Associations between exposure to heavy metals and the risk of chronic kidney disease: a systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
We performed a systematic review and meta-analysis to examine the relationship between heavy metals (HMs) exposure and the risk of chronic kidney disease (CKD). Databases of Web of Science, Embase, MEDLINE, and Scopus were searched through June 2020 to identify studies assessing the relationships between exposure to HMs (i.e. cadmium, lead, arsenic, mercury) and the risk of CKD, evaluated by decreased estimated glomerular filtration rate (eGFR) and/or increased proteinuria risks in adults (≥18 years). Data were pooled by random-effects models and expressed as weighted mean differences and 95% confidence intervals. The risk of bias was assessed by the Newcastle–Ottawa scale (NOS). Twenty-eight eligible articles (n = 107,539 participants) were included. Unlike eGFR risk (p = 0.10), Cadmium exposure was associated with an increased proteinuria risk (OR = 1.35; 95% CI: 1.13, 1.61; p < 0.001; I2 = 79.7%). Lead exposure was associated with decreased eGFR (OR = 1.12; 95%CI: 1.03, 1.22; p = 0.008; I2 = 87.8%) and increased proteinuria (OR = 1.25; 95% CI: 1.04, 1.49; p = 0.02; I2 = 79.6) risks. Further, arsenic exposure was linked to a decreased eGFR risk (OR = 1.55; 95% CI: 1.05, 2.28; p = 0.03; I2 = 89.1%) in contrast to mercury exposure (p = 0.89). Only two studies reported the link between arsenic exposure and proteinuria risk, while no study reported the link between mercury exposure and proteinuria risk. Exposure to cadmium, lead, and arsenic may increase CKD risk in adults, albeit studies were heterogeneous, warranting further investigations. Our observations support the consideration of these associations for preventative, diagnostic, monitoring, and management practices of CKD.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it