Both plasma and saliva cotinine levels have been reported in surveys of smoking behavior, and it is of interest to know how closely these two measures correspond. Plasma and saliva specimens were gathered from a sample of 605 respondents in the 1998 Health Survey for England and assayed for cotinine by a well-proven gas chromatographic method. Plasma and saliva cotinine concentrations were highly correlated (r=.99). On average, concentrations in saliva were 25% higher than in plasma, and this ratio applied both at the low levels attributable to passive smoking and across the range of active smoking values. The ratio was somewhat lower in younger people than in older people and also varied significantly by body mass index but did not differ by gender. Calculation of the limits of agreement revealed substantial uncertainty in the predicted plasma value corresponding to a given saliva cotinine, and vice versa. For comparisons across subjects, the mean plasma cotinine level corresponding to a mean saliva cotinine level can be estimated with confidence, but at the level of the individual, considerable predictive uncertainty remains.
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http://dx.doi.org/10.1080/1462220031000094213 | DOI Listing |
Clin Exp Dent Res
December 2024
Laboratory of Clinical, Forensic, Industrial and Environmental Toxicology, University Hospital of Liège, Liège, Belgium.
Objectives: Smoking is considered a major risk factor for periodontitis genesis and progression. In clinical studies, specific indicators have been used to characterize the smoking status of the patient as the number of cigarettes consumed (NCC), the pack-years (PY), or Fagerström Test for Nicotine Dependence (FTND). However, available literature is missing on the relationship between cotinine gingival intoxication and smoking indicators.
View Article and Find Full Text PDFbioRxiv
November 2024
Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Saliva is a widely used sample in epigenetic research with children due to its non-invasive nature. Since DNA methylation (DNAm) profile is cell type (CT) specific, salivary DNAm associations with exposures may be influenced by CT compositions, which is highly variable in saliva as it contains immune and buccal epithelial cells (BEC). Reference-based CT deconvolution and statistically adjusting estimated CT in DNAm analyses have become an increasingly common practice.
View Article and Find Full Text PDFSci Rep
November 2024
SeNSE, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016, India.
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for biomolecule sensing. When combined with a broadband plasmonic structure, label-free, highly sensitive detection of specific molecules is possible. It is non-invasive, sensitive, fast, and can be used for in-situ analysis, unlike enzyme-linked immunosorbent assay, fluorescence immunoassay, and radioimmunoassay.
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Instituto de Ciência e Tecnologia, Universidade Estadual Paulista (UNESP), Câmpus São José dos Campos, Av. Eng. Francisco José Longo, 777, São Dimas, São José dos Campos 12245-000, São Paulo, Brazil.
In recent years, the use of electronic cigarettes (e-cigs) has increased. However, their long-term effects on oral health and saliva remain poorly understood. Therefore, this study aimed to evaluate the saliva of e-cig users and investigate possible biomarkers.
View Article and Find Full Text PDFMolecules
September 2024
School of Pharmacy, Shujitsu University, Okayama 703-8516, Japan.
Passive smoking from environmental tobacco smoke not only increases the risk of lung cancer and cardiovascular disease but may also be a stressor triggering neuropsychiatric and other disorders. To prevent these diseases, understanding the relationship between passive smoking and stress is vital. In this study, we developed a simple and sensitive method to simultaneously measure nicotine (Nic) and cotinine (Cot) as tobacco smoke exposure biomarkers, and cortisol (CRT), serotonin (5-HT), melatonin (MEL), dopamine (DA), and oxytocin (OXT) as stress-related biomarkers.
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