Association Between Interleukin 1 Gene Cluster Polymorphisms and Bilateral Distal Interphalangeal Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the association of the interleukin 1 gene (IL1) cluster polymorphisms and their haplotypes with bilateral distal interphalangeal joint osteoarthritis (DIP OA). METHODS: Radiographs of both hands of 295 dentists and 248 teachers were examined and classified for the presence of OA using reference images. Bilateral DIP OA was defined by the presence of radiographic findings of grade 2 or more in at least 1 symmetrical pair of the DIP joints. We genotyped 10 single-nucleotide polymorphisms (SNP) in the IL1R1, IL1RL2, IL1A, IL1B, and IL1RN genes using polymerase chain reaction-based methods. Haplotypes were statistically reconstructed using the PHASE program. The association between the genotypes/diplotypes and bilateral DIP OA was examined with logistic regression analysis. RESULTS: Two IL1B SNP (rs1143634 and rs1143633) were associated with bilateral DIP OA. The carriers of the IL1B rs1143634 minor allele had an increased OA risk [odds ratio (OR) 1.6; 95% confidence interval (CI) 1.08-2.26] compared to the noncarriers. The association was stronger in the dentists. The distribution of the IL1B rs1143633 genotype fit a recessive mode of inheritance (OR 3.03, 95% CI 1.35-6.83, p = 0.006). Two IL1B-IL1RN extended haplotype alleles (211-1 and 121-1) were associated with bilateral DIP OA. An interaction between the IL1B rs1143634 and the IL1R1-IL1RL2 and IL1B-IL1RN extended haplotypes and occupation (increased risk of OA among dentists only) was observed. CONCLUSION: Our results provide further evidence for the role of IL1 gene cluster polymorphisms in the etiology of OA and suggest that some of these may predispose DIP joints to the effects of mechanical overload.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".