| Literature DB >> 28535811 |
Mathieu Valcke1,2, Marie-Eve Levasseur1, Agnes Soares da Silva3, Catharina Wesseling4.
Abstract
The main causes of chronic kidney disease (CKD) globally are diabetes and hypertension but epidemics of chronic kidney disease of unknown etiology (CKDu) occur in Central America, Sri Lanka, India and beyond. Althoug also being observed in women, CKDu concentrates among men in agricultural sectors. Therefore, suspicions fell initially on pesticide exposure, but currently chronic heat stress and dehydration are considered key etiologic factors. Responding to persistent community and scientific concerns about the role of pesticides, we performed a systematic review of epidemiologic studies that addressed associations between any indicator of pesticide exposure and any outcome measure of CKD. Of the 21 analytical studies we identified, seven were categorized as with low, ten with medium and four with relatively high explanation value. Thirteen (62%) studies reported one or more positive associations, but four had a low explanation value and three presented equivocal results. The main limitations of both positive and negative studies were unspecific and unquantified exposure measurement ('pesticides'), the cross-sectional nature of most studies, confounding and selection bias. The four studies with stronger designs and better exposure assessment (from Sri Lanka, India and USA) all showed exposure-responses or clear associations, but for different pesticides in each study, and three of these studies were conducted in areas without CKDu epidemics. No study investigated interactions between pesticides and other concommittant exposures in agricultural occupations, in particular heat stress and dehydration. In conclusion, existing studies provide scarce evidence for an association between pesticides and regional CKDu epidemics but, given the poor pesticide exposure assessment in the majority, a role of nephrotoxic agrochemicals cannot be conclusively discarded. Future research should procure assessment of lifetime exposures to relevant specific pesticides and enough power to look into interactions with other major risk factors, in particular heat stress.Entities:
Keywords: Agrochemicals; Chronic kidney disease of unknown etiology (CKDu); Etiology; Exposure; Pesticides; Review
Mesh:
Substances:
Year: 2017 PMID: 28535811 PMCID: PMC5442867 DOI: 10.1186/s12940-017-0254-0
Source DB: PubMed Journal: Environ Health ISSN: 1476-069X Impact factor: 5.984
Fig. 1Process of selection and preliminary analysis of relevant studies
Mesoamerican studies assessing the role of pesticides in CKD
| Reference & country | Study design | Study population | Pesticide exposure assessment | Case definition/outcome(s) | Main findings | Pesticide association |
|---|---|---|---|---|---|---|
| Rugama et al., 2001 [ | Retrospective hospital-based case-control | CKD hospitalizations during 2000: 165 cases, 334 non-CKD random hospital controls | Pesticide use yes/no, extracted from clinical records | CKD diagnosis at admission | OR pesticide exposure = 5.5 [2.8 – 10.7] | Positive association with pesticide exposure |
| Gracía-Trabanino et al., 2005 [ | Cross-sectional survey | Volunteer sample of 353 adult M, 292 coastal and 62 at 500 m above sea level (masl) | Questionnaire: | Proteinuria >15 mg/L | For proteinuria: | No association with agricultural work |
| Torres-LaCourt et al., 2008 [ | Cross-sectional population-based survey | Random sample of 337 adults aged 20-60 (129 M, 208 F) from 2 rural communities | Questionnaire: | CKD stage 3 or higher (eGFR <60 ml/min/1.73m2) | Results reported separately for the two communities, analyses not adjusted for potential confounders: | Positive association with agricultural work |
| Sanoff et al., 2010 [ | Volunteer screening program with nested case-control analysis | Screening: 997 volunteers aged >18 y (848 M, 149 F) | Questionnaire: | eGFR | Screening: | Positive association for agricultural field labor |
| O’Donnell et al., 2011 [ | Cross-sectional population-based survey; nested case-control analysis | Random sample of 771 individuals aged ≥18 (298 M, 473 F) from 300 eligible households | Questionnaire: | CKD ≥ stage 3 (eGFR <60 ml/min/1.73m2) | Unadjusted/sex and age adjusted logistic regressions: | No association with agricultural work |
| Orantes et al., 2011 [ | Community screening and cross-sectional survey | 775 individuals age ≥ 18, (343 M, 432 F) | Questionnaire: | CKD stages 1-5 (2 determinations with a 3-month interval) | OR agricultural occupation = 1.35 [0.63–2.88] | No association for agricultural occupation |
| Laux et al., 2012 [ | Community-based cross-sectional survey | 267 adults (120 M, 147 F) | Questionnaire: | Proteinuria | OR work with pesticides = 1.09 [0.6–1.98] | No association with pesticide exposure |
| Raines et al., 2014 [ | Cross-sectional population-based survey; nested case-control analysis | 424 adults (166 M, 258 F) | Questionnaire: | eGFR <60 ml/min/1.73 m2
| OR agricultural worker 2.05 [0.61-6.90] | Weak association with agricultural work |
| Laws et al., 2015 & Laws et al., 2016 [ | Cohort | 284 sugarcane workers (251 M, 33 F), incl. 29 agrichemical applicators | Job title: agrichemical applicator | eGFR (ml/min/1.73m2) | Mean change eGFR for pesticide applicators during harvest season −3.8 (−9.9, 2.3) | No association between a job of spraying pesticides with decrease in kidney function or increase in indicators of early tubular damage over one cutting season |
| García-Trabanino et al., 2015 [ | Cross-sectional occupational survey | 189 sugarcane cutters (168 M, 21 F) | Questionnaire: | eGFR <60 ml/min/1.73 m2 | Ever use of any pesticide not associated with low eGFR | Association with ever use of carbamate insecticides |
| Wesseling et al., 2016 [ | Occupational cross-sectional study | 86 sugarcane cutters, 56 construction workers, 52 subsistence farmers, all males | Questionnaire: | eGFR <80 ml/min/1.73 m2 | Ever use of any pesticide and ever use of specific pesticides not associated with reduced eGFR, for all workers combined and in analyses restricted to cane cutters | No association with ever use of pesticides |
Abbreviations: CKD chronic kidney disease (u: of unknown etiology; nt: not related to traditional risk factors), eGFR estimated glomerular filtration rate, ESRD end-stage renal disease, F female, M male, OR odds ratio [95% confidence interval], SCr serum creatinine, NGAL neutrophil gelatinase-associated lipocalin, NAG N-acetyl-D-glucosaminidase, IL-18 interleukin-18, ACR urinary albumin-to-creatinine ratio
aExplanation value: The study’s ability to contribute to knowledge about potential associations between pesticides and CKD or CKDu (according to the objective of the study), based on a qualitative evaluation of design and the validity of the results. For details see Additional file 2: Table S1 and the main text
Studies from Sri Lanka and other non-Mesoamerican countries assessing the role of pesticides in CKD
| Reference & country | Study design | Population | Exposure assessment | Case definition/outcome(s) | Main findings | Pesticide association |
|---|---|---|---|---|---|---|
| Sri Lanka | ||||||
| Peiris-John et al., 2006 [ | 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 (18.6 U/g) < unexposed CRF (26.6) < exposed non-CRF (29.1) < non-exposed non-CRF (32.6) | Possible association between long-term low-level OP-exposures, cholinesterase levels and CKD |
| Wanigasuriya et al., 2007 [ | 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: | SCr > 2 mg/dL | Bivariate analyses: | No associations in multivariate analyses with farming, pesticide use and well-water |
| Athuraliya et al., 2011 [ | Cross-sectional population-based survey with case –control analyses | 6153 (2889 M, 3264 F): age >19 | Questionnaire | Proteinuric chronic kidney disease | Entire study population: | 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. |
| Wanigasuriya et al., 2011 [ | Cross-sectional population-based survey with case –control analyses | 886 (461 M, 425 F) household members aged ≥18 | Questionnaire: | Micro-proteinuria | Bivariate analyses: | Positive association with drinking from well-water in the field |
| Jayasumana et al., 2015 [ | Hospital-based case-control (prevalent cases) | 125 cases (89 M, 36 F), 180 controls (98 M, 82 F) | Questionnaire: | CKDu | Bivariate logistic regression with significantly increased ORs for farming, use of fertilizers, and use of organophosphates, paraquat, MCPA, glyphosate, bispyribac and mancozeb | Positive association with pesticide applications |
| Other countries | ||||||
| Kamel & El-Minshawy, 2010 [ | Hospital-based case-control (prevalent cases) | 216 ESRD cases (141 M, 75 F) from unknown cause | Questionnaire: | ESRD of unknown cause (clinical exams) | Bivariate analyses: rural living, drinking unsafe water, being a farmer and pesticide exposure associated with ESRD ( | Possible association with pesticide exposures |
| Siddharth et al., 2012 [ | Hospital-based case-control (prevalent cases) | 150 CKD cases (77 M, 73 F): patients attending nephrology departments | Levels of organochlorine (OC) pesticides in blood | CKDu: eGFr <60 ml/min/1.73m2 for >3 months | Significantly higher blood levels in cases for α–HCH, γ-HCH, total HCH, α-endosulfan, β-endosulfan, aldrin, p,p’-DDE, and TPL. | Association of blood levels of OCs (from environmental exposures) with CKDu, mediated partially through genotype |
| Siddarth et al., 2014 [ | Hospital-based case-control (prevalent cases) | 270 cases (140 M, 130 F): patients attending nephrology departments | Concentrations of organochlorine pesticides in blood | 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 | |
| Lebov et al., 2016 [ | Cohort (follow-up since 1993-1997) | 55,580 licensed pesticide applicators (320 ESRD) | Self-administered questionnaires: | ESRD | Significantly increased HR for highest category of use vs non-users and significant exposure-response trends: | Association between use of specific pesticides and ESRD |
| Lebov et al., 2015 [ | Cohort (follow-up since 1993-1997) | 31,142 wives of licensed pesticide applicators (98 ESRD) | Self-administered questionnaires or telephone interview | ERSD | Highest category of cumulative lifetime-days of pesticide use in general vs never personal use: HR 4.22 [1.26-14.2] | Association between direct general pesticide use and husband’s use of paraquat and ESRD in women |
| Aroonvilairat et al., 2015 [ | 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), | No association between occupation in highly pesticide exposed farming and decreased kidney function |
Abbreviations: AChE red blood cell acetylcholinesterase, ACR albumin to creatinine ratio, ANOVA analysis of variance, CKD chronic kidney disease (u, of unknown etiology), BUN blood urea nitrogen, CRF chronic renal failure, DB diabetes, DW drinking water, DDE dichlorodiphenyldichloroethylene, eGFR glomerular filtration rate, ESRD end-stage renal disease, F female, GST glutathione-S-transferase, HCH hexachlorocyclohexane, HT hypertension, M male, MVLR multivariate logistic regression, OP organophosphate pesticides, SCr serum creatinine
Explanation value: The study’s ability to address potential associations between pesticdes and CKD or CKDu. For details see Additional file 2: Table S1 and the main text
Reviewed studies ranked by their explanatory potential on the etiological role of pesticide for CKD/CKDu
| Study | CKD marker | Potential to explain pesticide role in CKD/CKDu | Associations | ||
|---|---|---|---|---|---|
| Low | Medium | High | |||
| Pesticide exposure indicator | |||||
| Rugama, 2001 [ | CKD diagnosis at hospital admission | Pesticide use | Positive | ||
| Gracía-Trabanino et al., 2005 [ | Proteinuria >15 mg/L | Pesticide use | No | ||
| SCr >1.5 mg/dL | Pesticide use | No | |||
| Peiris-John et al., 2006 [ | Chronic renal failure diagnosis at hospital | Acetyl cholinesterase levels in four groups (exposed CRF, unexposed CRF, exposed non-CRF and unexposed non-CRF) | Positive | ||
| Wanigasuriya et al., 2007 [ | CKDu hospital diagnosis | Pesticides | No | ||
| Torres-Lacourt et al. 2008 [ | eGFR <60 ml/min1.73/m2 | Pesticide use | Positive | ||
| Pesticide intoxication | No | ||||
| Kamel & El Minshawy, 2010 [ | ESRDu | Pesticide exposure | Positive | ||
| Aroonvilairat et al., 2015 [ | BUN and SCr | Pesticide mixing and spraying in orchid for at least three months | No | ||
| Orantes et al., 2011 [ | Persistent CKD stages 1-5 determined twice with 3-months interval | Contact with agrichemicals | No | ||
| Wanigasuriya et al., 2011 [ | Micro-proteinuria | Pesticides | No | ||
| Laux et al., 2012 [ | Proteinuria | Work with pesticides | No | ||
| Laws et al., 2015 & 2016 [ | Change in eGFR (ml/min/1.73 m2) | Job as pesticide applicator over 6-month period | No | ||
| Change in early kidney injury markers | No | ||||
| Wesseling et al., 2016 [ | eGFR <80 ml/min/1.73m2 | Any pesticide use | No | ||
| Specific pesticides: glyphosate, paraquat, 2,4-D, chlorpyrifos, cypermethrin | No | ||||
| Sanoff et al., 2010 [ | eGFR <60 ml/min/1.73m2 | Pesticides | Weak positive | ||
| O’Donnell et al., 2011 [ | eGFR <60 ml/min/1.73m2 | Any pesticide exposure | Weak positive | ||
| Mixing/applying pesticides | No | ||||
| Athuraliya et al., 2011 [ | Proteinuric CKD | Pesticides | Negative in CKDu endemic area | ||
| Raines et al., 2014 [ | eGFR <60 ml/min/1.73m2 | Lifetime days mixing/applying | No | ||
| History of accidentally inhaling pesticides | Reported positive, but not interpretable | ||||
| García-Trabanino et al., 2015 [ | eGFR <60 ml/min/1.73m2 | Any pesticide use | No | ||
| Carbamate insecticides | Positive | ||||
| Glyphosate, paraquat, 2,4-D, triazines, organo-phosphates, pyrethroids | No | ||||
| Jayasumana et al., 2015 [ | Use of fertilizers, organo-phosphates, paraquat, MCPA, bispyribac, mancozeb | Positive only in unadjusted analyses | |||
| Use of glyphosate | Positive also in multivariate analyses | ||||
| Drinking water from serving wells and from abandoned wells (hardest water and highest glyphosate levels) | Positive with dose response | ||||
| Siddharth et al., 2012 and Siddharth et al., 2014 [ | CKDu with eGFR <60 ml/min/1.73m2 for >3 months | Urinary organochlorine pesticides and metabolites and interaction with GST polymorphism | Positive | ||
| Lebov et al., 2016 [ | ESRD among male applicators | Intensity weighted lifetime days for 39 pesticides: | Positive with dose-response | ||
| Petroleum oil, imazethapyr, coumaphos, parathion, phorate, aldicarb, chlordane, and metalaxyl | Weak positive without dose responses | ||||
| Glyphosate and 24 other pesticides | No | ||||
| Pesticide exposure resulting in medical visit or hospitalization | Positive | ||||
| Diagnosed pesticide poisoning | No | ||||
| High level pesticide exposure event | No | ||||
| Lebov et al., 2015 [ | ESRD among wives of licensed applicators | Intensity weighted lifetime days for applying | Positive | ||
| -Specific pesticides | No | ||||
| Husband’s use of paraquat | Positive | ||||
| Residential exposure | No | ||||