Associations between exposure to heavy metals and the risk of chronic kidney disease: a systematic review and meta-analysis
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 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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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".