Organophosphate pesticides and progression of chronic kidney disease among children: A prospective cohort study
Bibliographic record
Abstract
Growing evidence suggests that exposure to environmental chemicals, such as pesticides, impacts renal function and chronic kidney disease (CKD). However, it is not clear if pesticides may affect CKD progression and no studies exist in children. The objective of this study was to examine associations between serially measured urinary OP pesticide metabolites and clinical and laboratory measures of kidney function over time among children with CKD. This study used data on 618 participants enrolled in the CKD in Children study (CKiD), a cohort study of pediatric CKD patients from the US and Canada. Children were followed over an average of 3.0 years (standard deviation (SD) = 1.6) between 2005 and 2015. In serially collected urine samples over time, six nonspecific dialkyl phosphate (DAP) metabolites of OP pesticides were measured. Biomarkers of tubular injury (kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)) and oxidant stress (8-hydroxy-2′-deoxyguanosine (8-OHdG) and F2-isoprostane) were determined in the same specimens. Estimated glomerular filtration rate (eGFR), proteinuria, and blood pressure were assessed annually. DAPs were associated with increased KIM-1 and 8-OHdG throughout follow-up. A standard deviation increase in ∑diethyl metabolites was associated with increases of 11.9% (95% Confidence Interval (CI): 4.8%, 19.4%) and 13.2% (95% CI: 9.3%, 17.2%) in KIM-1 and 8-OHdG over time, respectively. DAPs were associated with lower eGFR at baseline and higher eGFR over subsequent years. These findings provide preliminary evidence suggesting that urinary DAP metabolites are associated with subclinical kidney injury among children with CKD, which may signal the potential for clinical events to manifest in the future. The results from this study are significant from both a clinical and public health perspective, given that OP pesticide exposure is a modifiable risk factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".