Polycyclic aromatic hydrocarbons are associated with sleep-related disorders in adults: the potential mediating role of inflammation
Bibliographic record
Abstract
BACKGROUND: The evidence on the effects of polycyclic aromatic hydrocarbons (PAHs) on sleep disorders is limited. This study aimed to explore the association between PAH exposure and sleep-related disorders, and the potential mediating roles of inflammation in these relationships were further been examined. METHODS: This study utilized data from adult participants (≥ 20 years of age) in the US National Health and Nutrition Examination Survey 2005-2008. Weighted logistic regression models were used to estimate the associations between each urinary PAH biomarker and sleep-related disorders. Structural Equation Modelling (SEM) was applied to evaluate the association between PAH exposure and sleep-related disorders and to assess the mediating effect of inflammatory markers that include C-reactive protein (CRP) and white blood cell counts (WBC). RESULTS: One or more urinary PAH metabolites were associated with an increased risk of long sleep-onset latency, obstructive sleep apnea (OSA), sleep problems, and daytime sleepiness. The latent PAH exposure variable in SEM was positively associated to long sleep-onset latency (β = 0.054, p = 0.035), OSA (β = 0.071, p = 0.002), sleep problems (β = 0.089, p < 0.001) and daytime sleepiness (β = 0.066, p = 0.003). Mediation analysis suggested that WBC mediated the link between PAH exposure and sleep problems (Indirect effect = 0.009, 95%CI: 0.002 ~ 0.017, Proportion: 10.8%), as well as daytime sleepiness (Indirect effect = 0.011, 95%CI: 0.005 ~ 0.019, Proportion: 15.7%). CONCLUSIONS: PAH exposure was associated with an increased risk of long sleep-onset latency, obstructive sleep apnea, sleep problems, and daytime sleepiness. Inflammation may be one potential mechanism by which PAH exposure contributes sleep problems and daytime sleepiness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".