Urinary Polycyclic Aromatic Hydrocarbons in a Longitudinal Cohort of Children with CKD: A Case of Reverse Causation?
Bibliographic record
Abstract
Key Points Serially measured polycyclic aromatic hydrocarbon (PAH) metabolites were associated with increased eGFR and lower proteinuria. PAHs were associated with higher tubular injury and oxidant stress biomarkers. Reduced renal function may affect excretion of urinary PAHs. Background Air pollution, which results in the formation of polycyclic aromatic hydrocarbons (PAHs), has been identified as a cause of renal function decline and a contributor to CKD. However, the results of cross-sectional studies investigating personal, integrated biomarkers of PAHs have been mixed. Longitudinal studies may be better suited to evaluate environmental drivers of kidney decline. The purpose of this study was to examine associations of serially measured urinary PAH metabolites with clinical and subclinical measures of kidney function over time among children with CKD. Methods This study was conducted among 618 participants in the Chronic Kidney Disease in Children study, a cohort study of pediatric patients with CKD from the United States and Canada, between 2005 and 2015. In serially collected urine samples over time, nine PAH metabolites were measured. Clinical outcomes measured annually included eGFR, proteinuria, and BP. Subclinical 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 F 2 -isoprostane) were assayed in urine samples. Results Children were followed over an average (SD) of 3.0 (1.6) years and 2469 study visits (mean±SD, 4.0±1.6). Hydroxynaphthalene (NAP) or hydroxyphenanthrene (PHEN) metabolites were detected in >99% of samples and NAP concentrations were greater than PHEN concentrations. PHEN metabolites, driven by 3-PHEN, were associated with increased eGFR and reduced proteinuria, diastolic BP z-score, and NGAL concentrations over time. However, PAH metabolites were consistently associated with increased KIM-1 and 8-OHdG concentrations. Conclusions Among children with CKD, these findings provoke the potential explanation of reverse causation, where renal function affects measured biomarker concentrations, even in the setting of a longitudinal study. Additional work is needed to determine if elevated KIM-1 and 8-OHdG excretion reflects site-specific injury to the proximal tubule mediated by low-grade oxidant 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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".