8277807 The impact of shift work on cognitive impairment in workers and changes in cholinergic-related factor levels
Bibliographic record
Abstract
<h3>Objectives</h3> Shift work affects approximately 30% of the global workforce and may lead to cognitive impairment. This study explores the impact of shift work on cognitive function and involves the potential mechanisms of the cholinergic pathway. <h3>Methods</h3> We recruited 1,025 manufacturing workers (Fuzhou, China) with ≥1 year of employment, retaining 677 after exclusions. Data collection included: 1) Shift work patterns (questionnaires); 2) Cognitive assessment using the Montreal Cognitive Assessment (MoCA); and 3) ELISA-measured circadian (BMAL1, CLOCK, PER1/2, CRY1/2) and cholinergic markers (ACh, AChE, ChAT, ChT, VAChT). Statistical analyses assessed cognitive outcome and cholinergic alterations. <h3>Results</h3> Among shift workers (58.1%), cognitive impairment prevalence was 37.9%. Cognitive impairment rates significantly differed by shift work duration groups. Shift work disrupted circadian regulation, with decreased BMAL1/CLOCK and increased PER/CRY levels (P<0.05), negatively correlating with shift duration. MoCA revealed deficits in visuospatial/executive function, attention, and abstraction among long-term shift workers. Logistic regression identified prolonged shift work as an independent risk factor for cognitive impairment. Cholinergic dysregulation in impaired shift workers included elevated AChE and reduced ChAT, VAChT, ChT, and ACh (P<0.05). Significant negative correlations emerged between shift work duration and peripheral cholinergic factor expression. <h3>Conclusion</h3> Shift work is a significant risk factor for cognitive impairment, with prolonged exposure increasing severity. Cognitive impairment was associated with circadian disruption and cholinergic dysfunction (elevated AChE, suppressed ACh synthesis/transport), suggesting cholinergic pathways may mediate the cognitive effects of shift work. Interventions targeting circadian and cholinergic systems may help mitigate these risks.
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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 | 0.001 |
| 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 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".