Increased oral inflammation, leukocytes, and leptin, and lower adiponectin in overweight or obesity
Bibliographic record
Abstract
Objectives The association between body mass index ( BMI ) and oral diseases was investigated, and levels of obesity‐related inflammatory mediators were evaluated. Subjects and Methods Participants ( n = 160) were clinically and radiographically examined for oral diseases. Blood profiles were recorded. Levels of adiponectin, leptin, and C‐reactive protein ( CRP ) were measured. Results One hundred and thirteen (70.6%) participants had overweight or obese status ( BMI ≥ 23.0 kg/m 2 ). Sum of dental diseases and severe periodontitis were higher in overweight or obese individuals than in normal‐weight participants ( p = .037 and p = .002, respectively). A significant difference in oral mucosal disorders between normal weight and overweight or obesity was not found. Plasma leukocyte counts, liver enzymes, leptin, and CRP levels were increased while adiponectin levels were decreased in individuals with BMI ≥23.0 kg/m 2 compared with normal‐weight participants. After adjusting for age, sex, fasting plasma glucose level, smoking, and exercise, obesity was associated with sum of dental diseases (ß = 0.239, p = .013), severe periodontitis ( OR =4.52; 95% CI 1.37, 14.95, p = .013), adiponectin (ß = –0.359, p < .001), leptin (ß = 0.630, p < .001), and CRP levels ( OR =12.66; 95% CI 3.07, 52.21, p < .001). Conclusion Overweight or obese Thai people were related to an increase in inflammatory dental and periodontal diseases with an altered health profile and plasma inflammatory mediators.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".