Meta-Analysis of Associations of IL1 Receptor Antagonist and Estrogen Receptor Gene Polymorphisms with Systemic Lupus Erythematosus Susceptibility
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease that affects a number of different organs and tissues. Interleukin-1 (IL1) and estrogen are considered potential elements in the pathology of SLE. Recently, the variable number of tandem repeats (VNTR) polymorphism in the IL1 receptor antagonist gene (IL1-RN) and PvuII (rs2234693) and XbaI (rs9340799) polymorphisms in the estrogen receptor 1 gene (ESR1) have been associated with a predisposition to SLE. However, the evidence for these associations is inconclusive. We therefore conducted a meta-analysis to validate the roles of these polymorphisms in SLE susceptibility. We searched four databases and identified a total of 17 eligible articles comprising 24 studies. The Newcastle-Ottawa quality assessment scale was used to assess the qualities of the selected studies. We assessed the strengths of the associations using odds ratios (ORs) with 95% confidence intervals (95% CIs). Regarding the IL-1RN VNTR, the 2 allele significantly increased SLE susceptibility (2 vs. L: OR = 1.34, 95% CI = 1.03-1.73, P = 0.03). The ESR1 PvuII CC/CT genotype was also associated with SLE susceptibility (CC/CT vs. TT: OR = 1.25, 95% CI = 1.06-1.47, P = 0.01), and the difference was especially pronounced among Asians (CC/CT vs. TT: OR = 1.33, 95% CI = 1.04-1.69, P = 0.02). No significant association between the ESR1 XbaI polymorphism and SLE susceptibility was observed in the overall analysis. However, a marginally significant association between the GG/GA genotype was found in individuals of Asian descent (GG/GA vs. AA: OR = 1.30, 95% CI = 1.01-1.67, P = 0.04). These results indicate that the IL1-RN VNTR 2 allele, ESR1 PvuII CC/CT genotype and ESR1 XbaI GG/GA genotype may increase SLE susceptibility, especially in Asian individuals.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".