The association of lead and cadmium exposure with periodontitis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Periodontitis, a microbiome-driven chronic inflammatory disease that destroys the supporting structures of the teeth, is influenced by various environmental factors, including exposure to heavy metals such as lead and cadmium. This systematic review and meta-analysis aimed to evaluate the association between exposure to lead and cadmium and periodontitis. METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, Scopus, and Embase up to February 1, 2025, following PRISMA guidelines. Observational studies examining the association between lead and/or cadmium exposure and periodontitis were included. Required clinical data were extracted, and study quality was assessed using the Newcastle-Ottawa Scale. Random-effects models were used to compute either standardized mean differences (SMD) of concentration or pooled adjusted odds ratios (aORs). Heterogeneity was assessed with I². RESULTS: Fourteen studies (13 datasets for either lead or cadmium) comprising 72,467 participants were eligible for inclusion. The meta-analysis found that cadmium and lead exposure were significantly associated with higher odds of periodontitis, with pooled aORs of 1.22 (95% CI: 1.08-1.37) and 1.85 (95% CI: 1.42-2.41), respectively. Sensitivity analyses confirmed the robustness of the findings. CONCLUSION: This study provides evidence that exposure to lead and cadmium is significantly associated with periodontitis. These findings highlight the importance of reducing environmental exposure to these heavy metals as part of preventive strategies for periodontal disease. Further research is needed to explore the underlying biological mechanisms and evaluate potential interventions to reduce exposure-associated periodontitis.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| 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".