Genetic links between atopy, allergy, and alopecia areata: insights from a Mendelian randomization study
Bibliographic record
Abstract
BACKGROUND: Alopecia areata (AA), a prevalent form of autoimmune hair loss, has a not well-defined relationship with atopic and allergic disorders, including eczema, hay fever, and asthma. OBJECTIVES: This study aims to elucidate the genetic relationship between atopy, allergies, and alopecia areata (AA) using Mendelian randomization. We hypothesize that atopic and allergic conditions contribute to the genetic predisposition of AA. METHODS: We analyzed extensive genetic data from Genome-wide Association Studies (GWAS) involving over one million individuals. This analysis focused on assessing the genetic correlation between AA and various allergic conditions, including hay fever, eczema, asthma, and allergies to pollen, dust, and cats. The inverse variance weighted method served as our primary analytical tool, complemented by sensitivity analyses to verify the robustness of our results. RESULTS: Our findings reveal a significant genetic correlation between atopy/allergies and an increased risk of AA. Notably, strong associations were observed for hay fever, eczema, asthma, and specific allergies (pollen, dust, and cats). The sensitivity analyses corroborated these associations, reinforcing the reliability of our primary results. CONCLUSIONS: This study provides compelling genetic evidence of an association between atopic and allergic conditions and the development of AA. These findings suggest that individuals with such conditions may benefit from enhanced surveillance for early signs of AA.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".