Gingival Tissue Transcriptomes Identify Distinct Periodontitis Phenotypes
Bibliographic record
Abstract
The currently recognized principal forms of periodontitis-chronic and aggressive-lack an unequivocal, pathobiology-based foundation. We explored whether gingival tissue transcriptomes can serve as the basis for an alternative classification of periodontitis. We used cross-sectional whole-genome gene expression data from 241 gingival tissue biopsies obtained from sites with periodontal pathology in 120 systemically healthy nonsmokers with periodontitis, with available data on clinical periodontal status, subgingival microbial profiles, and serum IgG antibodies to periodontal microbiota. Adjusted model-based clustering of transcriptomic data using finite mixtures generated two distinct clusters of patients that did not align with the current classification of chronic and aggressive periodontitis. Differential expression profiles primarily related to cell proliferation in cluster 1 and to lymphocyte activation and unfolded protein responses in cluster 2. Patients in the two clusters did not differ with respect to age but presented with distinct phenotypes (statistically significantly different whole-mouth clinical measures of extent/severity, subgingival microbial burden by several species, and selected serum antibody responses). Patients in cluster 2 showed more extensive/severe disease and were more often male. The findings suggest that distinct gene expression signatures in pathologic gingival tissues translate into phenotypic differences and can provide a basis for a novel classification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".