Blood Lead Level and Renal Impairment among Adults: A Meta-Analysis
Bibliographic record
Abstract
Background: The adult population in lead-related occupations or environmentally exposed to lead may be at risk for renal impairment and lead nephropathy. This meta-analysis aims to determine the impact of blood lead level (BLL) on renal function among middle-aged participants. Methods: Cross-sectional, longitudinal, or cohort studies that reported BLL and renal function tests among adult participants were retrieved from PubMed, Scopus, and ISI Web of Science. Relevant studies were included and assessed for quality using the Newcastle–Ottawa Scale (NOS). The pooled mean BLL of participants with a high BLL (≥30 µg/dL), moderate BLL (20–30 µg/dL), and low BLL (<20 µg/dL) was estimated using the random effects model. The pooled mean differences in BLL, blood urea nitrogen (BUN), creatinine, uric acid, and creatinine clearance between the exposed and non-exposed participants were estimated using the random effects model. Meta-regression was performed to demonstrate the association between the effect size (ES) of the pooled mean BLL and renal function. Heterogeneity among the included studies was assessed using the Cochrane Q and I2 statistics. Cochrane Q with a p value less than 0.05 and I2 more than 50% demonstrated substantial heterogeneity among the studies included. Publication bias was assessed using the funnel plot between the effect size and standard error of the effect size. Results: Out of 1657 articles, 43 were included in the meta-analysis. The meta-analysis demonstrated that the pooled mean BLL in the participants with a high BLL, moderate BLL, and low BLL was 42.41 µg/dL (95% confidence interval (CI): 42.14–42.67, I2: 99.1%), 22.18 µg/dL (95% CI: 21.68–22.68, I2: 60.4%), and 2.9 µg/dL (95% CI: 2.9–2.9, I2: 100%), respectively. The mean BLL of the exposed participants was higher than that of the non-exposed participants (weighted mean difference (WMD): 25.5, p < 0.0001, 95% CI: 18.59–32.45, I2: 99.8%, 17 studies). The mean BUN (WMD: 1.66, p < 0.0001, 95% CI: 0.76–2.55, I2: 76%, 10 studies) and mean creatinine (WMD: 0.05, p = 0.007, 95% CI: 0.01–0.08, I2: 76.8%, 15 studies) in the exposed participants were higher than those in the non-exposed participants. The mean creatinine clearance in the exposed participants was lower than that in the non-exposed participants (standard mean difference (SMD): −0.544, p = 0.03, 95% CI: −1.035–(−0.054), I2: 96.2%). The meta-regression demonstrated a significant positive effect of BLL on BUN (p = 0.022, coefficient: 0.75, constant: −3.7, 10 studies). Conclusions: BLL was observed to be associated with abnormal renal function test parameters, including high BUN, high creatinine, and low creatinine clearance. Moreover, BUN seemed to be the most valuable prognostic marker for lead-induced renal impairment. Therefore, regular checks for renal function among lead-exposed workers should be a priority and publicly promoted.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.067 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".