Correlations between lichen planus and autoimmune diseases: a Mendelian randomization study
Bibliographic record
Abstract
Abstract Background Previous observational studies have found that lichen planus (LP) is associated with autoimmune diseases. To determine the association between LP and 15 autoimmune diseases, we applied the Mendelian randomization (MR) approach, which uses genetic variants as a tool to improve causal inference. Methods We performed a two-sample MR with the genetic instruments identified for 15 autoimmune diseases. Genome-wide association study (GWAS) data for LP was sourced from the FinnGen (1,865 cases and 212,242 non-cases). The instrumental variables (IVs) for LP were genetic variations highly associated (P < 5 × 10− 6) with LP in the European population. UK Biobank, FinnGen data and IEU Open GWAS database were utilized for autoimmune diseases GWAS data. To calculate causal effects, odds ratios (ORs) with 95% confidence intervals (CIs) are employed. Results We found genetic liability to LP was associated with a decreased risk of atopic dermatitis and ankylosing spondylitis, OR [95%CI] = 0.96[0.94, 0.99], PIVW = 0.013 and OR [95%CI] = 0.88[0.77, 0.99], PIVW = 0.047, respectively. Furthermore, it presents increased risk of higher odds ratios in: Type 1 diabetes (OR [95%CI] = 1.07[1.01, 1.14], PIVW = 0.027). Conclusion The MR study suggests that there may not be a significant association for LP contributing to the development of vitiligo, thyroid disease, SLE and Sjogren’s syndrome. On the contrast, we found that patients with LP are at decreased risk of AD and ankylosing spondylitis. no strong causal evidence of LP on the risk of vitiligo, thyroid disease, SLE and Sjogren’s syndrome.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".