Electronic cigarettes (e-cigarettes) are designed to generate inhalable nicotine aerosol (vapor). When an e-cigarette user takes a puff, the nicotine solution is heated and the vapor is taken into lungs. Although no sidestream vapor is generated between puffs, some of the mainstream vapor is exhaled by e-cigarette user. The aim of this study was to evaluate the secondhand exposure to nicotine and other tobacco-related toxicants from e-cigarettes. We measured selected airborne markers of secondhand exposure: nicotine, aerosol particles (PM(2.5)), carbon monoxide, and volatile organic compounds (VOCs) in an exposure chamber. We generated e-cigarette vapor from 3 various brands of e-cigarette using a smoking machine and controlled exposure conditions. We also compared secondhand exposure with e-cigarette vapor and tobacco smoke generated by 5 dual users. The study showed that e-cigarettes are a source of secondhand exposure to nicotine but not to combustion toxicants. The air concentrations of nicotine emitted by various brands of e-cigarettes ranged from 0.82 to 6.23 µg/m(3). The average concentration of nicotine resulting from smoking tobacco cigarettes was 10 times higher than from e-cigarettes (31.60±6.91 vs. 3.32±2.49 µg/m(3), respectively; p = .0081). Using an e-cigarette in indoor environments may involuntarily expose nonusers to nicotine but not to toxic tobacco-specific combustion products. More research is needed to evaluate health consequences of secondhand exposure to nicotine, especially among vulnerable populations, including children, pregnant women, and people with cardiovascular conditions.

BACKGROUND: Electronic cigarettes (e-cigarettes) are battery-powered devices developed with the goal of mimicking the action of smoking, including nicotine delivery, without the toxic effects of tobacco smoke. Little is known about the uptake of e-cigarettes among young people. METHODS: A survey was conducted with a cluster sample of 20 240 students enrolled at 176 nationally representative Polish high schools and universities between September 2010 and June 2011. We estimated national e-cigarette prevalence among various demographic groups by using population weights. Multiple logistic regression was used to evaluate which demographic factors were independent predictors of 2 outcomes: ever use of e-cigarettes and use in the previous 30 days. RESULTS: Among high school students, aged 15 to 19 years, 23.5% had ever used e-cigarettes and 8.2% had done so within the previous 30 days. Among those in universities, aged 20 to 24 years, 19.0% had ever used an e-cigarette and 5.9% had done so in the previous 30 days. In multivariate analyses that controlled for covariates, smoking cigarettes, male gender, living in an urban area, and having parents who smoke were associated with ever use of e-cigarettes. Overall, 3.2% of never smoking students reported ever use of e-cigarettes. CONCLUSIONS: About one-fifth of Polish youth have tried e-cigarettes; most of them had previously smoked cigarettes. It is unclear whether e-cigarettes are just a novelty that young people try only once or whether they have potential to compete in the marketplace with conventional cigarettes.

4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) is tobacco specific and has a longer half-life than other tobacco biomarkers studied thus far. An accurate measurement of the NNAL half-life is important for optimal use to assess exposure to tobacco smoke. We determined the half-life of NNAL in urine in eight daily smokers on a clinical research ward and in five occasional smokers in a real-life environment. Total NNAL in urine was monitored for 14 days in daily smokers after stopping smoking and for up to 60 days in occasional smokers. The average half-life for the terminal phase in the daily smoker group using a two-compartmental body model was 10.3 days (beta phase), and using a noncompartmental model, it was 9.1 days. In the occasional group, these values were 17.6 and 16.0 days, respectively. The alpha-phase half-lives were 14.3 and 27.8 hours for the two groups, respectively. The inter-subject coefficient of variation of the NNAL terminal half-life ranged from 14% to 30%, and the intra-subject coefficient of variation ranged from 3% to 18%. There was very good agreement between the plasma and urinary half-lives in two subjects with plasma analyses: 7.4 versus 7.9 days and 9.2 versus 10.7 days. Mean renal clearance of NNAL was 13 ± 2.3 mL/min. The terminal half-life of NNAL of 10 to 18 days indicates that this biomarker can be used to detect tobacco smoke exposure for 6 to 12 weeks after cessation of exposure and requires a similar time to assess the steady levels of NNAL after switching from one tobacco product to another. (Cancer Epidemiol Biomarkers Prev 2009;18(12):3421–5)

Cotinine is the most widely used biomarker to distinguish active versus passive smoking. However, there is an overlap in cotinine levels when comparing light or occasional smokers versus heavily exposed passive smokers. 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) is a tobacco-specific nitrosamine measurable in urine with a much longer half-life than cotinine. The aim of the study was to determine optimal cutoff points to discriminate active versus passive smokers and to compare sensitivity and specificity for the use of cotinine, NNAL, and the ratio of the NNAL/cotinine in urine. Cotinine and NNAL were measured in urine of 373 active smokers and 228 passive smokers. Geometric mean cotinine levels were 2.03 ng/ml (interquartile interval: 0.43–8.60) and 1,043 ng/ml (658–2,251) and NNAL levels were 5.80 pg/ml (2.28–15.4) and 165 pg/ml (90.8–360) pg/ml in passive and active smokers, respectively. NNAL/cotinine ratio in urine was significantly higher for passive smokers when compared with active smokers (2.85 vs. 0.16, p < .01). The receiver operating characteristics analysis determined optimal cutoff points to discriminate passive versus active smokers: 31.5 ng/ml for cotinine (sensitivity: 97.1% and specificity: 93.9%), 47.3 pg/ml for NNAL (87.4% and 96.5%), and 0.74 × 10⁻³ for NNAL/cotinine ratio (97.3% and 87.3%). Both urine cotinine and NNAL are sensitive and specific biomarkers for discriminating the source of tobacco smoke exposure. Cotinine is the best overall discriminator when biomarkers are measured while a person has ongoing exposure to tobacco smoke. NNAL because of its long half-life would be particularly useful when there is a delay between exposure and biomarker measurement. The NNAL/cotinine ratio provides similar sensitivity but poorer specificity at discriminating passive versus active smokers when compared with NNAL alone.

An objective assessment of exposure to tobacco smoke may be accomplished by means of examining particular biomarkers in body fluids. The most common biomarker of tobacco smoke exposure is urinary, or serum, cotinine. In order to distinguish non-smokers from passive smokers and passive smokers from active smokers, it is necessary to estimate cotinine cut-off points. The objective of this article was to apply statistical distribution of urinary cotinine concentration to estimate cut-off points distinguishing the three above-mentioned groups. The examined group consisted of 327 volunteers (187 women and 140 men) who were ethnically homogenous inhabitants of the same urban agglomeration (Sosnowiec, Poland). The values which enabled differentiation of the examined population into groups and subgroups were as follows: 50 µg l⁻¹ (differentiation of non-smokers from passive smokers), 170 µg l⁻¹ (to divide the group of passive smokers into two subgroups: minimally and highly exposed to environmental tobacco smoke), 550 µg l⁻¹ (differentiation of passive smokers from active smokers), and 2100 µg l⁻¹ (to divide group of active smokers into two subgroups: minimally and highly exposed to tobacco smoke). The results suggest that statistical distribution of urinary cotinine concentration is useful for estimating urinary cotinine cut-off points and for assessing the smoking status of persons exposed to tobacco smoke.

Total plasma homocysteine (tHcy) and cysteine (tCys) levels are associated with cardiovascular diseases. One of the determinants that influence their levels is cigarette smoking. The aim of this study was to determine the relationship between plasma levels of both amino acids and urinary cotinine concentration as a reliable biomarker of tobacco smoke exposure. One hundred and seventeen volunteers (61 women and 56 men) aged 19–60 years (mean 40.3±11.0) were included in the study. The study subjects were qualified into non-smokers, passive smokers and active smokers based upon the urinary cotinine concentration. In each particular group, plasma tHcy and tCys levels were measured and evaluated in the whole population and separately in women and men. Statistically insignificant differences in plasma tHcy and tCys levels in the whole group of passive smokers in comparison with non-smokers were observed (11.47 vs. 10.94 μmol/l, p=0.414, and 253.0 vs. 266.9 μmol/l, p=0.163, respectively). However, statistically significant differences in plasma tHcy levels (13.29 vs. 10.94 μmol/l, p=0.011) and in plasma tCys levels (218.2 vs. 266.9 μmol/l, p<0.001) were found in the whole group of active smokers compared with nonsmokers. The Pearson's coefficient (r) for the correlation between plasma tHcy level and urinary cotinine concentration was r=0.630 (p<0.001) in the whole group of active smokers and r=0.480 (p=0.003) in the whole group of passive smokers. The correlation between plasma tCys level and urinary cotinine concentration in both study groups was insignificant. Similar results were obtained when calculated separately for men and women. The results suggest that cigarette smoking is a strong determinant of plasma tHcy level, but it is not a determinant of plasma tCys level.