Epidemiology, genetics and management of vitiligo in the USA: an All of Us investigation
Bibliographic record
Abstract
BACKGROUND: Vitiligo is an autoimmune skin depigmentation disorder significantly impacting quality of life. This condition is difficult to treat, with high relapse rates. Additionally, vitiligo associates with other autoimmune conditions, complicating patient management. Improving patient outcomes relies on understanding vitiligo's clinical landscape and genetic risk factors. OBJECTIVES: We aimed to understand vitiligo's patient distribution, current management practices, how comorbid autoimmune conditions influence treatment and how genetic risk factors vary in diverse populations. METHODS: We conducted a cross-sectional study of the All of Us research program, consisting of surveys, electronic health records and genomic data from 206,173 participants in the USA recruited between the summer of 2017 and 1 July 2022. We determined diagnostic and prescribing rates and elucidated differences in genetic risk within different populations. RESULTS: Oral corticosteroids are most frequently prescribed, followed by other immunosuppressive drugs and topical medications. Comorbid systemic lupus erythematosus impacted treatment choices. Single nucleotide polymorphisms associated with increased risk in patients of European decent were not always associated with increase risk in patients of other ancestry. CONCLUSIONS: This work highlights the current treatment landscape for vitiligo in the USA. We demonstrated that comorbid conditions impact treatment choices and genetic risk factors vary between ethnic groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".