The Association of Psoriasis With Sleep Disorders in a Diverse National Cohort
Bibliographic record
Abstract
ABSTRACT Background Higher prevalence of sleep disorders is seen in persons with psoriasis (PsO). However, the extent to which sleep conditions are linked to PsO severity, particularly across diverse populations, remains unknown. Bridging this knowledge gap is vital for developing comprehensive, equitable and personalized care strategies. Objectives We aimed to quantify the extent to which psoriasis severity correlates with the risk of developing specific sleep disorders, and to identify how these associations vary by racial/ethnic group, using robust covariate adjustments. Methods Health records for 7743 adults with psoriasis were obtained from the All of Us sample—an NIH database initiative that oversamples underrepresented populations. Cases were selectively matched 1:4 to age‐, sex‐, and race/ethnicity‐matched controls, and differences were interrogated by multivariable regression. Results Mild PsO was significantly associated with restless leg syndrome, insomnia and obstructive sleep apnoea after adjusting for sociodemographic variables and comorbidities. Moderate‐to‐severe PsO demonstrated greater magnitudes of association. We additionally observed a magnified sleep disorder risk in non‐White patients, particularly for insomnia and OSA. Conclusions Both mild and moderate‐to‐severe psoriasis is significantly associated with an increased risk of sleep disorders, with notable variations across different racial/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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".