Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Public Health. 2024 Jul 15;12:1423771. doi: 10.3389/fpubh.2024.1423771. eCollection 2024.
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.
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.
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 -acetyl--(-methylcarbamoyl)-l-cysteine (AMCC) in poor sleep patterns and its components. The depression score was a mediator between the positive VOC mixture index and the four sleep outcomes.
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.
环境污染对睡眠的影响已得到广泛研究，但接触挥发性有机化合物（VOCs）与睡眠健康之间的关系仍需要进一步探讨。本研究旨在调查尿中 VOC 代谢物对睡眠健康的单一和混合影响，并确定潜在的介导因素。
本横断面研究的数据来自国家健康和营养检查调查（NHANES）（2005-2006 年、2011-2014 年）。建立了加权多变量逻辑回归模型，以探讨 16 种 VOC 与 4 种睡眠结果之间的关联。通过最小绝对收缩和选择算子（LASSO）回归选择重要的 VOC 后，进行主成分分析（PCA）、权重分位数总和（WQS）和贝叶斯核机器回归（BKMR）分析，以探讨暴露于单一和混合 VOC 与睡眠结果之间的关联，并确定最有贡献的成分。进行中介分析以探讨抑郁评分的潜在影响。
在纳入的 3473 名参与者中，共有 618 名被诊断为睡眠模式不佳。在逻辑回归分析中，7、10、1 和 5 种 VOC 与睡眠模式不佳、睡眠时间异常、入睡困难和睡眠障碍分别呈显著正相关。PCA 分析表明，PC1 与较高的睡眠模式不佳及其成分风险显著相关。WQS 模型显示，VOC 混合物浓度增加与睡眠模式不佳[比值比（95%置信区间）：1.285（1.107，1.493）]、睡眠时间异常[比值比（95%置信区间）：1.154（1.030，1.295）]、入睡困难[比值比（95%置信区间）：1.236（1.090，1.403）]和睡眠障碍[比值比（95%置信区间）：1.378（1.118，1.705）]呈正相关。BKMR 模型发现，总体 VOC 暴露与睡眠模式不佳、入睡困难和睡眠障碍呈正相关。PCA、WQS 和 BKMR 模型均证实了 -乙酰基-(-甲基氨基甲酰基)-l-半胱氨酸（AMCC）在睡眠模式不佳及其成分中的重要作用。抑郁评分是 VOC 混合指数与四项睡眠结果之间关联的中介。
接触单一和混合 VOC 会对美国人群的睡眠健康产生负面影响，其中 AMCC 发挥了重要作用。抑郁评分表明，VOC 混合物与睡眠模式不佳及其成分之间存在关联。
