Exploring the link between periodontal disease and sperm quality: a comprehensive systematic review study
Bibliographic record
Abstract
BACKGROUND: Periodontal diseases are among the most prevalent oral health conditions globally, with evidence suggesting their systemic effects, including potential impacts on male reproductive health. Inflammation associated with periodontitis might interfere with spermatogenesis and alter sperm parameters such as count, motility, morphology, and DNA integrity. This systematic review aims to critically assess existing studies on the relationship between periodontal disease and sperm quality to delineate its clinical implications for male infertility. METHODS: The research was registered in the International Prospective Register of Systematic Reviews (PROSPERO) at the National Institute for Health Research (PROSPERO code: CRD420251005800). A comprehensive literature search was conducted across multiple databases, including PubMed, EMBASE, ProQuest, Scopus, Web of Science (WOS), and Google Scholar, up to September 2024. Inclusion criteria targeted human studies reporting associations between periodontal health and sperm quality parameters. To assess the quality of the included studies, the Newcastle-Ottawa Scale (NOS) was used. Data were synthesized qualitatively and analyzed for trends. RESULTS: Nine studies encompassing 1,386 participants were included. Evidence suggests a significant association between periodontitis and decreased sperm motility, abnormal morphology, and increased DNA fragmentation. However, findings on sperm count and concentration were inconsistent. Mechanistic pathways suggest that inflammatory cytokines and oxidative stress contribute to these disruptions. CONCLUSION: This review highlights the systemic implications of periodontal disease on male reproductive health. Given the global decline in male fertility, maintaining oral health may serve as an adjunct strategy in managing infertility. Future research should prioritize large-scale, longitudinal studies to establish causality and explore preventive interventions.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".