Interleukin-1 genetic polymorphisms in knee osteoarthritis: What do we know? A meta-analysis and systematic review
Bibliographic record
Abstract
PURPOSE: Interleukin-1 is the main proinflammatory cytokine in osteoarthritis (OA). Several single-nucleotide polymorphisms (SNPs) within the IL-1 gene cluster (IL-1β, IL-1R1, and IL-1RN) have been determined, but their associations with knee OA remain poorly understood. The present study aimed to identify the associations between IL-1 SNPs and knee osteoarthritis. METHODS: This meta-analysis and systematic review included all comparative studies published in the MEDLINE/PubMed, Embase, Google Scholar, and Cochrane Library databases. We performed a systematic search to identify relevant studies on the evaluation of the correlation between the IL-1 gene and knee OA published up to February 2020 that met the eligibility criteria. Nine studies on a total of 2256 knees with OA and 3527 healthy knees met the eligibility criteria. Results associated with IL-1A, IL-1B, IL-1R1, and IL-1RN SNPs were extracted and compared between knees with OA and healthy knees. Methodological quality was assessed using the Newcastle-Ottawa scale (NOS). All studies with fair or good quality were included. RESULTS: The meta-analysis showed that the risk of knee OA is decreased by the IL-1RN*1 and IL-1RN*1/*1 genotypes and increased by the IL-1RN*2 and I-L1RN*1/*2 genotypes. The systematic review revealed only two studies associating the IL-1RN allele, none associating the IL-1B polymorphism, and only one study associating IL-1A and IL-1R1 polymorphisms with knee OA. CONCLUSIONS: Several IL-1RN alleles and genotypes play a role in knee OA but other genetic variations in the IL-1 region were still conflicting in its association with knee OA.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.025 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".