Exploring the Causal Relationship Between Atopic Dermatitis and Malignancies: A Comprehensive Observational and Genetic Study
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by immune dysregulation. The global rise in the incidence of both AD and cancer suggests a potential link through shared immune-inflammatory pathways. Objective: This study aimed to investigate the causal relationship between AD and multiple cancer types and to explore potential underlying biological mechanisms. Methods: A two-sample Mendelian randomization (MR) analysis was conducted using data from a genome-wide association study of 6,224 AD patients. Seventeen cancer types were assessed. Additionally, expression quantitative trait loci analysis, pathway enrichment, and survival analyses were performed. Results : MR analysis identified a significant causal relationship between AD and the risk of esophageal cancer (odds ratio [OR] = 0.88, P = 0.0247) as well as colorectal cancer (OR = 0.94, P = 0.0154). Subsequent research revealed differential expression between tumor and normal tissues, with pathway enrichment highlighting immune processes. No survival association was found in esophageal cancer, but MFN2 and SIPA1 expression levels could affect outcomes in colorectal cancer. Conclusion: This study provides evidence for a protective causal relationship between AD and the risks of esophageal and colorectal cancers. Immune and inflammatory pathways may mediate this link.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".