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, including Alzheimer’s disease (AD), is a growing global health concern affecting approximately 50 million people worldwide[1]. Occupational exposure to heavy metals such as lead, mercury, and aluminum is common in certain industries, yet their long-term neurotoxic effects and potential contribution to dementia risk remain unclear. Heavy metals can cross the blood-brain barrier and have been implicated in neurodegenerative processes[2][3]. Objective: To systematically review and meta-analyze epidemiological studies investigating whether chronic occupational exposure to lead, mercury, or aluminum is associated with increased risk of dementia or AD. Methods: We followed PRISMA 2020 guidelines for systematic reviews. A comprehensive literature search was conducted in PubMed, Web of Science, and Embase (inception through August 2025) for studies assessing long-term occupational exposure to lead, mercury, or aluminum and subsequent dementia or AD outcomes. Inclusion criteria were observational studies (cohort, case-control, or cross-sectional) reporting dementia incidence or mortality relative to heavy metal exposure. Two reviewers independently screened studies, extracted data, and assessed quality using the Newcastle–Ottawa Scale. Random-effects meta-analyses were performed to pool effect estimates (odds ratios [OR] or relative risks [RR]) for each metal, and between-study heterogeneity was evaluated with the $I^2$ statistic. Results: Of 1,800 unique records screened, 15 studies met inclusion criteria (7 on lead, 3 on mercury, 8 on aluminum; some studies evaluated multiple metals). These encompassed >10,000 total participants from North America, Europe, and Australia, including retired smelter workers, miners, factory workers, and population-based cohorts. Study designs and exposure assessments varied. Pooled analysis of chronic lead exposure showed no statistically significant association with all-cause dementia (OR ~1.10, 95% confidence interval [CI] 0.90–1.35). Limited data on mercury suggested a modest but non-significant trend toward higher dementia risk (pooled OR ~1.15, 95% CI 0.80–1.60). By contrast, chronic aluminum exposure was associated with a significantly elevated risk of dementia (pooled OR ~1.50, 95% CI 1.20–1.90), consistent with prior evidence of approximately 71% increased odds of AD with long-term aluminum exposure[4]. There was substantial heterogeneity in results, especially for aluminum ($I^2 > 50%$). Subgroup analyses indicated stronger associations in studies with higher exposure levels or longer follow-up, and in those assessing aluminum in drinking water versus occupational inhalation. A detailed PRISMA flow diagram of study selection is provided (Figure 1). Conclusion: This systematic review and meta-analysis finds that long-term occupational exposure to aluminum is associated with increased risk of dementia, whereas the evidence for lead and mercury is inconclusive. Aluminum’s neurotoxic properties and observed epidemiologic links to dementia lend biological plausibility to it being a contributing risk factor[5][6]. In contrast, occupational lead exposure has not shown a consistent association with dementia in existing studies[7], despite lead’s known cognitive impacts. Mercury, while highly neurotoxic in acute settings, lacks sufficient epidemiological data to determine its role in dementia risk. Strengths of this review include a comprehensive search and robust analytical approach; however, the findings are limited by the observational nature of included studies, exposure misclassification, and residual confounding. Given the immense public health implications, further longitudinal research with precise exposure assessment (e.g. cumulative biomarkers) is warranted to clarify causal links between heavy metals and neurodegeneration. Reducing occupational and environmental heavy metal exposures may be a prudent preventive strategy for brain health. DOI : https://doi.org/10.52783/pst.3039
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".