Abstract
Exposure to certain environmental toxicants may be associated with increased risk of developing diabetes. Our aim was to investigate the relationship between lifetime exposure to specific agricultural pesticides and diabetes incidence among pesticide applicators. The study included 33,457 licensed applicators, predominantly non-Hispanic white males, enrolled in the Agricultural Health Study. Incident diabetes was self-reported in a 5-year follow-up interview (1999–2003), giving 1,176 diabetics and 30,611 non-diabetics for analysis. Lifetime exposure to pesticides and covariate information was reported by participants at enrollment (1993–1997). Using logistic regression, we considered two primary measures of pesticide exposure: ever use and cumulative lifetime days of use. We found seven specific pesticides (aldrin, chlordane, heptachlor, dichlorvos, trichlorfon, alachlor, and cyanazine) for which the odds of diabetes incidence increased with both ever use and cumulative days of use. Applicators who had used the organochlorine insecticides aldrin, chlordane, and heptachlor more than 100 lifetime days had 51%, 63%, and 94% increased odds of diabetes, respectively. The observed association of organochlorine and organophosphate insecticides with diabetes is consistent with previous human and animal studies. Long-term exposure from handling certain pesticides, in particular organochlorine and organophosphate insecticides, may be associated with increased risk of diabetes.
MeSH: diabetes mellitus, pesticides, insecticides, agrochemicals, environmental exposure, chlorinated hydrocarbons, phosphoric acid esters
Identifying modifiable risk factors for diabetes is important given that approximately 8.7 percent of all Americans over 20 years of age have diabetes (1). In addition to diet and obesity, there is increasing evidence that environmental exposures should also be considered as potential risk factors. Dioxins and other persistent organic pollutants have received particular attention (2, 3). As a result of findings of a positive association between dioxin, a contaminant of some herbicide formulations, and diabetes, the Department of Veterans Affairs now offers compensation to veterans who were involved in the application of herbicides during the Vietnam War and subsequently developed Type 2 diabetes (4). Other studies have demonstrated that organophosphate insecticides disrupt glucose homeostasis in animal models and can lead to hyperglycemia after poisonings in humans. However, the effects of chronic exposure to more moderate levels of organophosphate insecticides on glucose metabolism and diabetes in humans and the extent to which exposure to pesticides in other classes may contribute to diabetes risk is unclear.
Here we have examined the risk of incident adult onset diabetes associated with lifetime pesticide exposure in licensed pesticide applicators. In particular, we have assessed diabetes risk associated with general agricultural pesticide use and use of specific pesticides.
MATERIALS AND METHODS
Population
The Agricultural Health Study is a prospective study of licensed pesticide applicators and their spouses in Iowa and North Carolina. Study details have been previously reported (5), and questionnaires are available on the study website (www.aghealth.org). The institutional review boards of the National Institutes of Health (Bethesda, Maryland), Westat, Inc. (Rockville, Maryland; Coordinating Center), the University of Iowa (Iowa City, Iowa; Iowa Field Station), and Battelle (Durham, North Carolina; North Carolina Field Station) approved the study. Between 1993 and 1997 private pesticide applicators applying for a license to use restricted-use pesticides were invited to participate. Approximately 82 percent of eligible applicators enrolled (52,393) by completing a baseline survey on lifetime pesticide application practices and exposures and a brief medical history (enrollment questionnaire). Additionally, 44 percent of enrolled pesticide applicators completed a more detailed, self-administered questionnaire by mail (take-home questionnaire).
Participants were recontacted between 1999 and 2003 for a follow-up telephone interview. In this second phase, 33,457 applicators (64 percent) provided updated information on medical conditions. Participants who did not complete the follow-up interview were more likely to be older, to have less education, and to have had diabetes at enrollment. However, applicators who were lost to follow-up were similar to those who completed the interview with respect to the cumulative days of pesticide use and the number of acres farmed in the year before enrollment (data not shown). This study focused on the private pesticide applicators who participated in the follow-up interview and who were predominantly white, non-Hispanic (97 percent) and male (97 percent).
For this analysis, we excluded 1,330 participants who had diabetes at baseline (Figure 1). Of the 32,127 participants at risk, 238 participants (0.7 percent) were missing information on diabetes, and 102 participants (0.3 percent) were missing information on at least one key covariate (age, state, or body mass index). Thus, 31,787 applicators were included in the analysis.
Study outcome
The primary outcome was self-reported, incident diabetes. Participants were asked, “Has a doctor ever told you that you had been diagnosed with diabetes (other than while pregnant)?” on the enrollment questionnaire, on the take-home questionnaire, and in the follow-up interview. Age at diagnosis (in years) was reported during the follow-up interview. To define diabetes as incident, we excluded participants who reported diabetes on either the enrollment or take-home questionnaire. We also considered the age at diagnosis and excluded participants whose diagnosis ocurred more than one year prior to enrollment. Excluding diabetics who were diagnosed in the year preceding enrollment (n = 47) had only a negligible influence on the results. Although we cannot be certain, it is likely that most diabetics (>95 percent) were Type 2 given the predominance of Type 2 over Type 1 incidence in adults. Non-diabetics included all participants who reported never having had a diagnosis of diabetes.
Exposure assessment
We used questionnaire responses at baseline (either at enrollment or on the take-home questionnaire) to estimate lifetime exposure to pesticides. On the enrollment questionnaire, participants reported ever personally mixing or applying any pesticide as well as 50 individual pesticides. For 22 of these pesticides, participants also reported, in categories, the duration (years) and frequency (days per year) of use. Duration and frequency of use of the remaining 28 pesticides were reported on the take-home questionnaire. The percent agreement between repeated interviews in this cohort for self-reporting pesticide exposure typically ranged from 70–90% for ever use and from 50–60% for duration and frequency (6).
We calculated lifetime cumulative days of use by multiplying the midpoints of the reported categories for duration and frequency. For use of any pesticide, applicators were divided into quartiles of cumulative days of use. Categories for cumulative days of use for specific pesticides were: 0.01 to 10 days, 10.01 to 100 days, and more than 100 days, with never use as the reference category. These cut-points were selected to retain a sufficient number of observations in each category for analysis and to be consistent across most pesticides. For a few pesticides characterized by infrequent use, the two highest categories were combined into a single category of “more than 10 days”.
Pesticide applicators reported using multiple pesticides in their lifetime. In practice, over a lifetime applicators may substitute use of one pesticide with another in the same functional group. We were interested, therefore, in considering the effect of pesticide groups in addition to the effect of the individual pesticides. We considered four functional groups: herbicides, insecticides, fumigants, and fungicides, and three insecticide classes: organochlorines, organophosphates, and carbamates.
Statistical analyses
Analyses were performed using SAS 9.1 (Cary, NC) and the Agricultural Health Study data sets P1REL0506.01 and P2REL0506.03. Odds ratios (OR) and 95 percent confidence intervals (CI) were calculated using multiple logistic regression. Some analyses included information from the take-home questionnaire. These analyses were restricted to the subset of applicators (15,851) who completed the take-home questionnaire and are indicated in the results.
RESULTS
Characteristics of study population
Age and body mass index (BMI) were positively associated with diabetes, while hours of recreational exercise per week and education were inversely associated with diabetes (table 1). Participants from Iowa had a 56 percent reduced odds of diabetes compared to participants from North Carolina. Both former and current smokers at enrollment had a slightly higher odds of diabetes than never smokers. The reduced odds in Iowa remained after adjusting for smoking (OR 0.46, 95 percent CI: 0.40, 0.52). Applicators in the highest quartile of cumulative days of use of any pesticide had an increased odds of diabetes incidence compared to the lowest quartile of use. The odds of diabetes increased in a dose-dependent relationship with the number of acres farmed in the year prior to enrollment, though the highest odds was among those who reported farming zero acres. Although the numbers were few, participants who reported mixing or applying herbicides during military operations had an increased odds of diabetes.
TABLE 1.
Characteristics of incident diabetics and non-diabetics among licensed private pesticide applicators enrolled in the Agricultural Health Study.
| Characteristic | Diabetics | Non-diabetics | Adjusted odds ratio* |
95% confidence interval |
||
|---|---|---|---|---|---|---|
| N† | % | N† | % | |||
| Age at enrollment | ||||||
| Under 40 | 147 | 13 | 10,611 | 35 | 1.00 | Reference |
| 41 to 50 | 327 | 28 | 8,489 | 28 | 2.44 | 2.00 , 2.98 |
| 51 to 60 | 394 | 34 | 6,542 | 21 | 3.90 | 3.21 , 4.73 |
| 61 to 70 | 261 | 22 | 3,990 | 13 | 4.59 | 3.73 , 5.66 |
| Over 70 | 47 | 4 | 979 | 3 | 3.68 | 2.62 , 5.18 |
| Sex | ||||||
| Male | 1,153 | 98 | 29,802 | 97 | 1.00 | Reference |
| Female | 23 | 2 | 809 | 3 | 0.67 | 0.43 , 1.02 |
| State | ||||||
| North Carolina | 659 | 56 | 10,691 | 35 | 1.00 | Reference |
| Iowa | 517 | 44 | 19,920 | 65 | 0.44 | 0.39 , 0.49 |
| Body mass index (kg/m2) | ||||||
| Under 25 | 89 | 8 | 8,169 | 27 | 1.00 | Reference |
| 25 to 30 | 514 | 44 | 15,717 | 51 | 3.01 | 2.40 , 3.79 |
| 30 to 32 | 222 | 19 | 3,177 | 10 | 6.54 | 5.08 , 8.41 |
| Over 32 | 351 | 30 | 3,548 | 12 | 9.77 | 7.69 , 12.4 |
| Summer exercise (hours per week)‡ | ||||||
| None | 203 | 37 | 3,920 | 26 | 1.00 | Reference |
| Up to 2 | 177 | 32 | 5,476 | 37 | 0.77 | 0.62 , 0.95 |
| ?3 | 176 | 32 | 5,542 | 37 | 0.76 | 0.61 , 0.93 |
| Education | ||||||
| Did not complete high school | 186 | 16 | 2,446 | 8 | 1.18 | 0.98 , 1.41 |
| Completed high school or GED | 554 | 49 | 13,795 | 47 | 1.00 | Reference |
| At least some college | 392 | 35 | 13,265 | 45 | 0.86 | 0.75 , 0.99 |
| Smoking status at enrollment | ||||||
| Never | 484 | 43 | 16,597 | 56 | 1.00 | Reference |
| Former | 477 | 42 | 8,988 | 30 | 1.17 | 1.03 , 1.34 |
| Current | 168 | 15 | 4,249 | 14 | 1.18 | 0.98 , 1.42 |
| Cumulative days of any pesticide use | ||||||
| 0 to 64 | 265 | 25 | 7,551 | 26 | 1.00 | Reference |
| 65 to 200 | 186 | 17 | 6,065 | 21 | 0.91 | 0.75 , 1.11 |
| 201 to 396 | 260 | 24 | 7,480 | 26 | 1.04 | 0.87 , 1.25 |
| 397 to 7000 | 368 | 34 | 7,709 | 27 | 1.17 | 0.99 , 1.38 |
| Acres farmed in year prior to enrollment | ||||||
| Didn’t work on farm | 31 | 3 | 549 | 2 | 1.17 | 0.73 , 1.89 |
| None | 41 | 4 | 445 | 2 | 2.21 | 1.41 , 3.45 |
| <5 | 46 | 5 | 1,062 | 4 | 1.00 | Reference |
| 5 to 49 | 144 | 14 | 2,630 | 9 | 1.18 | 0.83 , 1.66 |
| 50 to 199 | 213 | 21 | 4,955 | 18 | 1.32 | 0.94 , 1.85 |
| 200 to 499 | 248 | 24 | 7,994 | 29 | 1.31 | 0.93 , 1.85 |
| 500 to 999 | 177 | 17 | 6,261 | 23 | 1.31 | 0.92 , 1.88 |
| ?1000 | 116 | 11 | 3,903 | 14 | 1.48 | 1.02 , 2.15 |
| Mixed herbicides in the military‡ | ||||||
| Never served in military | 136 | 26 | 4,392 | 30 | 1.00 | Reference |
| No | 384 | 73 | 9,951 | 69 | 0.95 | 0.77 , 1.18 |
| Yes | 6 | 1 | 83 | 1 | 1.46 | 0.61 , 3.48 |
Variables for age (under 40, 40–49, 50–59, 60–69, over 70), state, and body mass index (under 25, 25–30, 30–32, over 32) were included in all adjusted models.
Number of diabetics and non-diabetics range from (1,016 – 1,176) and (27,799 – 30,611), respectively, due to missing data.
Asked on take-home questionnaire. Number of diabetics and non-diabetics range from (526 – 556) and (14,426 – 14,938), respectively, due to missing observations.
Ever use of specific pesticides
Based on responses at enrollment, ever use of eight pesticides (two organochlorine insecticides: chlordane and heptachlor; four organophosphate insecticides: coumaphos, phorate, terbufos, and trichlorfon; and two herbicides: alachlor and cyanazine) was statistically significantly associated with incident diabetes in models accounting for age, state, and BMI (table 2). The herbicide glyphosate was inversely associated with diabetes. Including categorical variables for education (did not complete high school, completed high school or a GED, or beyond high school) or years of smoking (none, 1 to 5, 6 to 15, 16 to 25, or more than 25) had a negligible effect on the point estimates in these models.
TABLE 2.
Ever use of specific pesticides comparing incident diabetics and non-diabetics among applicators enrolled in the Agricultural Health Study.
| Pesticide name | Diabetics | % | Non-diabetics | % | Age-adjusted odds ratio* |
95% confidence interval |
Adjusted odds ratio† |
95% confidence interval |
|---|---|---|---|---|---|---|---|---|
| 1,176 | exposed | 30,611 | exposed | |||||
| Insecticide classes (any) | 1,042 | 89 | 27,646 | 90 | 0.84 | 0.70 , 1.01 | 1.03 | 0.85 , 1.24 |
| Organochlorines (any) | 666 | 57 | 14,659 | 48 | 0.98 | 0.87 , 1.11 | 1.03 | 0.91 , 1.16 |
| Aldrin | 261 | 27 | 5,417 | 20 | 0.99 | 0.85 , 1.15 | 1.14 | 0.97 , 1.33 |
| Chlordane | 372 | 38 | 7,365 | 27 | 1.25 | 1.09 , 1.43 | 1.16 | 1.01 , 1.34 |
| DDT | 409 | 41 | 7,110 | 26 | 1.26 | 1.09 , 1.46 | 1.09 | 0.94 , 1.27 |
| Dieldrin | 96 | 10 | 2,031 | 7 | 0.93 | 0.75 , 1.16 | 1.03 | 0.83 , 1.30 |
| Heptachlor | 209 | 22 | 4,569 | 17 | 0.95 | 0.80 , 1.11 | 1.20 | 1.01 , 1.43 |
| Lindane | 192 | 20 | 5,728 | 21 | 0.83 | 0.70 , 0.97 | 0.94 | 0.80 , 1.11 |
| Toxaphene | 224 | 23 | 4,011 | 15 | 1.33 | 1.14 , 1.56 | 1.14 | 0.97 , 1.33 |
| Organophosphates (any) | 978 | 83 | 26,329 | 86 | 0.82 | 0.70 , 0.96 | 1.02 | 0.86 , 1.20 |
| Chlorpyrifos | 466 | 41 | 12,707 | 43 | 0.98 | 0.87 , 1.10 | 1.03 | 0.91 , 1.17 |
| Coumaphos | 111 | 11 | 2,528 | 9 | 1.18 | 0.97 , 1.45 | 1.26 | 1.03 , 1.55 |
| Diazinon | 387 | 40 | 9,122 | 33 | 1.19 | 1.04 , 1.36 | 0.98 | 0.85 , 1.13 |
| Dichlorvos | 110 | 11 | 3,105 | 11 | 0.92 | 0.75 , 1.13 | 1.21 | 0.98 , 1.49 |
| Fonofos | 193 | 19 | 6,293 | 23 | 0.77 | 0.65 , 0.90 | 1.02 | 0.86 , 1.21 |
| Malathion | 766 | 75 | 20,397 | 72 | 1.05 | 0.91 , 1.21 | 1.10 | 0.95 , 1.27 |
| Parathion | 218 | 23 | 4,279 | 16 | 1.34 | 1.15 , 1.57 | 1.03 | 0.88 , 1.22 |
| Phorate | 345 | 36 | 9,310 | 34 | 0.97 | 0.84 , 1.11 | 1.22 | 1.06 , 1.42 |
| Terbufos | 392 | 39 | 11,277 | 40 | 0.96 | 0.85 , 1.10 | 1.17 | 1.02 , 1.35 |
| Trichlorfon | 13 | 1 | 169 | 1 | 2.03 | 1.15 , 3.60 | 1.85 | 1.03 , 3.33 |
| Carbamates (any) | 804 | 68 | 19,267 | 63 | 1.14 | 1.00 , 1.29 | 1.00 | 0.88 , 1.14 |
| Aldicarb | 163 | 17 | 3,011 | 11 | 1.71 | 1.44 , 2.03 | 1.10 | 0.91 , 1.34 |
| Carbaryl | 702 | 68 | 16,198 | 57 | 1.42 | 1.24 , 1.62 | 1.10 | 0.95 , 1.28 |
| Carbofuran | 330 | 33 | 7,937 | 29 | 1.06 | 0.92 , 1.21 | 1.05 | 0.91 , 1.20 |
| Pyrethroids (any) | 235 | 20 | 7,006 | 23 | 0.97 | 0.84 , 1.13 | 1.07 | 0.92 , 1.25 |
| Permethrin (crops) | 150 | 15 | 3,849 | 14 | 1.21 | 1.01 , 1.44 | 1.09 | 0.91 , 1.31 |
| Permethrin (animals) | 106 | 11 | 3,997 | 14 | 0.81 | 0.66 , 0.99 | 1.04 | 0.84 , 1.29 |
| Herbicides (any) | 1,102 | 94 | 29,184 | 95 | 0.74 | 0.58 , 0.94 | 0.92 | 0.72 , 1.18 |
| 2,4,5-T | 273 | 28 | 6,220 | 23 | 0.99 | 0.85 , 1.14 | 1.02 | 0.88 , 1.19 |
| 2,4,5-TP | 120 | 13 | 2,562 | 9 | 1.12 | 0.92 , 1.36 | 1.04 | 0.85 , 1.27 |
| 2,4-D | 822 | 73 | 22,911 | 77 | 0.75 | 0.66 , 0.86 | 0.92 | 0.79 , 1.06 |
| Alachlor | 585 | 58 | 15,194 | 54 | 1.06 | 0.94 , 1.21 | 1.14 | 1.00 , 1.30 |
| Atrazine | 796 | 70 | 21,577 | 72 | 0.88 | 0.77 , 1.01 | 1.07 | 0.93 , 1.23 |
| Butylate | 328 | 34 | 9,109 | 33 | 0.98 | 0.86 , 1.13 | 1.07 | 0.93 , 1.24 |
| Chlorimuron-ethyl | 339 | 35 | 10,464 | 38 | 0.98 | 0.86 , 1.13 | 1.01 | 0.88 , 1.16 |
| Cyanazine | 408 | 40 | 12,034 | 43 | 0.86 | 0.75 , 0.97 | 1.27 | 1.09 , 1.47 |
| Dicamba | 434 | 43 | 14,639 | 53 | 0.68 | 0.60 , 0.78 | 0.99 | 0.85 , 1.15 |
| EPTC | 180 | 18 | 5,733 | 21 | 0.89 | 0.75 , 1.04 | 1.10 | 0.93 , 1.31 |
| Glyphosate | 865 | 76 | 23,072 | 77 | 0.97 | 0.85 , 1.12 | 0.85 | 0.74 , 0.98 |
| Imazethapyr | 318 | 32 | 12,391 | 45 | 0.65 | 0.57 , 0.75 | 0.92 | 0.78 , 1.09 |
| Metolachlor | 444 | 44 | 13,204 | 47 | 0.91 | 0.80 , 1.04 | 1.05 | 0.92 , 1.20 |
| Metribuzin | 400 | 42 | 12,998 | 47 | 0.78 | 0.69 , 0.89 | 0.96 | 0.83 , 1.10 |
| Paraquat | 313 | 32 | 6,509 | 24 | 1.45 | 1.26 , 1.66 | 1.01 | 0.87 , 1.18 |
| Pendimethalin | 453 | 46 | 12,454 | 45 | 1.11 | 0.98 , 1.26 | 1.04 | 0.92 , 1.19 |
| Petroleum oil | 479 | 50 | 13,415 | 49 | 1.03 | 0.90 , 1.17 | 1.13 | 0.99 , 1.29 |
| Trifluralin | 493 | 49 | 15,132 | 54 | 0.80 | 0.70 , 0.90 | 1.01 | 0.88 , 1.16 |
| Fungicides (any) | 496 | 42 | 10,515 | 34 | 1.39 | 1.23 , 1.56 | 1.01 | 0.88 , 1.15 |
| Benomyl | 144 | 14 | 2,782 | 10 | 1.39 | 1.16 , 1.67 | 0.90 | 0.74 , 1.10 |
| Captan | 115 | 12 | 3,361 | 12 | 0.96 | 0.78 , 1.17 | 1.00 | 0.82 , 1.23 |
| Chlorothalonil | 132 | 12 | 2,277 | 8 | 1.63 | 1.35 , 1.96 | 1.04 | 0.85 , 1.27 |
| Maneb | 146 | 15 | 2,717 | 10 | 1.44 | 1.20 , 1.72 | 0.96 | 0.79 , 1.16 |
| Metalaxyl | 322 | 32 | 6,496 | 23 | 1.54 | 1.34 , 1.77 | 1.02 | 0.88 , 1.20 |
| Ziram | 19 | 2 | 447 | 2 | 1.14 | 0.71 , 1.81 | 0.92 | 0.57 , 1.47 |
| Fumigants (any) | 357 | 30 | 6,738 | 22 | 1.41 | 1.24 , 1.60 | 1.04 | 0.90 , 1.19 |
| Aluminum phosphide | 59 | 6 | 1,363 | 5 | 1.26 | 0.96 , 1.65 | 1.18 | 0.90 , 1.55 |
| Carbon tetrachloride | 73 | 7 | 1,599 | 6 | 0.99 | 0.77 , 1.26 | 1.03 | 0.80 , 1.33 |
| Ethylene dibromide | 43 | 4 | 990 | 4 | 1.10 | 0.81 , 1.51 | 0.82 | 0.60 , 1.13 |
| Methyl bromide | 268 | 24 | 4,621 | 15 | 1.60 | 1.39 , 1.84 | 0.99 | 0.84 , 1.16 |
Model adjusted for age (under 40, 40–49, 50–59, 60–69, over 70).
Model includes variables for age (under 40, 40–49, 50–59, 60–69, over 70), state, and body mass index (under 25, 25–30, 30–32, over 32).
A number of fungicides and fumigants were related to diabetes risk in age-adjusted models but not after further adjustment for state and BMI. Because these chemicals are used more frequently in North Carolina, we carried out state-stratified analyses. We found no significant association between any of these chemicals and diabetes in either state. DDT, aldicarb, carbaryl, and paraquat use were also proportionately more common in North Carolina (data not shown). The odds of diabetes did not differ substantially between North Carolina and Iowa for either carbaryl (OR 1.06 (0.83 – 1.35) and 1.13 (0.94 – 1.35), respectively) or paraquat (0.95 (0.79 – 1.15) and 1.14 (0.89 – 1.47), respectively). DDT and aldicarb were associated with non-significantly increased odds of diabetes in Iowa (OR 1.21 (0.97 – 1.50) and 1.41 (0.87 – 2.27), respectively) but not in North Carolina (OR 1.02 (0.83 – 1.25) and 1.06 (0.86 – 1.30), respectively).
Cumulative days of pesticide use
Of the sixteen pesticides for which ever use from the enrollment questionnaire had an increased odds of diabetes in fully adjusted models (OR > 1.10), six also demonstrated a dose-response (p for trend < 0.10, table 3). These included aldrin, chlordane, heptachlor, trichlorfon, alachlor, and cyanazine. Dichlorvos showed a moderate dose-response trend with diabetes. Additionally, four pesticides (chlorpyrifos, diazinon, atrazine, and metribuzin) showed a positive dose-response with diabetes that was not detected in ever use analyses. The remaining pesticides showed no dose-response association with diabetes incidence (data not shown). Information on cumulative days of use for certain pesticides was only asked on the take-home questionnaire, as indicated in table 3. Ever use of these pesticides based on take-home questionnaire information is included in the Appendix. In general, the estimates do not vary significantly between the two questionnaires.
TABLE 3.
Dose-response for specific pesticides among incident diabetics and non-diabetics enrolled in the Agricultural Health Study.
| Pesticide name | Cumulative days of use |
Diabetics |
Non-diabetics | Adjusted odds ratio* |
95% confidence interval |
p for trend |
|---|---|---|---|---|---|---|
| Insecticides | ||||||
| Organochlorines Aldrin† |
Never | 429 | 12,213 | Reference | ||
| 0.01 to 10 | 39 | 1,197 | 0.84 | 0.59 , 1.19 | ||
| 10.01 to 100 | 55 | 1,100 | 1.21 | 0.89 , 1.65 | ||
| Over 100 | 16 | 225 | 1.51 | 0.88 , 2.58 | 0.08 | |
| Chlordane† | Never | 392 | 11,922 | Reference | ||
| 0.01 to 10 | 92 | 1,881 | 1.15 | 0.90 , 1.46 | ||
| 10.01 to 100 | 43 | 762 | 1.18 | 0.85 , 1.65 | ||
| Over 100 | 15 | 159 | 1.63 | 0.93 , 2.86 | 0.05 | |
| Heptachlor† | Never | 454 | 12,969 | Reference | ||
| 0.01 to 10 | 41 | 946 | 1.31 | 0.93 , 1.85 | ||
| 10.01 to 100 | 32 | 753 | 1.26 | 0.85 , 1.85 | ||
| Over 100 | 11 | 147 | 1.94 | 1.02 , 3.69 | 0.02 | |
| Toxaphene† | Never | 440 | 13,172 | Reference | ||
| 0.01 to 10 | 45 | 825 | 1.28 | 0.93 , 1.77 | ||
| 10.01 to 100 | 38 | 569 | 1.30 | 0.91 , 1.85 | ||
| Over 100 | 13 | 237 | 0.82 | 0.46 , 1.46 | 0.80 | |
| Organophosphates Chlorpyrifos |
Never | 672 | 17,126 | Reference | ||
| 0.01 to 10 | 141 | 4,154 | 0.96 | 0.80 , 1.16 | ||
| 10.01 to 100 | 185 | 5,661 | 0.95 | 0.80 , 1.12 | ||
| Over 100 | 123 | 2,695 | 1.24 | 1.02 , 1.52 | 0.04 | |
| Coumaphos | Never | 865 | 24,668 | Reference | ||
| 0.01 to 10 | 40 | 1,053 | 1.12 | 0.81 , 1.56 | ||
| 10.01 to 100 | 49 | 931 | 1.60 | 1.18 , 2.17 | ||
| Over 100 | 16 | 440 | 0.94 | 0.56 , 1.56 | 0.79 | |
| Diazinon† | Never | 393 | 11,612 | Reference | ||
| 0.01 to 10 | 39 | 1,499 | 0.68 | 0.48 , 0.95 | ||
| 10.01 to 100 | 70 | 1,149 | 1.30 | 0.99 , 1.71 | ||
| Over 100 | 36 | 435 | 1.59 | 1.09 , 2.31 | 0.006 | |
| Dichlorvos | Never | 880 | 24,466 | Reference | ||
| 0.01 to 10 | 30 | 921 | 1.15 | 0.78 , 1.67 | ||
| 10.01 to 100 | 32 | 917 | 1.19 | 0.82 , 1.72 | ||
| Over 100 | 44 | 1,187 | 1.26 | 0.91 , 1.73 | 0.15 | |
| Phorate† | Never | 396 | 10,264 | Reference | ||
| 0.01 to 10 | 50 | 1,879 | 0.97 | 0.71 , 1.34 | ||
| 10.01 to 100 | 71 | 1,959 | 1.14 | 0.86 , 1.49 | ||
| Over 100 | 27 | 644 | 1.05 | 0.70 , 1.58 | 0.68 | |
| Terbufos | Never | 610 | 16572 | Reference | ||
| 0.01 to 10 | 81 | 2,779 | 1.08 | 0.85 , 1.38 | ||
| 10.01 to 100 | 181 | 5,020 | 1.23 | 1.03 , 1.47 | ||
| Over 100 | 116 | 3,236 | 1.14 | 0.93 , 1.41 | 0.19 | |
| Trichlorfon | Never | 968 | 27,180 | Reference | ||
| 0.01 to 10 | 5 | 61 | 1.92 | 0.75 , 4.94 | ||
| Over 10 | 7 | 75 | 2.47 | 1.10 , 5.56 | 0.02 | |
| Carbamates Aldicarb† |
Never | 492 | 13,745 | Reference | ||
| 0.01 to 10 | 12 | 370 | 0.62 | 0.34 , 1.12 | ||
| 10.01 to 100 | 23 | 410 | 1.00 | 0.64 , 1.57 | ||
| Over 100 | 15 | 259 | 0.97 | 0.56 , 1.68 | 0.90 | |
| Carbaryl† | Never | 254 | 8,393 | Reference | ||
| 0.01 to 10 | 89 | 2,532 | 1.03 | 0.80 , 1.32 | ||
| 10.01 to 100 | 101 | 2,147 | 0.98 | 0.75 , 1.26 | ||
| Over 100 | 94 | 1,600 | 0.95 | 0.72 , 1.25 | 0.67 | |
| Herbicides | ||||||
| Alachlor | Never | 431 | 12,716 | Reference | ||
| 0.01 to 10 | 110 | 3,421 | 1.00 | 0.81 , 1.25 | ||
| 10.01 to 100 | 208 | 6,312 | 1.05 | 0.88 , 1.25 | ||
| Over 100 | 240 | 4,974 | 1.31 | 1.11 , 1.55 | 0.001 | |
| Atrazine | Never | 340 | 8,342 | Reference | ||
| 0.01 to 10 | 118 | 3,778 | 0.95 | 0.77 , 1.19 | ||
| 10.01 to 100 | 285 | 8,529 | 0.99 | 0.83 , 1.17 | ||
| Over 100 | 370 | 9,005 | 1.15 | 0.98 , 1.36 | 0.02 | |
| Cyanazine | Never | 609 | 15,817 | Reference | ||
| 0.01 to 10 | 104 | 3,618 | 1.04 | 0.83 , 1.30 | ||
| 10.01 to 100 | 173 | 5,003 | 1.32 | 1.09 , 1.60 | ||
| Over 100 | 115 | 3,179 | 1.38 | 1.10 , 1.72 | 0.004 | |
| EPTC | Never | 805 | 21,707 | Reference | ||
| 0.01 to 10 | 77 | 2,488 | 1.08 | 0.84 , 1.38 | ||
| 10.01 to 100 | 65 | 2,145 | 1.15 | 0.88 , 1.50 | ||
| Over 100 | 31 | 949 | 1.07 | 0.74 , 1.56 | 0.56 | |
| Metribuzin† | Never | 351 | 9,104 | Reference | ||
| 0.01 to 10 | 80 | 2,759 | 1.02 | 0.78 , 1.32 | ||
| 10.01 to 100 | 81 | 2,337 | 1.22 | 0.93 , 1.58 | ||
| Over 100 | 25 | 574 | 1.44 | 0.94 , 2.21 | 0.06 | |
| Petroleum oil† | Never | 436 | 11,551 | Reference | ||
| 0.01 to 10 | 28 | 1,035 | 0.87 | 0.58 , 1.29 | ||
| 10.01 to 100 | 42 | 1,226 | 0.99 | 0.71 , 1.38 | ||
| Over 100 | 30 | 882 | 0.93 | 0.63 , 1.37 | 0.72 | |
| Fumigant | ||||||
| Aluminum phosphide | Never | 524 | 14,345 | Reference | ||
| 0.01 to 10 | 11 | 282 | 1.15 | 0.62 , 2.15 | ||
| Over 10 | 10 | 231 | 1.10 | 0.57 , 2.12 | 0.69 |
Models include variables for age (under 40, 40–49, 50–59, 60–69, over 70), state, and body mass index (under 25, 25–30, 30–32, over 32).
Information provided on take-home questionnaire completed by 44% of enrolled applicators.
Pesticides with no dose-response association with diabetes are not shown.
APPENDIX.
Association of diabetes incidence with ever use of specific pesticides asked on both enrollment and take-home questionnaires among diabetics and non-diabetics enrolled in the Agricultural Health Study.
| Enrollment (from Table 2) | Take-home questionnaire | |||||
|---|---|---|---|---|---|---|
| Pesticide name | Adjusted odds ratio* |
95% confidence interval |
Diabetics 573 |
Non-diabetics 15,278 |
Adjusted odds ratio* |
95% confidence interval |
| Insecticide classes | ||||||
| Organochlorines | ||||||
| Aldrin | 1.14 | 0.97 , 1.33 | 113 | 2,606 | 1.06 | 0.84 , 1.34 |
| Chlordane | 1.16 | 1.01 , 1.34 | 154 | 2,902 | 1.18 | 0.97 , 1.44 |
| DDT | 1.09 | 0.94 , 1.27 | 190 | 3,411 | 1.07 | 0.87 , 1.30 |
| Dieldrin | 1.03 | 0.83 , 1.30 | 32 | 571 | 1.39 | 0.94 , 2.03 |
| Heptachlor | 1.20 | 1.01 , 1.43 | 87 | 1,896 | 1.34 | 1.04 , 1.74 |
| Lindane | 0.94 | 0.80 , 1.11 | 81 | 2,161 | 1.05 | 0.82 , 1.34 |
| Toxaphene | 1.14 | 0.97 , 1.33 | 100 | 1,671 | 1.22 | 0.97 , 1.54 |
| Organophosphates | ||||||
| Dianzinon | 0.98 | 0.85 , 1.13 | 149 | 3,174 | 1.06 | 0.87 , 1.30 |
| Malathion | 1.10 | 0.95 , 1.27 | 364 | 9,727 | 1.03 | 0.85 , 1.23 |
| Parathion | 1.03 | 0.88 , 1.22 | 61 | 1,166 | 0.94 | 0.71 , 1.25 |
| Phorate | 1.22 | 1.06 , 1.42 | 151 | 4,572 | 1.06 | 0.86 , 1.30 |
| Carbamates | ||||||
| Aldicarb | 1.10 | 0.91 , 1.34 | 51 | 1,072 | 0.85 | 0.62 , 1.16 |
| Carbaryl | 1.10 | 0.95 , 1.28 | 294 | 6,495 | 0.99 | 0.82 , 1.20 |
| Herbicides | ||||||
| 2,4,5-T | 1.02 | 0.88 , 1.19 | 117 | 2,911 | 0.95 | 0.76 , 1.18 |
| 2,4,5-TP | 1.04 | 0.85 , 1.27 | 30 | 762 | 0.84 | 0.57 , 1.23 |
| Butylate | 1.07 | 0.93 , 1.24 | 140 | 4,110 | 1.01 | 0.82 , 1.24 |
| Chlorimuron-ethyl | 1.01 | 0.88 , 1.16 | 156 | 4,790 | 0.94 | 0.77 , 1.14 |
| Metribuzin | 0.96 | 0.83 , 1.10 | 191 | 5,776 | 1.14 | 0.94 , 1.40 |
| Paraquat | 1.01 | 0.87 , 1.18 | 112 | 2,371 | 0.89 | 0.71 , 1.12 |
| Pendimethalin | 1.04 | 0.92 , 1.19 | 204 | 5,534 | 1.04 | 0.86 , 1.24 |
| Petroleum oil | 1.13 | 0.99 , 1.29 | 104 | 3,249 | 0.94 | 0.75 , 1.17 |
| Fungicides | ||||||
| Benomyl | 0.90 | 0.74 , 1.10 | 58 | 1,201 | 0.83 | 0.62 , 1.11 |
| Maneb | 0.96 | 0.79 , 1.16 | 60 | 1,267 | 0.80 | 0.60 , 1.08 |
| Metalaxyl | 1.02 | 0.88 , 1.20 | 151 | 2,931 | 1.03 | 0.83 , 1.28 |
| Ziram† | ||||||
| Fumigants | ||||||
| Aluminum phosphide | 1.18 | 0.90 , 1.55 | 21 | 523 | 1.10 | 0.70 , 1.74 |
| Carbon tetrachloride | 1.03 | 0.80 , 1.33 | 17 | 663 | 0.58 | 0.35 , 0.95 |
| Ethylene dibromide | 0.82 | 0.60 , 1.13 | 26 | 678 | 0.64 | 0.42 , 0.97 |
Model includes variables for age (under 40, 40–49, 50–59, 60–69, over 70), state, and body mass index (under 25, 25–30, 30–32, over 32).
Only two diabetics were exposed to ziram on the take-home questionnaire.
Ever use stratified by age and state
Analyses stratified on age, state, and BMI were carried out for pesticides where the odds of diabetes was increased in both ever-never and dose-response models (table 4 and table 5). We used lenient selection criteria because prior evidence for these pesticides was lacking. With the exception of heptachlor, which was associated with diabetes only in Iowa, pesticide estimates did not differ greatly between states although the confidence intervals widened due to smaller sample sizes. For age-specific strata, however, pesticide associations with diabetes were more obvious among applicators under 60 years old. In general, the associations of pesticides with diabetes were also stronger among participants with higher BMI.
TABLE 4.
Relation between specific pesticide use and incident diabetes stratified by age and state among applicators enrolled in the Agricultural Health Study.
| Age | ||||||||
|---|---|---|---|---|---|---|---|---|
| Under 60 years old | 60 years and older | |||||||
| Pesticide | Diabetics | Non-diabetics | Adjusted odds ratio* |
95% confidence interval |
Diabetics | Non-diabetics | Adjusted odds ratio* |
95% confidence interval |
| Insecticides, organochlorines | ||||||||
| Aldrin | 169 | 3,757 | 1.11 | 0.92 , 1.34 | 92 | 1,660 | 1.10 | 0.82 , 1.47 |
| Chlordane | 268 | 5,450 | 1.26 | 1.08 , 1.48 | 104 | 1,915 | 0.88 | 0.67 , 1.15 |
| Heptachlor | 140 | 3,163 | 1.25 | 1.01 , 1.54 | 69 | 1,406 | 1.00 | 0.72 , 1.38 |
| Insecticides, organophosphates | ||||||||
| Dichlorvos | 86 | 2,673 | 1.22 | 0.96 , 1.54 | 24 | 432 | 1.16 | 0.74 , 1.81 |
| Trichlorfon | 11 | 142 | 2.03 | 1.07 , 3.86 | 2 | 27 | 1.31 | 0.30 , 5.68 |
| Herbicides | ||||||||
| Alachlor | 451 | 12,896 | 1.18 | 1.01 , 1.37 | 134 | 2,298 | 1.00 | 0.77 , 1.30 |
| Cyanazine | 322 | 10,331 | 1.33 | 1.13 , 1.58 | 86 | 1,703 | 1.04 | 0.76 , 1.41 |
| State | ||||||||
| North Carolina | Iowa | |||||||
| Diabetics | Non-diabetics | Adjusted odds ratio† |
95% confidence interval |
Diabetics | Non-diabetics | Adjusted odds ratio† |
95% confidence interval |
|
| Insecticides, organochlorines | ||||||||
| Aldrin | 82 | 956 | 1.20 | 0.93 , 1.56 | 179 | 4,461 | 1.10 | 0.89 , 1.34 |
| Chlordane | 210 | 3,045 | 1.14 | 0.94 , 1.38 | 162 | 4,320 | 1.20 | 0.98 , 1.46 |
| Heptachlor | 26 | 429 | 0.80 | 0.53 , 1.22 | 183 | 4,140 | 1.34 | 1.09 , 1.64 |
| Insecticides, organophosphates | ||||||||
| Dichlorvos | 22 | 314 | 1.08 | 0.69 , 1.70 | 88 | 2,791 | 1.25 | 0.98 , 1.59 |
| Trichlorfon | 8 | 89 | 1.54 | 0.73 , 3.25 | 5 | 80 | 2.67 | 1.05 , 6.81 |
| Herbicides | ||||||||
| Alachlor | 276 | 3,898 | 1.25 | 1.04 , 1.49 | 309 | 11,296 | 1.03 | 0.85 , 1.24 |
| Cyanazine | 105 | 1,252 | 1.43 | 1.14 , 1.79 | 303 | 10,782 | 1.17 | 0.97 , 1.41 |
Adjusted for age (continuous), body mass index (under 25, 25–30, 30–32, over 32), and state.
Adjusted for age (under 40, 40–49, 50–59, 60–69, over 70) and body mass index (under 25, 25–30, 30–32, over 32).
TABLE 5.
Relation between specific pesticide use and incident diabetes stratified by body mass index among diabetics and non-diabetics enrolled in the Agricultural Health Study.
| Under and Normal Weight (<25 kg/m2) | Overweight (25–30 kg/m2) | Obese (≥30 kg/m2) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Diabetics | Non- diabetics |
Adjusted odds ratio* |
95% confidence interval |
Diabetics | Non- diabetics |
Adjusted odds ratio* |
95% confidence interval |
Diabetics | Non- diabetics |
Adjusted odds ratio* |
95% confidence interval |
|
| Aldrin | 10 | 1,210 | 0.53 | 0.26 , 1.08 | 106 | 2,903 | 1.07 | 0.84 , 1.36 | 145 | 1,304 | 1.31 | 1.05 , 1.63 |
| Chlordane | 28 | 1,860 | 1.03 | 0.63 , 1.68 | 165 | 3,904 | 1.16 | 0.94 , 1.42 | 179 | 1,601 | 1.19 | 0.97 , 1.45 |
| Heptachlor | 7 | 1,036 | 0.48 | 0.21 , 1.10 | 81 | 2,408 | 1.09 | 0.83 , 1.43 | 121 | 1,125 | 1.42 | 1.11 , 1.81 |
| Dichlorvos | 7 | 832 | 1.03 | 0.46 , 2.32 | 44 | 1,628 | 1.15 | 0.83 , 1.60 | 59 | 645 | 1.30 | 0.97 , 1.75 |
| Trichlorfon | 0 | 52 | N/A | 9 | 83 | 3.19 | 1.56 , 6.51 | 4 | 34 | 1.29 | 0.45 , 3.69 | |
| Alachlor | 32 | 3,659 | 0.82 | 0.51 , 1.32 | 239 | 8,020 | 1.09 | 0.89 , 1.327 | 314 | 3,515 | 1.24 | 1.03 , 1.50 |
| Cyanazine | 18 | 2,978 | 0.62 | 0.34 , 1.13 | 176 | 6,357 | 1.41 | 1.13 , 1.77 | 214 | 2,699 | 1.27 | 1.03 , 1.57 |
Adjusted for age (under 40, 40–49, 50–59, 60–69, over 70), state, and body mass index (continuous).
DISCUSSION
This study is to our knowledge the largest study to evaluate the potential effects of pesticides on diabetes incidence in adults. The prospective design of the study ensures that exposures were reported prior to the diagnosis of diabetes and reduces the potential for recall bias. Of the fifty pesticides evaluated, seven displayed suggestive evidence of an association with diabetes incidence in both ever use and cumulative days of use models: aldrin, chlordane, heptachlor, dichlorvos, trichlorfon, alachlor, and cyanazine. It is noteworthy that all of these pesticides are chlorinated compounds while only half of the pesticides investigated were chlorinated.
Few studies, if any, have considered the potential diabetogenic effects of alachlor and cyanazine, which both showed a dose-response association with diabetes in the present study. However, the biologic plausibility of a diabetogenic effect of exposure to persistent organic pollutants (POPs, e.g., dioxins, polychlorinated biphenyls, and organochlorine insecticides), and organophosphate insecticides is supported by numerous studies.
Persistent organic pollutants (organochlorine insecticides)
Because POPs are lipid soluble and bioaccumulate in animal tissues, studies of the relationship of chronic exposure to POPs to diabetes can be conducted using human biologic samples (3, 7, 8). Of the seven pesticides for which the odds of diabetes was increased in both ever-never and dose-response analyses in the present study, three (aldrin, chlordane, and heptachlor) are POPs. Although the organochlorine insecticides in this study are no longer available on the market, measurable levels of these and other POPs are still detectable in the general population and in food products, making these findings potentially relevant to the general population (2, 9, 10).
Studies using NHANES data have found associations of POPs with both diabetes and insulin resistance and have noted in particular the association of diabetes with organochlorine insecticides (2, 11, 12). A metabolite and an impurity of chlordane were most strongly associated with insulin resistance in non-diabetics (12). Animal studies of exposure to chlordane have demonstrated increased lipids and triglycerides in liver (13, 14) and altered glucose metabolism (14, 15). Our finding that chlordane exposure followed a dose-response association with diabetes incidence strengthens the chlordane-diabetes hypothesis. Heptachlor is a frequent component of chlordane mixtures and is structurally very similar (16), but few studies have considered the diabetogenic actions of heptachlor itself. There is some evidence that heptachlor affects lipid metabolism (17). Similarly, few studies have examined aldrin in relation to diabetes although it has been shown that aldrin disrupts carbohydrate metabolism in fish (18, 19).
Dioxin, a frequenct contaminant of herbicides used for military purposes, is a POP that has been studied repeatedly for its potential diabetogenic effect in humans. Studies have suggested that exposure to dioxin as a contaminant of the herbicide Agent Orange increased the risk of diabetes and disrupted glucose and insulin homeostasis among exposed veterans (20, 21). Although the number of exposed diabetics was small, our findings that participants who reported mixing herbicides in the military had an increased odds of diabetes incidence compared to participants who did not mix herbicides in the military or who were not in the military are consistent with these studies.
Organophosphate insecticides
Unlike POPs, organophosphate insecticides (OPs) are readily degraded, and consequently, studies have more frequently been conducted in animal models where the outcome is typically short-term disruption of glucose homeostasis. An advantage of the Agricultural Health Study is the ability to consider the risk of diabetes in humans in relation to long-term exposure to lower levels of OPs. Of the ten OP insecticides investigated, we found seven (chlorpyrifos, coumaphos, diazinon, dichlorvos, phorate, terbufos, and trichlorfon) that had increased odds of diabetes, three of which (chlorpyrifos, diazinon, and trichlorfon) were associated in a dose-dependent manner.
Type 2 diabetes is characterized by insulin resistance, which initially is compensated by an increase in insulin production. Over time, the pancreas fails to produce sufficient insulin to stimulate adequate glucose uptake in adipose and muscle tissues, leading to hyperglycemia and type 2 diabetes. Pancreatic β-cells contain muscarinic acetylcholine receptors, which are involved in the glucose-dependent production of insulin (22). OPs are known inhibitors of acetylcholinesterase, the enzyme responsible for the degradation of acetylcholine. Thus, exposure to sufficiently high levels of OPs would be expected to result in increased accumulation of acetylcholine, potentially leading to over-stimulation and eventual down-regulation of its receptors (23) and reducing insulin production.
Indeed, organophosphate exposure has been shown repeatedly to be associated with hyperglycemia in animal models (24). Dichlorvos specifically has been shown to disrupt glucose homeostasis in male Wistar rats (25). We found that applicators exposed to dichlorvos had an increased odds of diabetes and that the odds increased with increasing cumulative days of use, although the test for trend was of borderline significance. Furthermore, the pesticide most strongly associated with diabetes among applicators was the organophosphate insecticide trichlorfon, which is converted to dichlorvos in mammals (26).
In addition to the effects of short-term exposure to OPs, studies that have considered the effects of long-term, low-level exposure may be of greater relevance to our study population. Studies of long-term exposure to OPs with respect to diabetes in humans have not previously been conducted. However, animal studies have demonstrated that tolerance to OP exposure develops over time, likely as a result of decreased expression of muscarinic receptors (27). Because these 10 receptors mediate the production of insulin in β-cells, a decrease in muscarinic receptors could potentially lead to decreased insulin production. Additionally, prolonged stimulation by acetylcholine may reduce β-cell sensitivity to glucose (28).
As opposed to organophosphate insecticides, the inhibition of acetylcholinesterase by carbamate insecticides is reversible and short-lived, and therefore the effects of exposure would be expected to be less severe. We found that the carbamate insecticides showed very weak, if any, evidence of an association with diabetes in fully-adjusted models. Pesticides from the fungicide and fumigant groups also showed no convincing association with diabetes, indicating that our findings were exclusive to organochlorine and organophosphate insecticides and a limited number of herbicides.
The role of body mass index
Because POPs are lipophilic, people with higher BMI may be more likely to store higher levels of POPs than people with lower BMI with equivalent exposure. A study in the NHANES population found that obesity and diabetes were associated only among participants with detectable levels of POPs (2). The diabetogenic effect of dioxin exposure has also been shown to be stronger among obese compared to lean individuals (29). The effects of the seven pesticides that showed an association in both ever-never and dose-response analyses were strongest in obese participants. Although BMI may be related to pesticide exposure in the case of lipophilic compounds, it is not clearly in the causal pathway (i.e., pesticide exposure has not been shown to cause weight gain in adults), allaying the concern that adjusting for BMI would result in an over-adjustment of the effect of pesticide exposure.
Limitations
One limitation of this study was the use of self-reported diagnosis of diabetes. Among the 1,055 participants who indicated a diagnosis of diabetes at baseline and completed the follow-up 15 interview, 92 percent (972) confirmed this diagnosis in the follow-up interview. This suggests a high level of reliability in self-reporting diabetes in this cohort. Furthermore, it is reassuring that age and BMI were associated with the outcome as these are well established risk factors for diabetes. A second limitation was our inability to control for exercise and diet.
A drawback in studies of occupationally exposed cohorts that has been raised in previous studies is the ability of the group identified as unexposed to represent a truly low-risk group (2). Inclusion of participants exposed to other potentially diabetogenic pesticides in the unexposed group may have resulted in an underestimation of the true effects.
In regard to age, which is associated with cumulative pesticide exposure and causally associated with diabetes, there may be some concern about residual confounding. However, estimates from models with age treated as a continuous or as a quadratic term were nearly identical to those from the model using age as a categorical variable. In age-stratified analyses, the observation that pesticide effects were more prominent among younger applicators may be due to an increased number of competing risk factors for diabetes at older ages.
There was a strong relationship between diabetes incidence and state of residence; applicators from North Carolina had a two-fold increased odds of diabetes compared to applicators in Iowa, even after adjusting for age, BMI, and smoking. This may reflect differences in health and lifestyle status between the states that were not completely controlled for by age, BMI, and smoking alone. State, therefore, was included in all fully-adjusted models and state-stratified analyses were conducted when necessary.
Although we had relatively good follow-up of the cohort after 5 years, participants who did not complete the follow-up interview were more likely to have had diabetes at enrollment. While the cumulative days of pesticide use did not differ significantly, ever use of pesticide groups was lower among participants who were lost to follow-up. The loss of prevalent diabetics does not necessarily imply the loss of incident diabetics, and it is impossible to know whether the loss of diabetics would be related to level of exposure. However, we cannot exclude the possibility that selection would have biased our results.
Summary
Pesticide applicators who reported exposure to certain organochlorine and organophosphate insecticides and two herbicides showed an increased risk of diabetes independent of age, state of residence, and body mass index. These results extend previous findings of persistent organic pollutants and organophosphate insecticides to a much larger cohort where diabetes onset was assessed prospectively and exposure was measured in a semi-quantitative manner. Although based in an occupationally-exposed cohort, the findings may have relevance to the general population in the case of environmentally-persistent chemicals. Apart from organochlorine insecticides, most pesticides in this study are considered general use pesticides and are available to the general public. The increasing burden of diabetes in populations world-wide warrants our improved understanding of the possible relationship of diabetes risk to long-term, low-levels of pesticide exposure.
ACKNOWLEDGEMENTS
The authors appreciate the help of Dr. Marie Richards at Westat, Inc. for assistance in programming, and the staff of the Agricultural Health Study. This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences.
Abbreviations
- BMI
body mass index
- CI
confidence interval
- DDT
dichloro-diphenyl-trichloroethane
- NHANES
National Health and Nutrition Examination Survey
- OP
organophosphate insecticide
- OR
odds ratio
- POP
persistent organic pollutant.
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