Association of active/passive smoking and urinary 1-hydroxypyrene with poor sleep quality: A cross-sectional survey among Chinese male enterprise workers

Introduction Tobacco use has been implicated as an important factor for poor sleep quality. However, in most studies, the sleep quality of smokers was only assessed though a self-reported questionnaire, without measuring any internal biomarkers that reflect the levels of tobacco exposure. We examined the association of active and passive smoking with sleep quality, assessed smoking exposure using urinary 1-hydroxypyrene (1-HOP) as an internal biomarker, and further explored the relationship between 1-HOP and sleep quality. Methods A cross-sectional survey was conducted in Liuzhou city, Guangxi, China. A total of 1787 male enterprise workers were enrolled. The smoking attribute data were collected by self-reported questionnaire, and individual sleep quality was evaluated through the Pittsburgh Sleep Quality Index (PSQI). The concentration of urinary 1-HOP was measured by highperformance liquid chromatography. Results Compared with non-smoking, active smoking and passive smoking were significantly associated with long sleep latency (odds ratio, OR=1.84, 95% confidence interval, CI=1.28–2.64; 1.45, 1.00–2.11, respectively), short sleep duration (OR=2.72, 95% CI=1.45–5.09; 1.94, 1.01–3.71, respectively), daytime dysfunction (OR=1.54, 95% CI=1.10–2.17; 1.44, 1.02–2.03, respectively), and overall poor sleep quality with PSQI total score >5 (OR=1.41, 95% CI=1.05–1.88; 1.34, 1.00–1.79, respectively). Compared with non-smokers, active smokers had higher urinary 1-OHP concentrations that were significant (p=0.004), while passive smokers had no significant difference in urinary 1-OHP concentration (p=0.344). The high concentration group was significantly associated with daytime dysfunction and overall poor sleep quality with PSQI total score >5 (OR = 1.73, 95% CI=1.06–2.81; 1.76, 1.18–2.63, respectively). Conclusions Both active smoking and passive smoking are risk factors for poor sleep quality among Chinese male enterprise workers. Active smokers had significantly higher levels of urinary 1-OHP than non-smokers, and high concentration of 1-OHP was associated with daytime dysfunction and overall poor sleep quality

Expression Profiles of Long Noncoding RNAs and Messenger RNAs in Mn-Exposed Hippocampal Neurons of Sprague-Dawley Rats Ascertained by Microarray: Implications for Mn-Induced Neurotoxicity.

Manganese (Mn) is an essential trace element, while excessive expose may induce neurotoxicity. Recently, lncRNAs have been extensively studied and it has been confirmed that lncRNAs participate in neural functions and aberrantly expressed lncRNAs are involved in neurological diseases. However, the pathological effects of lncRNAs on Mn-induced neurotoxicity remain unclear. In this study, the expression profiles of lncRNAs and messenger RNAs (mRNAs) were identified in Mn-treated hippocampal neurons and control neurons via microarray. Bioinformatic methods and intersection analysis were also employed. Results indicated that 566, 1161, and 1474 lncRNAs meanwhile 1848, 3228, and 4022 mRNAs were aberrantly expressed in low, intermediate, and high Mn-exposed groups compared with the control group, respectively. Go analysis determined that differentially expressed mRNAs were targeted to biological processes, cellular components, and molecular functions. Pathway analysis indicated that these mRNAs were enriched in insulin secretion, cell cycle, and DNA replication. Intersection analysis denominated that 135 lncRNAs and 373 mRNAs were consistently up-regulated while 150 lncRNAs and 560 mRNAs were consistently down-regulated. Meanwhile, lncRNA BC079195 was significantly up-regulated while lncRNAs uc.229- and BC089928 were significantly down-regulated in three comparison groups. The relative expression levels of 3 lncRNAs and 4 mRNAs were validated through qRT-PCR. To the best of our knowledge, this study is the first to identify the expression patterns of lncRNAs and mRNAs in hippocampal neurons of Sprague-Dawley rats. The results may provide evidence on underlying mechanisms of Mn-induced neurotoxicity, and aberrantly expressed lncRNAs/mRNAs may be useful in further investigations to detect early symptoms of Mn-induced neuropsychiatric disorders in the central nervous system

Association Between Chinese Cooking Oil Fumes and Sleep Quality Among a Middle-Aged Chinese Population

Poor sleep quality is an important symptom of many medical or psychiatric disorders. However, the impact of cooking oil fumes (COFs) on sleep quality has not been studied. This population-based cross-sectional study was conducted to examine the association between COFs of Chinese household cooking and sleep quality. Individual sleep quality assessment was completed in 2197 participants with an average age of 37.52 years, through Pittsburgh Sleep Quality Index (PSQI). Information about their cooking practice were also collected by self-reported questionnaire. As an internal biomarker of COFs, urinary 1-hydroxypyrene (1-HOP) (n = 562) was further measured using high-performance liquid chromatography. Binary logistic regression models were performed to evaluate the association between exposure to COFs and individual sleep quality. We found that, subjective poor kitchen ventilation, preheating oil to smoking, and cooking for over 30 minutes were positively associated with overall poor sleep quality (global PSQI score \u3e5) [odds ratio (OR) = 1.75, 95% confidence interval (CI) = 1.43–2.16; 1.25, (1.03–1.52); 1.42, (1.15–1.76), respectively]. After adjusting for potential confounders, subjective poor kitchen ventilation still tend to increase the risk of long sleep latency, sleep disturbances, and daytime dysfunction [OR = 1.37, 95% CI = 1.09–1.73; 1.91, (1.39–2.61); 1.54, (1.23–1.93), respectively]. Similar results were observed in participants who preheated oil to smoking [OR = 1.36, 95% CI = 1.08–1.72; 1.55, (1.14–2.14); 1.25, (1.02–1.55), respectively] and cooked for over 30 minutes [OR = 1.34, 95% CI = 1.05–1.72; 1.46, (1.03–2.06); 1.36, (1.08–1.72), respectively]. Furthermore, high urinary 1-HOP level was also positively associated with overall poor sleep quality (OR = 2.30, 95% CI = 1.31–4.05). The results indicated that exposure to COFs from Chinese household cooking may be a risk factor for poor sleep quality among middle-aged Chinese population

Overview of the microarray signatures.

<p>(A) Scatter plots showing the variations in the mRNA expression between two groups (1, L/C; 2, M/C; 3, H/C). A scatter plot is a visualization method used to assess the variation (or reproducibility) between chips. The values of the X and Y axes are the averaged normalized signal values of groups of samples (X: log2 scaled; and Y: −log10 scaled). The green lines are fold change lines (FC = 2). The color of the points indicates the intensities from low (blue) to high (red). The mRNAs above the top green line and below the bottom green line indicate 2 FC between two groups of samples. (B) Volcano plots of the differentially expressed mRNAs (1, L/C; 2, M/C; 3, H/C). A volcano plot is a useful tool for visualizing differential expression between two different conditions. The vertical lines correspond to twofold up and down, and the horizontal line represents a p-value of 0.05. The red point in the plot represents the differentially expressed mRNAs with statistical significance.</p

Gene ontology (GO) enrichment analysis for differentially expressed genes.

<p>GO analysis provides a controlled vocabulary to describe differentially expressed transcript attributes in all organisms. The ontology covers three domains: biological process (BP), cellular component (CC), and molecular function (MF). The p-value denotes the significance of GO term enrichment in the DE genes. GO terms are considered statistically significant at p ≤ 0.05. 1, L/C; 2, M/C; 3, H/C.</p

Pathway analysis of the differentially expressed genes.

<p>(A) Significant pathways of up-regulated genes. (B) Significant pathways of down-regulated genes. Pathway analysis is a functional analysis that maps genes to Kyoto Encyclopedia of Genes and Genomes pathways (<a href="http://www.genome.jp/kegg/" target="_blank">http://www.genome.jp/kegg/</a>). The p-value (Fisher p-value) denotes the significance of the pathway correlated with the conditions. The pathways are considered statistically significant at p < 0.05. 1, L/C; 2, M/C; 3, H/C.</p

Number of differentially expressed lncRNAs (A) and mRNAs (B) between different groups (1, L/C; 2, M/C; 3, H/C).

<p>Number of differentially expressed lncRNAs (A) and mRNAs (B) between different groups (1, L/C; 2, M/C; 3, H/C).</p

Overview of the microarray signatures.

<p>(A) Scatter plots showing the variations in lncRNA expression between two groups (1, L/C; 2, M/C; 3, H/C). A scatter plot is a visualization method used to assess the variation (or reproducibility) between chips. The values of the X and Y axes are the averaged normalized signal values of groups of samples (X: log2 scaled; and Y: −log10 scaled). The green lines are fold change lines (FC = 2). The color of the points indicates the intensities from low (blue) to high (red). The lncRNAs above the top green line and below the bottom green line indicate 2 FC between two groups of samples. (B) Volcano plots of the differentially expressed lncRNAs (1, L/C; 2, M/C; 3, H/C). A volcano plot is a useful tool for visualizing differential expression between two different conditions. The vertical lines correspond to twofold up and down, and the horizontal line represents a p-value of 0.05. The red point in the plot represents the differentially expressed lncRNAs with statistical significance.</p