Subgingival biodiversity in subjects with uncontrolled type‐2 diabetes and chronic periodontitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There is a bidirectional relationship between periodontal disease and type-2 diabetes mellitus (DM). Inflammatory mediators may negatively affect glycemic control, and increased glucose levels and resultant glycation end-products may alter the host response against bacterial infection. However, no agreement has been reached regarding the effect of DM on periodontal subgingival microbiota. Therefore, the purpose of the present study was to compare the subgingival biodiversity in deep periodontal pockets of subjects with chronic periodontitis and either uncontrolled type-2 diabetes or no diabetes using 16S rRNA gene cloning and sequencing. MATERIAL AND METHODS: Twelve subjects with uncontrolled type-2 diabetes (glycated hemoglobin > 8%) and eleven nondiabetic subjects presenting severe and generalized chronic periodontitis were selected. Subgingival biofilm from periodontal pockets > 5 mm were assessed using the 16S rRNA gene cloning and sequencing technique. RESULTS: Significant differences were observed in subgingival microbiota between diabetic and nondiabetic subjects. Diabetic subjects presented higher percentages of total clones of TM7, Aggregatibacter, Neisseria, Gemella, Eikenella, Selenomonas, Actinomyces, Capnocytophaga, Fusobacterium, Veillonella and Streptococcus genera, and lower percentages of Porphyromonas, Filifactor, Eubacterium, Synergistetes, Tannerella and Treponema genera than nondiabetic individuals (p < 0.05). Moreover, some phylotypes, such as Fusobacterium nucleatum, Veillonella parvula, V. dispar and Eikenella corrodens were detected significantly more often in diabetic subjects than in nondiabetic subjects (p < 0.05). CONCLUSION: Subjects with uncontrolled type-2 diabetes and chronic periodontitis presented significant dissimilarities in subgingival biodiversity compared with nondiabetic subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".