Association between metal(loid)s in different biospecimens and dementia: A systematic review and meta-analysis
Bibliographic record
Abstract
Metal (loid)s are widely present in the environment and affect human health, especially the central nervous system. Dementia is a syndrome characterized by cognitive decline that can be caused by neurological degeneration. We aimed to review the current state of knowledge with respect to associations between various metal(loid)s in different biospecimens and dementia. We searched PubMed, Embase, and Web of Science for original research in English up to January 15th, 2025. We evaluated the synthesized using random effects and cumulative meta-analysis and assessed the risk of bias using the Newcastle-Ottawa Scale tool and the certainty of evidence by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Of the 7390 publications identified, 82 articles met the eligibility criteria. In the present meta-analysis, hair iron, serum zinc, cerebrospinal fluid zinc, and blood selenium showed significant negative associations with dementia. Urine iron, serum lead, and serum aluminum were significantly positively associated with dementia. It may be hypothesized that zinc/selenium/iron deficiency was the pathogenic factor for the onset or development of dementia. Increasing serum lead/aluminum may lead to dementia, especially in lower economic level countries. Because of the high heterogeneity, methodological limitations, and quality of evidence, the present findings should be interpreted with caution. The review was prospectively registered on PROSPERO CRD42024502024.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".