Association between Periodontitis and HbA1c Levels in Non-Diabetic Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A high detection rate of diabetes among dental visitors has been reported recently. This systematic review aimed to evaluate the association between periodontitis and glycated hemoglobin (HbA1c) levels among non-diabetic individuals. Methods: The EMBASE, MEDLINE, Web of Science, Cochrane Library, PubMed, and Open GREY databases were searched, and observational studies published until 1st June 2023 were identified. A methodological quality assessment was conducted based on the original and modified versions of the Newcastle–Ottawa scale. Cohort, case–control, and cross-sectional studies that performed clinical periodontal examinations and measured HbA1c levels in non-diabetic adults were included. A meta-analysis was conducted to estimate the weighted mean difference (WMD) between individuals with and without periodontitis. Results: In total, 29 case–control and 5 cross-sectional studies were selected from 2583 potentially eligible articles. Among them, sixteen case–control and three cross-sectional studies with moderate to high quality were selected for the meta-analyses. The HbA1c levels in periodontitis patients were significantly higher than those in individuals with healthy periodontal conditions (WMD = 0.16; p < 0.001) among the non-diabetic populations. Conclusions: This study reveals a significant association between periodontitis and HbA1c levels in non-diabetic populations. Thus, HbA1c screening may be recommended to detect potential hyperglycemia in non-diabetic periodontitis patients.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".