The multifaceted impact of circadian disruption on cancer risk: a systematic review of insights and economic implications
Bibliographic record
Abstract
Background: Circadian disruption has emerged as a significant risk factor for cancer, driven by mechanisms such as hormonal imbalances, impaired DNA repair, immune suppression, and metabolic dysregulation. Modern societal patterns-shift work, artificial light at night, and irregular sleep schedules-have exacerbated these risks. Methods: We conducted a systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, screening over 500 studies published between 2003 and 2023 from PubMed, Scopus, Embase, ScienceDirect, and Web of Science. Inclusion criteria focused on peer-reviewed epidemiological and mechanistic studies linking circadian disruption with cancer risk. The Newcastle-Ottawa Scale was used for methodological quality assessment. Results: A total of 75 high-quality studies were included. Strong evidence supports associations between circadian disruption and breast, prostate, and colorectal cancers, with limited but emerging evidence for melanoma and bladder cancer. Mechanistic pathways involve melatonin suppression, dysregulation of CLOCK and BMAL1 genes, reduced natural killer cell activity, and chronic inflammation due to metabolic imbalance. Light-at-night (LAN) exposure and prolonged night shift work were consistently identified as major risk factors. Furthermore, economic analyses reveal a substantial burden due to increased healthcare costs and productivity losses, particularly in shift work-dominated sectors. Conclusions: Circadian misalignment is a critical, yet often overlooked, contributor to cancer incidence and associated economic burdens. Public health strategies-such as regulating shift schedules, reducing LAN exposure, and promoting chronotherapy-are essential to mitigate these risks. Further research should address sex-based differences, improve exposure measurement, and extend investigations to low- and middle-income countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".