From: Pesticide exposures and chronic kidney disease of unknown etiology: an epidemiologic review
Reference & country | Study design | Population | Exposure assessment | Case definition/outcome(s) | Main findings | Pesticide association ------------- Validity and explanation valuea |
---|---|---|---|---|---|---|
Sri Lanka | ||||||
Peiris-John et al., 2006 [87] Sri Lanka | Cross-sectional | 4 groups: 23 OP-exposed farmers with chronic renal failure (CRF) vs 18 unexposed patients with CRF vs 239 OP-exposed farmers without CRF vs 50 unexposed fishermen without CRF | Red blood cell acetyl cholinesterase (AChE) levels (U/g) as proxy of organophosphate exposures | CRF (not further specified) | Significant differences in AChE levels: exposed CRF (18.6Â U/g)Â <Â unexposed CRF (26.6)Â <Â exposed non-CRF (29.1)Â <Â non-exposed non-CRF (32.6) | Possible association between long-term low-level OP-exposures, cholinesterase levels and CKD -------------- Exploratory aim with unconventional cross-sectional design; inadequate selection of study participants; high risk of bias from exposure misclassification; high risk of confounding Explanation value: low |
Wanigasuriya et al., 2007 [36] Sri Lanka | Hospital- based case – control (prevalent cases) | 183 CKDu cases (136 M, 47 F), 200 controls among HT and DM patients (139 M, 61 F), age 36-67 | Questionnaire: Farmer yes/no Pesticide exposure yes/no Drinking water source (well-water home, well-water field, pipe born) | SCr > 2 mg/dL | Bivariate analyses: Males: OR farmer = 4.68 [2.50- 8.82) OR pesticides = 2.94 [1.73-5.01] OR drinking well-water field = 1.72 [0.92-3.22] Females: OR farmer = 1.28 [0.55-2.99) OR pesticides: 0 cases OR drinking well-water home = 4.24 [1.51-12.32] Multivariate logistic regressions: NO associations for farming, pesticide use and drinking well-water | No associations in multivariate analyses with farming, pesticide use and well-water ------------- Prevalent cases; high risk of bias from exposure misclassification; inadequate reporting of statistical analyses and pesticide results Explanation value: low |
Athuraliya et al., 2011 [19] Sri Lanka | Cross-sectional population-based survey with case –control analyses | 6153 (2889 M, 3264 F): age >19 ᅟ CKDu endemic area Medawachchiya 2600 Two non-endemica areas Yatinuwara708 Hambantota 2844 ᅟ 109 CKDu patients in Medawachchiya (66 M, 43 F) | Questionnaire -Farmer yes/no -Spraying or handling agrochemicals yes/no | Proteinuric chronic kidney disease | Entire study population: Adj OR farmer 2.6 (1.9–3.4) Adj OR agrochemical exposure 2.3 (1.4–3.9) Medawachchiya (CKDu region) Adj OR farmer 2.1 (1.4–3.3) Adj OR agrochemical exposure 1.1 (0.7–1.9) ᅟ Yatinuwara (non-CKDu region) Adj OR farmer 1.5 (0.5–3.9) Adj OR agrochemical exposure 1.6 (0.8–3.2) ᅟ Hambantota (non-CKDu region) Adj OR farmer 1.6 (1.0–2.7) Adj OR agrochemical exposure 5.6 (2.3–13.2) | Pesticide use was not associated to proteinuric CKD in the CKDu region, but it was associated to CKD of known causes in one of the two non-CKDu regions. ------------- Cross-sectional, crude pesticide exposure assessment, misclassification of disease Explanation value: medium |
Wanigasuriya et al., 2011 [92] Sri Lanka | Cross-sectional population-based survey with case –control analyses | 886 (461 M, 425 F) household members aged ≥18 | Questionnaire: Farmer yes/no Pesticide spraying yes/no Drinking water source | Micro-proteinuria | Bivariate analyses: OR farmer = 1.38 [0.71- 2.70) OR pesticides = 1.01 [0.60-1.72] OR well-water in the field = 1.79 [1.07-3.01] ᅟ Multivariate logistic regression: OR pesticides = 0.43 [0.21-0.90] OR well-water in the field = 1.92 [1.04-3.53] | Positive association with drinking from well-water in the field Negative association with pesticide spraying ------------- Cross-sectional, crude pesticide exposure assessment, misclassification of disease Explanation value: medium |
Jayasumana et al., 2015 [95] Sri Lanka | Hospital-based case-control (prevalent cases) | 125 cases (89 M, 36 F), 180 controls (98 M, 82 F) | Questionnaire: Usual occupation last 10 years, farming yes/no Use of fertilizer and specific pesticides over last 10 years yes/no (organophosphates, paraquat, MCPA, glyphosate, bispyribac, carbofuran, mancozeb and other common pesticides) Glyphosate, metals and hardness measured in water of serving and abandoned wells | CKDu | Bivariate logistic regression with significantly increased ORs for farming, use of fertilizers, and use of organophosphates, paraquat, MCPA, glyphosate, bispyribac and mancozeb ᅟ Multivariate logistic regression: OR drinking well water = 2.52 [1.12-5.70] OR history drinking water from abandoned well = 5.43 [2.88-10.26] OR pesticide application = 2.34 [0.97- 5.57] OR use of glyphosate = 5.12 [2.33-11.26] ᅟ Water hardness: abandoned wells: very high; serving wells: moderate to hard; reservoir and pipeline: soft Glyphosate concentration in water from abandoned well significantly higher than in serving wells (median 3.2 μg/L and 0.6 μg/L, respectively). | Positive association with pesticide applications Positive association with use of glyphosate Positive association with drinking well-water and, especially, with history of drinking water from abandoned (with hardest water and highest glyphosate levels) -------------- Prevalent cases; relatively good case ascertainment; specific, unquantified pesticide exposure assessment Exposure response for glyphosate in water; control of potential confounders Explanation value: high |
Other countries | ||||||
Kamel & El-Minshawy, 2010 [6] Egypt | Hospital-based case-control (prevalent cases) | 216 ESRD cases (141 M, 75 F) from unknown cause 220 random controls (152 M, 68 F) from other patients | Questionnaire: Rural residency yes/no Drinking unsafe (non-pipe) water yes/no Farming occupation yes/no Pesticide exposures by any mean yes/no | ESRD of unknown cause (clinical exams) | Bivariate analyses: rural living, drinking unsafe water, being a farmer and pesticide exposure associated with ESRD (p < 0.001) Multivariate analyses (model not specified): OR pesticide exposure 2.08 [1.42 – 3.06] | Possible association with pesticide exposures ------------- Prevalent cases; no data to evaluate potential selection bias; crude exposure assessment, statistical methods not well described Explanation value: low |
Siddharth et al., 2012 [75] India Note: this study is an interim report of Siddarth et al., 2014 [76] | Hospital-based case-control (prevalent cases) | 150 CKD cases (77 M, 73 F): patients attending nephrology departments 96 controls (51 M, 45 F): staff or persons accompanying CKD patients in the hospital Age 30-50 | Levels of organochlorine (OC) pesticides in blood | CKDu: eGFr <60 ml/min/1.73m2 for >3 months Oxidative stress markers | Significantly higher blood levels in cases for α–HCH, γ-HCH, total HCH, α-endosulfan, β-endosulfan, aldrin, p,p’-DDE, and TPL. Among cases, adjusted Spearman correlations between eGFR and different pesticide analytes varied between −0.07 and −0.23 (significant for γ-HCH, total HCH and aldrin). When adjusting additionally for levels of other analytes, the association with eGFR remained significant only for aldrin. In addition, significant correlation between eGFR and TPL (r = −0.26). | Association of blood levels of OCs (from environmental exposures) with CKDu, mediated partially through genotype -------------- Prevalent cases; specific and quantitative assessment for non-occupational exposures to OCs, study in a non-CKDu setting; some potential for inverse causation; low risk for confounding Explanation value: high |
Siddarth et al., 2014 [76] India | Hospital-based case-control (prevalent cases) | 270 cases (140 M, 130 F): patients attending nephrology departments 270 age and sex matched controls: staff or persons accompanying CKD patients in the hospital | Concentrations of organochlorine pesticides in blood GST genotyping | CKDu: eGFR <90 ml/min/1.73m2 with or without proteinuria, for 3 months | Cases had significantly higher blood concentrations of α–HCH, γ-HCH, total HCH, α-endosulfan, β-endosulfan, aldrin, p,p’-DDE, and total pesticides Significant associations with CKDu for 3rd versus 1st tertile for α-HCH (OR = 2.52), γ-HCH (OR = 2.70), total-HCH (OR = 3.18), aldrin (OR = 3.07), α-endosulfan (OR = 2.99), and β-endosulfan (OR = 3.06). Total pesticides 3rd to 1st tertile OR = 2.73 [(1.46–9.47). CKDu patients having either one null or two null genotypes tend to accumulate majority of pesticides, whereas in healthy controls only in the subset with both null genotypes for some pesticides. | |
Lebov et al., 2016 [97] USA | Cohort (follow-up since 1993-1997) | 55,580 licensed pesticide applicators (320 ESRD) | Self-administered questionnaires: Ordinal categories of intensity-weighted lifetime days for 39 specific pesticides Pesticide exposure resulting in medical visit or hospitalization Diagnosed pesticide poisoning High level pesticide exposure event | ESRD | Significantly increased HR for highest category of use vs non-users and significant exposure-response trends: Alachlor HRÂ =Â 1.51 [1.08-2.13], p for trend 0.015 Atrazine HRÂ =Â 1.52 [1.11-2.09], p for trend 0.008 Metolachlor HRÂ =Â 1.53 [1.08-2.13], p for trend 0.008 Paraquat HRÂ =Â 2.15 [1.11-4.15], p for trend 0.016 Pendimethalin HRÂ =Â 2.13 [1.20-3.78], p for trend 0.006 Permethrin HRÂ =Â 2.00 [1.08-3.68], p for trend 0.031 More than one medical visit due to pesticide use HRÂ =Â 2.13 [1.17 - 3.89], p for trend for increasing number of doctor visits 0.04. Hospitalization due to pesticide use HRÂ =Â 3.05 [1.67 to 5.58] | Association between use of specific pesticides and ESRD Association between ESRD and exposures resulting in medical visits or hospitalization and ESRD -------------- Large cohort with long follow-up; study in non-CKDu endemic regions; specific and quantitative exposure assessment; multiple comparisons; low risk for confounding Explanation value: high |
Lebov et al., 2015 [96] USA | Cohort (follow-up since 1993-1997) | 31,142 wives of licensed pesticide applicators (98 ESRD) | Self-administered questionnaires or telephone interview -direct exposures (n = 17,425): ordinal categories of intensity weighted lifetime use of any pesticide, 10 specific pesticides and 6 chemical classes -Indirect pesticide exposures (husband’s pesticide use) among wives without personal use (n = 13,717) -Indicators of residential pesticide exposure | ERSD | Highest category of cumulative lifetime-days of pesticide use in general vs never personal use: HR 4.22 [1.26-14.2] Exposure-response trends for husband’s use of paraquat HR 1.99 [1.14-3.47] and butylate HR 1.71 [1.00-2.95] No excess risk for indicators of residential exposures | Association between direct general pesticide use and husband’s use of paraquat and ESRD in women No associations with residential exposures --------------- Large cohort with long follow-up; study in non-CKDu endemic regions; specific and quantitative exposure assessment; multiple comparisons; low risk for confounding Explanation value: high |
Aroonvilairat et al., 2015 [98] Thailand | Cross-sectional | 64 workers of orchids (30 M, 34 F) and 60 controls (33 M, 27 F) | Mixing and spraying pesticides during work at orchard for at least three months | Difference in BUN and SCr | BUN (mg/dL) exposed 12.64 ± 3.7 (3.7% abnormal) vs BUN unexposed 12.43 ± 2.9 (1.7% abnormal), p = 0.76 SCr (mg/dL) exposed females 0.86 ± 0.11 (3.7% abnormal) vs unexposed females 0.82 ± 0.11 (2.9% abnormal), p = 0.11 SCr exposed males 1.09 ± 0.11 (0% abnormal) vs unexposed males 1.09 ± 0.10 (0% abnormal), p = 0.95 | No association between occupation in highly pesticide exposed farming and decreased kidney function ------------- Cross-sectional; crude exposure assessment, selection of study population not well described; no confounding adjustment Explanation value: low |