Gingival Crevicular Fluid as Biomarker’s Source for Alzheimer’s Disease
Bibliographic record
Abstract
Periodontitis is a low-grade inflammatory disease caused by a subgingival dysbiotic microbiota. Multiple studies have determined the higher prevalence of tooth loss and poor oral hygiene in patients with Alzheimer’s disease (AD). However, the periodontal diagnosis, periodontal bacteria or mediators has not been measured to date. Aim: To determine the periodontal status, the pro-inflammatory mediators, Porphyromonas gingivalis load, and Apoliporpotein E (ApoE) in patients with AD. A complete dental examination was performed on 30 patients, and cognitive status was determined by the Montreal Cognitive Assessment (MoCA). Subgingival microbiota and GCF samples were then taken from all patients from the deepest sites. Total DNA was isolated from the microbiota samples for the quantification of the 16S ribosomal subunit. Pro-inflammatory mediators and ApoE were quantified from the gingival crevicular fluid (GCF). Patients with AD had periodontitis stage III-IV in 80%, a higher concentration of pro-inflammatory and ApoE mediators, and a higher P. gingivalis load compared to healthy subjects. The pro-inflammatory mediators, P. gingivalis load had a negative correlation with the MoCA test scores. Finally, a ROC curve was performed to assess the specificity and sensitivity of ApoE levels, detecting an area of 84.9%. In AD patients, we found a more severe periodontitis, a higher levels of pro-inflammatory mediators, and higher bacterial load. In addition, there is an increase in ApoE that allows to clearly determine patients with health, periodontitis and periodontitis and AD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".