The effects of alcohol-containing e-cigarettes on young adult smokers

Adolescent alcohol and nicotine exposure alters the adult response to alcohol use

Adolescence through young adulthood is a unique period of neuronal development and maturation. Numerous agents can alter this process, resulting in long-term neurological and biological consequences. In the clinical literature, it is frequently reported that adolescent alcohol consumption increases the propensity to develop addictions, including alcohol use disorder (AUD), during adulthood. A general limitation of both clinical and human pre-clinical adolescent alcohol research is the high rate of co-using/abusing more than one drug during adolescence, such as co-using/abusing alcohol with nicotine. A primary goal of basic research is elucidating neuroadaptations produced by adolescent alcohol exposure/consumption that promote alcohol and other drug self-administration in adulthood. The long-term goal is to develop pharmacotherapeutics for the prevention or amelioration of these neuroadaptations. This review will focus on studies that have examined the effects of adolescent alcohol and nicotine exposure on adult alcohol consumption, the hypersensitivity of the mesolimbic dopaminergic system, and enhanced responses not only to alcohol but also to nicotine during adulthood. Again, the long-term goal is to identify potential cholinergic agents to prevent or ameliorate the consequences of, peri-adolescent alcohol abuse.

The impact of vaping ethanol-containing electronic cigarette liquids on roadside impairment investigations

Legal professionals and others have suggested that vaping electronic cigarettes (e-cigs) prior to or during ethanol breath testing may produce false positives. Preliminary breath tests (PBTs) and evidentiary breath tests (EBTs) measure ethanol in exhaled breath and standardized field sobriety tests (SFSTs) are used to assess impairment. Ethanol has been identified in e-cig liquids (e-liquids). Presented are a series of experiments designed to determine the mechanics of vaping ethanol using an e-cig and the effects of vaping ethanol on the SFSTs and breath tests used by law enforcement officers (LEO). Twelve participants (five females, age: 21-32 and seven males, age: 21-55), vaped either one or ten puffs of an e-liquid (0% or 20% ethanol). LEOs assessed impairment using SFSTs (12 and 42 min), PBTs (<1, 27, 32, 37 and 57 min) and EBTs (2, 29, 34, 39 and 59 min) post-vaping. A self-assessment test was administered post-vaping (22 and 52 min). Baseline responses for all measures were collected prior to vaping. Results demonstrated that ethanol in the e-liquids was aerosolized by e-cigs and produced particles that could reach the deep lung tissue based on mean-mass diameter. Ethanol was detected by PBT <3 min after participants vaped one (0.007-0.030 g/210 L) or ten puffs (013-0.074 g/210 L) of a 20% ethanol e-liquid. Ethanol was not detected by PBT at any subsequent time point. Ethanol was not detected by the EBT under any condition. Impairment was not indicated by the SFST. Some subjective effects were reported, but few statistically significant differences between conditions were indicated. A wait period prior to ethanol breath testing is not always mandated, depending on jurisdiction, or observed in all applications, such as workplace testing. The results demonstrate that a wait period must be employed to prevent vaping-related false-positive breath ethanol results.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, although some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit smoking, and if they are safe to use for this purpose. This is a review update conducted as part of a living systematic review.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 July 2022, and reference-checked and contacted study authors.  SELECTION CRITERIA: We included randomized controlled trials (RCTs) and randomized cross-over trials, in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. Studies had to report abstinence from cigarettes at six months or longer or data on safety markers at one week or longer, or both.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included the proportion of people still using study product (EC or pharmacotherapy) at six or more months after randomization or starting EC use, changes in carbon monoxide (CO), blood pressure (BP), heart rate, arterial oxygen saturation, lung function, and levels of carcinogens or toxicants, or both. We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in meta-analyses.

MAIN RESULTS

We included 78 completed studies, representing 22,052 participants, of which 40 were RCTs. Seventeen of the 78 included studies were new to this review update. Of the included studies, we rated ten (all but one contributing to our main comparisons) at low risk of bias overall, 50 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There was high certainty that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (RR 1.63, 95% CI 1.30 to 2.04; I2 = 10%; 6 studies, 2378 participants). In absolute terms, this might translate to an additional four quitters per 100 (95% CI 2 to 6). There was moderate-certainty evidence (limited by imprecision) that the rate of occurrence of AEs was similar between groups (RR 1.02, 95% CI 0.88 to 1.19; I2 = 0%; 4 studies, 1702 participants). SAEs were rare, but there was insufficient evidence to determine whether rates differed between groups due to very serious imprecision (RR 1.12, 95% CI 0.82 to 1.52; I2 = 34%; 5 studies, 2411 participants). There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.94, 95% CI 1.21 to 3.13; I2 = 0%; 5 studies, 1447 participants). In absolute terms, this might lead to an additional seven quitters per 100 (95% CI 2 to 16). There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 5 studies, 1840 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 1.00, 95% CI 0.56 to 1.79; I2 = 0%; 8 studies, 1272 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.66, 95% CI 1.52 to 4.65; I2 = 0%; 7 studies, 3126 participants). In absolute terms, this represents an additional two quitters per 100 (95% CI 1 to 3). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was some evidence that (non-serious) AEs were more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants) and, again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 1.03, 95% CI 0.54 to 1.97; I2 = 38%; 9 studies, 1993 participants).  Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued EC use. Very few studies reported data on other outcomes or comparisons, hence evidence for these is limited, with CIs often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is high-certainty evidence that ECs with nicotine increase quit rates compared to NRT and moderate-certainty evidence that they increase quit rates compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the effect size. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs nor between nicotine ECs and NRT. Overall incidence of SAEs was low across all study arms. We did not detect evidence of serious harm from nicotine EC, but longest follow-up was two years and the number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates, but further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

A Retrospective Analysis of Chemical Constituents in Regulated and Unregulated E-Cigarette Liquids

E-cigarette or vaping use-associated lung injury (EVALI) was identified with the incidents of a multi-state outbreak of acute lung injuries associated with the use of electronic cigarettes (e-cigs) and attributed to vitamin E acetate in off-market cannabis-based e-liquids. Aside from EVALI, hypersecretion of mucus, irritated nasal passages, and watery, red eyes have been defined as complaints associated with vaping standard nicotine-based e-liquids. The chemical composition of e-liquids varies between manufacturers and robust oversight of ingredients is lacking. Manufacturers use chemicals deemed "generally recognized as safe" (GRAS) by the FDA, a designation for chemicals used in foodstuffs to be ingested. Most "GRAS" chemicals are associated with at least one Global Harmonization System (GHS) warning class, ranging from irritant to toxic. Untargeted chemical analysis is critical to evaluate e-liquid products to determine chemical composition; equally important is the quantitation of components to help elucidate the potential harms from exceeding recommended exposure limits. Untargeted screening of e-liquids was accomplished using gas chromatography-mass spectrometry (GC-MS) and Direct Analysis in Real Time-AccuTOF™ mass spectrometry (DART-ToF-MS) and has identified 350 chemical constituents from 241 products analyzed. Nicotine, caffeine, menthol, and vitamin E were confirmed and quantitated by GC-MS, ethanol was confirmed and quantitated by headspace-gas chromatography-dual flame ionization detection (HS-GC-FID), and olivetol and cannabinoids were confirmed and quantitated by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Maximum identified concentrations of nicotine, caffeine, menthol, vitamin E, ethanol, olivetol, Δ9-tetrahydrocannabinol, and cannabidiol were 56.4, 26.9, 4.28, 307.9, 217.2, 399.6, 497.7, and 332.6 mg/ml, respectively. Evaluation of untargeted analysis and quantitation of unlabeled chemical components of e-liquids is essential to improving etiology of acute lung injury and less severe impacts of vaping, both short-term and long-term. The historical documentation of unlabeled ingredients can provide some insight for a retrospective analysis of health consequences and inform policy discussions.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, but some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This is an update conducted as part of a living systematic review.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 May 2021, and reference-checked and contacted study authors. We screened abstracts from the Society for Research on Nicotine and Tobacco (SRNT) 2021 Annual Meeting.   SELECTION CRITERIA: We included randomized controlled trials (RCTs) and randomized cross-over trials, in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. Studies had to report abstinence from cigarettes at six months or longer or data on safety markers at one week or longer, or both.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included the proportion of people still using study product (EC or pharmacotherapy) at six or more months after randomization or starting EC use, changes in carbon monoxide (CO), blood pressure (BP), heart rate, arterial oxygen saturation, lung function, and levels of carcinogens or toxicants or both. We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in meta-analyses.

MAIN RESULTS

We included 61 completed studies, representing 16,759 participants, of which 34 were RCTs. Five of the 61 included studies were new to this review update. Of the included studies, we rated seven (all contributing to our main comparisons) at low risk of bias overall, 42 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.53, 95% confidence interval (CI) 1.21 to 1.93; I2 = 0%; 4 studies, 1924 participants). In absolute terms, this might translate to an additional three quitters per 100 (95% CI 1 to 6). There was low-certainty evidence (limited by very serious imprecision) that the rate of occurrence of AEs was similar (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs were rare, but there was insufficient evidence to determine whether rates differed between groups due to very serious imprecision (RR 1.30, 95% CI 0.89 to 1.90: I2 = 0; 4 studies, 1424 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.94, 95% CI 1.21 to 3.13; I2 = 0%; 5 studies, 1447 participants). In absolute terms, this might lead to an additional seven quitters per 100 (95% CI 2 to 16). There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 3 studies, 601 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 1.06, 95% CI 0.47 to 2.38; I2 = 0; 5 studies, 792 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.61, 95% CI 1.44 to 4.74; I2 = 0%; 6 studies, 2886 participants). In absolute terms this represents an additional six quitters per 100 (95% CI 2 to 15). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was some evidence that non-serious AEs were more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants), and again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 1.51, 95% CI 0.70 to 3.24; I2 = 0%; 7 studies, 1303 participants).  Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued use. Very few studies reported data on other outcomes or comparisons, hence evidence for these is limited, with CIs often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to NRT and compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the effect size. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs. Overall incidence of SAEs was low across all study arms. We did not detect  evidence of harm from nicotine EC, but longest follow-up was two years and the  number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates, but further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is now a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

A Rapid and Sensitive Chemical Screening Method for E-Cigarette Aerosols Based on Runtime Cavity Ringdown Spectroscopy

Growing demand of Juul and other electronic cigarettes, despite critical knowledge gaps about their chemical composition, has led to concerns regarding their potential health effects. We introduce a novel analytical approach, runtime cavity ringdown spectroscopy (rtCRDS) for rapid detection of oxidative products in e-cigarette aerosols, to facilitate the study of aerosol from a single puff of e-liquid. We report a systematic investigation of three flavors of commercial Juul pods (Virginia tobacco, mango, and menthol) and known commercial e-liquid ingredients (propylene glycol (PG), vegetable glycerin (VG), nicotine, ethyl maltol, benzoic acid, and nicotine benzoate) vaped using Juul devices. Juul e-liquids and neat chemical additives spiked into a 30:70 PG/VG solution were vaped and their aerosols were collected in 1-L Tedlar gas bags and analyzed using rtCRDS. Acetaldehyde, formaldehyde, and acetone were identified as primary oxidative products in aerosolized PG/VG. Ethanol was detected as a major constituent of the three commercial Juul flavors. Spectral intensities of carbonyl compounds increased with the addition of spikes, benzoic acid, ethyl maltol, and nicotine to PG/VG, suggesting that oxidative product generation increases with common additives. The method of direct, rapid analysis of e-cig aerosols introduced here can be used to complement traditional methods in vaping exposures.

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, but some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This is an update of a review first published in 2014.

OBJECTIVES

To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 February 2021, together with reference-checking and contact with study authors.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) and randomized cross-over trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. To be included, studies had to report abstinence from cigarettes at six months or longer and/or data on adverse events (AEs) or other markers of safety at one week or longer.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included changes in carbon monoxide, blood pressure, heart rate, blood oxygen saturation, lung function, and levels of known carcinogens/toxicants. We used a fixed-effect Mantel-Haenszel model to calculate the risk ratio (RR) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data from these studies in meta-analyses.

MAIN RESULTS

We included 56 completed studies, representing 12,804 participants, of which 29 were RCTs. Six of the 56 included studies were new to this review update. Of the included studies, we rated five (all contributing to our main comparisons) at low risk of bias overall, 41 at high risk overall (including the 25 non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.69, 95% confidence interval (CI) 1.25 to 2.27; I2 = 0%; 3 studies, 1498 participants). In absolute terms, this might translate to an additional four successful quitters per 100 (95% CI 2 to 8). There was low-certainty evidence (limited by very serious imprecision) that the rate of occurrence of AEs was similar) (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs occurred rarely, with no evidence that their frequency differed between nicotine EC and NRT, but very serious imprecision led to low certainty in this finding (RR 1.37, 95% CI 0.77 to 2.41: I2 = n/a; 2 studies, 727 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.70, 95% CI 1.03 to 2.81; I2 = 0%; 4 studies, 1057 participants). In absolute terms, this might again lead to an additional four successful quitters per 100 (95% CI 0 to 11). These trials mainly used older EC with relatively low nicotine delivery. There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 3 studies, 601 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 0.60, 95% CI 0.15 to 2.44; I2 = n/a; 4 studies, 494 participants). Compared to behavioral support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.70, 95% CI 1.39 to 5.26; I2 = 0%; 5 studies, 2561 participants). In absolute terms this represents an increase of seven per 100 (95% CI 2 to 17). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was no evidence that the rate of SAEs differed, but some evidence that non-serious AEs were more common in people randomized to nicotine EC (AEs: RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants; SAEs: RR 1.17, 95% CI 0.33 to 4.09; I2 = 5%; 6 studies, 1011 participants, very low certainty). Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued use. Very few studies reported data on other outcomes or comparisons and hence evidence for these is limited, with confidence intervals often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to ECs without nicotine and compared to NRT. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the size of effect, particularly when using modern EC products. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, though evidence indicated no difference in AEs between nicotine and non-nicotine ECs. Overall incidence of SAEs was low across all study arms. We did not detect any clear evidence of harm from nicotine EC, but longest follow-up was two years and the overall number of studies was small. The evidence is limited mainly by imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up-to-date information, this review is now a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

An Experimental Test of the Relationship between Electronic Nicotine Delivery System Use and Alcohol Consumption

BACKGROUND

Increasing research shows that the use of electronic nicotine delivery systems (ENDS) is associated with a higher rate and quantity of alcohol consumption.

METHODS

The present study used a 2-session, within-subjects design to experimentally examine the relationship between ENDS use and laboratory ad libitum alcohol consumption. A total of N = 31 (mean age = 28.71, SD = 11.17; 45.2% women; 54.8% White/Caucasian) healthy adults from the community who use ENDS and endorsed liking beer completed the study, which included a beer consumption taste-test task that assessed the volume of beer consumed by the participants across 2 counterbalanced sessions: 1 in which concurrent ENDS use was allowed and 1 in which it was not. All analyses controlled for age, race, and gender.

RESULTS

The effect of ENDS condition on the volume of beer consumed was not statistically significant, F(1, 30) = 0.03, p = 0.86). Results of linear mixed modeling showed that ENDS puffs were significantly related to alcohol sips (estimate = 0.23, SE = 0.07, p = 0.002) across the ad libitum session.

CONCLUSIONS

Overall, ENDS use did not increase alcohol consumption; however, the data suggest that ENDS puffs might act as a prime for beer sips or that these 2 behaviors are linked through habit. Future studies should more fully measure and compare global and event-level data on ENDS and alcohol use as they might show disparate patterns of relationships.

Modulation of "Protective" Nicotine Perception and Use Profile by Flavorants: Preliminary Findings in E-cigarettes

INTRODUCTION

Characterizing flavors are widely available in e-cigarettes and motivate initiation and continued use. Flavors may enhance appeal and facilitate development of addiction to tobacco products through modulation of tobacco products' reinforcing or aversive actions. Palatable flavors (eg, fruit) may increase appeal through primary reinforcing properties. Menthol's cooling and anesthetic effects may increase appeal by counteracting nicotine's aversive effects. Genetics provide a method for modeling individual differences in sensitivity to nicotine's effects. A common polymorphism, rs16969968, encoded in the α5 nicotinic acetylcholine receptor subunit gene (CHRNA5), is a well-recognized marker for smoking risk and reduces sensitivity to nicotine aversiveness.

METHODS

This pilot study tested how flavors impacted e-cigarette appeal and self-administration. In a single testing day, cigarette smokers (N = 32; 94% menthol-smokers) self-administered e-cigarettes containing e-liquids differing in nicotine level (0 mg/mL, 24 mg/mL) and flavor (unflavored, menthol, fruit-flavored) within directed and ad libitum e-cigarette paradigms. Subjective drug effects, number of puffs, rs16969968 genotype, plasma nicotine, and menthol glucuronide levels were collected.

RESULTS

Menthol partially ameliorated nicotine aversiveness; fruit did not. In nicotine's absence, fruit flavor increased self-reported preference and ad libitum use relative to menthol-containing or unflavored e-liquids. Individuals with high-smoking-risk rs16969968 genotype (N = 7) reported greater craving alleviation following directed administration of nicotine-containing e-liquids, showed a trend rating nicotine-containing e-liquids as less harsh, and self-administered more nicotine during ad libitum compared to individuals with low-smoking-risk genotype (N = 23).

CONCLUSIONS

While menthol countered aversiveness of nicotine-containing e-liquids, fruit flavor increased appeal of nicotine-free e-liquids. These preliminary findings suggest menthol and fruit flavor increase e-cigarettes' appeal through distinct mechanisms.

IMPLICATIONS

This study provides a detailed characterization of the effects of flavors (unflavored, menthol, fruit), nicotine (0 mg/mL, 24 mg/mL) and their interactions on the subjective drug effects and ad libitum self-administration of e-cigarettes. Genetics were used to assess these effects in higher-smoking-risk (diminished sensitivity to nicotine aversiveness) and lower-risk groups. Findings could inform impact of regulation of flavors or nicotine in e-cigarettes, and their impacts on vulnerable sub-populations.

Dual Substance Use of Electronic Cigarettes and Alcohol

Electronic cigarettes (ECs) are a modern nicotine delivery system that rapidly grew in widespread use, particularly in younger populations. Given the long history of the comorbidity of alcohol and nicotine use, the rising prevalence of ECs raises the question as to their role in the consumption of alcohol. Of the numerous models of ECs available, JUUL is the most popular. This narrative review aims to determine current trends in literature regarding the relationship between EC and alcohol dual use, as well as hypothesize potential pathogenic tissue damage and summarize areas for future study, including second-hand vapor exposure and calling for standardization among studies. In summary, EC users are more likely to participate in hazardous drinking and are at higher risk for alcohol use disorder (AUD). We surmise the pathogenic damage of dual use may exhibit an additive effect, particularly in pathogen clearance from the lungs, increased inflammation and decreased immune response, physical damage to epithelial cells, and exacerbation of chronic obstructive pulmonary disease (COPD)-like illnesses. A better understanding of pathogenic damages is critical to understand the risks placed on dual users when exposed to respiratory pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Electronic cigarettes for smoking cessation

BACKGROUND

Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e-liquid. People who smoke report using ECs to stop or reduce smoking, but some organisations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This review is an update of a review first published in 2014.

OBJECTIVES

To evaluate the effect and safety of using electronic cigarettes (ECs) to help people who smoke achieve long-term smoking abstinence.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO for relevant records to January 2020, together with reference-checking and contact with study authors.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) and randomized cross-over trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. To be included, studies had to report abstinence from cigarettes at six months or longer and/or data on adverse events (AEs) or other markers of safety at one week or longer.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, AEs, and serious adverse events (SAEs). Secondary outcomes included changes in carbon monoxide, blood pressure, heart rate, blood oxygen saturation, lung function, and levels of known carcinogens/toxicants. We used a fixed-effect Mantel-Haenszel model to calculate the risk ratio (RR) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data from these studies in meta-analyses.

MAIN RESULTS

We include 50 completed studies, representing 12,430 participants, of which 26 are RCTs. Thirty-five of the 50 included studies are new to this review update. Of the included studies, we rated four (all which contribute to our main comparisons) at low risk of bias overall, 37 at high risk overall (including the 24 non-randomized studies), and the remainder at unclear risk. There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.69, 95% confidence interval (CI) 1.25 to 2.27; I2 = 0%; 3 studies, 1498 participants). In absolute terms, this might translate to an additional four successful quitters per 100 (95% CI 2 to 8). There was low-certainty evidence (limited by very serious imprecision) of no difference in the rate of adverse events (AEs) (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs occurred rarely, with no evidence that their frequency differed between nicotine EC and NRT, but very serious imprecision led to low certainty in this finding (RR 1.37, 95% CI 0.77 to 2.41: I2 = n/a; 2 studies, 727 participants). There was moderate-certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.71, 95% CI 1.00 to 2.92; I2 = 0%; 3 studies, 802 participants). In absolute terms, this might again lead to an additional four successful quitters per 100 (95% CI 0 to 12). These trials used EC with relatively low nicotine delivery. There was low-certainty evidence, limited by very serious imprecision, that there was no difference in the rate of AEs between these groups (RR 1.00, 95% CI 0.73 to 1.36; I2 = 0%; 2 studies, 346 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 0.25, 95% CI 0.03 to 2.19; I2 = n/a; 4 studies, 494 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.50, 95% CI 1.24 to 5.04; I2 = 0%; 4 studies, 2312 participants). In absolute terms this represents an increase of six per 100 (95% CI 1 to 14). However, this finding was very low-certainty, due to issues with imprecision and risk of bias. There was no evidence that the rate of SAEs varied, but some evidence that non-serious AEs were more common in people randomized to nicotine EC (AEs: RR 1.17, 95% CI 1.04 to 1.31; I2 = 28%; 3 studies, 516 participants; SAEs: RR 1.33, 95% CI 0.25 to 6.96; I2 = 17%; 5 studies, 842 participants). Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate over time with continued use. Very few studies reported data on other outcomes or comparisons and hence evidence for these is limited, with confidence intervals often encompassing clinically significant harm and benefit.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that ECs with nicotine increase quit rates compared to ECs without nicotine and compared to NRT. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the degree of effect, particularly when using modern EC products. Confidence intervals were wide for data on AEs, SAEs and other safety markers. Overall incidence of SAEs was low across all study arms. We did not detect any clear evidence of harm from nicotine EC, but longest follow-up was two years and the overall number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up-to-date information for decision-makers, this review is now a living systematic review. We will run searches monthly from December 2020, with the review updated as relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.

Inhaling alcohol vapour or mist: An international study of use, effects and harms

OBJECTIVES

To determine the prevalence, patterns of use and consequences of inhaling alcohol vapour or mist.

DESIGN

Cross-sectional survey recruiting an international non-probability sample of people who use alcohol and other drugs as part of the annual Global Drug Survey (GDS).

SETTING

Online questionnaire translated into 19 languages.

PARTICIPANTS

110,557 people took part in GDS2020 and 88,124 completed a question about inhaling alcohol in the last 12 months. The focus of this study is GDS2020 respondents who reported inhaling alcohol mist/vapour from a balloon in the last 12 months.

MAIN OUTCOMES

Last 12-month use of alcohol vapour, onset duration, intensity of effects, value for money compared with alcohol, and incidence of falls/injuries.

RESULTS

A total of 803 people reported the use of alcohol vapour in the last 12 months, with 51% of the sample coming from 3 countries: Australia, Denmark and England. Two-thirds were male, and they were more likely to be under 25. 45.7% reported that they were a bit/very drunk before they inhaled alcohol. 51.3% reported that the effects lasted for less than 5 mins. Both the intensity of effect and perceived value for money showed a normal distribution on a 10-point scale. 12.7% of respondents reported falling/injuring themselves.

CONCLUSION

Inhaling alcohol vapour appears to be more common amongst younger, higher-risk drinkers. The rapid onset of action and inability to titrate consumption to effect is a risk for acute injury. We consider that alcohol is harmful enough through oral consumption without adding to the risk of injury by offering such a potentially risky additional administration method.

Prospective association of e-cigarette and cigarette use with alcohol use in two waves of the Population Assessment of Tobacco and Health

BACKGROUND AND AIMS

Prior cross-sectional research finds that electronic cigarette (e-cigarette) use clusters with higher rates of harmful alcohol consumption in the United States adult population. The current study examined prospectively the association between e-cigarette use, cigarette use and the combined use of e-cigarettes and tobacco cigarettes and alcohol use outcomes.

DESIGN

A nationally representative multi-wave cohort survey (wave 1: September 2013-December 2014, wave 2: October 2014-October 2015).

SETTING

United States.

PARTICIPANTS

A representative sample of civilian, non-institutionalized adults who completed waves 1 and 2 of the Population Assessment of Tobacco and Health survey (n = 26 427).

MEASUREMENTS

Participants were categorized into exposure groups according to their e-cigarette and cigarette use during wave 1. Past 30-day alcohol use outcomes were (1) National Institute on Alcohol Abuse and Alcoholism (NIAAA)-defined hazardous alcohol use, (2) total alcohol drinks consumed and (3) alcohol-related consequences.

FINDINGS

After controlling for socio-demographic risk factors and alcohol use at wave 1, all exposure groups showed higher odds of hazardous alcohol use [adjusted odds ratios (aORs) = 2.05-2.12, all P < 0.001] and reported higher past-month total drinks (B = 0.46-0.70, all P < 0.001) and more alcohol consequences (B = 0.63-0.89, all P ≤ 0.10) at wave 2 compared with non-users. Cigarette users (B = 0.24, P = 0.038) and dual e-cigarette/cigarette users (B = 0.32, P = 0.038) reported higher past-month total drinks compared with e-cigarette users. There was no conclusive evidence that non-daily use of e-cigarettes or cigarettes predicted poorer alcohol use outcomes compared with daily use.

CONCLUSIONS

In the United States between 2013 and 2015, after adjustment for socio-demographic characteristics, cigarette and e-cigarette use were associated with alcohol use 1 year later.

Review of Health Consequences of Electronic Cigarettes and the Outbreak of Electronic Cigarette, or Vaping, Product Use-Associated Lung Injury

Electronic cigarettes (e-cigarettes) are battery-operated devices to insufflate nicotine or other psychoactive e-liquid aerosols. Despite initial claims of e-cigarettes as a nicotine-cessation device, aggressive marketing of e-cigarettes has led to an explosion in adolescents' and young adults' use over the last few years. Coupled with a lack of adequate investigation and regulation of e-cigarettes, the USA is facing an outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) starting in mid-2019. While little long-term health hazard data are available, the components and constituents of e-cigarettes may adversely impact health. Propylene glycol and glycerin are humectants (water-retaining excipients) that generate pulmonary irritants and carcinogenic carbonyl compounds (e.g., formaldehyde, acetaldehyde, and acrolein) when heated in e-cigarettes. Metals contained in heating coils and cartridge casings may leach metals such as aluminum, chromium, iron, lead, manganese, nickel, and tin. Flavoring agents are considered safe for ingestion but lack safety data for inhalational exposures. Diacetyl, a common buttery flavoring agent, has known pulmonary toxicity with inhalational exposures leading to bronchiolitis obliterans. In 2019, clusters of lung injury associated with e-cigarette use were identified in Wisconsin and Illinois. Patients with EVALI present with a constellation of respiratory, gastrointestinal, and constitutional symptoms. Radiographically, patients have bilateral ground glass opacifications. As of February 18, 2020, the Centers for Disease Control has identified 2807 hospitalized patients diagnosed with either "confirmed" or "probable" EVALI in the US. Currently, vitamin E acetate (VEA) used as a diluent in tetrahydrocannabinol vape cartridges is implicated in EVALI. VEA cuts tetrahydrocannabinol oil without changing the appearance or viscosity. When inhaled, pulmonary tissue lacks the mechanism to metabolize and absorb VEA, which may lead to its accumulation. While most EVALI patients were hospitalized, treatment remains largely supportive, and use of corticosteroids has been associated with clinical improvement. The outbreak of EVALI highlights the need for regulation of e-cigarette devices and e-liquids. Clinicians need to be aware of the health hazards of e-cigarettes and be vigilant in asking about vaping.

Association between preference for using alcohol beverage-named e-liquids and alcohol use among high school youth

BACKGROUND

There are thousands of e-liquid flavors available, and some are named after alcohol beverages (e.g., "pina colada"). It is unclear whether use of e-liquids with alcohol beverage names is associated with adolescent alcohol use. E-cigarettes and alcohol are co-used in adolescents; therefore, it is important to investigate these associations.

METHODS

Eight Southeastern Connecticut high schools were surveyed in Spring 2015 (N = 7045). We examined the association between preference for using e-liquid flavors and alcohol drinking status (i.e., no past month alcohol use, past month alcohol use but no binge drinking, and past month binge drinking) in ever e-cigarette users (N = 1311).

RESULTS

Among ever e-cigarette users who preferred using e-liquids with alcohol beverage names (N = 111), 30.6 % had no past month alcohol use, 19.8 % had past month alcohol use but did not binge drink in the past month, and 49.5 % binge drank in the past month. Multinomial logistic regression (controlling for demographics and including other e-cigarette flavors that were highly endorsed, i.e., fruit and candy) revealed that the preferences of alcohol beverage-named-e-liquid (OR: 2.84, CI: 1.70-4.75) and fruit flavored e-liquids (OR: 1.55, CI: 1.14-2.11), but not candy flavored e-liquids was associated with past-month binge drinking compared to no past-month alcohol use.

CONCLUSION

This evidence suggests that the preference for using alcohol beverage- and fruit-named e-liquid flavors is associated with past-month binge drinking among adolescents. Understanding the associations between alcohol beverage-named e-liquids and alcohol use in adolescents may help inform tobacco regulatory strategies that aim to decrease the use/appeal of e-cigarettes.

PBPK modeling characterization of potential acute impairment effects from inhalation of ethanol during e-cigarette use

Objective: Ethanol is used as a solvent for flavoring chemicals in some electronic cigarette (e-cigarette) liquids (e-liquids). However, there are limited data available regarding the effects of inhalation of ethanol on blood alcohol concentration (BAC) during e-cigarette use. In this study, a modified physiologically based pharmacokinetic (PBPK) model for inhalation of ethanol was used to estimate the BAC time-profile of e-cigarette users who puffed an e-liquid containing 23.5% ethanol. Materials and Methods: A modified PBPK model for inhalation of ethanol was developed. Use characteristics were estimated based on first-generation and second-generation e-cigarette topography parameters. Three representative use-case puffing profiles were modeled: a user that took many, short puffs; a typical user with intermediate puff counts and puff durations; and a user that took fewer, long puffs. Results and Discussion: The estimated peak BACs for these three user profiles were 0.22, 0.22, and 0.30 mg/L for first-generation devices, respectively, and 0.85, 0.58, and 0.34 mg/L for second-generation devices, respectively. Additionally, peak BACs for individual first-generation users with directly measured puffing parameters were estimated to range from 0.06 to 0.67 mg/L. None of the scenarios modeled predicted a peak BAC result that approached toxicological or regulatory thresholds that would be associated with physiological impairment (roughly 0.01% or 100 mg/L). Conclusions: The approach used in this study, combining a validated PBPK model for a toxicant with peer-reviewed topographical parameters, can serve as a screening-level exposure assessment useful for evaluation of the safety of e-liquid formulations. Abbreviations: BAC: blood alcohol concentration; e-cigarette: electronic cigarette; e-liquid: e-cigarette liquid or propylene glycol and/or vegetable glycerin-based liquid; HS-GC-FID: headspace gas chromatography with flame-ionization detection; HS-GC-MS: headspace gas chromatography-mass spectrometry; PBPK: physiologically based pharmacokinetic; C_air: puff concentration expressed as ppm; C_air,mass: ethanol air concentration expressed on a mass basis; C_v: ethanol concentration in the venous blood; ρ: density; EC: ethanol concentration in the liquid; PLC: liquid consumption per puff; PAV: air volume of the puff; C_air,mass: puff concentration expressed as ppm; MW: molecular weight; P: pressure; T: temperature; PK: pharmacokinetic.

Washout kinetics of ethanol from the airways following inhalation of ethanol vapors and use of mouthwash

Introduction: Breath analyzers are commonly used to test for alcohol intoxication, i.e., elevated systemic levels of ethanol, at workplaces and among vehicle drivers. However, local low-dose exposure to ethanol in the mouth or airways may temporarily increase the breath-alcohol concentration (BrAC) without the systemic ethanol level being affected, leading to false positive test results. The aim of this study was to assess the impact of local ethanol exposure on the BrAC.Methods: Eleven healthy adults (six women) were exposed to on average 856 mg/m³ ethanol vapor for 15 min, followed by repeat collection of exhaled breath in Tedlar bags. One hour later, the subjects washed their mouth for 30 s with a typical mouthwash containing 22% ethanol and post-exposure breaths were again collected repeatedly. Negligible systemic uptake of ethanol was confirmed by analysis of blood sampled before, between and after the exposures. Ethanol in breath and blood was analyzed by gas chromatography.Results: No or very low levels (less than 0.002 mg/g) of ethanol were detected in blood at any time point, indicating negligible systemic uptake. The decline in breath was mono-exponential after both exposures with average half times of 0.4 (range 0.3-0.8) min after inhalation exposure and 1.9 (1.1-3.0) min after mouthwash. BrAC levels in the first sample, collected a few seconds after exposure, were 0.14 (0.07-0.13) mg/L after inhalation and 4.4 (2.7-6.0) mg/L after mouth wash. On average, it took 0.5 (0.06-0.7) min and 11 (6-15) min, respectively, for the BrAC to fall below the Swedish statutory limit of 0.1 mg/L air.Conclusion: In practice, use of breath analysis should not be a problem even if the subject inhaled ethanol vapors before the test. In contrast, use of ethanol-containing mouthwash results in a false positive test if sampling is done within 15 min.

Therapeutic challenges for concurrent ethanol and nicotine consumption: naltrexone and varenicline fail to alter simultaneous ethanol and nicotine intake by female alcohol-preferring (P) rats

RATIONALE AND OBJECTIVES

Simultaneous alcohol and nicotine consumption occurs in the majority of individuals with alcohol use disorder (AUD) and nicotine dependence. Varenicline (Var) is used to assist in the cessation of nicotine use, while naltrexone (Nal) is the standard treatment for AUD. Despite evidence that ethanol (EtOH) and nicotine (NIC) co-use produces unique neuroadaptations, preclinical research has focused on the effects of pharmacotherapeutics on a single reinforcer. The current experiments examined the effects of Var and Nal on EtOH, NIC, or EtOH+NIC intake.

METHODS

Animals were randomly assigned to one of four drinking conditions of 24-h access to a three-bottle choice paradigm, one of which always contained water. Drinking conditions were water only, 0.07 and 0.14 mg/mL NIC (NIC only), 15% and 30% EtOH (EtOH only), or 15% and 30% EtOH with 0.14 mg/mL NIC (EtOH+NIC). The effects of Var (0, 1, or 2 mg/kg) or Nal (0, 1, or 10 mg/kg) injections on maintenance and relapse consumption were determined during four consecutive days.

RESULTS

Var reduced maintenance and relapse NIC intake but had no effect on EtOH or EtOH+NIC drinking. Conversely, Nal reduced EtOH maintenance and relapse drinking, but had no effect on NIC or EtOH+NIC drinking.

DISCUSSION

The results indicate the standard pharmacological treatments for nicotine dependence and AUD were effective at reducing consumption of the targeted reinforcer but neither reduced EtOH+NIC co-use/abuse. These findings suggest that co-abuse may promote unique neuroadaptations that require models of polysubstance abuse to develop pharmacotherapeutics to treat AUD and nicotine dependence.

Ethyl glucuronide hair testing: A review

Ethyl glucuronide (EtG) is a minor, non-oxidative ethanol metabolite that can be detected in several matrices (e.g. blood, urine, hair, meconium) for variable periods of time. Quantification of EtG in hair (hEtG) has established itself, over recent years, as one of the most reliable biomarkers of long-term alcohol consumption habits, with the Society of Hair Testing (SoHT) offering cut-off values for assessment of both abstinence and heavy drinking (>60 g/day). Despite its high diagnostic performance, however, issues concerning inter- and intra-laboratory variability as well as data interpretation are still being investigated and represent the ultimate barrier to widespread acceptance of hEtG in the forensic context. The aim of this review is to summarize currently available analytical methods of hEtG testing, provide a framework to understand current hEtG cut-offs and their possible upcoming changes (in particular, a lower abstinence cut-off has been proposed for the 2019 revision of the SoHT consensus), and offer a schematic but exhaustive overview of the pitfalls in result reproducibility and interpretation that may limit applications of hEtG testing in the forensic context. Ultimately, the purpose of the authors is not to undermine the reliability of hEtG as an alcohol use marker, but rather to enhance it by promoting familiarization with all aspects related to it, from ethanol pharmacokinetics and EtG incorporation into hair, to sample preparation and analytical methods, to specific cases warranting close attention and additional tests for correct interpretation of hEtG results.

E-cigarettes: Tobacco policy and regulation

Purpose of the review

The purpose of this review is to describe current trends in e-cigarette use, provide an overview of the potential health risks and benefits, and discuss tobacco policy and regulatory considerations.

Recent Findings

E-cigarette use is popular among youth and adults, despite limited evidence about the long-term risks or benefits. Internationally there is wide variability in how e-cigarettes are classified, which has also led to large differences in regulations on these products. Regulatory policies that strike a balance between the potential benefit of these devices for adult smokers who are trying to quit smoking with reducing the appeal and addictive potential for youth are critically needed.

Summary

Continued research on the health effects and consequences of e-cigarette use will be essential to inform e-cigarette product standards and tobacco regulatory policies as scientific research strives to keep pace with the ever-changing landscape of e-cigarette technology.

Headspace analysis for screening of volatile organic compound profiles of electronic juice bulk material

The use of electronic nicotine delivery systems continues to gain popularity, and there is concern for potential health risks from inhalation of aerosol and vapor produced by these devices. An analytical method was developed that provided quantitative and qualitative chemical information for characterizing the volatile constituents of bulk electronic cigarette liquids (e-liquids) using a static headspace technique. Volatile organic compounds (VOCs) were screened from a convenience sample of 146 e-liquids by equilibrating 1 g of each e-liquid in amber vials for 24 h at room temperature. Headspace was transferred to an evacuated canister and quantitatively analyzed for 20 VOCs as well as tentatively identified compounds using a preconcentrator/gas chromatography/mass spectrometer system. The e-liquids were classified into flavor categories including brown, fruit, hybrid dairy, menthol, mint, none, tobacco, and other. 2,3-Butanedione was found at the highest concentration in brown flavor types, but was also found in fruit, hybrid dairy, and menthol flavor types. Benzene was observed at concentrations that are concerning given the carcinogenicity of this compound (max 1.6 ppm in a fruit flavor type). The proposed headspace analysis technique coupled with partition coefficients allows for a rapid and sensitive prediction of the volatile content in the liquid. The technique does not require onerous sample preparation, dilution with organic solvents, or sampling at elevated temperatures. Static headspace screening of e-liquids allows for the identification of volatile chemical constituents which is critical for identifying and controlling emission of potentially hazardous constituents in the workplace.

E-cigarettes: Impact of E-Liquid Components and Device Characteristics on Nicotine Exposure

BACKGROUND

Electronic cigarette (e-cigarette) use has increased substantially in recent years. While e-cigarettes have been proposed as a potentially effective smoking cessation tool, dualuse in smokers is common and e-cigarettes are widely used by non-smokers, including youth and young-adult non-smokers. Nicotine, the primary addictive component in cigarettes, is present at varying levels in many e-liquids. E-cigarettes may lead to initiation of nicotine use in adult and youth non-smokers, re-initiation of nicotine dependence in ex-smokers or increased severity of nicotine dependence in dual-users of cigarettes and e-cigarettes. As such, there are important clinical and policy implications to understanding factors impacting nicotine exposure from e-cigarettes. However, the broad and rapidly changing range of e-liquid constituents and e-cigarette hardware which could impact nicotine exposure presents a challenge. Recent changes in regulatory oversight of e-cigarettes underscore the importance of synthesizing current knowledge on common factors which may impact nicotine exposure.

METHODS

This review focuses on factors which may impact nicotine exposure by changing e-cigarette use behavior, puff topography, altering the nicotine yield (amount of nicotine exiting the e-cigarette mouth piece including nicotine exhaled as vapor) or more directly by altering nicotine absorption and bioavailability.

RESULTS

Topics reviewed include e-liquid components or characteristics including flavor additives (e.g., menthol), base e-liquid ingredients (propylene glycol, vegetable glycerin), components commonly used to dissolve flavorants (e.g., ethanol), and resulting properties of the e-liquid (e.g., pH), e-cigarette device characteristics (e.g., wattage, temperature, model) and user behavior (e.g., puff topography) which may impact nicotine exposure.

CONCLUSION

E-liquid characteristics and components, e-cigarette hardware and settings, and user behavior can all contribute substantially to nicotine exposure from e-cigarettes.

Ethanol concentration in 56 refillable electronic cigarettes liquid formulations determined by headspace gas chromatography with flame ionization detector (HS-GC-FID)

Personal battery-powered vaporizers or electronic cigarettes were developed as an alternative to traditional cigarettes. The modern electronic cigarettes were patented in 2004 by Hon Lik in China. In May 2016, the US Food and Drug Administration (FDA) imposed regulatory statutes on e-cigarettes and their liquid formulations (e-liquids); prior to that, they were unregulated. E-liquids are typically composed of propylene glycol and/or glycerin, flavouring component(s), and active ingredient(s), such as nicotine. Fifty-six commercially available e-liquids, purchased from various sources, contained a variety of flavours and active ingredients. A headspace gas chromatography with flame ionization detector (HS-GC-FID) method was used to analyze these e-liquids for volatiles content. Only one of the e-liquids listed ethanol as a component. The chromatographic separation of volatiles was performed on a Restek BAC-1 column. A linear calibration was generated for ethanol with limits of detection and quantification (LOD/LOQ) of 0.05 mg/mL. Ethanol concentrations in the 56 e-liquids ranged from none detected to 206 mg/mL. The ethanol determined in these products may have been used in flavourants or a solvent; the reason for inclusion cannot be fully ascertained. The implications of vaporizing ethanol as an e-liquid component are unknown. Copyright © 2017 John Wiley & Sons, Ltd.

"Essentially, All Models are Wrong, but Some Are Useful": A Preliminary Conceptual Model Of Co-Occurring E-Cig and Alcohol Use

PURPOSE OF REVIEW

As prevalence rates of electronic cigarette (e-cig) use increase, researchers and clinicians should not only be paying careful attention to the direct health effects of e-cigs, but also the potential impact e-cigs may have on alcohol use behaviors. We review the current state of the literature and propose a conceptual model for the relationship between e-cig and alcohol use, including important consequences, mechanisms, and moderators of this relationship.

RECENT FINDINGS

The model is based in emerging literature examining the direct relationship between e-cig and alcohol use, as well as indirect evidence concerning potential mechanisms from research on cigarette and alcohol use. Overall, research indicates a robust relationship between e-cig and alcohol use.

SUMMARY

We suggest that a relationship between e-cig use and alcohol use could be particularly problematic, especially for adolescents and for those with or at risk for alcohol use disorders. We hope the presented conceptual model can stimulate research in this area. We make research recommendations, including the need for more methodological rigor, including improved measurement of e-cig use, and expanding research to longitudinal and experimental designs.

Overview of Electronic Nicotine Delivery Systems: A Systematic Review

CONTEXT

Rapid developments in e-cigarettes, or electronic nicotine delivery systems (ENDS), and the evolution of the overall tobacco product marketplace warrant frequent evaluation of the published literature. The purpose of this article is to report updated findings from a comprehensive review of the published scientific literature on ENDS.

EVIDENCE ACQUISITION

The authors conducted a systematic review of published empirical research literature on ENDS through May 31, 2016, using a detailed search strategy in the PubMed electronic database, expert review, and additional targeted searches. Included studies presented empirical findings and were coded to at least one of nine topics: (1) Product Features; (2) Health Effects; (3) Consumer Perceptions; (4) Patterns of Use; (5) Potential to Induce Dependence; (6) Smoking Cessation; (7) Marketing and Communication; (8) Sales; and (9) Policies; reviews and commentaries were excluded. Data from included studies were extracted by multiple coders (October 2015 to August 2016) into a standardized form and synthesized qualitatively by topic.

EVIDENCE SYNTHESIS

There were 687 articles included in this systematic review. The majority of studies assessed patterns of ENDS use and consumer perceptions of ENDS, followed by studies examining health effects of vaping and product features.

CONCLUSIONS

Studies indicate that ENDS are increasing in use, particularly among current smokers, pose substantially less harm to smokers than cigarettes, are being used to reduce/quit smoking, and are widely available. More longitudinal studies and controlled trials are needed to evaluate the impact of ENDS on population-level tobacco use and determine the health effects of longer-term vaping.

Inhalation of Alcohol Vapor: Measurement and Implications

Decades of alcohol research have established the health risks and pharmacodynamic profile of oral alcohol consumption. Despite isolated periods of public health concern, comparatively less research has evaluated exposure to alcohol vapor. Inhaled alcohol initially bypasses first-pass metabolism and rapidly reaches the arterial circulation and the brain, suggesting that this route of administration may be associated with pharmacological effects that increase the risk of addiction. However, detailed reviews assessing the possible effects of inhaled alcohol in humans are lacking. A comprehensive, systematic literature review was conducted using Google Scholar and PubMed to examine manuscripts studying exposure to inhaled alcohol and measurement of biomarkers (biochemical or functional) associated with alcohol consumption in human participants. Twenty-one publications reported on alcohol inhalation. Fourteen studies examined inhalation of alcohol vapor associated with occupational exposure (e.g., hand sanitizer) in a variety of settings (e.g., naturalistic, laboratory). Six publications measured inhalation of alcohol in a controlled laboratory chamber, and 1 evaluated direct inhalation of an e-cigarette with ethanol-containing "e-liquid." Some studies have reported that inhalation of alcohol vapor results in measurable biomarkers of acute alcohol exposure, most notably ethyl glucuronide. Despite the lack of significantly elevated blood alcohol concentrations, the behavioral consequences and subjective effects associated with repeated use of devices capable of delivering alcohol vapor are yet to be determined. No studies have focused on vulnerable populations, such as adolescents or individuals with alcohol use disorder, who may be most at risk of problems associated with alcohol inhalation.