Association between diabetes and pesticides: a case-control study among Thai farmers

Diabetes is an important public health problem with 415 million adults (aged 20–79 years) worldwide suffering the disease in 2015. By 2040, the figure will rise to 642 million [1]. In Thailand, diabetes among adult aged ≥ 18 years drastically increased from 6.9% in 2009 to 8.9% (4.8 million people) in 2014 [2]. The confirmed causal factors were gender, genetic, and overweight [3]. In addition, the disease was also strongly associated with many lifestyle behaviors, including lack of active exercise [4], cigarette smoking [5] and alcohol consumption [3], sleeping patterns [6, 7], and pregnancy status [8]. Recent research has also suggested environmental contamination by agricultural chemicals to be a causal factor [9]. The suspected compounds are arsenic, mercury, phthalates, bisphenol A (BPA), and persistent organic pollutants (POPs), especially PCBs, dioxin, and pesticides [10‐12].

Pesticides have received greater attention as they are the most commonly compounds in use today, with more than 2.3 billion kilograms used worldwide in 2001 [13]. In an Agricultural Health Study (AHS) project in the USA, a large cohort study of 33,457 pesticide applicators reported an association between diabetes and seven organochlorine pesticides, including aldrin, chlordane, heptachlor, dichlorvos, trichlorfon, alachlor, and cyanazine [14]. Further study of this program using data of the wives of 13,637 pesticide applicators identified more diabetic risk compounds, including three organophosphates, including fonofos (OR = 1.56; 95%CI 1.11–2.19), phorate (OR = 1.57; 95%CI 1.14–2.16), and parathion (OR = 1.61; 95%CI 1.05–2.46); one organochlorine, dieldrin (OR = 1.99; 95%CI 1.12–3.54); and one herbicide, 2,4,5-T/2,4,5-TP (OR = 1.59; 95%CI 1.00–2.51) [15]. Study among women who were involved in mixing or applying pesticides, or fixing pesticide appliances, also found diabetes and pesticide exposure with odds ratios of 2.2 (95%CI 1.5–3.3) [16]. From these studies, it is evident that gender plays a significant role, as well as the several other factors which influence diabetic occurrence, including race, ethnicity and economic status, education level, and nutritional conditions [17]. A study among Mexican-American farmers found an association with several organochlorine pesticides, including trans-nonachlor, oxychlordane, β-hexachlorocyclohexane, p,p′-DDT, and p,p′-DDE [18]. Another study using data from Native Americans found PCBs, dichlorodiphenyldichloroethylene (DDE), and hexachlorobenzene (HCB) to be associated with diabetes [19].

With currently limited information, several mechanisms have been suggested to explain the linkage between environmental compounds and diabetes. In animal and cell studies, it was found that organochlorine pesticide and some other POPs reduce cell metabolic functions and cause obesity [20]. There has been evidence that pollutant compounds affect insulin secretion, insulin action, and glucose homeostasis [21‐23], and elsewhere, physiological and oxidative stress, and cholinesterase inhibition [24]. Recently, Park et al. [25] proposed a molecular scheme to explain how these chemicals caused metabolic disorders by damaging the mitochondria, which are organelles that act as powerhouses of the cells.

Information on diabetes and pesticide linkage is relatively new, and a significant data gap still remains [12]. This purpose of this case-control study was to identify more specific compounds which are, or may be, associated with diabetes, using data on Thai rural residents. With the differences in the health backgrounds, life styles, and agricultural practices and behaviors of this particular group, the results can be compared to those previously reported in the literature which has mostly been conducted in the Western world.

This study was a population-based case-control study. It was conducted from February to May 2016 in Bang Rakam district, located in Phitsanulok Province, 377 km north of Bangkok, Thailand. The district has a population of 94,980, most of whom are rice farmers [26].

Both case and control groups had a similar demographic data with a comparable proportion of men, 35% for control and 33% for case (P = 0.36) (Table 1). Comparing case to control, the mean age (57.68 ± 9.00 vs. 57.64 ± 9.17) and age in each categories did not differ significantly (P = 0.99). However, case had a higher average BMI (25.67 ± 6.99) than control (24.06 ± 6.04). Most of the study subjects are farmer, 78.9% for case and 75.3% for control. An average number of alcohol consumption of case was 7.16 ± 2.34 glass per year, significantly higher than that of control (6.85 ± 2.60) (P < 0.01). However, case smoked cigarette in less amount (1.41 ± 5.80 vs 2.56 ± 9.36 pack/year, case to control) (P < 0.01). The two groups of subjects had no difference in lifestyle (morningness-eveningness), ever exercise during the past 12 months, and frequency of TV watching during leisure time; and an amount of vegetable, fruit, and sweet intake.

Association between different types of pesticide and diabetes were presented in Table 2. It was found that ever use of any pesticides cannot predict risk of diabetes. Experience using any types of pesticides was positively associated with diabetes but the association were statistically significant only for rodenticide (adjusted OR = 1.35; 95%CI 1.04–1.76). For insecticides, year of exposure to insecticides and cumulative days failed to predict risk of diabetes. For fungicides, diabetic risk seems to correlate with both year of exposure and exposure days, but none with statistical significance. For herbicides and molluscicides, both year of exposure and exposure days were inversely associated with the disease. For rodenticides, only exposure days was positively correlated with diabetes.

Table 3 presents the ORs of each individual pesticide for diabetes. Among 20 compound investigated, 15 were positively associated with diabetes, 3 with statistical significance [endosulfan (OR = 1.40; 95%CI 1.01–1.95), mevinphos (OR = 2.22; 95%CI 1.17–4.19), and carbaryl/Sevin (OR = 1.50; 95%CI 1.02–2.19)]. For herbicides, of the 6 compounds investigated, 4 showed increased ORs. Study also found 7 out of 9 fungicides to be correlated with diabetes, 1 with statistical significant [benlate (OR = 2.08; 95%CI 1.03–4.20).

In this study, the OR of diabetes was statistically associated with three types of insecticides investigated, including one organochlorine (endosulfan), one organophosphate (mevinphos), and one carbamate (carbaryl/Sevin), as well as one fungicide (benlate). The results were well supported by previous findings either from epidemiological studies or an animal study. A recent study also suggested that pesticides could affect the pancreases and interfere with insulin secretion [27] by damaging the mitochondria cells [27].

Of the six organochlorine pesticides investigated, three [chlordane (OR = 1.35; 95%CI 0.56–3.24), endosulfan (OR = 1.40; 95%CI 1.01–1.95), and heptachlor (OR = 1.13; 95%CI 0.46–2.80)] were positively associated with diabetes. This finding was consistent with a study which used US National Health and Examination Survey (NHANES) data that reported a strong association between diabetes and PCB, two dioxin congeners, and three organochlorine pesticides (oxychlordane, DDE, and trans-nonachlor) [10]. A study in the USA under the Agricultural Health Study program also found diabetes to be correlated with seven organochlorine pesticides (aldrin, chlordane, heptachlor, dichlorvos, trichlorfon, alachlor, and cyanazine) [14]. Compared with these previous results, ORs of chlordane in our study were higher (1.35 vs 1.16 in the previous study) but lower for heptachlor (1.13 vs 1.20). Our study is the first to show that endosulfan is strongly associated with diabetes. However, although endosulfan has long been banned in the Western world a long time ago, being one of the most toxic POPs, it was still widely used in Thailand until quite recently. In an animal study, exposure to endosulfan was shown to affect the endocrine cells of the pancreas of a rabbit and also that it decreased insulin secretion [27].

For organophosphates, in our study, specific compounds found to be positively associated with diabetes were chlorpyrifos (OR = 1.20; 95%CI 0.83–1.75), dicrotophos (OR = 2.12; 95%CI 0.97–4.61), dichlorvos (OR = 1.03; 95%CI 0.41–2.62), EPN (ethyl-p-nitrophenyl) (OR = 1.29; 95%CI 0.83–2.02), mevinphos (OR = 2.22; 95%CI 1.17–4.19), monocrotophos (OR = 1.29; 95%CI 0.72–2.32), and methamidophos (OR = 1.04; 95%CI 0.65–1.65). This is consistent with a study in the USA by Montgomery et al. [14], which reported an increased ORs of diabetes among subject exposure to chlorpyrifos (OR = 1.03; 95%CI 0.91–1.17), coumaphos (OR = 1.26; 95%CI 1.03–1.55), dichlorvos (OR = 1.21; 95%CI 0.98–1.49), phorate (OR = 1.22; 95%CI 1.06–1.42), terbufos (OR = 1.17; 95%CI 1.02–1.35), and trichlorfon (OR = 1.85; 95%CI 1.03–3.33). The ORs of chlorpyrifos and dichlorvos in our study were higher than those in the previous study. This issue will be discussed later. In an animal study, it was found that organophosphate inhibits acetylcholinesterase and causes an accumulation of acetylcholine and reduced insulin production [22, 28].

For carbamates, our results are in contrast with that reported by Montgomery et al. [14] in the USA. Of the four carbamate insecticides investigated (carbofuran/Furadan, carbosulfan, methomyl, and carbaryl/Sevin), we found that all were positively associated with diabetes. For carbaryl, the ORs were statistically significant (OR = 1.50; 95%CI 1.02–2.19). The OR in our study was higher than the results reported by Montgomery et al. [14] (OR = 1.10; 95%CI 0.95–1.28). In another animal study, carbaryl affected glucose homeostasis and insulin secretion [29].

We also found permethrin/Ambush, a pyrethoid insecticide, to be associated with diabetes (OR = 1.39; 95%CI 0.64–3.04). This finding has never been reported in an epidemiological study before. In molecular study, carbamate and pyrethoid compounds demonstrated a potential effect on circadian disruption which is linked to an increased diabetic risk [30].

For herbicides, of the 6 compounds investigated, four of them (butachlor, glyphosate, paraquat/gramoxone, and 2,4-D) could predict risk of diabetes but not statistically significant. A similar result was also reported in the Agricultural Health Study [14]. Evidence of the diabetic effect of herbicides was first recognized among Vietnam veterans who had used Agent Orange, which is an herbicide containing dioxins [31]. Dioxins were strongly associated with diabetes in [10]. A study in Australia compared the mortality of 1999 outdoor staff who had used insecticides in the period 1935–1996 to those of the general population [32] and that study reported an association between mortality as a result of diabetes and herbicide use, at the standard mortality rate (SMR) of 3.57 (95%CI 1.16–8.32). This is a high level of association.

Of the nine fungicides investigated, eight (benlate, carbendazim, copper sulfate, mancozeb, maneb, metalaxyl, thiophanate-methyl, and zineb) were positively associated with diabetes. For benlate, the association is statistically significant with OR of 2.08 (95%CI 1.03–4.20). Although associations between diabetes and fungicides have never been reported in any epidemiological study prior to this, a recent tissue-culture study reported that fungicide exposure can cause insulin resistance in fat cells and causes higher blood glucose [33].

Compared with the study in the USA [14], most of ORs found in our study were higher. The possible explanation for this is that Thai people are more likely to have poorer health backgrounds, life styles, and risky agricultural practices and behaviors. Data on PPE use and pesticide use behaviors that were also collected in this study showed that the rate of PPE utilized was lower than 30% and 41% of users did not change their clothes immediately after spraying the pesticides. A further localizing factor is that Thai people, particularly rural Thais, tend to eat less protein in their diet with greater amounts of vegetables and have poorer educational and socioeconomic status, all of which are confirmed diabetic risk factors [17].

The major sources of error in this type of study were recall and selection bias. Recall bias usually occurs when using past exposure data which may be biased due to differences in recall ability between case and control [34]. However, as information on pesticide and diabetic linkage was not well known in Thailand, the recall problem, if it did occur, would affect both case and control groups equally. As for selection bias, it often occurs when exposure or disease information are not accurate [35]. In our situation, the error for case selection was expected to be minimal since each case was a medically diagnosed diabetes patient. However, by using neighboring controls, it was very likely that the control subject was also exposed to some type of pesticides. This phenomenon would have the effect of lowering the degree of association between diabetes and any of the pesticides.

Data on exercise and the eating of various types of food, including vegetables, fruit, and sweet were also collected in this study for analysis (Table 1). However, since most of the study subjects were farmers who had similar lifestyles, exercise and eating factors failed to predict diabetic risk and therefore they were not included in the analytical model.

Our results suggest that the occurrence of diabetes among Thai farmer was associated with pesticide exposure. The results supported the existing literature that pesticide exposure increases the risk of diabetes. Of the 35 individual pesticides investigated, those found to show statistically significant increased risk of diabetes were endosulfan, mevinphos, carbaryl/Sevin, and benlate. The ORs in this study were higher than previous studies in Western populations, and the difference is considered to be explainable by subject discrepancy in terms of health background, safety practices, and nutritional status. It must be noted that recall and selection bias tend to obscure the true association between diabetes and pesticides. It is suggested that more studies employing a larger sample size and better exposure tracking are still needed to confirm our results and to identify more individual compounds that may be a causal effect of diabetes. The issue should certainly receive more attention by public institutions, and practical measures to control those harmful pesticides are urgently needed.