Evidence map›Paper›PMID 39076421›Full record

ArticleFrontiers in public health2024

Associations of exposure to volatile organic compounds with sleep health and potential mediators: analysis of NHANES data.

Yan Zhuang, Laifu Li, Yanqi Zhang, Fei Dai

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yan ZhuangDepartment of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Laifu LiDepartment of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yanqi ZhangDepartment of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Fei DaiDepartment of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The effect of environmental pollution on sleep has been widely studied, yet the relationship between exposure to volatile organic compounds (VOCs) and sleep health requires further exploration. We aimed to investigate the single and mixed effect of urinary VOC metabolites on sleep health and identify potential mediators. Methods: Data for this cross-sectional study was collected from the National Health and Nutrition Examination Surveys (NHANES) (2005-2006, 2011-2014). A weighted multivariate logistic regression was established to explore the associations of 16 VOCs with four sleep outcomes. Following the selection of important VOCs through the least absolute shrinkage and selection operator (LASSO) regression, principal component analyses (PCA), weight quantile sum (WQS), and Bayesian kernel machine regression (BKMR) analyses were conducted to explore the associations between exposure to single and mixed VOCs and sleep outcomes, as well as identify the most contributing components. A mediation analysis was performed to explore the potential effect of depression scores. Results: Of the 3,473 participants included in the study, a total of 618 were diagnosed with poor sleep patterns. In logistic regression analyses, 7, 10, 1, and 5 VOCs were significantly positively correlated with poor sleep patterns, abnormal sleep duration, trouble sleeping, and sleep disorders, respectively. The PCA analysis showed that PC1 was substantially linked to a higher risk of poor sleep patterns and its components. The WQS model revealed a positive association between VOC mixture of increased concentrations and poor sleep patterns [OR (95% CI): 1.285 (1.107, 1.493)], abnormal sleep duration [OR (95% CI): 1.154 (1.030, 1.295)], trouble sleeping [OR (95% CI): 1.236 (1.090, 1.403)] and sleep disorders [OR (95% CI): 1.378 (1.118, 1.705)]. The BKMR model found positive associations of the overall VOC exposure with poor sleep patterns, trouble sleeping, and sleep disorders. PCA, WQS, and BKMR models all confirmed the significant role of Conclusion: Exposure to single and mixed VOCs negatively affected the sleep health of American population, with AMCC playing a significant role. The depression score was shown to mediate the associations of VOC mixtures with poor sleep patterns and its components.

Indexed as

Nutrition SurveysVolatile Organic CompoundsAdultBayes TheoremCross-Sectional StudiesEnvironmental ExposureFemaleHumansLogistic ModelsMaleMiddle AgedPrincipal Component AnalysisSleepSleep Wake DisordersUnited StatesVolatile Organic CompoundsBayesian kernel machine regressiondepression scoreNHANESsleep healthVOCsweighted quantile sum regression

Identifiers

PMID39076421
PMCPMC11284068

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.