Long-Term occupational exposure to heavy metals (lead, mercury, aluminum) and risk of dementia: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Dementia, is a major global health challenge. Occupational exposure to heavy metals such as lead, mercury, and aluminum is common in several industries, yet their long-term contribution to dementia risk remains uncertain. Objective: To systematically review and meta-analyze epidemiological evidence on the association between chronic occupational exposure to lead, mercury, or aluminum and risk of dementia or AD. Methods: Following PRISMA 2020 guidelines, PubMed, Web of Science, and Embase were searched through August 2025 for observational studies assessing long-term occupational heavy metal exposure and dementia outcomes. Study quality was assessed using the Newcastle–Ottawa Scale. Random-effects meta-analyses pooled odds ratios (ORs), and heterogeneity was evaluated using the I² statistic. Results: Fifteen studies involving over 10,000 participants met inclusion criteria. Chronic lead exposure was not significantly associated with dementia risk (OR ≈ 1.10, 95% CI 0.90–1.35). Mercury exposure showed a non-significant trend toward increased risk (OR ≈ 1.15, 95% CI 0.80–1.60). In contrast, chronic aluminum exposure was associated with a significantly higher risk of dementia (OR ≈ 1.50, 95% CI 1.20–1.90), with moderate heterogeneity. Conclusion: Long-term aluminum exposure appears to increase dementia risk, whereas evidence for lead and mercury remains inconclusive. Further longitudinal studies with precise exposure assessment are warranted.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".