Chronic Kidney Disease and Chronic Oral Inflammatory Diseases: A Systematic Review and Meta-Analysis of Periodontitis and Apical Periodontitis
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) has been increasingly associated with oral chronic inflammatory conditions, including periodontitis (PD) and apical periodontitis (AP). Both share common pathophysiological pathways involving systemic inflammation, immune dysregulation, and oxidative stress. This systematic review and meta-analysis aimed to synthesize current evidence on the association between CKD and chronic oral inflammatory diseases. Methods: The PRISMA guidelines were followed and the proto-col was registered in PROSPERO: CRD420251167323. A comprehensive electronic search was conducted in PubMed, Scopus, Web of Science, and ProQuest up to September 2025. Observational studies reporting prevalence of chronic oral inflammatory diseases in CKD patients and controls subjects were included. The Newcastle–Ottawa scale was used for assessing risk of bias. Pooled odds ratios (ORs) were calculated using a random-effects model. Results: Seven studies published between 2011 and 2025, including 13,139 participants, met the inclusion criteria. CKD patients had significantly higher prevalence of oral inflammatory disease than controls (OR = 4.2; 95% CI = 2.5–7.2; p < 0.00001). Heterogeneity was high (I2 = 83.0%). Subgroup analysis showed an OR of 4.3 (95% CI = 2.6–7.0; p < 0.0001) for AP and 4.3 (95% CI = 2.2–8.7) for PD. The overall risk of bias was moderate, and the certainty of evidence according to GRADE was rated as low. Conclusions This systematic review and meta-analysis highlight a potential link between chronic oral inflammatory disease, including both AP and PD, and chronic kidney disease (CKD). However, the certainty of the evidence is low, and substantial heterogeneity exists across studies.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".