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Introduction This study examined user behavior, e-cigarette dependence, and device characteristics on nicotine intake among users of pod-mod e-cigarettes. Aims and Methods In 2019–2020, people who use pod-mods in the San Francisco Bay Area completed questionnaires and provided a urine sample for analysis of total nicotine equivalents (TNE). The relationship between TNE and e-cigarette use, e-cigarette brands, e-liquid nicotine strength, e-cigarette dependence, and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), as a measure of combustible cigarette exposure, were examined. Results Of 100 participants (64% male, 71% in the 18–34 age group, 45% white), 53 used JUUL primarily, 12 used Puff Bar primarily, and 35 used other brands, including Suorin; 48 participants reported current cigarette smoking. Participants most often reported use of e-liquid with 4.5%–6.0% nicotine (68%), fruit (35%), tobacco (28%), and menthol or mint flavors (26%), used e-cigarettes on 25.5 (SD = 6.3) days a month, 10.2 (SD = 14.2) times a day, and 40% used 1–2 pods/cartridges per week. In bivariate analysis, urinary TNE was higher with greater frequency (days used) and intensity (number of pods used) of e-cigarette use, e-cigarette dependence, and combustible cigarette use. In multivariable analysis, days of e-cigarette use in the last 30 days, number of pods used per week, and NNAL levels were significantly associated with TNE. There was no significant impact of e-liquid nicotine strength on TNE. Conclusions Nicotine intake among people who used pod-mod e-cigarettes increased with e-cigarette consumption and e-cigarette dependence, but not with e-liquid nicotine strength. Our findings may inform whether FDA adopts a nicotine standard for e-cigarettes. Implications The study examined how device and user characteristics influence nicotine intake among pod-mod e-cigarette users. Nicotine intake increased with frequency (days of e-cigarette use in past 30 days) intensity of use (number of pods used per day) and e-cigarette dependence but not with the flavor or nicotine concentration of the e-liquids. Regulation of nicotine concentration of e-liquids is unlikely to influence nicotine exposure among adult experienced pod-mod users.

PDF file - 481K, Table S-1. SRM Transitions and Collision Energies Table S-2. Equations for Mercapturic Acid Calibration Curves Table S-3. Precision and Accuracy for Mercapturic Acids in Urine, Intra-day (N=6) Table S-4. Concentrations of Mercapturic Acids in Urine of Cigarette Smokers. Comparison with Literature Values. Table S-5. Concentrations of PAH Metabolites in Urine of Smokers. Comparison with Literature Values. Figure S-1A. Chromatograms of urine extracts containing mercapturic acids: Metabolites of acrylamide (AAMA), ethylene oxide (HEMA), and acrolein (3-HPMA). Figure S-1B. Chromatograms of urine extracts containing mercapturic acids: Metabolites of 1,3-butadiene (MHBMA), acrylonitrile (CNEMA), and benzene (PMA). Figure S-1C. Chromatograms of urine extracts containing mercapturic acids: Metabolite of propylene oxide (2-HPMA) and separation from the isomer 3-HPMA. Figure S-2. Butadiene metabolite (MHBMA) calibration curves. Figure S-3. Benzene metabolite (PMA) calibration curves.

Background: Smoking tobacco preparations in a water pipe (hookah) is widespread in many places of the world and is perceived by many as relatively safe. We investigated biomarkers of toxicant exposure with water pipe compared with cigarette smoking.Methods: We conducted a crossover study to assess daily nicotine and carcinogen exposure with water pipe and cigarette smoking in 13 people who were experienced in using both products.Results: When smoking an average of 3 water pipe sessions compared with smoking 11 cigarettes per day (cpd), water pipe use was associated with a significantly lower intake of nicotine, greater exposure to carbon monoxide (CO), and a different pattern of carcinogen exposure compared with cigarette smoking, with greater exposure to benzene, and high molecular weight polycyclic aromatic hydrocarbon (PAH), but less exposure to tobacco-specific nitrosamines, 1,3-butadiene, acrolein, acrylonitrile, propylene oxide, ethylene oxide, and low molecular weight PAHs.Conclusions: A different pattern of carcinogen exposure might result in a different cancer risk profile between cigarette and water pipe smoking. Of particular concern is the risk of leukemia related to high levels of benzene exposure with water pipe use.Impact: Smoking tobacco in water pipes has gained popularity in the United States and around the world. Many believe that water pipe smoking is not addictive and less harmful than cigarette smoking. We provide data on toxicant exposure that will help guide regulation and public education regarding water pipe health risk. Cancer Epidemiol Biomarkers Prev; 22(5); 765–72. ©2013 AACR.

Introduction The aims of this study were to characterize particle size in a thirdhand smoke (THS) aerosol and measure the effects of controlled inhalational exposure to THS on biomarkers of tobacco smoke exposure, inflammation, and oxidative stress in human subjects. Secondhand cigarette smoke changes physically and chemically after release into the environment. Some of the resulting chemicals persist indoors as thirdhand cigarette smoke. THS that is sorbed to surfaces can emit particles back into the air. Aims and Methods Smoke particle size was measured with a scanning mobility particle sizer and condensation particle counter. Using a crossover study design, 18 healthy nonsmokers received a 3-hour inhalational exposure to THS and to filtered, conditioned air. THS was generated with a smoking machine and aged overnight in a chamber. The chamber was flushed with clean air to create the THS aerosol. The tobacco smoke metabolites cotinine, 3-hydroxycotinine and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) were measured in urine. Vascular endothelial growth factor and interleukin-6 in plasma, and 8-isoprostane in urine, were measured using enzyme-linked immunosorbent assay kits. Results Mean smoke particle size increased with aging (171 to 265 nm). We found significant increases in urinary cotinine and 3-hydroxycotinine after 3 hours of exposure to THS and no significant increases in NNAL, interleukin-6, vascular endothelial growth factor or 8-isoprostane. Conclusions Acute inhalational exposure to 22-hour old tobacco smoke aerosol caused increases in the metabolites of nicotine but not the metabolites of the tobacco-specific nitrosamine NNK (4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone). This corroborates the utility of cotinine and NNAL for secondhand and THS exposure screening. Implications This study shows that a 3-hour inhalational exposure to the tobacco smoke aerosol that forms in a room that has been smoked in and left unventilated overnight causes increases in urinary metabolites of nicotine, but not of the tobacco-specific nitrosamine NNK. This suggests that cleaning personnel and others who live and work in rooms polluted with aged or thirdhand cigarette smoke regularly may have inhalational exposures and potential health effects related to their exposure to nicotine and other smoke toxicants.

Tobacco-specific nitrosamines (TSNAs) are emitted during smoking and form indoors by nitrosation of nicotine. Two of them, N'-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are human carcinogens with No Significant Risk Levels (NSRLs) of 500 and 14 ng day-1, respectively. Another TSNA, 4-(methylnitrosamino)-4-(3-pyridyl) butanal (NNA), shows genotoxic and mutagenic activity in vitro. Here, we present additional evidence of genotoxicity of NNA, an assessment of TSNA dermal uptake, and predicted exposure risks through different pathways. Dermal uptake was investigated by evaluating the penetration of NNK and nicotine through mice skin. Comparable mouse urine metabolite profiles suggested that both compounds were absorbed and metabolized via similar mechanisms. We then investigated the effects of skin constituents on the reaction of adsorbed nicotine with nitrous acid (epidermal chemistry). Higher TSNA concentrations were formed on cellulose and cotton substrates that were precoated with human skin oils and sweat compared to clean substrates. These results were combined with reported air, dust, and surface concentrations to assess NNK intake. Five different exposure pathways exceeded the NSRL under realistic scenarios, including inhalation, dust ingestion, direct dermal contact, gas-to-skin deposition, and epidermal nitrosation of nicotine. These results illustrate potential long-term health risks for nonsmokers in homes contaminated with thirdhand tobacco smoke.

School children may be exposed to secondhand smoke (SHS) either at home, in transit or in social gatherings permitting smoking in their presence. Questionnaires about SHS often underestimate prevalence and extent of exposure. A more accurate tool is the use of biomarkers such as cotinine (COT) and trans-3'-hydrocycotinine (3HC) as biomarkers of SHS exposure, alongside 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), a reduction product in the body of the tobacco-specific nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), both potent carcinogens. We measured urinary COT, 3HC and total NNAL using sensitive and specific high-performance LC-MS/MS methods. The limit of quantification (LOQ) for each assay were 0.05ng/mL, 0.1ng/mL and 0.25pg/mL respectively. The aim of this study was to evaluate the exposure to SHS of school children (9-11 years), from five public schools in the island of Malta, from questionnaire information about smoking at home and verify it by urinary biomarker data of COT, 3HC and NNAL. These biomarkers were measurable in 99.4%, 95.4% and 98.3% of the participating children respectively. From the children reporting smoking at home, 11% had a history of asthma and had COT, 3HC and NNAL geometric mean concentrations double compared to the non-asthmatic group. In has been confirmed that non-smokers exposed to SHS and THS have a higher NNAL/COT ratio than the group identified as smokers according to specific and defined COT threshold levels (despite the fact that a priori, the entire study group was composed of non-smokers). The implication of high measured levels of urinary NNAL in children should be of concern given its potency. A main effects multifactor ANOVA model was developed and the children's house and school locations and the smoking frequency were statistically significant to predict the levels of the three metabolites. For 3HC only, the status of the employment of the mother was also an important predictor.