Oral inflammatory load predicts vascular function in a young adult population: a pilot study
Bibliographic record
Abstract
Background The periodontium is a highly vascularized area of the mouth, and periodontitis initiates negative functional and structural changes in the vasculature. However, mild oral inflammation, including levels experienced by many apparently healthy individuals, has an unclear impact on cardiovascular function. The purpose of this pilot study is to investigate the effects of objectively measured whole mouth oral inflammatory load (OIL) on vascular function in apparently healthy individuals. Methods In this cross-sectional and correlational analysis, we recruited 28 young (18–30 years) and systemically healthy participants (16 male, 12 female). Using oral neutrophil counts, a validated measure for OIL, we collected participant's mouth rinse samples and quantified OIL. Blood pressure, arterial stiffness (pulse-wave velocity) and endothelial function (brachial artery flow-mediated dilation) were also measured. Results Only oral neutrophil count significantly predicted flow-mediated dilation % (p = 0.04; R2 = 0.16, β = − 1.05) and those with OIL levels associated with >2.5 × 105 neutrophil counts (n = 8) had a lower flow-mediated dilation % (6.0 ± 2.3%) than those with counts associated with gingival health with less than 2.5 × 105 neutrophil counts (10.0 ± 5.2%, p = 0.05). There were no significant predictors for arterial stiffness. Conclusion We found that OIL was a predictor of reduced flow-mediated dilation. An impairment in flow-mediated dilation is an indicator of future possible risk of cardiovascular disease—one of the leading causes of death in North America. Therefore, this study provides evidence for the importance of oral health and that OIL may impact endothelial function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".