Association of Sleep Deprivation and the Risk of Developing Systemic Lupus Erythematosus Among Women
Bibliographic record
Abstract
Objective Sleep deprivation has been associated with risk of autoimmune diseases. Using the Nurses’ Health Study (NHS) (1986–2016) and NHSII (1989–2017) cohorts, we aimed in the present study to investigate whether sleep deprivation was associated with risk of developing systemic lupus erythematosus (SLE). Methods Average sleep duration in a 24‐hour period was reported in the NHS (1986–2014) and NHSII (1989–2009). Lifestyle, exposure, and medical information was collected on biennial questionnaires. Adjusted Cox regression analyses modeled associations between cumulative average sleep duration (categorical variables) and incident SLE. Interactions between sleep duration and shiftwork, bodily pain (using the Short Form 36 [SF‐36] questionnaire), and depression were examined. Results We included 186,072 women with 187 incident SLE cases during 4,246,094 person‐years of follow‐up. Chronic low sleep duration (≤5 hours/night versus reference >7–8 hours) was associated with increased SLE risk (adjusted hazard ratio [HRadj] 2.47 [95% confidence interval (95% CI) 1.29, 4.75]), which persisted after the analysis was lagged (4 years; HRadj 3.14 [95% CI 1.57, 6.29]) and adjusted for shiftwork, bodily pain, and depression (HRadj 2.13 [95% CI 1.11, 4.10]). We detected additive interactions between low sleep duration and high bodily pain (SF‐36 score <75) with an attributable proportion (AP) of 64% (95% CI 40%, 87%) and an HR for SLE of 2.97 (95% CI 1.86, 4.75) for those with both risk factors compared to those with neither. Similarly, there was an interaction between low sleep duration and depression, with an AP of 68% (95% CI 49%, 88%) and an HR for SLE of 2.82 (95% CI 1.64, 4.85). Conclusion Chronic low sleep duration was associated with higher SLE risk, with stronger effects among those with bodily pain and depression, highlighting the potential role of adequate sleep in disease prevention.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".