Abstract：
BACKGROUND: As a chronic occupational disease, silicosis could cause irreversible and incurable impair to the lung. The current diagnosis of silicosis relies on imaging of X-ray or CT, but these methods cannot detect lung lesions in the early stage of silicosis.
OBJECTIVE: To establish a regular screening and early diagnosis methods for silicosis, which could be helpful for the prevention and treatment of silicosis.
METHODS: A total of 161 subjects were enrolled in the study, including 69 patients with silicosis (SILs) and 92 healthy controls. The exhaled breath samples of the subjects were collected with breath sampler and Tedlar bag. The analysis of volatile organic compounds (VOCs) in exhaled breath was performed by solid-phase microextraction (SPME) combined with gas chromatography mass spectrometry (GC-MS).
RESULTS: After excluding the pollutants from sampling bags and instruments, 86 VOCs have been identified in the exhaled breath. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for the screening of potential biomarkers of silicosis. Those components that related to smoking were also excluded from the biomarkers. Finally, nine possible biomarkers for silicosis were screened out, including 2,3-butanedione, ethyl acetate, chlorobenzene, o-cymene, 4-ethylhex-2-ynal, 3,5-dimethyl-3-heptanol, hydroquinone, phthalic anhydride and 5-(2-methylpropyl)nonane. Based on these biomarkers screened, a predicted model for silicosis was generated with the accuracy of 89.61%.
CONCLUSIONS: The nine biomarkers in exhaled breath were preliminarily screened out for the early diagnosis of silicosis, which can be helpful to the establishment of a noninvasive screening method for silicosis. Follow-up studies should be conducted to further verify these markers.
OBJECTIVE: To establish a regular screening and early diagnosis methods for silicosis, which could be helpful for the prevention and treatment of silicosis.
METHODS: A total of 161 subjects were enrolled in the study, including 69 patients with silicosis (SILs) and 92 healthy controls. The exhaled breath samples of the subjects were collected with breath sampler and Tedlar bag. The analysis of volatile organic compounds (VOCs) in exhaled breath was performed by solid-phase microextraction (SPME) combined with gas chromatography mass spectrometry (GC-MS).
RESULTS: After excluding the pollutants from sampling bags and instruments, 86 VOCs have been identified in the exhaled breath. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for the screening of potential biomarkers of silicosis. Those components that related to smoking were also excluded from the biomarkers. Finally, nine possible biomarkers for silicosis were screened out, including 2,3-butanedione, ethyl acetate, chlorobenzene, o-cymene, 4-ethylhex-2-ynal, 3,5-dimethyl-3-heptanol, hydroquinone, phthalic anhydride and 5-(2-methylpropyl)nonane. Based on these biomarkers screened, a predicted model for silicosis was generated with the accuracy of 89.61%.
CONCLUSIONS: The nine biomarkers in exhaled breath were preliminarily screened out for the early diagnosis of silicosis, which can be helpful to the establishment of a noninvasive screening method for silicosis. Follow-up studies should be conducted to further verify these markers.
摘要：
背景：作为一种慢性职业病，矽肺可导致肺部不可逆和无法治愈的损害。目前矽肺的诊断依赖于X线或CT的成像,但是这些方法不能在矽肺的早期发现肺部病变。
目的：建立矽肺的常规筛查和早期诊断方法，有助于矽肺的预防和治疗。
方法：本研究共纳入161名受试者，包括69例矽肺病（SILs）患者和92例健康对照。用呼吸采样器和Tedlar袋收集受试者的呼出呼吸样本。通过固相微萃取（SPME）结合气相色谱质谱（GC-MS）对呼气中的挥发性有机化合物（VOC）进行分析。
结果：从取样袋和仪器中排除污染物后，已在呼出的气体中鉴定出86种VOC。采用正交偏最小二乘判别分析（OPLS-DA）筛选矽肺的潜在生物标志物。与吸烟相关的那些成分也被排除在生物标志物之外。最后,筛选出九种可能的矽肺生物标志物，包括2,3-丁二酮,乙酸乙酯,氯苯,o-cymene,4-乙基hex-2-ynal，3,5-二甲基-3-庚醇,对苯二酚,邻苯二甲酸酐和5-(2-甲基丙基)壬烷。根据这些生物标志物筛选，建立了矽肺预测模型,准确率为89.61%.
结论：初步筛选出呼出气中的9种生物标志物，用于矽肺的早期诊断。有助于建立矽肺的无创筛查方法。应进行后续研究以进一步验证这些标志物。
目的：建立矽肺的常规筛查和早期诊断方法，有助于矽肺的预防和治疗。
方法：本研究共纳入161名受试者，包括69例矽肺病（SILs）患者和92例健康对照。用呼吸采样器和Tedlar袋收集受试者的呼出呼吸样本。通过固相微萃取（SPME）结合气相色谱质谱（GC-MS）对呼气中的挥发性有机化合物（VOC）进行分析。
结果：从取样袋和仪器中排除污染物后，已在呼出的气体中鉴定出86种VOC。采用正交偏最小二乘判别分析（OPLS-DA）筛选矽肺的潜在生物标志物。与吸烟相关的那些成分也被排除在生物标志物之外。最后,筛选出九种可能的矽肺生物标志物，包括2,3-丁二酮,乙酸乙酯,氯苯,o-cymene,4-乙基hex-2-ynal，3,5-二甲基-3-庚醇,对苯二酚,邻苯二甲酸酐和5-(2-甲基丙基)壬烷。根据这些生物标志物筛选，建立了矽肺预测模型,准确率为89.61%.
结论：初步筛选出呼出气中的9种生物标志物，用于矽肺的早期诊断。有助于建立矽肺的无创筛查方法。应进行后续研究以进一步验证这些标志物。
