Abstract
Objectives
Experimental studies suggest a relationship between pesticide exposure and renal impairment, but epidemiological evidence is limited. We evaluated the association between exposure to 41 specific pesticides and end-stage renal disease (ESRD) incidence in the Agricultural Health Study (AHS), a prospective cohort study of licensed pesticide applicators in Iowa and North Carolina.
Methods
Via linkage to the United States Renal Data System, we identified 320 ESRD cases diagnosed between enrollment (1993-1997) and December 2011 among 55,580 male licensed pesticide applicators. Participants provided pesticide use information via self-administered questionnaires. Lifetime pesticide use was defined as the product of duration and frequency of use and then modified by an intensity factor to account for differences in pesticide application practices. Cox proportional hazards models, adjusted for age and state, were used to estimate associations between ESRD and: 1) ordinal categories of intensity-weighted lifetime use of 41 pesticides, 2) poisoning and high-level pesticide exposures, and 3) pesticide exposure resulting in a medical visit or hospitalization.
Results
Positive exposure-response trends were observed for the herbicides alachlor, atrazine, metolachlor, paraquat, and pendimethalin, and the insecticide chlordane. More than one medical visit due to pesticide use (HR = 2.13; 95% CI: 1.17, 3.89) and hospitalization due to pesticide use (HR = 3.05; 95% CI: 1.67, 5.58) were significantly associated with ESRD.
Conclusions
Our findings support an association between ESRD and chronic exposure to specific pesticides and suggest pesticide exposures resulting in medical visits may increase the risk of ESRD.
Keywords: Pesticide exposure, chronic kidney disease, end-stage renal disease
Introduction
In 2011, over 600,000 United States residents were receiving treatment for end-stage renal disease (ESRD), a life-threatening condition requiring dialysis or kidney transplant for survival. Although much research has been conducted on clinical precursors to ESRD, such as diabetes and hypertension, research is limited on the impact of environmental and occupational factors. Literature on the nephrotoxic effects of pesticides in humans is extremely limited, and mainly comes from case reports of both fatal and non-fatal pesticide poisoning, which have described acute nephrotoxicity with a variety of pesticide classes. 1-5 Most of the evidence regarding nephrotoxicity of pesticides is restricted to experimental animal studies. Renal damage and dysfunction has been observed in experimental animal studies with exposure to specific pesticides in a dose-dependent6-9 and/or exposure duration-dependent7,9-11 manner. A variety of pesticide classes have been shown to cause renal damage and dysfunction in animals, including organophosphate, 6,9 organochlorine, 10,12 carbamate, 13 and pyrethroid 14 insecticides and triazine 15 and chlorophenoxy8 herbicides.
The impact of long-term pesticide exposure on human kidney function remains largely unknown. Generally, studies conducted in El Salvador, Nicaragua, and Sri Lanka indicate an elevated prevalence of chronic kidney disease among agricultural workers 16-19 compared to those who have never worked in agriculture, particularly among male agricultural workers20; pesticide exposure is postulated to be a contributor to kidney disease in these regions, but existing evidence has not confirmed this hypothesis. 16-18 These studies lack specificity with regard to pesticide type and have not been able to adequately assess the long-term effects of chronic low-level or acute high-level pesticide exposure on ESRD risk. To our knowledge, the only study to assess pesticide exposure and ESRD found self-reported work in a place with frequent or daily exposure to insect or plant spray to be associated with increased ESRD risk. 21
The Agricultural Health Study (AHS) is the largest prospective study of pesticide applicators in the United States. Linking the AHS to the United States Renal Data System (USRDS) provides a unique opportunity to evaluate the relationship between pesticide use and ESRD risk. Using this linkage, we evaluated associations between chronic and acute pesticide exposure and ESRD risk.
Methods
Population and case definition
Details of the AHS design have been described previously.22 Briefly, the AHS recruited private pesticide applicators (mainly farmers) (N= 52,394) in North Carolina and Iowa and commercial pesticide applicators (N=4,916) in Iowa who applied for or renewed a restricted-use pesticide license between 1993 and 1997. At the licensing facility, each pesticide applicator was asked to complete a brief enrollment questionnaire. Participating applicators were also given a packet of additional questionnaires to complete at home and mail back (take-home questionnaire). Approximately 82% of eligible private applicators and 47% of eligible commercial applicators enrolled in the study. Farmworkers were not included in the AHS. At enrollment, applicators provided information on lifetime pesticide use and pesticide use practices, demographic characteristics, lifestyle factors, farm information, and medical history in a self-administered questionnaire. 22 Of enrolled applicators, 44% also completed the take-home questionnaire with additional questions about medical history and pesticide use. 23 Questionnaires are available on the AHS web site: http://aghealth.nih.gov/collaboration/questionnaires.html.
We identified ESRD cases diagnosed between study enrollment and end of follow-up (December 31, 2011) through linkage with the USRDS. The USRDS collects data on all ESRD cases in the United States through Medical Evidence Form CMS-2728, which is required for all new ESRD patients, regardless of Medicare eligibility. The USRDS derives the first ESRD service date (FSD) by taking the earliest of: a) the date of the start of dialysis for chronic renal failure, as reported on the Medical Evidence form, b) the date of a kidney transplant, or c) the date of the first Medicare dialysis claim. 24 The FSD was used to estimate age at ESRD diagnosis. Date of death was obtained from state mortality files and the National Death Index. Because the distribution of ESRD risk factors differs by gender, and because few applicators were female, we excluded female applicators (N= 1,562; 2.7%) from this analysis. We also excluded applicators under age 18 (N=127; <1.0%) and ESRD cases diagnosed prior to enrollment (N=42; 11.5% of cases). This left us with 55,580 participants for analyses of enrollment questionnaire variables, and 24,565 participants for analyses of take-home questionnaire variables.
Exposure assessment
Information provided on the enrollment and take-home questionnaires (Phase 1) was used to estimate lifetime pesticide exposure. We limited these analyses to enrollment information because recent exposure was anticipated to have a minor impact on ESRD risk, given the typical decades-long progression of this disease from chronic stage 1 to ESRD, and this information was not available for all participants. Participants provided information on years of use (duration) and average days per year of use (frequency) for 22 pesticides on the enrollment questionnaire. Duration and frequency of use data were obtained on the take-home questionnaire for 28 additional pesticides. For each pesticide, an intensity-weighted lifetime exposure metric was generated by multiplying lifetime-days of use (product of duration and frequency of use) by an intensity score that accounts for differences in exposure resulting from variation in pesticide application methods, repair of pesticide application equipment, and use of personal protective equipment 25. The intensity score algorithm was developed using AHS-specific pesticide exposure monitoring data, in conjunction with expert judgment from published studies on pesticide exposure, including information from the Pesticide Handlers Exposure Database.25 We used intensity-weighted lifetime-days as our primary exposure metric. Due to the relatively small number of cases, we used the distribution of use among cases to create cut-points for intensity-weighted lifetime use of specific pesticides. For pesticides used by ≥15% of cases, we categorized non-zero intensity-weighted lifetime-days into tertiles with non-users as the referent group. For less frequently used pesticides, non-zero intensity-weighted lifetime-days of use were split at the median (<median vs. ≥ median), with non-users as the referent group. Analyses were restricted to pesticides for which there were at least 5 cases in each exposure stratum.
To assess overall pesticide use, we evaluated risk related to duration, frequency and lifetime-days of use of any pesticide. Duration and frequency of use were categorized into three levels (lowest category of use (referent), > lowest category of use to the median value, and > median). Cumulative lifetime-days of use was categorized into quartiles.
Participant report of medical visits due to pesticide use (enrollment questionnaire), unusually high personal exposure to any pesticide (take-home questionnaire), and doctor-diagnosed pesticide poisoning (take-home questionnaire) were also evaluated in relation to ESRD risk.
Statistical Analysis
We used Cox proportional hazards models to calculate hazard ratios for risk of ESRD, using age as the timescale and adjusting for state as a covariate in all models. Person-time was accrued from the date of study enrollment until the earliest of ESRD diagnosis, death, or the end of study follow-up (December 31, 2011). The proportional hazards assumption was evaluated for each model by entering a product term (exposure of interest * time on study) into each model. A product term coefficient which differed significantly from zero (chi-square p-value <0.10) indicated a potential violation of the proportional hazards assumption.
Private and commercial applicators were analyzed together because there were too few ESRD cases among the latter to analyze them separately. Race and education level were identified as additional potential confounders through directed acyclic graph analyses (DAGs) and review of prior literature. Because adjustment for these factors did not substantially change hazard ratio estimates and power was reduced due to incomplete ascertainment of education and race data, we did not adjust for these factors in the final analyses. Though diabetes and body mass index (BMI) were associated with ESRD risk in this and other studies, 26,27 it was unknown whether these conditions affected pesticide use, and it is possible that use of specific pesticides may increase the risk of diabetes and BMI28-31, which may be on the causal pathway to ESRD. Hazard ratio estimates obtained from models adjusted for diabetes and BMI were not meaningfully different from crude estimates; therefore, these two conditions were not included in final adjusted models. Hypertension was not adjusted for because it is largely asymptomatic and therefore is unlikely to have affected pesticide use practices. Additionally, in our study, there was no association between cumulative pesticide use and hypertension (data not shown).
To assess potential confounding by other pesticides, we examined pairwise correlations between pesticides that were strongly (HR in any strata ≥1.5 or ≤0.65) or significantly associated with ESRD in single pesticide adjusted exposure-response models. For pesticides with a Spearman correlation coefficient ≥0.3, we constructed models with both pesticides, using the intensity-weighted variables included in the main analyses. For pesticides that were correlated with more than one pesticide, we first evaluated each pesticide pair and then added correlated pesticides one at a time into subsequent models. We assessed model fit using Akaike information criteria (AIC) and selected that with the lowest AIC as the final model.
To assess linear exposure-response trends in intensity-weighted lifetime use, we used within-category medians as the score for each level of use for each chemical. Exposure–response trends were also evaluated for duration, frequency, and cumulative lifetime-days of use of any pesticide, and number of doctor visits related to pesticide use.
ESRD is the final stage of chronic kidney disease (CKD), which is often debilitating in later stages of the disease. Cases may have already experienced the effects of CKD prior to study enrollment, which could have influenced their pesticide use. If those with earlier stages of renal disease have reduced exposure due to modified application practices, effect estimates for specific pesticide use would be biased towards the null. This bias is commonly referred to as the healthy worker survivor effect. 32 To evaluate the potential for this effect to influence our findings, we repeated analyses, excluding person-time for all participants for the first five years after enrollment under the assumption that ESRD cases diagnosed within 5 years following study enrollment likely had poor renal health at enrollment.
To evaluate whether patterns of association were consistent across states, we entered a product term for state into pesticide use models for those pesticides for which there were at least 5 cases in each stratum of use in both states.
We used the AHS dataset releases P1REL201209, P3REL201209.00, and AHSREL201304.00. All statistical analyses were done using SAS v9.3 (Cary, NC).
Results
Of the 55,580 participants eligible for analysis, 320 (308 private and 12 commercial) were diagnosed with ESRD over an average 15.7-year follow-up period (incidence rate: 36.6 ESRD cases per 100,000 person-years). Among the subset of 24,565 participants who returned the take-home questionnaire, there were 136 cases (incidence rate: 35.1 ESRD cases per 100,000 person-years). ESRD incidence was significantly higher in North Carolina compared to Iowa, regardless of age, which follows the pattern of ESRD incidence in the general population.24. In age- and state-adjusted models, education level greater than high school and obesity at enrollment were associated with increased risk of ESRD (Table 1). Self-reported doctor diagnosis of diabetes, high blood pressure, and kidney disease were significantly associated with increased risk of ESRD. There was a suggestive but non-significant association between pack years of cigarettes smoked and ESRD, but applicator type, number of years living on a farm, and alcohol consumption at enrollment were not associated with ESRD risk.
Table 1. Association between ESRD and demographic and medical conditions among private and commercial applicators, adjusted for age and state, Agricultural Health Study (1993-1997).
| Variable (at enrollment) | Non-cases (N= 55,260) N (%) | ESRD Cases (N=320) N (%) | HR (95% CI) | |
|---|---|---|---|---|
| State (where enrolled)* | Iowa | 35943 (65.0) | 134 (41.9) | |
| North Carolina | 19317 (35.0) | 186 (58.1) | 2.02 (1.61, 2.53) | |
| Applicator type* | Private | 50575 (91.5) | 308 (96.3) | |
| Commercial | 4685 (8.5) | 12 (3.7) | 0.75 (0.42, 1.35) | |
| Age category (years) | 18-30 | 6306 (11.4) | 12 (3.8) | |
| 31-49 | 27380 (49.6) | 58 (18.1) | 1.15 (0.62, 2.15) | |
| 50-69 | 18924 (34.3) | 210 (65.6) | 6.04 (3.38, 10.81) | |
| ≥ 70 | 2650 (4.8) | 40 (12.5) | 10.04 (5.25, 19.20) | |
| Race | White | 52763 (97.3) | 273 (85.3) | |
| non-White | 1440 (2.7) | 47 (14.7) | 4.42 (3.18, 6.13) | |
| Education level | High school or less | 22452 (41.7) | 74 (24.3) | |
| More than high school | 31369 (58.3) | 231 (75.7) | 1.49 (1.14 1.95) | |
| Number years lived or worked on a farm† | 0-20 | 2181 (10.0) | 10 (7.9) | |
| 21-30 | 2928 (13.4) | 13 (10.2) | 1.43 (0.62 3.28) | |
| >30 | 16668 (76.5) | 104 (81.9) | 0.90 (0.46 1.75) | |
| Number of days per month drink alcohol in the last year | 0 | 16504 (31.9) | 147 (52.1) | |
| 1-23 | 31833 (61.5) | 121 (42.9) | 0.86 (0.66 1.11) | |
| ≥24 | 3447 (6.7) | 14 (5.0) | 0.78 (0.45 1.36) | |
| Number of pack-years smoked | None | 28059 (53.8) | 124 (43.5) | |
| 1-11 | 10956 (21.0) | 45 (15.8) | 0.80 (0.57 1.13) | |
| 12-30 | 8708 (16.7) | 67 (23.5) | 1.23 (0.91 1.66) | |
| >30 | 4464 (8.6) | 49 (17.2) | 1.36 (0.97 1.91) | |
| Body mass index (kg/m2) | <25 | 9658 (25.5) | 44 (19.8) | |
| 25-29.99 | 19341 (51.1) | 108 (48.6) | 1.26 (0.89 1.79) | |
| ≥30 | 8823 (23.3) | 70 (31.5) | 2.00 (1.37 2.93) | |
| Self-reported doctor diagnosis of: | ||||
| Diabetes | No | 49616 (97.2) | 192 (70.1) | |
| Yes | 1411 (2.8) | 82 (29.9) | 8.78 (6.72, 11.45) | |
| High Blood Pressure† | No | 20081 (83.6) | 52 (39.4) | |
| Yes | 3945 (16.4) | 80 (60.6) | 4.66 (3.25, 6.68) | |
| Kidney disease (not counting kidney stones) | No | 50935 (99.2) | 245 (88.4) | |
| Yes | 415 (0.8) | 32 (11.6) | 10.35 (7.14, 15.02) | |
ESRD = end-stage renal disease.
Adjusted for age only.
Reported on the take-home questionnaire only
More than one doctor visit due to pesticide use and hospitalization due to pesticide use were both significantly associated with ESRD with a significant trend observed for increasing number of pesticide-related doctor visits (p for trend=0.038) (Table 2). ESRD risk was not associated with either self-reported unusually high personal pesticide exposure or pesticide poisoning, though only 5 ESRD cases reported a pesticide poisoning diagnosis. No exposure–response relationships were observed for duration and frequency of general pesticide use (data not shown), or for cumulative lifetime-days of general pesticide use (Table 2).
Table 2. Association between ESRD risk and acute and cumulative pesticide exposure, adjusted for age and state, among male pesticide applicators, Agricultural Health Study (1993-1997).
| Variable | Non-cases (N= 55,260) N (%) | ESRD Cases (N=320) N (%) | HR (95% CI) | P for trend | |
|---|---|---|---|---|---|
| Number of times ever visited a medical doctor due to pesticide use | None | 49764 (93.4) | 272 (91.3) | ||
| Once | 2635 (4.9) | 15 (5) | 1.07 (0.64, 1.8) | ||
| More than once | 884 (1.7) | 11 (3.7) | 2.13 (1.17, 3.89) | ||
| 0.0384 | |||||
| Ever hospitalized due to pesticide use | No | 51619 (98.9) | 275 (96.2) | ||
| Yes | 572 (1.1) | 11 (3.8) | 3.05 (1.67, 5.58) | ||
| Ever experienced unusually high personal pesticide exposure* | No | 20112 (84.8) | 115 (87.1) | ||
| Yes | 3596 (15.2) | 17 (12.9) | 1.08 (0.65, 1.81) | ||
| Ever diagnosed with Pesticide poisoning* | No | 23506 (98.0) | 126 (96.2) | ||
| Yes | 484 (2.0) | 5 (3.8) | 1.59 (0.65, 3.89) | ||
| Cumulative lifetime-days personally mixed or applied pesticides | 0-64 | 13850 (27.1) | 78 (28.0) | ||
| 65-225 | 15637 (30.6) | 69 (24.7) | 0.89 (0.64, 1.24) | ||
| 226-457 | 7307 (14.3) | 41 (14.7) | 0.95 (0.65, 1.39) | ||
| >457 | 14314 (28.0) | 91 (32.6) | 1.07 (0.79, 1.44) | ||
| 0.4667 |
ESRD = end-stage renal disease
Question asked only on the take-home questionnaire: N (non-cases) = 24,429 and N (cases) =136.
In intensity-weighted cumulative use analyses, positive associations were observed primarily among herbicides (Table 3). ESRD risk was associated with the highest tertile of intensity-weighted use of five herbicides: atrazine, metolachlor, alachlor, paraquat, and pendimethalin, compared to no use. We observed a significant (p for trend <0.05) exposure-response trend with increasing use levels for all of these herbicides. Although exposure-response trends were not seen for the herbicides petroleum oil or imazethapyr, ever use of these chemicals was significantly associated with risk (HR=1.63; 95% CI: 1.11, 2.41 and HR=1.46; 95% CI: 1.08, 1.99, respectively; data not shown). The proportional hazards assumption held for all exposures of interest.
Table 3. Intensity-weighted lifetime-days of use of specific pesticides and ESRD risk, adjusted for age and state, among male pesticide applicators in the Agricultural Health Study (1993-1997).
| Pesticide | Intensity-weighted lifetime-days | Non-cases N=55,260 | ESRD Cases N=320 | HR (95% CI) | P for trend |
|---|---|---|---|---|---|
|
|
|
||||
| N (%) | N (%) | ||||
| FUMIGANTS | |||||
|
| |||||
| Methyl Bromide | <490 | 3180 (6) | 17 (5.7) | 0.63 (0.38, 1.04) | |
| 490-1873 | 2449 (4.6) | 20 (6.8) | 0.87 (0.54, 1.40) | ||
| ≥1874 | 2038 (3.8) | 20 (6.8) | 0.97 (0.61, 1.57) | ||
| 0.9304 | |||||
|
| |||||
| FUNGICIDES | |||||
|
| |||||
| Chlorothalonil* | <588 | 1341 (2.5) | 13 (4.4) | 1.47 (0.84, 2.59) | |
| 588-3254 | 1483 (2.8) | 14 (4.7) | 1.38 (0.80, 2.40) | ||
| ≥3255 | 1330 (2.5) | 14 (4.7) | 1.54 (0.89, 2.67) | ||
| 0.1119 | |||||
| Metalaxyl* | <294 | 1315 (5.6) | 11 (8.7) | 1.62 (0.86, 3.06) | |
| 294-1679 | 1531 (6.6) | 12 (9.5) | 1.58 (0.83, 3.01) | ||
| ≥ 1680 | 1339 (5.7) | 13 (10.3) | 1.92 (1.01, 3.66) | ||
| 0.067 | |||||
|
| |||||
| HERBICIDES | |||||
|
| |||||
| Phenoxy herbicides | |||||
| 2,4-D* | <1721 | 15671 (29.8) | 67 (22.9) | 0.74 (0.54, 1.02) | |
| 1721-6614 | 12980 (24.7) | 66 (22.5) | 0.87 (0.62, 1.2) | ||
| ≥ 6615 | 10788 (20.5) | 71 (24.2) | 1.00 (0.73, 1.39) | ||
| 0.3247 | |||||
| 2,4,5,T* | <780 | 2425 (10.3) | 12 (9.4) | 0.60 (0.33, 1.09) | |
| ≥ 780 | 1776 (7.6) | 13 (10.2) | 0.83 (0.46, 1.48) | ||
| 0.5508 | |||||
|
| |||||
| Triazine herbicides | |||||
| Atrazine | <1302 | 13386 (25.3) | 67 (22.3) | 1.10 (0.80, 1.52) | |
| 1302-6439 | 14134 (26.7) | 67 (22.3) | 1.00 (0.73, 1.39) | ||
| ≥ 6440 | 9065 (17.1) | 72 (24) | 1.51 (1.11, 2.06) | ||
| 0.023 | |||||
| Cyanazine | <780 | 7791 (15.7) | 27 (10.6) | 0.82 (0.54, 1.26) | |
| 780-2787 | 6479 (13.1) | 29 (11.4) | 1.07 (0.71, 1.62) | ||
| ≥ 2788 | 6201 (12.5) | 28 (11) | 1.16 (0.76, 1.77) | ||
| 0.3698 | |||||
| Metribuzin a | <455 | 3555 (15.1) | 14 (10.9) | 0.90 (0.50, 1.62) | |
| 455-1322 | 2497 (10.6) | 15 (11.6) | 1.39 (0.79, 2.45) | ||
| ≥ 1323 | 2627 (11.2) | 16 (12.4) | 1.49 (0.86, 2.59) | ||
| 0.1075 | |||||
|
| |||||
| Dinitroaniline herbicides | |||||
| Pendimethalin* | <793 | 4318 (18.3) | 14 (11) | 0.7 (0.4, 1.23) | |
| 793-3023 | 2829 (12) | 14 (11) | 1.19 (0.67, 2.1) | ||
| ≥ 3024 | 1779 (7.6) | 15 (11.8) | 2.15 (1.23, 3.77) | ||
| 0.0041 | |||||
| Trifluralin | <1008 | 8255 (16.8) | 37 (14.7) | 0.95 (0.66, 1.38) | |
| 1008-3417 | 8060 (16.4) | 39 (15.5) | 1.10 (0.76, 1.59) | ||
| ≥ 3418 | 9425 (19.1) | 38 (15.1) | 0.91 (0.63, 1.31) | ||
| 0.6499 | |||||
|
| |||||
| Chloroacetanilide herbicides | |||||
| Metolachlor | <1006 | 9206 (18.7) | 38 (14.5) | 1.02 (0.71, 1.46) | |
| 1006-3827 | 7157 (14.5) | 38 (14.5) | 1.39 (0.96, 2.00) | ||
| ≥ 3828 | 6443 (13.1) | 40 (15.3) | 1.53 (1.08, 2.18) | ||
| 0.0121 | |||||
| Alachlor | <1008 | 9683 (19.7) | 46 (17.6) | 1.04 (0.74, 1.48) | |
| 1008-5486 | 9776 (19.9) | 47 (18) | 1.02 (0.73, 1.44) | ||
| ≥ 5487 | 6111 (12.4) | 49 (18.8) | 1.56 (1.12, 2.18) | ||
| 0.0077 | |||||
|
| |||||
| all other herbicides | |||||
| Dicamba | <473 | 6981 (14.2) | 32 (12.4) | 1.01 (0.67, 1.51) | |
| 473-2603 | 9899 (20.2) | 29 (11.2) | 0.66 (0.43, 1.01) | ||
| ≥ 2604 | 7766 (15.8) | 34 (13.1) | 1.05 (0.71, 1.57) | ||
| 0.7251 | |||||
| Chlorimuron-ethyl* | <351 | 3368 (14.3) | 12 (9.3) | 0.90 (0.49, 1.65) | |
| 351-787 | 1361 (5.8) | 14 (10.9) | 2.47 (1.4, 4.34) | ||
| ≥ 788 | 2863 (12.2) | 13 (10.1) | 1.06 (0.59, 1.9) | ||
| 0.5893 | |||||
| EPTC | <15 | 4525 (9.3) | 11 (4.4) | 0.64 (0.35, 1.19) | |
| 15-55 | 2753 (5.6) | 12 (4.8) | 1.18 (0.65, 2.13) | ||
| >55 | 2713 (5.6) | 12 (4.8) | 1.25 (0.69, 2.24) | ||
| 0.3894 | |||||
| Paraquat* | <638 | 1881 (8) | 11 (8.5) | 1.15 (0.61, 2.15) | |
| 638-2087 | 1034 (4.4) | 10 (7.7) | 1.82 (0.93, 3.58) | ||
| ≥ 2088 | 1012 (4.3) | 12 (9.2) | 2.23 (1.18, 4.21) | ||
| 0.0121 | |||||
| Petroleum Oil* | <784 | 1950 (8.3) | 11 (8.5) | 1.27 (0.68, 2.38) | |
| 784-2024 | 1006 (4.3) | 12 (9.3) | 3.20 (1.75, 5.85) | ||
| ≥ 2025 | 1969 (8.4) | 12 (9.3) | 1.42 (0.78, 2.59) | ||
| 0.1906 | |||||
| Imazethapyr | <350 | 6748 (13.8) | 29 (11.2) | 1.34 (0.87, 2.07) | |
| 350-839 | 4819 (9.8) | 29 (11.2) | 2.03 (1.31, 3.15) | ||
| ≥ 840 | 9393 (19.2) | 31 (12) | 1.26 (0.83, 1.93) | ||
| 0.3169 | |||||
| Glyphosate | <600 | 12104 (22.9) | 70 (23.4) | 0.91 (0.67, 1.26) | |
| 600-2687 | 14471 (27.3) | 72 (24.1) | 0.75 (0.54, 1.03) | ||
| ≥ 2688 | 13375 (25.3) | 74 (24.7) | 0.85 (0.62, 1.17) | ||
| 0.5843 | |||||
| Butylate* | <1006 | 3237 (13.8) | 13 (10.2) | 0.78 (0.43, 1.41) | |
| ≥ 1006 | 2888 (12.3) | 12 (9.4) | 0.87 (0.48, 1.60) | ||
| 0.6548 | |||||
|
| |||||
| INSECTICIDES | |||||
|
| |||||
| Organochlorines | |||||
| Aldrin* | <327 | 1292 (5.5) | 12 (9.8) | 1.27 (0.68, 2.37) | |
| 327-1018 | 1196 (5.1) | 11 (9) | 1.21 (0.63, 2.31) | ||
| ≥ 1019 | 1227 (5.2) | 12 (9.8) | 1.23 (0.66, 2.29) | ||
| 0.5243 | |||||
| Chlordane* | <560 | 2581 (11) | 11 (8.7) | 0.57 (0.30, 1.06) | |
| 560-1224 | 805 (3.4) | 10 (7.9) | 1.44 (0.75, 2.78) | ||
| ≥ 1225 | 874 (3.7) | 13 (10.2) | 1.70 (0.95, 3.06) | ||
| 0.0409 | |||||
| DDT* | <438 | 1904 (8.2) | 16 (12.7) | 0.80 (0.46, 1.38) | |
| 438-2327 | 1689 (7.2) | 16 (12.7) | 0.82 (0.47, 1.42) | ||
| ≥ 2328 | 1328 (5.7) | 15 (11.9) | 0.99 (0.56, 1.74) | ||
| 0.9263 | |||||
| Heptachlor* | >408 | 1235 (5.2) | 11 (8.7) | 1.29 (0.68, 2.46) | |
| ≥ 408 | 1433 (6.1) | 15 (11.9) | 1.44 (0.82, 2.52) | ||
| 0.2068 | |||||
| Toxaphene* | <1006 | 1554 (6.6) | 8 (6.5) | 0.69 (0.34, 1.42) | |
| ≥ 1006 | 975 (4.1) | 9 (7.3) | 0.99 (0.49, 1.99) | ||
| 0.9583 | |||||
|
| |||||
| Organophosphates | |||||
| Terbufos | <827 | 7124 (14.4) | 28 (11) | 1.0 (0.66, 1.51) | |
| 827-2159 | 4413 (8.9) | 27 (10.6) | 1.41 (0.93, 2.13) | ||
| ≥ 2160 | 6833 (13.8) | 29 (11.4) | 0.97 (0.65, 1.44) | ||
| 0.9823 | |||||
| Fonofos | <588 | 3761 (7.6) | 10 (3.9) | 0.62 (0.33, 1.19) | |
| 588-1619 | 2834 (5.7) | 11 (4.3) | 0.9 (0.48, 1.66) | ||
| ≥ 1620 | 3670 (7.4) | 12 (4.7) | 0.7 (0.39, 1.27) | ||
| 0.257 | |||||
| Chlorpyrifos | <438 | 5989 (13.1) | 29 (11) | 0.85 (0.57, 1.26) | |
| 438-2139 | 7603 (16.7) | 28 (10.6) | 0.65 (0.43, 0.97) | ||
| ≥ 2140 | 6078 (13.3) | 29 (11) | 0.84 (0.57, 1.25) | ||
| 0.3738 | |||||
| Malathion* | <644 | 6577 (28.2) | 27 (21.4) | 0.87 (0.54, 1.42) | |
| 644-1743 | 3567 (15.3) | 28 (22.2) | 1.47 (0.91, 2.36) | ||
| ≥ 1744 | 4403 (18.9) | 28 (22.2) | 1.01 (0.62, 1.63) | ||
| 0.9247 | |||||
| Parathion* | <1392 | 1022 (4.4) | 8 (6.5) | 1.11 (0.53, 2.29) | |
| ≥1392 | 687 (2.9) | 8 (6.5) | 1.64 (0.79, 3.43) | ||
| 0.1856 | |||||
| Diazinon* | <1184 | 3124 (13.4) | 11 (8.7) | 0.57 (0.31, 1.07) | |
| ≥1184 | 1762 (7.5) | 14 (11.1) | 1.11 (0.62, 1.97) | ||
| 0.6733 | |||||
| Phorate* | <408 | 2514 (10.7) | 10 (8.3) | 0.76 (0.39, 1.51) | |
| 408-2169 | 2625 (11.2) | 12 (9.9) | 0.87 (0.47, 1.63) | ||
| ≥ 2170 | 1486 (6.3) | 12 (9.9) | 1.47 (0.80, 2.71) | ||
| 0.1941 | |||||
| Coumaphos | <957 | 2100 (4.3) | 14 (5.6) | 1.29 (0.75, 2.22) | |
| ≥957 | 1651 (3.4) | 14 (5.6) | 1.63 (0.95, 2.79) | ||
| 0.0689 | |||||
| Dichlorvos | <3136 | 2987 (6.1) | 10 (4.1) | 0.78 (0.41, 1.47) | |
| ≥ 3136 | 1682 (3.4) | 10 (4.1) | 1.41 (0.74, 2.67) | ||
| 0.2862 | |||||
|
| |||||
| Pyrethroids | |||||
| Permethrin for crops | <368 | 2737 (5.6) | 11 (4.3) | 1.06 (0.58, 1.94) | |
| 368-2572 | 2611 (5.3) | 11 (4.3) | 0.99 (0.54, 1.82) | ||
| ≥ 2573 | 1576 (3.2) | 11 (4.3) | 1.50 (0.82, 2.77) | ||
| 0.201 | |||||
| Permethrin for animals | <646 | 2709 (5.4) | 13 (5.1) | 1.60 (0.91, 2.83) | |
| ≥ 646 | 3225 (6.5) | 12 (4.7) | 1.24 (0.69, 2.22) | ||
| 0.4335 | |||||
|
| |||||
| Carbamates | |||||
| Carbofuran | <696 | 5740 (11.7) | 21 (8.4) | 0.65 (0.41, 1.03) | |
| 696-2299 | 3753 (7.6) | 21 (8.4) | 0.88 (0.56, 1.39) | ||
| ≥ 2300 | 3210 (6.5) | 22 (8.8) | 1.06 (0.68, 1.65) | ||
| 0.755 | |||||
| Carbaryl* | <919 | 4818 (20.6) | 18 (14.4) | 0.57 (0.34, 0.97) | |
| 919-6874 | 3353 (14.4) | 17 (13.6) | 0.59 (0.33, 1.05) | ||
| ≥ 6875 | 1649 (7.1) | 19 (15.2) | 1.05 (0.58, 1.88) | ||
| 0.3186 | |||||
| Aldicarb* | <1323 | 878 (3.7) | 8 (6.3) | 1.66 (0.79, 3.5) | |
| ≥ 1323 | 856 (3.6) | 7 (5.5) | 1.71 (0.77, 3.79) | ||
| 0.1876 | |||||
Indicates pesticide with duration and frequency information only available on the take-home questionnaire: N (non-cases) = 24,429 and N (cases) =136.
Among non-herbicide pesticides, ESRD risk was associated with the highest tertile of metalaxyl (fungicide) use (HR = 1.92; 95% CI: 1.01, 3.66), with evidence of a positive exposure-response trend (Table 3). Associations for the insecticides coumaphos and parathion (organophosphates), aldicarb (carbamate), and chlordane (organochlorine) were elevated (i.e. >1.6), but did not reach statistical significance. A significant positive exposure-response trend was present for chlordane.
In analyses of correlated pesticides, we found fourteen pesticide pairs with a Spearman correlation coefficient ≥0.30. Adjustment for correlated pesticides resulted in reduced overall sample size due to missing data for each chemical. Adjusted estimates were similar in magnitude and direction, but were less precise. Patterns of exposure-response also did not change. After adjustment for correlated pesticides, the association between ESRD risk and the top tertile of intensity-weighted use remained significant only for pendimethalin, and the association became significant for chlordane (HR=1.93; 95% CI: 1.01, 3.70). Estimates for atrazine, alachlor, metolachlor, and aldicarb remained elevated but were no longer significantly associated with ESRD risk after adjustment for correlated pesticides. We did not observe a correlation coefficient ≥0.3 for the following pesticides: 2,4,5 T, chlorimuron ethyl, paraquat, petroleum oil, coumaphos, fonofos, parathion, and permethrin (for animals) (data not shown).
In sensitivity analysis evaluating the potential for a ‘healthy worker survivor effect’, we excluded 53 cases that were diagnosed with ESRD within 5 years after enrollment and 277,900 person-years. The greatest percent reductions of case numbers were observed in the ‘None’ use category for all pesticides. In general, associations for intensity-weighted lifetime-use were in the same direction and of very similar magnitude compared to estimates in the main analyses. Of note, age- and state-adjusted estimates for the highest quantile of intensity-weighted chlordane (HR= 1.99; 95% CI: 1.07, 3.68) and coumaphos (HR=1.81, 95% CI: 1.03, 3.17) use became significant. (Supplemental Table 1).
Results did not change substantially when we restricted analyses to private applicators (data not shown). Thirteen pesticides had 5 exposed cases in each exposure stratum in both states, and were therefore included in analyses of interaction with state. P-values for interaction with state were consistently > 0.10 (data not shown), suggesting no differences by state.
Discussion
To our knowledge, this is the first study to evaluate the association between ESRD risk and cumulative lifetime use of specific pesticides. Among pesticide applicators in the AHS, we found significant positive associations between intensity-weighted use of several specific pesticides and ESRD; excluding cases that arose within 5 years after enrollment strengthened some of these associations, though estimates were less precise due to the reduction in sample size. This is also the first epidemiological study of ESRD risk associated with non-fatal pesticide poisoning, acute high-level exposure, and pesticide exposure requiring medical attention. Participants who reported doctor visits and hospitalization due to pesticide use had a significantly higher risk of ESRD diagnosis compared to those who did not, but we did not observe increased risk with applicator report of doctor-diagnosed pesticide poisoning or unusually high personal pesticide exposure.
Prior published epidemiological research on pesticide exposure and kidney disease is minimal. Results from several cross-sectional studies evaluating the relationship between agricultural work and CKD suggest a potential association between agricultural work, particularly field work, and CKD prevalence17,20. Studies that have evaluated overall pesticide exposure have found positive associations with CKD17,18,35,36. The only study to assess the relationship between ESRD risk and agricultural exposures observed an increased risk of ESRD among a large population of insured patients in the San Francisco Bay area who reported that they worked in a place with “frequent or daily exposure to insect or plant spray” (unadjusted hazard ratio: 1.78; 95% CI: 1.36-2.34).21 In contrast, results from our analyses of general overall pesticide use did not show an association with ESRD; however few participants in this licensed applicator cohort reported no pesticide use, and evaluation of overall pesticide use may obscure associations because only some pesticides appear to be associated with ESRD.
Epidemiologic studies of renal effects of specific pesticides are rare. Hernandez et al (2006) found no difference in serum creatinine levels among greenhouse workers with higher vs. lower levels of apparent cholinesterase inhibition (used as a marker for organophosphate pesticide exposure).37 Serum levels of several organochlorine insecticides among chronic kidney disease patients were inversely associated with kidney function, potentially indicating a renal filtration deficiency resulting in an accumulation of organochlorine pesticides in the body.38 In our study, ESRD risk was elevated for three cholinesterase-inhibiting insecticides (the carbamate aldicarb and the organophosphates coumaphos and parathion), with a moderate positive trend observed for coumaphos. No associations were seen with organochlorine use, except for chlordane, which was significantly positively associated with ESRD risk after adjustment for correlated pesticides and in analyses excluding cases diagnosed within five years after enrollment. Glyphosate was recently partially banned in Sri Lanka due to its hypothesized association with kidney disease, though the ban has since been lifted. Studies that informed this partial ban suggested that glyphosate exposure leads to renal failure only when combined with high-level exposure to heavy metals.39 We found no evidence of an association between ESRD risk and glyphosate exposure.
Experimental evidence supports our findings of positive associations with exposure to the herbicides atrazine, alachlor, paraquat, and pendimethalin and the fungicide metalaxyl, with evidence of dose-response as well as renal damage and dysfunction at low dose levels. Glomerular lesions and renal tubular necrosis due to oxidative stress-induced cell damage have been observed in animal models with exposure to metalaxyl and paraquat,40,41 and kidney damage and dysfunction have been observed in rats exposed to atrazine 15 and fish exposed to alachlor.42 There have been no reports of renal effects of pendimethalin among mammals; however, at least one formulation of pendimethalin contains monochlorobenzene as an inert ingredient, which has been shown to cause kidney damage in rats.43 Although we observed a positive exposure-response trend for metolachlor, we found no published studies implicating this chemical in renal dysfunction or oxidative stress pathways. Sub-acute tubulointerstitial and glomerular damage, such as that observed in animal studies with prolonged low dosing of pesticides, 7,11 can initiate a feed-forward loop of kidney injury and progressive loss of renal function. 44 In humans, pesticide poisoning can lead to acute kidney injury,45 which has been associated with increased risk of subsequent chronic kidney disease and ESRD. 46,47
When we adjusted for correlated pesticides, we found that estimates for atrazine, alachlor, metolachlor, and aldicarb were positively associated but no longer statistically significant. We lacked power to formally test interaction among specific pesticides. In addition, we saw significant associations for chemicals from a number of chemical groups. Given that there is no required renal toxicity testing for pesticides, we have no information regarding which chemicals we would expect to be associated with CKD. A large portion of commercial pesticide products are “other ingredients” including solvents; therefore, it is possible that one or more of these “other ingredients” are driving the risk observed here for unrelated chemicals. Unfortunately, we do not have access to what these “other ingredients” are because they are regarded as confidential business information.
A significant positive exposure-response trend was observed for pesticide-related doctor visits, and participants who reported being hospitalized due to pesticide use had three times the risk of ESRD compared to those who did not. Though information about the route and type of pesticide involved in these exposures was not available, these findings support the hypothesis that frequent and/or severe pesticide exposures may increase the risk of ESRD. We did not see an association between pesticide poisoning or self-reported unusually high pesticide exposure and ESRD; however, power to detect an association was limited because information for those exposures was available only for participants who returned the take-home questionnaire, and pesticide poisoning was rarely diagnosed in the cohort. Pesticide poisoning is frequently under-diagnosed 48; thus, doctor visits or hospitalization related to pesticide use may represent a more sensitive indicator for acute high-level exposure than pesticide poisoning diagnosis.
The study improves upon prior research in several ways. First, we were able to evaluate associations of ESRD risk with a wide range of specific chemicals that vary in toxicity and extent of use. The prospective design of the study mitigates concerns about differential misclassification of exposure. Whereas prior studies were limited by small sample size and exposure to few chemicals, the large size of the AHS cohort allowed us to assess exposure-response trends for many individual pesticides. Also, use of a validated exposure-intensity metric 25 allowed for a better estimate of each participant's likely pesticide exposure as opposed to non-specific pesticide use. Until now, the relationship between short-term high-level pesticide exposures and kidney disease has been evaluated only with respect to the immediate effects of pesticide poisonings. Here, we were able to evaluate measures of non-poisoning short-term high-level pesticide exposures, thereby providing an important contribution to the scientific literature regarding occupational risk factors for kidney disease. Additionally, the fact that almost all ESRD cases in the United States are captured in the USRDS reduces concerns about loss to follow-up or outcome misclassification.
Because exposure data were collected prior to disease onset and ESRD diagnosis data were obtained from a third party linkage rather than participant report, any exposure misclassification due to self-report is likely to be non-differential with respect to the outcome, which would bias estimates towards the null. Additionally, evidence suggests that report of pesticide exposure by AHS participants is reasonably reliable 49 and plausible. 50 The accuracy and reliability of reporting acute pesticide exposures has not been investigated.
Analyses of lifetime use of pesticides that were assessed only on the take-home questionnaire could be subject to selection bias if applicators who returned the take-home questionnaire were significantly different from those who did not by exposure or outcome, or by factors associated with the exposure or the outcome. State of enrollment, age, and pesticide use characteristics were similar for those who returned the take-home questionnaire compared to those who did not, and the percentage of cases was essentially the same for take-home questionnaire respondents as it was for non-respondents (0.59% vs. 0.57%). Still, differences in unmeasured factors remain a possibility, and we had limited power to evaluate associations between ESRD and lifetime use of the pesticides for which duration and frequency information was collected only on the take-home questionnaire.
Lifetime use estimates could also be biased if participants with prevalent pre-end stage kidney disease modified their pesticide application practices in the period prior to enrollment. This ‘healthy worker survivor effect’ is a common problem in occupational health studies, including among pesticide applicators, 51 frequently biasing estimates toward the null. 32 Results of our sensitivity analysis suggest a minimal impact of this potential effect on HR estimates. The median time from enrollment to ESRD diagnosis was 9.7 years. It is possible that unmeasured post-enrollment pesticide use may differ by case status; if recent exposures are stronger contributors to ESRD risk than pre-enrollment exposures, then our results would still be biased towards the null. However, progression of renal disease from chronic stage 1 to ESRD can take several decades; if pesticide use does contribute to kidney disease incidence, it is probable that this pathway would have been initiated prior to enrollment.
Because the AHS is a cohort of US farmers in NC and IA, the risks for this cohort may be different from the risks experienced by farmworkers in the US or elsewhere. However, the AHS, with its detailed exposure characterization, limited loss to follow up over 20 years, and the ability to link to a population-based kidney disease registry, provides important human data for evaluation of the role of pesticides and kidney disease.
Lastly, in these analyses we estimated associations between a large number of exposures and ESRD. We did not employ statistical methods to adjust for multiple comparisons, such as the Bonferroni correction. Previous authors have cautioned against employing Bonferroni-type corrections in an epidemiological analysis of associations between multiple environmental or occupational exposures and disease, 52-54 and such methods have largely fallen out of use in such settings because Bonferroni adjustments are concerned with testing of a general null hypothesis which is rarely of interest. Alternatives to Bonferroni methods exist for inference in settings where multiple exposure effects are estimated, including Bayesian methods;55 however, we have not employed such methods here, in preference for simply describing what statistical quantities have been estimated.
Conclusions
Our study provides evidence for an association between ESRD risk and chronic exposure to specific chemicals among pesticide applicators in Iowa and North Carolina. Results from this study also suggest that pesticide exposures resulting in medical visits increase the risk of incident ESRD, raising concerns that multiple high-level pesticide exposures may contribute to irreversible kidney damage and resultant disease. Efforts to better characterize the pathway between pesticide exposure and kidney disease should include assessments of earlier disease stages, rate of progression from CKD to ESRD, and other potential routes of pesticide exposure, such as spray drift and carry-home exposures. Caution should be taken in interpreting results of such studies when diagnosis dates or disease severity information is not available, because the healthy worker survivor effect may bias estimates towards the null. Additional epidemiological studies are needed to confirm the findings of our study, given the limited research on the role of pesticide exposure in the development of renal disease, and research on the direct renal toxicity of specific chemicals must be expanded to facilitate interpretation of epidemiological results.
Supplementary Material
What this paper adds.
Much is known about clinical risk factors for end-stage renal disease (chronic kidney disease requiring dialysis or kidney transplant for survival), but research on environmental risk factors for kidney disease is limited.
In this study of male pesticide applicators, risk of end-stage renal disease increased with increasing cumulative exposure to several pesticides, including the herbicides metolachlor, paraquat and pendimethalin, and the insecticide chlordane.
Risk of end-stage renal disease was significantly greater for pesticide applicators who reported multiple doctor visits due to pesticide use and hospitalization due to pesticide use, compared to those who reported no medical visits due to pesticide use.
Exposure to certain pesticides may increase the risk of end-stage renal disease; however, additional studies are needed to support these findings.
Acknowledgments
This research was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (ZO1 ES 049030) and by an NIH, National Institute of Diabetes and Digestive and Kidney Diseases Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant (T32 DK 007750; PI RJ Falk).
We thank the participants of the AHS for their contribution to this research. We also thank Aaron Blair, Freya Kamel, Honglei Chen, Cynthia J. Hines, and Laura Beane Freeman for reviewing this manuscript, and Stuart Long at Westat for his assistance with data management.
Footnotes
The authors declare they have no actual or potential competing financial interests.
Contributor Information
Jill F. Lebov, Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Lawrence S. Engel, Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
David Richardson, Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Susan L. Hogan, Department of Medicine, Division of Nephrology and Hypertension, University of North Carolina, Chapel Hill, NC, USA.
Jane A. Hoppin, Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA.
Dale P. Sandler, Epidemiology Branch/Chronic Disease Epidemiology Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
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