheroid attachment onto endometrial epithelial cells through downregulation of estrogen receptor β and integrin β3 in Ishikawa cells. Ecotoxicol Environ Saf. 2021;208:111606. https://doi.org/10.1016/j.ecoenv.2020.111606

    Article  CAS  Google Scholar 

  37. Gastiazoro MP, Durando M, Milesi MM, et al. Glyphosate induces epithelial mesenchymal transition-related changes in human endometrial Ishikawa cells via estrogen receptor pathway. Mol Cell Endocrinol. 2020;510:110841. https://doi.org/10.1016/j.mce.2020.110841

    Article  CAS  Google Scholar 

  38. Caserta D, De Marco MP, Besharat AR, Costanzi F. Endocrine disruptors and endometrial cancer: molecular mechanisms of action and clinical implications, a systematic review. Int J Mol Sci. 2022;23(6):2956. https://doi.org/10.3390/ijms23062956

  39. Dopart PJ, Friesen MC. New opportunities in exposure assessment of occupational epidemiology: use of measurements to aid exposure reconstruction in population-based studies. Curr Environ Health Rep. 2017;4(3):355–63. https://doi.org/10.1007/s40572-017-0153-0

    Article  Google Scholar 

  40. Gunier RB, Kang A, Hammond SK, et al. A task-based assessment of parental occupational exposure to pesticides and childhood acute lymphoblastic leukemia. Environ Res. 2017;156:57–62. https://doi.org/10.1016/j.envres.2017.03.001

    Article  CAS  Google Scholar 

  41. Salerno C, Carcagnì A, Sacco S, et al. An Italian population-based case-control study on the association between farming and cancer: are pesticides a plausible risk factor? Arch Environ Occup Health. 2016;71(3):147–56. https://doi.org/10.1080/19338244.2015.1027808

    Article  CAS  Google Scholar 

  42. Sookhtanlou M, Allahyari MS. Farmers’ health risk and the use of personal protective equipment (PPE) during pesticide application. Environ Sci Pollut Res Int. 2021;28(22):28168–78. https://doi.org/10.1007/s11356-021-12502-y

  43. Le Moual N, Bakke P, Orlowski E, et al. Performance of population specific job exposure matrices (JEMs): European collaborative analyses on occupational risk factors for chronic obstructive pulmonary disease with job exposure matrices (ECOJEM). Occup Environ Med. 2000;57(2):126–32. https://doi.org/10.1136/oem.57.2.126

    Article  Google Scholar 

  44. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Vol. 3. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2008.

    Google Scholar 

  45. Dosemeci M, Cocco P, Gómez M, Stewart PA, Heineman EF. Effects of three features of a job-exposure matrix on risk estimates. Epidemiology. 1994;5(1):124–27. https://doi.org/10.1097/00001648-199401000-00019

    Article  CAS  Google Scholar 

  46. Pearce N, Checkoway H, Kriebel D. Bias in occupational epidemiology studies. Occup Environ Med. 2007;64(8):562–68. https://doi.org/10.1136/oem.2006.026690

    Article  Google Scholar 

Download references

Funding

We thank the CERCA Programme/Generalitat de Catalunya for institutional support. This study was funded by competitive grants from Instituto de Salud Carlos III through the projects PIE16/00049, PI19/01835, PI23/00790, and FI20/00031, CIBERESP CB06/02/0073 and CIBERONC CB16/12/00231, CB16/12/00234 (Co-funded by European Regional Development Fund. ERDF: A way to build Europe). Samples and data were provided by Biobank HUB-ICO-IDIBELL, integrated into the Spanish Biobank Network, and funded by the Instituto de Salud Carlos III (PT20/00171) and by Xarxa de Bancs de Tumors de Catalunya (XBTC) sponsored by Pla Director d’Oncologia de Catalunya. This work was supported in part by the AECC, Grupos estables (GCTRA18014MATI). It also counts with the support of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112.

Author information

Authors and Affiliations

  1. Unit of Molecular Epidemiology and Genetics in Infections and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology. IDIBELL, Av Gran Vía 199-203, L’Hospitalet de Llobregat, Barcelona, 08908, Spain

    Arnau Peñalver-Piñol, Yolanda Benavente, Jon Frias-Gomez, Paula Peremiquel-Trillas, Marta López-Querol, Sonia Paytubi, Beatriz Pelegrina, Irene Onieva, Francesc Xavier Bosch, Laia Alemany & Laura Costas

  2. Servei de Medicina Preventiva i Epidemiologia, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, Barcelona, 08035, Spain

    Arnau Peñalver-Piñol

  3. Consortium for Biomedical Research in Epidemiology and Public Health - CIBERESP. Carlos III In-stitute of Health, Av. De Monforte de Lemos 5, Madrid, 28029, Spain

    Yolanda Benavente, Jon Frias-Gomez, Juan Alguacil, Paula Peremiquel-Trillas, Sonia Paytubi, Beatriz Pelegrina, Francesc Xavier Bosch, Laia Alemany & Laura Costas

  4. Faculty of Medicine, University of Barcelona, Barcelona, Spain

    Jon Frias-Gomez & Paula Peremiquel-Trillas

  5. Dept. of Sociology, Social Work and Public Health, Research Group “Preventive Medicine and Public Health”, Center for Research in Health and Environment (CYSMA), Huelva, Spain

    Juan Alguacil & Manuel Contreras-Llanes

  6. University of Cantabria - IDIVAL, Santander, Spain

    Miguel Santibañez

  7. Dept. of Integrated Sciences, Center for Research in Natural Resources, Health and Environment (RENSMA), Faculty of Experimental Sciences, Research Group “Radiation Physics and Environment” (FRYMA), Campus El Carmen s/n, Huelva, 21007, Spain

    Manuel Contreras-Llanes

  8. Department of Gynecology, Hospital Universitari de Bellvitge, IDIBELL. Hospitalet de Llobregat, Barcelona, Spain

    José Manuel Martínez, Sergi Fernandez-Gonzalez & Jordi Ponce

  9. Department of Anesthesiology, Hospital Universitari de Bellvitge, IDIBELL. Hospitalet de Llobregat, Barcelona, Spain

    Javier de Francisco & Víctor Caño

  10. Consortium for Biomedical Research in Cancer – CIBERONC. Carlos III Institute of Health, Av. De Monforte de Lemos 5, Madrid, 28029, Spain

    Joan Brunet, Marta Pineda & Xavier Matias-Guiu

  11. Hereditary Cancer Program, IDIBELL, ONCOBELL Program, Catalan Institute of Oncology, L’Hospitalet, Barcelona, Spain

    Joan Brunet & Marta Pineda

  12. Medical Oncology Department, Catalan Institute of Oncology, Doctor Josep Trueta Girona University Hospital, Girona, Spain

    Joan Brunet

  13. Hereditary Cancer Program, Catalan Institute of Oncology, IDIBGI, Girona, Spain

    Joan Brunet

  14. Department of Pathology, Hospital Universitari de Bellvitge, IDIBELL. Hospitalet de Llobregat, Bar-celona, Spain

    Xavier Matias-Guiu

  15. Universitat Oberta de Catalunya, Barcelona, Spain

    Francesc Xavier Bosch

  16. ISGlobal, Barcelona, Spain

    Silvia de Sanjosé

Authors
  1. Arnau Peñalver-Piñol
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yolanda Benavente
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jon Frias-Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Alguacil
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miguel Santibañez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manuel Contreras-Llanes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paula Peremiquel-Trillas
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marta López-Querol
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sonia Paytubi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Beatriz Pelegrina
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Irene Onieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. José Manuel Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sergi Fernandez-Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Javier de Francisco
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Víctor Caño
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Joan Brunet
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Marta Pineda
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jordi Ponce
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xavier Matias-Guiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Francesc Xavier Bosch
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Silvia de Sanjosé
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Laia Alemany
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Laura Costas
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Laura Costas; Formal analysis, Arnau Peñalver-Piñol, Yolanda Benavente and Jon Frias-Gomez; Funding acquisition, Silvia De San-jose and Laura Costas; Investigation, Juan Alguacil, Miguel Santibañez, Manuel Contreras Llanes, Sonia Paytubi, José Manuel Martínez, Javier De Francisco, Victor Caño, Jordi Ponce, Xavier Matias-Guiu and Laia Alemany; Methodology, Yolanda Benavente, Jon Frias-Gomez, Marta López-Querol, Sonia Paytubi, Javier De Francisco, Jordi Ponce, Xavier Matias-Guiu, Francesc Xavier Bosch, Silvia De Sanjose, Laia Alemany and Laura Costas; Project administration, Laura Costas; Resources, Juan Alguacil, Miguel Santibañez, Manuel Contreras Llanes, Paula Peremiquel-Trillas, Marta López-Querol, José Manuel Martínez, Sergi Fernandez-Gonzalez, Javier De Francisco, Victor Caño, Jordi Ponce and Xavier Matias-Guiu; Supervision, Francesc Xavier Bosch, Silvia De Sanjose and Laura Costas; Visualization, Beatriz Pelegrina and Irene Onieva; Writing – original draft, Arnau Peñalver-Piñol; Writing – review & editing, Miguel Santibañez, Manuel Contreras Llanes, Sonia Paytubi, Beatriz Pelegrina, Irene Onieva, Sergi Fernandez-Gonzalez, Joan Brunet, Marta Pineda and Laura Costas. All authors accepted the final version of the manuscript.

Corresponding author

Correspondence to Laura Costas.

Ethics declarations

Institutional Review Board Statement

The Screenwide study followed the national and international directives on ethics and data protection (Declaration of Helsinki and subsequent amendments; EU Regla-ment 2016/679) and the Spanish laws on data protection (Organic Law 3/2018; Law 14/2007 biomedical research). Study protocol was approved by the Ethics Committee for Clinical Research from the Bellvitge University Hospital (PR348/19).

Informed consent

Participation in the study was voluntary, and all eligible subjects signed an informed consent form after receiving information about the study, before participating in any intervention.

Conflict of interest

L.C. received supplies from Integrated DNA Technologies (IDT) and Roche Diagnostics at a 50% discount for the pilot study of this project and received a speaker’s honoraria from Roche. Idibell and Roche signed a contract to collaborate in the development of the bioinformatics pipeline. L.C. has received a competitive grant from Novosanis/European Association for Cancer Research (EARC) and received Colli-Pee® devices (Novosanis) for a research project free of charge. JHcispoly and IDIBELL signed a service agreement for another project on endometrial cancer.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12940_2023_1028_MOESM1_ESM.docx

Supplementary Material 1: Table S1: Occupations considered exposed to pesticides in the Screenwide case-control study. Table S2: Pearson’s correlation coefficients between cumulative exposure scores (CES) for pesticides and for each pesticide application group, among exposed to pesticides.; Table S3: Associations between endometrial cancer and pesticide exposure, among the unexposed to solvents. Table S4: Associations between endometrial cancer and pesticide exposure, by type of control.; Table S5: Associations between endometrial cancer and pesticide exposure, by BMI; Figure S1: Flow chart; Figure S2: Directed acyclic graph (DAG).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peñalver-Piñol, A., Benavente, Y., Frias-Gomez, J. et al. Occupational exposure to pesticides and endometrial cancer in the Screenwide case-control study. Environ Health 22, 77 (2023). https://doi.org/10.1186/s12940-023-01028-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12940-023-01028-0

Keywords

Environmental Health

ISSN: 1476-069X