Mercury Exposure, Blood Pressure, and Hypertension: A Systematic Review and Dose–response Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Body burden of mercury has been linked to hypertension in populations exposed to high mercury levels. OBJECTIVES: We summarized, extracted, and pooled the results of published studies that investigated mercury biomarkers and hypertension or blood pressure (BP) measurements to examine this potential relationship. METHODS: defined inclusion criteria. Study quality was assessed by the Newcastle-Ottawa scale for cohort and case-control studies and the Quality Assessment Tool for cross-sectional studies. Study estimates were pooled using inverse-variance weighted random-effects models. Dose-response meta-analysis was performed with studies reporting hypertension and systolic BP for at least three mercury categories. RESULTS: A total of 29 studies were included in the meta-analysis. The pooled odds ratio (OR) for hypertension, comparing the highest and lowest mercury exposure categories, was 1.35 [95% confidence interval (CI): 0.99, 1.83] for populations with hair mercury ≥2 μg/g in comparison with the OR of 1.12 (95% CI: 0.82, 1.52) for populations with hair mercury <2 μg/g. Positive associations were also observed for highest versus lowest mercury exposure categories on systolic and diastolic BP. Heterogeneity was observed for mercury species and exposure groups across different studies. Associations estimated using different mercury biomarkers generally agree with each other in the same study. A nonlinear dose-response relationship with an inflection point at 3 μg/g was identified, for both hypertension and systolic BP. CONCLUSIONS: A significant positive association between mercury and hypertension and between mercury and BP was identified. The exposure dose is an important factor in determining the toxic effects of mercury on hypertension. https://doi.org/10.1289/EHP2863.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".