Impact of artificial light at night on obesity and overweight: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Global obesity rates are rising sharply, prompting interest in environmental drivers beyond diet and activity. Widespread artificial light at night disrupts circadian rhythms and metabolism. potentially elevating obesity risk, but evidence syntheses are outdated and limited. METHODS: We conducted a systematic search of databases including PubMed, EMbase, Cochrane Library, and Web of Science. Eligible observational studies (cohort or cross-sectional) were required to report adjusted risk estimates (odds ratio [OR], relative risk [RR], or hazard ratio [HR] with 95% confidence intervals [95% CI]) for obesity or overweight, with quantifiable LAN exposure. Study quality was evaluated using the Newcastle-Ottawa Scale (NOS) for cohort studies and the AHRQ criteria for cross-sectional studies. A random-effects model was applied for effect size pooling, alongside subgroup analyses stratified by geographic region, age, and sex, supported by sensitivity analyses and Egger's test for publication bias assessment. RESULTS: Our analysis included 13 studies involving 867,647 participants (9 cross-sectional and 4 cohort studies). A significant 14% increased risk of obesity was observed in the highest LAN exposure group compared to the lowest (OR = 1.14, 95% CI: 1.07-1.22; I² = 92.5%, P < 0.001). For overweight risk, a 7% increase was noted (OR = 1.07, 95% CI: 1.00-1.15; I² = 92.5%, P < 0.001). Regionally, the strongest association was in North America (OR = 1.21, 95% CI: 1.10-1.32; I² = 46.6%, P = 0.132) and significant in Asia (OR = 1.14, 95% CI: 1.06-1.21; I² = 85.5%, P < 0.001), but not in Europe. Age-wise, both adults (OR = 1.16) and adolescents (OR = 1.17) exhibited significant associations (P < 0.05), with no notable differences between sexes. All studies were deemed high quality (mean NOS score: 7.50; mean AHRQ score: 7.56), and sensitivity analyses confirmed the findings with no evidence of publication bias (P > 0.05). CONCLUSIONS: Exposure to LAN significantly correlates with increased obesity and overweight risks, influenced by geographic and age-specific factors. These results highlight LAN as a noteworthy environmental risk factor for metabolic health, advocating for informed public health strategies, including region-specific lighting regulations and "screen curfews" for adolescents.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".