{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:51:02Z","timestamp":1787021462626,"version":"build-2736575974"},"update-to":[{"DOI":"10.12688\/f1000research.53261.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000}},{"DOI":"10.12688\/f1000research.53261.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000}}],"reference-count":24,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017170","name":"Thailand Science Research and Innovation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100017170","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns5:p>\n                    <ns5:bold>Background:<\/ns5:bold>\n                    Obesity is\u00a0a\u00a0serious\u00a0condition\u00a0because it is associated with other chronic diseases\u00a0which\u00a0affect the quality of life. In addition to problems associated with diet and exercise, recent research has found\u00a0that\u00a0pesticide exposure might be another important risk factor.\u00a0The objective of this study was to determine the association between pesticide exposure and obesity\u00a0among farmers in Nakhon Sawan and\u00a0Phitsanulok\u00a0province, Thailand.\u00a0\n                  <\/ns5:p>\n                  <ns5:p>\n                    <ns5:bold>Methods:<\/ns5:bold>\n                    This study was a population-based cross-sectional study. Data on pesticide use and obesity prevalence from 20,295 farmers aged 20 years\u00a0and\u00a0older were collected using\u00a0an in-person interview\u00a0questionnaire. The association\u00a0was analysed using multivariable logistic regression, adjusted for its potential confounding factors.\u00a0\n                  <\/ns5:p>\n                  <ns5:p>\n                    <ns5:bold>Results:<\/ns5:bold>\n                    \u00a0Obesity was found to be associated with pesticide use in the past. The risk of obesity was significantly predicted by types of pesticides, including insecticides\u00a0(OR\u00a0=\u00a02.10, 95% CI 1.00-4.38), herbicides (OR\u00a0=\u00a04.56, 95% CI 1.11-18.62), fungicides (OR\u00a0=\u00a02.12, 95% CI 1.34-3.36), rodenticides (OR\u00a0=\u00a02.55, 95% CI 1.61-4.05),\u00a0and molluscicides\u00a0(OR\u00a0=\u00a03.40, 95% CI 2.15-5.40).\u00a0Among 35 surveyed individual pesticides, 22 were significantly associated with higher obesity prevalence (OR = 1.78, 95% CI 1.10-2.88 to OR = 8.30, 95% CI 2.54-27.19),\u00a0including herbicide butachlor, 15 insecticides (two carbamate insecticides,\u00a0five\u00a0organochlorine insecticides, and eight organophosphate insecticides), and\u00a0six\u00a0fungicides.\u00a0\n                  <\/ns5:p>\n                  <ns5:p>\n                    <ns5:bold>Conclusion:<\/ns5:bold>\n                    \u00a0This study found obesity in farmers in Nakhon Sawan and\u00a0Phitsanulok\u00a0province,\u00a0Thailand,\u00a0to be associated with\u00a0the\u00a0long-term\u00a0use of several types of pesticides. The issue should receive more public attention, and pesticide use should be strictly controlled.\n                  <\/ns5:p>","DOI":"10.12688\/f1000research.53261.3","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T05:30:15Z","timestamp":1652765415000},"page":"445","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Association between pesticide exposure and obesity: A cross-sectional study of 20,295 farmers in Thailand"],"prefix":"10.12688","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5783-8766","authenticated-orcid":false,"given":"Kajohnsak","family":"Noppakun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0321-5325","authenticated-orcid":false,"given":"Chudchawal","family":"Juntarawijit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","article-title":"Obesity and Overweight.","journal-title":"WHO.","DOI":"10.1007\/978-1-4419-1695-2_447"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e242","DOI":"10.1007\/BF03327467","article-title":"Prevalence of overweight and obesity in Thai population: Results of the National Thai Food Consumption Survey.","volume":"16","author":"N Jitnarin","year":"2011","journal-title":"Eat Weight Disord."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"21-33","DOI":"10.3810\/pgm.2009.11.2074","article-title":"The medical risks of obesity.","volume":"121","author":"X Pi-Sunyer","year":"2009","journal-title":"Postgrad Med."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6134362","article-title":"Epidemiology, Predisposing Factors, Biomarkers, and Prevention Mechanism of Obesity: A Systematic Review.","volume":"2020","author":"M Endalifer","year":"2020","journal-title":"J Obes."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"108685","DOI":"10.1016\/j.envres.2019.108685","article-title":"Organophosphorus pesticides can influence the development of obesity and type 2 diabetes with concomitant metabolic changes.","volume":"178","author":"M Czajka","year":"2019","journal-title":"Environ Res."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40168-019-0635-4","article-title":"Organophosphorus pesticide chlorpyrifos intake promotes obesity and insulin resistance through impacting gut and gut microbiota.","volume":"7","author":"Y Liang","year":"2019","journal-title":"Microbiome."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e27773","DOI":"10.1371\/journal.pone.0027773","article-title":"Associations among organochlorine pesticides, methanobacteriales, and obesity in Korean women. 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Epidemiol."}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/10-445\/v3\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/10-445\/v3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/10-445\/v3\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T07:00:13Z","timestamp":1653289213000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/10-445\/v3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,17]]},"references-count":24,"URL":"https:\/\/doi.org\/10.12688\/f1000research.53261.3","relation":{"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.134032.r138054","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.134032.r138054","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,17]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/10-445\/v3#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.79778.r122025, Yankai Xia, State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China, 07 Feb 2022, version 2, 1 approved, 1 approved with reservations, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/10-445\/v2#referee-response-122025","order":0,"name":"referee-response-122025","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.56626.r95935, Yuki Ito, Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan, 15 Oct 2021, version 1, 1 approved with reservations, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/10-445\/v1#referee-response-95935","order":1,"name":"referee-response-95935","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Chudchawal Juntarawijit<\/b>; \n<i>Posted: 24 Nov 2021<\/i>; \n<b>Comment<\/b> The authors have investigated the relationships of pesticides used by farmers and obesity following the Thailand Ministry of Public Health definitions. They found a positive association between pesticide use and obesity. However, there are major points to be concerned as mentioned below. Also, the paper may benefit from an English language edit.  \n<b>Response<\/b> Thank you, we really appreciate your time and effort to review the manuscript.  We admitted that this study may be far from perfect. By using a descriptive cross-sectional study, this study has several limitations. As compared to an analytical cross-sectional study, a better study design with a control group is often found in medical studies. With the descriptive design, this study had some limitations and could not be compared with the analytical one. This study lacks data on some confounding variables and relies only on the questionnaire method for pesticide exposure data. However, the results supported the usefulness of ICD-10 data for studying obesity. We hope that the result could be useful as a small piece of information to the relatively rare literature on the association of pesticides and obesity.  The English language was edited by Mr. Kevin.  \n<b>Abstract: Comment<\/b> Background in the Abstract should be specified by demonstrating the relationships between pesticides exposure and obesity.  \n<b>Response<\/b> Currently, there was not enough evidence demonstrating the relationship between pesticides exposure and obesity. With 250 words as the maximum word count limited by F1000Research, the issue, therefore, was only briefly described here.  \n<b>Comment<\/b> The authors should write the objective in the background section, not in the method section.  \n<b>Response<\/b> We agree and the objective has been revised and moved to the background section, as follows:  \n<i>Background: ...The objective of this study was to determine the association between pesticide exposure and obesity&nbsp;among farmers in Nakhon Sawan and&nbsp;Phitsanulok&nbsp;province, Thailand.  Methods: This study was a population-based cross-sectional study.<\/i>  \n<b>Background:<\/b> \n<b>Comment<\/b> The background should address the rationale of the study.  \n<b>Response<\/b> The main reason why this study is needed was that obesity is an important public health problem and exposure to pesticides might be one of its causes. However, currently, there was only limited evidence. This study was among a few studies trying to investigate the association between obesity and pesticide exposure using a large cross-sectional study design.  \n<b>Comment<\/b> In the last line of the first paragraph of the background section, the authors have stated lots of factors associated with obesity. Unfortunately, they cited only one article. More corresponding references should be added in this section. \n<b>&nbsp;<\/b> \n<b>Response<\/b> The reference was a systematic review that mentioned various factors associated with obesity.  \n<b>Comment<\/b> In the second paragraph, the authors described the association of PCBs, bisphenols, and one phthalate with obesity. Instead of these descriptions, the authors should cite other epidemiological studies examining the relationship between pesticide exposure and obesity. \n<b>&nbsp;<\/b> \n<b>Response<\/b> In this section, we try to present information on pesticides and obesity. To our knowledge, all of the epidemiological studies on pesticides and obesity were already included. In the last statement, we just want to mention that not only pesticides but also some other compounds, e.g., PCBs, bisphenols, and one phthalate might also cause obesity.  To make it clear, the statement has been revised.  \n<b>Comment<\/b> Authors stated that \u201cidentifications of individual pesticides to predict the risk of obesity was the main interest\u201d. However, they did not assess the risk of pesticide exposure. They only investigated the observational relationships between the groups of pesticide use and obesity. The last line has no basis to write in this section.  \n<b>Response<\/b> To be more precise, the statement \u201c\n<i>identifications of individual pesticides to predict the risk of obesity was the main interest<\/i>\u201d has been revised as follows:  \n<i>&quot;The main interest was to associate obesity to pesticides, either by group or individual chemical.\u201d<\/i>  \n<b>Methods: Comment<\/b> The authors should remove the duplicate statement such as, \u201cthe major crops in the province are rice, sugar cane, cassava and vegetables\u201d from the study settings and design section.  \n<b>Response<\/b> Actually, that statement was not duplicated but they described major crops of the two different provinces, Nakhon Sawan and Phitsanulok.  \n<b>Comment<\/b> Please clarify the last two lines of \u201cstudy participants and sampling procedure\u201d. Define the term exposed and unexposed. \n<b>&nbsp;<\/b> \n<b>Response<\/b> Exposed refers to the exposure group or those who used pesticides. Unexposed refers to the unexposed group or those who do not use pesticides.  This information has been added to the paper.  \n<b>Comment<\/b> A major concern is the definition of obesity. The authors investigated the relationship between pesticide use and obesity following the definition of the Thailand Ministry of Public Health. However, the definition of obesity is not clear. Based on the author\u2019s statement, it seems like the definition of a metabolic syndrome but not obesity (see the article by Engin, 2017\n<u><sup>1<\/sup><\/u>). The authors should clarify it. They can consider BMI, WC, and\/or other anthropometric indices of overweight and obesity if data is available.  \n<b>Response<\/b> We thank you the reviewer for making a very important point and agreeing to revise our manuscript as we picked up the case with obesity using a diagnosis in ICD-10. We have deleted the statement \u201c\n<i>In Thailand, the Ministry of Public Health follows the International Diabetes Federation\u2019s definition (2005), which defines obesity as a waist circumference of no more than 90 cm for men and 80 cm for women, plus two out of the following four criteria:..<\/i>\u201d and replaced with the following, \u201c\n<i>In Thailand, the Ministry of Public Health follows The World Health Organization obesity criteria for Asia Pacific Region in which body mass index over 25.0 kg\/m2 was identified as obesity.&quot;.<\/i>  \n<b>Results: Comment<\/b> Out of 20,295, only 78 study participants were obese. The overall prevalence of obesity was very low, although the authors did not calculate prevalence both in males and females. \n<b>&nbsp;<\/b> \n<b>Response<\/b> Prevalence of obesity was very low since the study use data from ICD-10 records, instead of survey data on BMI or waist circumstance. The issue was already discussed in the manuscript.  \n<b>Comment<\/b> Tables 1 and 2 were not prepared carefully. These two tables should merge and put data in each column and row carefully. \n<b>&nbsp;<\/b> \n<b>Response<\/b> The two tables have been merged. An error in the table head has been corrected.  \n<b>Comment<\/b> If possible, please provide all the demographic data (collected by questionnaires), including socioeconomic status, education, ethnicity, etc.  \n<b>Response<\/b> In this survey, we collected data from respondents who are farmers living in a rural area of six districts but found obesity cases in five districts (Table 1).  For socioeconomic data, only the district location of participants, and their education, and monthly income were collected.&nbsp; However, these factors were not included in the model because of several reasons. For district location, most of the participants were farmers from rural areas, thus there was no reason to believe district location to associate with obesity. Moreover, the district was closely related to pesticide use and will cause multicollinearity problems, if included in the model (Bhandari, 2020). For income, there was no relationship with obesity in middle-income countries, e.g. Thailand (Ameye and Swinnen, 2019). &nbsp;For education, previous research found a higher risk of obesity to be inversely associated with education (Anyanwu, 2010). However, in this study more obesity was found among those with higher education (Table 1). In addition, when these two variables were included in the model, not much effects on OR were found. For example, for molluscicide the OR was changed from 2.28 (1.48-3.64) to 2.37 (1.48-3.79) when added education and to 2.39 (1.49-3.83) when added income (Table 2-1). For baseline diseases, most of them should be considered as the so-called \u2018downstream outcome\u2019 closely related to obesity and thus, should not be included in the model (DVL, 2020). More demographic information has been added to Table 1.  For ethnicity, in Thailand, we did not have ethnic groups.  \n<b>Comment<\/b> For investigating the OR, they have adjusted the results only by gender, age, smoking, and alcohol consumption. They have no data about diet, physical activity, socioeconomic status or income that may affect the results.  \n<b>Response<\/b> Yes, we agree that that information on diet and physical activity would be useful and should be used for the adjustment. It is a study limitation and the issue was already discussed in the study limitation. The problem should not seriously affect study results since all of the participants were farmers from rural areas.  For data on socioeconomic, actually, we also collected data on district location, education, and monthly income.&nbsp; However, these factors were not included in the model because of several reasons. For district location, most of the participants were farmers from rural areas, thus there was no reason to believe district location to associate with obesity. However, the district was closely related to pesticide use and caused a multicollinearity problem, therefore, it was not be included in the model (Bhandari, 2020). For education and income, previous research found a higher risk of obesity to inversely associate with education (Anyanwu, 2010). For income, there was no relationship with obesity in middle-income countries, e.g. Thailand (Ameye and Swinnen, 2019). &nbsp;When the model included these two variables, the OR was not much changed. For example, for Molluscicide the OR was changed from 2.28 (1.48-3.64) to 2.37 (1.48-3.79) when added education and to 2.39 (1.49-3.83) when added income (Table 2-1). For baseline diseases, most of them should be considered as the so called \u2018downstream outcome\u2019 closely related to obesity but not confounding and thus, should not be included in the model as well (DVL, 2020).  \n<b>Discussion: Comment<\/b> This section should be written&nbsp;based on results deeply and sharply. They should explain their findings, e.g.&nbsp;why and how by comparing other studies.  \n<b>Response<\/b> Thank you for the suggestion. We agree and try to comply with your suggestion as much as we can.  \n<b>Comment<\/b> Additionally, this study is only based on questionnaire data and did not monitor the individual exposure levels of pesticides. This is another major limitation of this study.&nbsp;  \n<b>Response<\/b> For a large study of long-term effects of pesticides, a questionnaire might be the only option. Since pesticide dose is a good marker for short-term pesticide exposure. However, it was costly and may not be suitable for a large survey study. The questionnaire method is probably the best alternative to collect data on long-term pesticide exposure. The method is commonly used in epidemiological studies e.g., Agricultural Health Study in the United State. For studies using a large group of participants, the method has been proved to be useful without any serious bias (Coble \n<i>et al<\/i>., 2011) (Hoppin \n<i>et al<\/i>., 2002).  The limitation was further discussed as suggested.  \n<b>Conclusion: Comment<\/b> The conclusion should be short and specific based on the results. \n<b>&nbsp;<\/b> \n<b>Response<\/b> Thank you for the suggestion. We agree and try to comply with your suggestion as much as we can.  \n<b>Comment<\/b> The authors should not use \u201cincreased the risk of obesity\u201d.  \n<b>Response<\/b> The statement has been changed to \u201c\n<i>The study additionally found exposure to 24 individual pesticides was significantly associated with obesity<\/i>.&quot;  \n<u><b>References<\/b><\/u>  Anyanwu, G. E., Ekezie, J., Danborno, B., &amp; Ugochukwu, A. I. (2010). Impact of education on obesity and blood pressure in developing countries: A study on the Ibos of Nigeria.&nbsp;North American Journal of Medical Sciences,&nbsp;2(7), 320\u2013324. https:\/\/doi.org\/10.4297\/najms.2010.2320  Bhandari A. (2020). What is multicollinearity? Here\u2019s everything you need to know. Analytics Vidhya. Available: Multicollinearity | Detecting Multicollinearity with VIF (analyticsvidhya.com)  Coble \n<i>et al<\/i>., (2011). An updated algorithm for estimation of pesticide exposure intensity in the agricultural health study, Int. J. Environ. Res. Public Health; 8(12), 4608\u20134622. &nbsp;doi: 10.3390\/ijerph8124608.  Hannah Ameye and Johan Swinnen, (2019). Obesity, income and gender: The changing global relationship, Global Food Security, 23, 267-281. https:\/\/doi.org\/10.1016\/j.gfs.2019.09.003.  Hoppin, F. Yucel, M. Dosemeci, and D. P. Sandler, (2002). Accuracy of self-reported pesticide use duration information from licensed pesticide applicators in the Agricultural Health Study, J. Expo. Anal. Environ. Epidemiol.; 12(5), 313\u2013318. &nbsp;doi: 10.1038\/sj.jea.7500232.  \n<b>Table 2-1 Odds ratio when more variables were added to the model.<\/b>  Pesticide: Molluscicide OR (in the table 2)&nbsp; 3.36 (2.13-5.31) OR (when add baseline disease)&nbsp;&nbsp; 3.30 (2.08-5.22) OR (add district location)&nbsp;&nbsp; 2.28 (1.43-3.64) OR (add education)&nbsp;&nbsp; 2.37 (1.48-3.79) OR (add income)&nbsp; 2.39 (1.49-3.83)","URL":"https:\/\/f1000research.com\/articles\/10-445\/v3#referee-comment-7492","order":2,"name":"referee-comment-7492","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.134032.r138054, Erik Jors, Clinic of Occupational Medicine, Clinical Institute, University of Southern Denmark, Odense, Denmark, 23 May 2022, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/10-445\/v3#referee-response-138054","order":3,"name":"referee-response-138054","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This project received grant support from the Thailand Science Research and Innovation (SRI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":4,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}