Molecular Differences between Chronic and Aggressive Periodontitis
Bibliographic record
Abstract
The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 'healthy' or 'diseased' gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with ≥ 2 'diseased' gingival papillae (n = 241; with bleeding-on-probing, probing depth ≥ 4 mm, and clinical attachment loss ≥ 3 mm), and, when available, a 'healthy' papilla (n = 69; no bleeding-on-probing, probing depth ≤ 4 mm, and clinical attachment loss ≤ 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, 'intrinsic' classification of periodontitis based on molecular profiling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".