Association of Oral Microbiome with Periodontal Disease Progression: A Longitudinal Study
Bibliographic record
Abstract
Periodontal disease, a prevalent oral health condition, is characterized by the inflammation and destruction of the supporting tissues around the teeth and poses significant challenges to global public health. Objectives: To examine the association between the oral microbiome and periodontal disease progression in a Pakistani population. Methods: A total of 350 patients aged ≥ 18 years, diagnosed with periodontal disease, were registered from August 2023 to February 2024. Participants were evaluated for periodontal health indicators, including probing depth and clinical attachment loss, and their oral microbiome profiles were analyzed using high-throughput sequencing of the 16S rRNA gene. Machine learning algorithms, including Random Forest and Support Vector Machines, were applied to predict disease progression based on microbial profiles. Results: Porphyromonas gingivalis and Tannerella forsythia were strongly associated with greater probing depths and clinical attachment loss (β = 0.45, p < 0.01), indicating their role in disease progression. Conversely, Streptococcus and Lactobacillus were linked to reduced disease severity (β = -0.30, p < 0.05). The oral microbiome exhibited high diversity, with Firmicutes (35%), Bacteroidetes (25%), Proteobacteria (20%), and Actinobacteria (15%) being the predominant species. The Random Forest model predicted disease progression with 85% accuracy (Area under the curve (AUC) = 0.87), emphasizing the predictive value of microbial profiles. Conclusions: It was concluded that the study confirms a strong link between specific oral microbiota and periodontal disease progression, emphasizing the importance of microbial analysis in predicting and managing periodontal health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".