Associations of Long‐Term Night Shift Work With Incident Irritable Bowel Syndrome: A Population‐Based Cohort Study
Bibliographic record
Abstract
BACKGROUND AND AIM: To explore the impact of long-term night shift work on the incidence of irritable bowel syndrome (IBS) and the underlying mechanism. METHODS: This cohort study included 239 760 participants who were in paid employment or self-employed from the UK Biobank. The start date refers to the date when a participant joined the cohort between 2006 and 2010, whereas the end of follow-up was December 31, 2021. In-depth lifetime employment information was used to calculate the duration and frequency of night shifts. Low-grade inflammation index (INFLA score) was calculated from five circulating inflammatory biomarkers. Cox proportional hazard models were used to estimate the relationships between long-term night shifts and IBS risk. RESULTS: An increasing trend of IBS incidence was observed from day workers to regular night shift workers. Compared to day workers, rarely/some night shift workers (HR 1.097, 95% CI 1.007-1.195) and usual/permanent night shift workers (HR 1.213, 95% CI 1.046-1.407) had a higher risk of IBS. INFLA score significantly mediated this association (mediation proportion 3.6%, p < 0.05). Workers with a longer duration (≥ 3 years) (HR 1.241, 95% CI 1.073-1.436) and a higher frequency of night shifts (> 7 shifts/month) (HR 1.248, 95% CI 1.045-1.491) also showed higher IBS risks. CONCLUSION: Night shift work, longer night shift duration, and higher night shift frequency were associated with higher risks of IBS. The potential underlying mechanism may be heightened low-grade inflammation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".