Exposure to Artificial Light-at-Night and Breast and Prostate Cancer Risk in Spain (MCC-Spain study)
Bibliographic record
Abstract
Background/Aim: Night shift work, exposure to light-at-night and the consequent circadian disruption may increase the risk of hormone-dependent cancers. We evaluated the association of exposure to artificial light at night (ALAN) during sleeping time with breast and prostate cancer in a population based multicase-control study (MCC-Spain), among subjects who had never worked at night. We took into account chronotype, a characteristic that may relate to adaptation to light-at-night. Methods: We enrolled 1219 breast cancer cases, 1385 female controls, 623 prostate cancer cases and 879 male controls from 11 Spanish regions, 2008-2013. Indoor-ALAN information was obtained through questionnaires and outdoor-ALAN was analyzed using images from the International Space Station (ISS) available for Barcelona and Madrid, including data of remotely sensed upward light intensity and blue light spectrum information for each geocoded longest residence of each MCC-Spain subject. Results: Among participants with information on both indoor and outdoor ALAN, exposure to higher levels of blue light spectrum (outdoor-ALAN) was associated with an increased risk of breast (adjusted odds ratio OR=1.54, 95%CI 1.0-2.4) and prostate cancer (OR=1.90, 95%CI 1.2-2.9). Overall light intensity (outdoor-ALAN) was not associated with cancer risk. Those sleeping in more illuminated bedrooms (indoor-ALAN) had a higher risk of prostate cancer [OR=2.82, 95%CI 1.5-5.3] while there was no clear association for breast cancer (OR=1.19, 95%CI 0.6-2.6). Evening types tended to have slightly higher prostate cancer risks. Conclusions: Both indoor and outdoor ALAN and particularly blue enriched light spectrum were associated with an increased risk of breast and prostate cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".