Tumor Necrosis Factor-<i>α</i>and Interleukin-6: Potential Interorgan Inflammatory Mediators Contributing to Destructive Periodontal Disease in Obesity or Metabolic Syndrome
Bibliographic record
Abstract
Obesity has become a worldwide health burden in the last two decades. Obesity has been associated with increased comorbidities, such as coronary artery disease, diabetes, and destructive periodontal disease. Obesity is also part of a group of risk factors occurring together in an individual, which is referred to as metabolic syndrome. Clinical studies have shown higher risk for destructive periodontal disease in obesity and metabolic syndrome. However, the role of obesity and metabolic syndrome in the onset and development of destructive periodontal disease has not yet been fully understood. In this review, we discuss a working model, which focuses on interorgan inflammation as a common etiological factor for destructive periodontal disease associated with obesity and metabolic syndrome. Specifically, we suggest that elevated levels of tumor necrosis factor- α (TNF- α ) or interleukin 6 (IL-6)--both adipokines and known risk factors for destructive periodontal disease--in obesity and metabolic syndrome contribute to the onset and development of destructive periodontal disease. The connections between destructive periodontal disease and systemic conditions, such as obesity or metabolic syndrome, are complex and potentially multidirectional. This review largely focuses on TNF- α and IL-6, inflammatory mediators, as potential common risk factors and does not exclude other biological mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".