Urinary volatomic profile of traditional tobacco smokers and electronic cigarettes users as a strategy to unveil potential health issues.
Cristina V BerenguerJorge A M PereiraJosé S CâmaraPublished in: Journal of separation science (2021)
Many harmful and potentially harmful constituents are present in tobacco products. Cigarette smoke is known to cause different forms of cancer and trigger the development of chronic diseases. In the last decade, electronic cigarettes have emerged as a healthier alternative associated to less harmful effects in comparison to traditional tobacco. However, the lack of standardization of electronic cigarettes products makes it difficult to establish and compare the real effects on health of products from different manufacturers. To better understand the impact of smoking and vaping, the volatomic composition of urine samples from traditional tobacco smokers and electronic cigarette users was established and compared with nonsmokers (control group), using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry. A total of 45 urinary volatile organic metabolites belonging to different chemical families were identified in the urine of the studied groups. Benzene derivatives, terpenes, and aromatics were the chemical families that contributed the most to the urinary profile of smokers. The vapers urinary volatomic pattern was also dominated by terpenes and aromatics, in addition to alcohols. The orthogonal partial least squares-discriminant analysis of the data obtained indicated that the urinary profile of vapers is more closely related to the control group, reinforcing the hypothesis of the lowest harmfulness of electronic cigarettes. Further studies recruiting a higher number of subjects are therefore necessary to consolidate the data obtained.
Keyphrases
- smoking cessation
- gas chromatography mass spectrometry
- replacement therapy
- healthcare
- public health
- mental health
- gas chromatography
- big data
- papillary thyroid
- ms ms
- health information
- squamous cell carcinoma
- lymph node metastasis
- high resolution
- machine learning
- mass spectrometry
- human health
- deep learning
- artificial intelligence
- data analysis
- health promotion