Exploring the impact of night shift work on methylation of circadian genes
Bibliographic record
Abstract
Night shift work is associated with increased breast cancer risk, but the molecular mechanisms are not well-understood. The objective of this study was to explore the relationship between night shift work parameters (current status, duration/years, and intensity) and methylation in circadian genes as a potential mechanism underlying the carcinogenic effects of night shift work. A cross-sectional study was conducted among 74 female healthcare employees (n = 38 day workers, n = 36 night shift workers). The Illumina Infinium MethylationEPIC beadchip was applied to DNA extracted from blood samples to measure methylation using a candidate gene approach at 1150 CpG loci across 22 circadian genes. Linear regression models were used to examine the association between night shift work parameters and continuous methylation measurements (β-values) for each CpG site. The false-discovery rate (q = 0.2) was used to account for multiple comparisons. Compared to day workers, current night shift workers demonstrated hypermethylation in the 5ʹUTR region of CSNK1E (q = 0.15). Individuals that worked night shifts for ≥10 years exhibited hypomethylation in the gene body of NR1D1 (q = 0.08) compared to those that worked <10 years. Hypermethylation in the gene body of ARNTL was also apparent in those who worked ≥3 consecutive night shifts a week (q = 0.18). These findings suggest that night shift work is associated with differential methylation in core circadian genes, including CSNK1E, NR1D1 and ARNTL. Future, larger-scale studies with long-term follow-up and detailed night shift work assessment are needed to confirm and expand on these findings.
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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 | 0.000 | 0.000 |
| 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".