Immune‐Mediated Disorders in Patients With Alopecia Areata: Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Alopecia areata (AA) is a nonscarring, organ-specific autoimmune disorder characterized by hair loss. Emerging evidence suggests that AA frequently coexists with other immune-mediated diseases, but the strength and consistency of these associations remain unclear. This meta-analysis aimed to systematically evaluate the prevalence and risk of immune-mediated comorbidities in patients with AA, focusing on dermatological, endocrinological, gastrointestinal, and systemic immune-mediated conditions. A comprehensive literature search was conducted in Medline, Web of Science, and Google Scholar for studies published from January 2000 through July 2024. Case-control studies reporting immune-mediated comorbidities in AA with sufficient data to calculate odds ratios (ORs) were included. Eighteen studies met the inclusion criteria, encompassing 462,945 AA patients and 11,488,192 healthy controls. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and study quality was assessed using the Newcastle-Ottawa Scale. The meta-analysis was performed using a random-effects model. The analysis revealed significantly increased odds of comorbid immune-mediated diseases in patients with AA, including atopic dermatitis (OR 2.63), autoimmune thyroiditis (OR 1.57), inflammatory bowel disease (OR 1.20), systemic lupus erythematosus (OR 3.28), and vitiligo (OR 6.61). The prevalence of these comorbidities ranged from 0.34% for multiple sclerosis to 38.65% for atopic disorders. These findings confirm that AA is associated with a heightened risk of various immune-mediated conditions. The strong links, particularly with vitiligo, systemic lupus erythematosus, and autoimmune thyroiditis, underscore the need for comprehensive screening and interdisciplinary management of patients with AA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".