Association between prediabetes and periodontitis: a meta-analysis of observational studies with multivariate analysis
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests that prediabetes may increase the risk of periodontitis, though the extent of this association remains unclear. To provide a clearer understanding, this meta-analysis focused on observational studies that utilized multivariate analyses to adjust for key confounding factors. MATERIAL AND METHODS: A comprehensive search of PubMed, Embase, and Web of Science was conducted to identify observational studies assessing the relationship between prediabetes and periodontitis. Only studies that utilized multivariate analyses were included to minimize confounding bias. The quality of the studies was evaluated with the Newcastle-Ottawa Scale (NOS). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random-effects model, with heterogeneity assessed by the I² statistic. RESULTS: Ten observational studies with 38,727 participants were included. Overall, individuals with prediabetes had a significantly higher risk of periodontitis compared to normoglycemic individuals (OR: 1.27, 95% CI: 1.09 to 1.48, p < 0.001) with moderate heterogeneity (I² = 53%). Subgroup analyses revealed a stronger association in studies where the proportion of men was < 45% compared to those ≥ 45% (OR: 1.75 vs. 1.15, p for subgroup difference = 0.01). Studies with lower quality (NOS score = 7) showed a stronger association compared to higher-quality studies (NOS score = 8 or 9, p for subgroup difference = 0.003). CONCLUSION: This meta-analysis found that prediabetes may be independently associated with an increased risk of periodontitis. Further research is needed to explore the mechanisms underlying this association and potential sex-specific effects.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.075 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".