Pesticide exposures and chronic kidney disease of unknown etiology: an epidemiologic review
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".