Smoking and Polymorphisms of the Interleukin‐1 Gene Cluster (IL‐1β, IL‐1α, and IL‐1RN) in Patients with Periodontal Disease
Bibliographic record
Abstract
BACKGROUND: Polymorphisms within the interleukin-1 cluster are known to be associated with adult periodontal disease. However, interactions of genetic with other risk factors, especially smoking, remain questionable. The aim of this cross-sectional study was to evaluate the genetic influence on periodontal variables in relation to environmental factors. METHODS: One-hundred fifty-four (154) Caucasian subjects were clinically and radiographically assessed for their periodontal status, their smoking history recorded, and their allelic pattern of IL-1alpha, IL-1beta, and IL-1RN polymorphisms determined by genotyping. RESULTS: In assessing periodontitis with mean probing depth, mean attachment loss, or mean bone loss, no differences were found in allele frequencies or combined allotypes between subjects with mild or moderate versus those with severe signs of periodontitis. However, the extent of attachment loss defined as percentage of sites >4 mm was significantly associated with the composite genotype of IL-1alpha/1beta in smokers (odds ratio [OR] = 4.00; 95% confidence interval [CI] 1.03 to 16.70; P= 0.02). No differences were found in genotype negative subjects irrespective of their smoking status. They had nearly identical attachment loss as genotype positive non-smokers. Similar non-significant results were found with respect to extent of bone loss. An increased risk of more extended attachment loss was observed also in individuals carrying mutations of the combined genotype IL-1alpha/IL-1RN, again showing enhanced risk only in genotype-positive and smoking subjects. CONCLUSIONS: The results provide evidence that the composite genotypes studied show interaction with smoking, the main exposition-related risk factor of periodontal disease. Non-smoking subjects are not at increased risk, even if they are genotype-positive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".