Association Between Outdoor Light-at-night Exposure and Colorectal Cancer in Spain
Bibliographic record
Abstract
BACKGROUND: Night-shift work, exposure to artificial light-at-night (ALAN) and particularly blue light spectrum, and the consequent circadian disruption may increase the risk of breast and prostate cancer. Colorectal cancer risk may also be increased among night-shift workers. We investigated the association between exposure to ALAN according to light spectrum and colorectal cancer among subjects who had never worked at night in a general population case-control study in Spain. METHODS: We examined information on 661 incident histologically verified colorectal cancer cases and 1,322 controls from Barcelona and Madrid, 2007-2013. Outdoor ALAN exposure was based on images from the International Space Station (ISS) including data on remotely sensed upward light intensity. We derived adjusted odds ratio (OR) estimates and confidence intervals (CI) for visual light, blue light, and spectral sensitivities of the five human photopigments assigned to participant's geocoded longest residence. RESULTS: Exposure to blue light spectrum was positively associated with colorectal cancer (OR = 1.6; 95% CI: 1.2-2.2; highest vs. lowest tertile). ORs were similar (OR = 1.7; 95% CI: 1.3-2.3) when further adjusting for area socioeconomic status, diet patterns, smoking, sleep, and family history. We observed no association for outdoor visual light (full spectrum) (OR = 1.0; 95% CI, 0.7-1.2; highest vs. lowest tertile). Analysis of the five photopigments gave similar results with increased risks for shorter wavelengths overlapping with the blue spectrum and no association for longer wavelengths. CONCLUSIONS: Outdoor blue light spectrum exposure that is increasingly prevalent in recent years may be associated with colorectal cancer risk. See video abstract: http://links.lww.com/EDE/B708.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".