Nocturnal light exposure aggravates schizophrenia via gut microbiota mediated lipid metabolism: Human and animal multi-omics evidence
Bibliographic record
Abstract
BACKGROUND: Artificial light at night (ALAN) is common in psychiatric inpatient settings, yet evidence suggests it may exacerbate mental disorders. We aimed to examine the effects of ALAN intervention on schizophrenia relapse risk and the mediating roles of gut microbiota and metabolic pathways. METHODS: Schizophrenia patients in hospital rooms randomly received usual ALAN or reduced ALAN by partially covering lights, and swapped after a two-week washout interval. Outcomes were assessed using the Early Signs Scale (ESS) and Montreal Cognitive Assessment (MoCA). Complementary experiments in a dizocilpine-induced schizophrenia mouse model under ALAN exposure evaluated gut microbiota, metabolomic profiles, and behavioral effects following linoleic acid supplementation. RESULTS: Reduced ALAN exposure was associated with lower ESS scores on depression and incipient psychosis, as well as higher scores on MoCA. These benefits were linked to stabilized gut microbiota and linoleic acid metabolism. There were mediation effects by 13(S)-HpODE (linoleic acid metabolite) on associations of ALAN with higher depression and lower MoCA scores. Mice exposed to ALAN showed gut dysbiosis, disrupted linoleic acid metabolism, and schizophrenia-like behaviors, which were partially ameliorated by linoleic acid supplementation. CONCLUSIONS: ALAN exposure may increase the risk of schizophrenia relapse, potentially mediated by gut microbiota and linoleic acid metabolism. Linoleic acid supplementation indicates potential benefits in rodent models, suggesting a potential translational intervention worthy of further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".