Modeling the relationship between shift work and cardiometabolic risk through circadian disruption, sleep and stress pathways
Bibliographic record
Abstract
The purpose of this study is to elucidate the multiple pathways linking shift work exposure to cardiometabolic risk (CMR) through the intermediates of circadian disruption, sleep disturbances, and stress. A cross-sectional study was conducted at Kingston Health Sciences Center that included female hospital workers, 160 who worked a day-only schedule and 168 who worked rotating days and nights. Participants completed questionnaires, a clinical exam, and wore accelerometers to collect sleep data for 8 days. Participants also collected urine samples at each void during a 24-h collection period, on the day shift for day-only workers and the night shift for rotating shift workers, for cortisol and melatonin measures. We adapted and tested a conceptual model proposed by Knutsson and Boggild for circadian disruption, sleep, and stress mechanistic pathways linking shift work to CMR using structural equation modeling techniques. Status as a rotating shift worker was associated with increased circadian disruption of cortisol and melatonin production compared to day-only workers (P < .001). Increased circadian disruption was associated with an increased CMR (P = .01). Rotating shift work was associated with sleep disturbances (P = .002) and increased job stress (P < .001), but neither was associated with CMR. We conclude that rotating shift work is associated indirectly with increased CMR. This association is mediated by circadian disruption as indicated by attenuated melatonin and cortisol, and flatter cortisol curves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".