Association of IL2RA and multiple sclerosis risk: A case control, systematic review, and meta-analysis study
Bibliographic record
Abstract
The interleukin-2 receptor alpha chain (IL2RA) gene has been implicated in multiple sclerosis (MS) susceptibility, particularly through the rs2104286 and rs12722489 SNPs. However, previous studies have yielded inconsistent results across different populations, likely due to small sample sizes and ethnic variations. This study aimed to investigate the association of IL2RA SNPs with MS risk in eastern Iranian population and through a comprehensive meta-analysis. Our case-control study included 400 Iranian individuals from North Khorasan and Sistan & Baluchistan provinces, comprising 200 MS patients across all subtypes and genders and 200 controls. The meta-analysis incorporated pooled odds ratios (ORs) with 95 % confidence intervals (CIs) to assess the strength of these associations. Our case-control findings demonstrated a significant association between rs2104286 and MS risk, which was further supported by the meta-analysis in Iranian populations. Specifically, the association was observed at both the genotype (P = 0.001) and allelic (P = 0.003) levels in North Khorasan and at the genotype level in Sistan & Baluchistan (P = 0.001). A global meta-analysis of rs2104286, encompassing 24,931 MS patients and 36,036 controls, revealed a significant association between the A allele and all genotype models with increased MS risk (P < 0.05). Similarly, a meta-analysis of rs12722489, including 19,797 MS patients and 32,085 controls, identified the CC + CT genotype as a risk factor for MS (P = 0.04). In conclusion, our findings suggest that the rs2104286 A allele and rs12722489 CC + CT genotype are associated with an increased risk of MS in both Caucasian and Asian populations, including Iranians.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".