Anti‐<i>Porphyromonas gingivalis</i> Antibody Levels in Patients With Stroke and Atrial Fibrillation: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: Atrial fibrillation (AF) and stroke are two highly related conditions, with periodontitis and periodontal pathogens, such as Porphyromonas gingivalis (Pg), appearing to be the most prominent common risk factors. In this study, we evaluated studies assessing Pg infection via serum/plasma anti-Pg antibodies in patients with AF and/or stroke. MATERIAL AND METHODS: Online databases (PubMed, Scopus, Embase, and the Web of Science) were screened for studies showing the association between anti-Pg antibodies with stroke and/or AF. Relevant data were extracted, and a subsequent random-effects meta-analysis was performed to calculate the pooled odds ratio (OR) or standardized mean difference (SMD) and 95% confidence intervals (CIs) for Pg seropositivity or anti-Pg antibody levels in stroke patients compared to controls. RESULTS: Sixteen studies were included in the systematic review. Based on the meta-analysis performed, there was no significant difference in Pg IgA and IgG levels between patients with stroke and controls (IgA: SMD 0.11, 95% CI -0.02 to 0.25, p = 0.1; IgG: SMD -0.12, 95% CI -1.24 to 0.99, p = 0.83). Similarly, no difference was observed between these groups in terms of Pg IgA and IgG seropositivity (IgA: OR 1.63, 95% CI 1.06-2.50, p = 0.026; IgG: OR 2.30, 95% CI 1.39-3.78, p < 0.001). Subsequently, we reviewed the results of six articles investigating serum or plasma IgG antibodies against Pg in patients with AF. Our results revealed a strict association between Pg infection and AF, with AF patients exhibiting either higher anti-Pg antibody levels or a higher prevalence of positive serum Pg antibodies. CONCLUSIONS: Our study supports the clinical utility of Pg infection assessment in patients with periodontitis and those with AF and solicits more focused studies to corroborate its use in clinical settings to enhance overall outcomes, reduce the risk of complications like stroke, and help fine-tune personalized therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".