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How bad is vaping for your health? We’re finally getting answers

As more of us take up vaping and concerns rise about the long-term effects, we now have enough data to get a grip on the health impact – and how it compares to smoking

By Graham Lawton

6 December 2023

Klaus Kremmerz

AS THE old joke goes, when I read about the dangers of smoking, I gave up reading. If you are a vaper, you might feel like you want to stop reading now. Don’t: you need to know this.

I am a vaper. Like many others, I used to smoke and switched to vaping for health reasons. I plan to quit completely, but I haven’t managed it yet. I am sure vaping is better for me than smoking, but I am also sure it is worse than not vaping. I cough in the morning and feel massively addicted to the nicotine. I don’t even really know what I am inhaling. I worry that it will be hard to quit, that I am causing long-term damage to my body and that by vaping, I am susceptible to slipping back down the slope to cigarettes. I also have the same worries for the teenagers I see coming out of school and immediately enveloping themselves in sweet-smelling clouds.

Is CBD a wonder drug or waste of money? Here's what the evidence says

As vaping has increased throughout the Western world, these fears have been repeated often. Part of last month’s King’s Speech in the UK focused on new legislation aiming to create a smoke-free generation in part by cracking down on youth vaping. Worldwide, there have been calls for tougher regulation and more investigation into vaping’s health effects as increasing numbers of children admit to taking up the habit.

But there hasn’t been a huge amount to say on whether fears over health effects are well-founded – until recently. Now, vaping…

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• Open access
• Published: 18 May 2021

An updated overview of e-cigarette impact on human health

• Patrice Marques   ORCID: orcid.org/0000-0003-0465-1727 1 , 2 ,
• Laura Piqueras   ORCID: orcid.org/0000-0001-8010-5168 1 , 2 , 3 &
• Maria-Jesus Sanz   ORCID: orcid.org/0000-0002-8885-294X 1 , 2 , 3  

Respiratory Research volume  22 , Article number:  151 ( 2021 ) Cite this article

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The electronic cigarette ( e-cigarette ), for many considered as a safe alternative to conventional cigarettes, has revolutionised the tobacco industry in the last decades. In e-cigarettes , tobacco combustion is replaced by e-liquid heating, leading some manufacturers to propose that e-cigarettes have less harmful respiratory effects than tobacco consumption. Other innovative features such as the adjustment of nicotine content and the choice of pleasant flavours have won over many users. Nevertheless, the safety of e-cigarette consumption and its potential as a smoking cessation method remain controversial due to limited evidence. Moreover, it has been reported that the heating process itself can lead to the formation of new decomposition compounds of questionable toxicity. Numerous in vivo and in vitro studies have been performed to better understand the impact of these new inhalable compounds on human health. Results of toxicological analyses suggest that e-cigarettes can be safer than conventional cigarettes, although harmful effects from short-term e-cigarette use have been described. Worryingly, the potential long-term effects of e-cigarette consumption have been scarcely investigated. In this review, we take stock of the main findings in this field and their consequences for human health including coronavirus disease 2019 (COVID-19).

Electronic nicotine dispensing systems (ENDS), commonly known as electronic cigarettes or e-cigarettes , have been popularly considered a less harmful alternative to conventional cigarette smoking since they first appeared on the market more than a decade ago. E-cigarettes are electronic devices, essentially consisting of a cartridge, filled with an e-liquid, a heating element/atomiser necessary to heat the e-liquid to create a vapour that can be inhaled through a mouthpiece, and a rechargeable battery (Fig.  1 ) [ 1 , 2 ]. Both the electronic devices and the different e-liquids are easily available in shops or online stores.

Effect of the heating process on aerosol composition. Main harmful effects documented. Several compounds detected in e-cigarette aerosols are not present in e-liquid s and the device material also seems to contribute to the presence of metal and silicate particles in the aerosols. The heating conditions especially on humectants, flavourings and the low-quality material used have been identified as the generator of the new compounds in aerosols. Some compounds generated from humectants (propylene glycol and glycerol) and flavourings, have been associated with clear airways impact, inflammation, impairment of cardiovascular function and toxicity. In addition, some of them are carcinogens or potential carcinogens

The e-liquid typically contains humectants and flavourings, with or without nicotine; once vapourised by the atomiser, the aerosol (vapour) provides a sensation similar to tobacco smoking, but purportedly without harmful effects [ 3 ]. However, it has been reported that the heating process can lead to the generation of new decomposition compounds that may be hazardous [ 4 , 5 ]. The levels of nicotine, which is the key addictive component of tobacco, can also vary between the commercially available e-liquids, and even nicotine-free options are available. For this particular reason, e-cigarettes are often viewed as a smoking cessation tool, given that those with nicotine can prevent smoking craving, yet this idea has not been fully demonstrated [ 2 , 6 , 7 ].

Because e-cigarettes are combustion-free, and because most of the damaging and well-known effects of tobacco are derived from this reaction, there is a common and widely spread assumption that e-cigarette consumption or “vaping” is safer than conventional cigarette smoking. However, are they risk-free? Is there sufficient toxicological data on all the components employed in e-liquids ? Do we really know the composition of the inhaled vapour during the heating process and its impact on health? Can e-cigarettes be used to curb tobacco use? Do their consumption impact on coronavirus disease 2019 (COVID-19)? In the present review, we have attempted to clarify these questions based on the existing scientific literature, and we have compiled new insights related with the toxicity derived from the use of these devices.

Effect of e-cigarette vapour versus conventional cigarette exposure: in vivo and in vitro effects

Numerous studies have been performed to evaluate the safety/toxicity of e-cigarette use both in vivo and in in vitro cell culture.

One of the first studies in humans involved the analysis of 9 volunteers that consumed e-cigarettes , with or without nicotine, in a ventilated room for 2 h [ 8 ]. Pollutants in indoor air, exhaled nitric oxide (NO) and urinary metabolite profiles were analysed. The results of this acute experiment revealed that e-cigarettes are not emission-free, and ultrafine particles formed from propylene glycol (PG) could be detected in the lungs. The study also suggested that the presence of nicotine in e-cigarettes increased the levels of NO exhaled from consumers and provoked marked airway inflammation; however, no differences were found in the levels of exhaled carbon monoxide (CO), an oxidative stress marker, before and after e-cigarette consumption [ 8 ]. A more recent human study detected significantly higher levels of metabolites of hazardous compounds including benzene, ethylene oxide, acrylonitrile, acrolein and acrylamide in the urine of adolescent dual users ( e-cigarettes and conventional tobacco consumers) than in adolescent e-cigarette -only users (Table 1 ) [ 9 ]. Moreover, the urine levels of metabolites of acrylonitrile, acrolein, propylene oxide, acrylamide and crotonaldehyde, all of which are detrimental for human health, were significantly higher in e-cigarette -only users than in non-smoker controls, reaching up to twice the registered values of those from non-smoker subjects (Table 1 ) [ 9 ]. In line with these observations, dysregulation of lung homeostasis has been documented in non-smokers subjected to acute inhalation of e-cigarette aerosols [ 10 ].

Little is known about the effect of vaping on the immune system. Interestingly, both traditional and e-cigarette consumption by non-smokers was found to provoke short-term effects on platelet function, increasing platelet activation (levels of soluble CD40 ligand and the adhesion molecule P-selectin) and platelet aggregation, although to a lesser extent with e-cigarettes [ 11 ]. As found with platelets, the exposure of neutrophils to e-cigarette aerosol resulted in increased CD11b and CD66b expression being both markers of neutrophil activation [ 12 ]. Additionally, increased oxidative stress, vascular endothelial damage, impaired endothelial function, and changes in vascular tone have all been reported in different human studies on vaping [ 13 , 14 , 15 , 16 , 17 ]. In this context, it is widely accepted that platelet and leukocyte activation as well as endothelial dysfunction are associated with atherogenesis and cardiovascular morbidity [ 18 , 19 ]. In line with these observations the potential association of daily e-cigarettes consumption and the increased risk of myocardial infarction remains controversial but benefits may occur when switching from tobacco to chronic e-cigarette use in blood pressure regulation, endothelial function and vascular stiffness (reviewed in [ 20 ]). Nevertheless, whether or not e-cigarette vaping has cardiovascular consequences requires further investigation.

More recently, in August 2019, the US Centers for Disease Control and Prevention (CDC) declared an outbreak of the e-cigarette or vaping product use-associated lung injury (EVALI) which caused several deaths in young population (reviewed in [ 20 ]). Indeed, computed tomography (CT scan) revealed local inflammation that impaired gas exchange caused by aerosolised oils from e-cigarettes [ 21 ]. However, most of the reported cases of lung injury were associated with use of e-cigarettes for tetrahydrocannabinol (THC) consumption as well as vitamin E additives [ 20 ] and not necessarily attributable to other e-cigarette components.

On the other hand, in a comparative study of mice subjected to either lab air, e-cigarette aerosol or cigarette smoke (CS) for 3 days (6 h-exposure per day), those exposed to e-cigarette aerosols showed significant increases in interleukin (IL)-6 but normal lung parenchyma with no evidence of apoptotic activity or elevations in IL-1β or tumour necrosis factor-α (TNFα) [ 22 ]. By contrast, animals exposed to CS showed lung inflammatory cell infiltration and elevations in inflammatory marker expression such as IL-6, IL-1β and TNFα [ 22 ]. Beyond airway disease, exposure to aerosols from e-liquids with or without nicotine has also been also associated with neurotoxicity in an early-life murine model [ 23 ].

Results from in vitro studies are in general agreement with the limited number of in vivo studies. For example, in an analysis using primary human umbilical vein endothelial cells (HUVEC) exposed to 11 commercially-available vapours, 5 were found to be acutely cytotoxic, and only 3 of those contained nicotine [ 24 ]. In addition, 5 of the 11 vapours tested (including 4 that were cytotoxic) reduced HUVEC proliferation and one of them increased the production of intracellular reactive oxygen species (ROS) [ 24 ]. Three of the most cytotoxic vapours—with effects similar to those of conventional high-nicotine CS extracts—also caused comparable morphological changes [ 24 ]. Endothelial cell migration is an important mechanism of vascular repair than can be disrupted in smokers due to endothelial dysfunction [ 25 , 26 ]. In a comparative study of CS and e-cigarette aerosols, Taylor et al . found that exposure of HUVEC to e-cigarette aqueous extracts for 20 h did not affect migration in a scratch wound assay [ 27 ], whereas equivalent cells exposed to CS extract showed a significant inhibition in migration that was concentration dependent [ 27 ].

In cultured human airway epithelial cells, both e-cigarette aerosol and CS extract induced IL-8/CXCL8 (neutrophil chemoattractant) release [ 28 ]. In contrast, while CS extract reduced epithelial barrier integrity (determined by the translocation of dextran from the apical to the basolateral side of the cell layer), e-cigarette aerosol did not, suggesting that only CS extract negatively affected host defence [ 28 ]. Moreover, Higham et al . also found that e-cigarette aerosol caused IL-8/CXCL8 and matrix metallopeptidase 9 (MMP-9) release together with enhanced activity of elastase from neutrophils [ 12 ] which might facilitate neutrophil migration to the site of inflammation [ 12 ].

In a comparative study, repeated exposure of human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates led to alterations in morphology, suppression of proliferation and induction of apoptosis, with changes in all three parameters greater in cells exposed to CS condensate [ 29 ]. Likewise, both e-cigarette aerosol and CS extract increased cell death in adenocarcinomic human alveolar basal epithelial cells (A549 cells), and again the effect was more damaging with CS extract than with e-cigarette aerosol (detrimental effects found at 2 mg/mL of CS extract vs. 64 mg/mL of e-cigarette extract) [ 22 ], which is in agreement with another study examining battery output voltage and cytotoxicity [ 30 ].

All this evidence would suggest that e-cigarettes are potentially less harmful than conventional cigarettes (Fig.  2 ) [ 11 , 14 , 22 , 24 , 27 , 28 , 29 ]. Importantly, however, most of these studies have investigated only short-term effects [ 10 , 14 , 15 , 22 , 27 , 28 , 29 , 31 , 32 ], and the long-term effects of e-cigarette consumption on human health are still unclear and require further study.

Comparison of the degree of harmful effects documented from e-cigarette and conventional cigarette consumption. Human studies, in vivo mice exposure and in vitro studies. All of these effects from e-cigarettes were documented to be lower than those exerted by conventional cigarettes, which may suggest that e-cigarette consumption could be a safer option than conventional tobacco smoking but not a clear safe choice

Consequences of nicotine content

Beyond flavour, one of the major issues in the e-liquid market is the range of nicotine content available. Depending on the manufacturer, the concentration of this alkaloid can be presented as low , medium or high , or expressed as mg/mL or as a percentage (% v/v). The concentrations range from 0 (0%, nicotine-free option) to 20 mg/mL (2.0%)—the maximum nicotine threshold according to directive 2014/40/EU of the European Parliament and the European Union Council [ 33 , 34 ]. Despite this normative, however, some commercial e-liquids have nicotine concentrations close to 54 mg/mL [ 35 ], much higher than the limits established by the European Union.

The mislabelling of nicotine content in e-liquids has been previously addressed [ 8 , 34 ]. For instance, gas chromatography with a flame ionisation detector (GC-FID) revealed inconsistencies in the nicotine content with respect to the manufacturer´s declaration (average of 22 ± 0.8 mg/mL vs. 18 mg/mL) [ 8 ], which equates to a content ~ 22% higher than that indicated in the product label. Of note, several studies have detected nicotine in those e-liquids labelled as nicotine-free [ 5 , 35 , 36 ]. One study detected the presence of nicotine (0.11–6.90 mg/mL) in 5 of 23 nicotine-free labelled e-liquids by nuclear magnetic resonance spectroscopy [ 35 ], and another study found nicotine (average 8.9 mg/mL) in 13.6% (17/125) of the nicotine-free e-liquids as analysed by high performance liquid chromatography (HPLC) [ 36 ]. Among the 17 samples tested in this latter study 14 were identified to be counterfeit or suspected counterfeit. A third study detected nicotine in 7 of 10 nicotine-free refills, although the concentrations were lower than those identified in the previous analyses (0.1–15 µg/mL) [ 5 ]. Not only is there evidence of mislabelling of nicotine content among refills labelled as nicotine-free, but there also seems to be a history of poor labelling accuracy in nicotine-containing e-liquids [ 37 , 38 ].

A comparison of the serum levels of nicotine from e-cigarette or conventional cigarette consumption has been recently reported [ 39 ]. Participants took one vape from an e-cigarette , with at least 12 mg/mL of nicotine, or inhaled a conventional cigarette, every 20 s for 10 min. Blood samples were collected 1, 2, 4, 6, 8, 10, 12 and 15 min after the first puff, and nicotine serum levels were measured by liquid chromatography-mass spectrometry (LC–MS). The results revealed higher serum levels of nicotine in the conventional CS group than in the e-cigarette group (25.9 ± 16.7 ng/mL vs. 11.5 ± 9.8 ng/mL). However, e-cigarettes containing 20 mg/mL of nicotine are more equivalent to normal cigarettes, based on the delivery of approximately 1 mg of nicotine every 5 min [ 40 ].

In this line, a study compared the acute impact of CS vs. e-cigarette vaping with equivalent nicotine content in healthy smokers and non-smokers. Both increased markers of oxidative stress and decreased NO bioavailability, flow-mediated dilation, and vitamin E levels showing no significant differences between tobacco and e-cigarette exposure (reviewed in [ 20 ]). Inasmuch, short-term e-cigarette use in healthy smokers resulted in marked impairment of endothelial function and an increase in arterial stiffness (reviewed in [ 20 ]). Similar effects on endothelial dysfunction and arterial stiffness were found in animals when they were exposed to e-cigarette vapor either for several days or chronically (reviewed in [ 20 ]). In contrast, other studies found acute microvascular endothelial dysfunction, increased oxidative stress and arterial stiffness in smokers after exposure to e-cigarettes with nicotine, but not after e-cigarettes without nicotine (reviewed in [ 20 ]). In women smokers, a study found a significant difference in stiffness after smoking just one tobacco cigarette, but not after use of e-cigarettes (reviewed in [ 20 ]).

It is well known that nicotine is extremely addictive and has a multitude of harmful effects. Nicotine has significant biologic activity and adversely affects several physiological systems including the cardiovascular, respiratory, immunological and reproductive systems, and can also compromise lung and kidney function [ 41 ]. Recently, a sub-chronic whole-body exposure of e-liquid (2 h/day, 5 days/week, 30 days) containing PG alone or PG with nicotine (25 mg/mL) to wild type (WT) animals or knockout (KO) mice in α7 nicotinic acetylcholine receptor (nAChRα7-KO) revealed a partly nAChRα7-dependent lung inflammation [ 42 ]. While sub-chronic exposure to PG/nicotine promote nAChRα7-dependent increased levels of different cytokines and chemokines in the bronchoalveolar lavage fluid (BALF) such as IL-1α, IL-2, IL-9, interferon γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1/CCL2) and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5), the enhanced levels of IL-1β, IL-5 and TNFα were nAChRα7 independent. In general, most of the cytokines detected in BALF were significantly increased in WT mice exposed to PG with nicotine compared to PG alone or air control [ 42 ]. Some of these effects were found to be through nicotine activation of NF-κB signalling albeit in females but not in males. In addition, PG with nicotine caused increased macrophage and CD4 + /CD8 + T-lymphocytes cell counts in BALF compared to air control, but these effects were ameliorated when animals were sub-chronically exposed to PG alone [ 42 ].

Of note, another study indicated that although RANTES/CCL5 and CCR1 mRNA were upregulated in flavour/nicotine-containing e-cigarette users, vaping flavour and nicotine-less e-cigarettes did not significantly dysregulate cytokine and inflammasome activation [ 43 ].

In addition to its toxicological effects on foetus development, nicotine can disrupt brain development in adolescents and young adults [ 44 , 45 , 46 ]. Several studies have also suggested that nicotine is potentially carcinogenic (reviewed in [ 41 ]), but more work is needed to prove its carcinogenicity independently of the combustion products of tobacco [ 47 ]. In this latter regard, no differences were encountered in the frequency of tumour appearance in rats subjected to long-term (2 years) inhalation of nicotine when compared with control rats [ 48 ]. Despite the lack of carcinogenicity evidence, it has been reported that nicotine promotes tumour cell survival by decreasing apoptosis and increasing proliferation [ 49 ], indicating that it may work as a “tumour enhancer”. In a very recent study, chronic administration of nicotine to mice (1 mg/kg every 3 days for a 60-day period) enhanced brain metastasis by skewing the polarity of M2 microglia, which increases metastatic tumour growth [ 50 ]. Assuming that a conventional cigarette contains 0.172–1.702 mg of nicotine [ 51 ], the daily nicotine dose administered to these animals corresponds to 40–400 cigarettes for a 70 kg-adult, which is a dose of an extremely heavy smoker. We would argue that further studies with chronic administration of low doses of nicotine are required to clearly evaluate its impact on carcinogenicity.

In the aforementioned study exposing human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates [ 29 ], the detrimental effects were greater in cells exposed to nicotine-rich condensate than to nicotine-free condensate, suggesting that the possible injurious effects of nicotine should be considered when purchasing e-refills . It is also noteworthy that among the 3 most cytotoxic vapours for HUVEC evaluated in the Putzhammer et al . study, 2 were nicotine-free, which suggests that nicotine is not the only hazardous component in e-cigarettes [ 24 ] .

The lethal dose of nicotine for an adult is estimated at 30–60 mg [ 52 ]. Given that nicotine easily diffuses from the dermis to the bloodstream, acute nicotine exposure by e-liquid spilling (5 mL of a 20 mg/mL nicotine-containing refill is equivalent to 100 mg of nicotine) can easily be toxic or even deadly [ 8 ]. Thus, devices with rechargeable refills are another issue of concern with e-cigarettes , especially when e-liquids are not sold in child-safe containers, increasing the risk of spilling, swallowing or breathing.

These data overall indicate that the harmful effects of nicotine should not be underestimated. Despite the established regulations, some inaccuracies in nicotine content labelling remain in different brands of e-liquids . Consequently, stricter regulation and a higher quality control in the e-liquid industry are required.

Effect of humectants and their heating-related products

In this particular aspect, again the composition of the e-liquid varies significantly among different commercial brands [ 4 , 35 ]. The most common and major components of e-liquids are PG or 1,2-propanediol, and glycerol or glycerine (propane-1,2,3-triol). Both types of compounds are used as humectants to prevent the e-liquid from drying out [ 2 , 53 ] and are classified by the Food and Drug Administration (FDA) as “Generally Recognised as Safe” [ 54 ]. In fact, they are widely used as alimentary and pharmaceutical products [ 2 ]. In an analysis of 54 commercially available e-liquids , PG and glycerol were detected in almost all samples at concentrations ranging from 0.4% to 98% (average 57%) and from 0.3% to 95% (average 37%), respectively [ 35 ].

With regards to toxicity, little is known about the effects of humectants when they are heated and chronically inhaled. Studies have indicated that PG can induce respiratory irritation and increase the probability of asthma development [ 55 , 56 ], and both PG and glycerol from e-cigarettes might reach concentrations sufficiently high to potentially cause irritation of the airways [ 57 ]. Indeed, the latter study established that one e-cigarette puff results in a PG exposure of 430–603 mg/m 3 , which is higher than the levels reported to cause airway irritation (average 309 mg/m 3 ) based on a human study [ 55 ]. The same study established that one e-cigarette puff results in a glycerol exposure of 348–495 mg/m 3 [ 57 ], which is close to the levels reported to cause airway irritation in rats (662 mg/m 3 ) [ 58 ].

Airway epithelial injury induced by acute vaping of PG and glycerol aerosols (50:50 vol/vol), with or without nicotine, has been reported in two randomised clinical trials in young tobacco smokers [ 32 ]. In vitro, aerosols from glycerol only-containing refills showed cytotoxicity in A549 and human embryonic stem cells, even at a low battery output voltage [ 59 ]. PG was also found to affect early neurodevelopment in a zebrafish model [ 60 ]. Another important issue is that, under heating conditions PG can produce acetaldehyde or formaldehyde (119.2 or 143.7 ng/puff at 20 W, respectively, on average), while glycerol can also generate acrolein (53.0, 1000.0 or 5.9 ng/puff at 20 W, respectively, on average), all carbonyls with a well-documented toxicity [ 61 ]. Although, assuming 15 puffs per e-cigarette unit, carbonyls produced by PG or glycerol heating would be below the maximum levels found in a conventional cigarette combustion (Table 2 ) [ 51 , 62 ]. Nevertheless, further studies are required to properly test the deleterious effects of all these compounds at physiological doses resembling those to which individuals are chronically exposed.

Although PG and glycerol are the major components of e-liquids other components have been detected. When the aerosols of 4 commercially available e-liquids chosen from a top 10 list of “ Best E-Cigarettes of 2014” , were analysed by gas chromatography-mass spectrometry (GC–MS) after heating, numerous compounds were detected, with nearly half of them not previously identified [ 4 ], thus suggesting that the heating process per se generates new compounds of unknown consequence. Of note, the analysis identified formaldehyde, acetaldehyde and acrolein [ 4 ], 3 carbonyl compounds with known high toxicity [ 63 , 64 , 65 , 66 , 67 ]. While no information was given regarding formaldehyde and acetaldehyde concentrations, the authors calculated that one puff could result in an acrolein exposure of 0.003–0.015 μg/mL [ 4 ]. Assuming 40 mL per puff and 15 puffs per e-cigarette unit (according to several manufacturers) [ 4 ], each e-cigarette unit would generate approximately 1.8–9 μg of acrolein, which is less than the levels of acrolein emitted by a conventional tobacco cigarette (18.3–98.2 μg) [ 51 ]. However, given that e-cigarette units of vaping are not well established, users may puff intermittently throughout the whole day. Thus, assuming 400 to 500 puffs per cartridge, users could be exposed to up to 300 μg of acrolein.

In a similar study, acrolein was found in 11 of 12 aerosols tested, with a similar content range (approximately 0.07–4.19 μg per e-cigarette unit) [ 68 ]. In the same study, both formaldehyde and acetaldehyde were detected in all of the aerosols tested, with contents of 0.2–5.61 μg and 0.11–1.36 μg, respectively, per e-cigarette unit [ 68 ]. It is important to point out that the levels of these toxic products in e-cigarette aerosols are significantly lower than those found in CS: 9 times lower for formaldehyde, 450 times lower for acetaldehyde and 15 times lower for acrolein (Table 2 ) [ 62 , 68 ].

Other compounds that have been detected in aerosols include acetamide, a potential human carcinogen [ 5 ], and some aldehydes [ 69 ], although their levels were minimal. Interestingly, the existence of harmful concentrations of diethylene glycol, a known cytotoxic agent, in e-liquid aerosols is contentious with some studies detecting its presence [ 4 , 68 , 70 , 71 , 72 ], and others finding low subtoxic concentrations [ 73 , 74 ]. Similar observations were reported for the content ethylene glycol. In this regard, either it was detected at concentrations that did not exceed the authorised limit [ 73 ], or it was absent from the aerosols produced [ 4 , 71 , 72 ]. Only one study revealed its presence at high concentration in a very low number of samples [ 5 ]. Nevertheless, its presence above 1 mg/g is not allowed by the FDA [ 73 ]. Figure  1 lists the main compounds detected in aerosols derived from humectant heating and their potential damaging effects. It would seem that future studies should analyse the possible toxic effects of humectants and related products at concentrations similar to those that e-cigarette vapers are exposed to reach conclusive results.

Impact of flavouring compounds

The range of e-liquid flavours available to consumers is extensive and is used to attract both current smokers and new e-cigarette users, which is a growing public health concern [ 6 ]. In fact, over 5 million middle- and high-school students were current users of e-cigarettes in 2019 [ 75 ], and appealing flavours have been identified as the primary reason for e-cigarette consumption in 81% of young users [ 76 ]. Since 2016, the FDA regulates the flavours used in the e-cigarette market and has recently published an enforcement policy on unauthorised flavours, including fruit and mint flavours, which are more appealing to young users [ 77 ]. However, the long-term effects of all flavour chemicals used by this industry (which are more than 15,000) remain unknown and they are not usually included in the product label [ 78 ]. Furthermore, there is no safety guarantee since they may harbour potential toxic or irritating properties [ 5 ].

With regards to the multitude of available flavours, some have demonstrated cytotoxicity [ 59 , 79 ]. Bahl et al. evaluated the toxicity of 36 different e-liquids and 29 different flavours on human embryonic stem cells, mouse neural stem cells and human pulmonary fibroblasts using a metabolic activity assay. In general, those e-liquids that were bubblegum-, butterscotch- and caramel-flavoured did not show any overt cytotoxicity even at the highest dose tested. By contrast, those e-liquids with Freedom Smoke Menthol Arctic and Global Smoke Caramel flavours had marked cytotoxic effects on pulmonary fibroblasts and those with Cinnamon Ceylon flavour were the most cytotoxic in all cell lines [ 79 ]. A further study from the same group [ 80 ] revealed that high cytotoxicity is a recurrent feature of cinnamon-flavoured e-liquids. In this line, results from GC–MS and HPLC analyses indicated that cinnamaldehyde (CAD) and 2-methoxycinnamaldehyde, but not dipropylene glycol or vanillin, were mainly responsible for the high cytotoxicity of cinnamon-flavoured e-liquids [ 80 ]. Other flavouring-related compounds that are associated with respiratory complications [ 81 , 82 , 83 ], such as diacetyl, 2,3-pentanedione or acetoin, were found in 47 out of 51 aerosols of flavoured e-liquids tested [ 84 ] . Allen et al . calculated an average of 239 μg of diacetyl per cartridge [ 84 ]. Assuming again 400 puffs per cartridge and 40 mL per puff, is it is possible to estimate an average of 0.015 ppm of diacetyl per puff, which could compromise normal lung function in the long-term [ 85 ].

The cytotoxic and pro-inflammatory effects of different e-cigarette flavouring chemicals were also tested on two human monocytic cell lines—mono mac 6 (MM6) and U937 [ 86 ]. Among the flavouring chemicals tested, CAD was found to be the most toxic and O-vanillin and pentanedione also showed significant cytotoxicity; by contrast, acetoin, diacetyl, maltol, and coumarin did not show any toxicity at the concentrations assayed (10–1000 µM). Of interest, a higher toxicity was evident when combinations of different flavours or mixed equal proportions of e-liquids from 10 differently flavoured e-liquids were tested, suggesting that vaping a single flavour is less toxic than inhaling mixed flavours [ 86 ]. Also, all the tested flavours produced significant levels of ROS in a cell-free ROS production assay. Finally, diacetyl, pentanedione, O-vanillin, maltol, coumarin, and CAD induced significant IL-8 secretion from MM6 and U937 monocytes [ 86 ]. It should be borne in mind, however, that the concentrations assayed were in the supra-physiological range and it is likely that, once inhaled, these concentrations are not reached in the airway space. Indeed, one of the limitations of the study was that human cells are not exposed to e-liquids per se, but rather to the aerosols where the concentrations are lower [ 86 ]. In this line, the maximum concentration tested (1000 µM) would correspond to approximately 80 to 150 ppm, which is far higher than the levels found in aerosols of some of these compounds [ 84 ]. Moreover, on a day-to-day basis, lungs of e-cigarette users are not constantly exposed to these chemicals for 24 h at these concentrations. Similar limitations were found when five of seven flavourings were found to cause cytotoxicity in human bronchial epithelial cells [ 87 ].

Recently, a commonly commercialized crème brûlée -flavoured aerosol was found to contain high concentrations of benzoic acid (86.9 μg/puff), a well-established respiratory irritant [ 88 ]. When human lung epithelial cells (BEAS-2B and H292) were exposed to this aerosol for 1 h, a marked cytotoxicity was observed in BEAS-2B but not in H292 cells, 24 h later. However, increased ROS production was registered in H292 cells [ 88 ].

Therefore, to fully understand the effects of these compounds, it is relevant the cell cultures selected for performing these assays, as well as the use of in vivo models that mimic the real-life situation of chronic e-cigarette vapers to clarify their impact on human health.

The e-cigarette device

While the bulk of studies related to the impact of e-cigarette use on human health has focused on the e-liquid components and the resulting aerosols produced after heating, a few studies have addressed the material of the electronic device and its potential consequences—specifically, the potential presence of metals such as copper, nickel or silver particles in e-liquids and aerosols originating from the filaments and wires and the atomiser [ 89 , 90 , 91 ].

Other important components in the aerosols include silicate particles from the fiberglass wicks or silicone [ 89 , 90 , 91 ]. Many of these products are known to cause abnormalities in respiratory function and respiratory diseases [ 89 , 90 , 91 ], but more in-depth studies are required. Interestingly, the battery output voltage also seems to have an impact on the cytotoxicity of the aerosol vapours, with e-liquids from a higher battery output voltage showing more toxicity to A549 cells [ 30 ].

A recent study compared the acute effects of e-cigarette vapor (with PG/vegetable glycerine plus tobacco flavouring but without nicotine) generated from stainless‐steel atomizer (SS) heating element or from a nickel‐chromium alloy (NC) [ 92 ]. Some rats received a single e-cigarette exposure for 2 h from a NC heating element (60 or 70 W); other rats received a similar exposure of e-cigarette vapor using a SS heating element for the same period of time (60 or 70 W) and, a final group of animals were exposed for 2 h to air. Neither the air‐exposed rats nor those exposed to e-cigarette vapor using SS heating elements developed respiratory distress. In contrast, 80% of the rats exposed to e-cigarette vapor using NC heating units developed clinical acute respiratory distress when a 70‐W power setting was employed. Thus, suggesting that operating units at higher than recommended settings can cause adverse effects. Nevertheless, there is no doubt that the deleterious effects of battery output voltage are not comparable to those exerted by CS extracts [ 30 ] (Figs.  1 and 2 ).

E-cigarettes as a smoking cessation tool

CS contains a large number of substances—about 7000 different constituents in total, with sizes ranging from atoms to particulate matter, and with many hundreds likely responsible for the harmful effects of this habit [ 93 ]. Given that tobacco is being substituted in great part by e-cigarettes with different chemical compositions, manufacturers claim that e -cigarette will not cause lung diseases such as lung cancer, chronic obstructive pulmonary disease, or cardiovascular disorders often associated with conventional cigarette consumption [ 3 , 94 ]. However, the World Health Organisation suggests that e-cigarettes cannot be considered as a viable method to quit smoking, due to a lack of evidence [ 7 , 95 ]. Indeed, the results of studies addressing the use of e-cigarettes as a smoking cessation tool remain controversial [ 96 , 97 , 98 , 99 , 100 ]. Moreover, both FDA and CDC are actively investigating the incidence of severe respiratory symptoms associated with the use of vaping products [ 77 ]. Because many e-liquids contain nicotine, which is well known for its powerful addictive properties [ 41 ], e-cigarette users can easily switch to conventional cigarette smoking, avoiding smoking cessation. Nevertheless, the possibility of vaping nicotine-free e-cigarettes has led to the branding of these devices as smoking cessation tools [ 2 , 6 , 7 ].

In a recently published randomised trial of 886 subjects who were willing to quit smoking [ 100 ], the abstinence rate was found to be twice as high in the e-cigarette group than in the nicotine-replacement group (18.0% vs. 9.9%) after 1 year. Of note, the abstinence rate found in the nicotine-replacement group was lower than what is usually expected with this therapy. Nevertheless, the incidence of throat and mouth irritation was higher in the e-cigarette group than in the nicotine-replacement group (65.3% vs. 51.2%, respectively). Also, the participant adherence to the treatment after 1-year abstinence was significantly higher in the e-cigarette group (80%) than in nicotine-replacement products group (9%) [ 100 ].

On the other hand, it is estimated that COPD could become the third leading cause of death in 2030 [ 101 ]. Given that COPD is generally associated with smoking habits (approximately 15 to 20% of smokers develop COPD) [ 101 ], smoking cessation is imperative among COPD smokers. Published data revealed a clear reduction of conventional cigarette consumption in COPD smokers that switched to e-cigarettes [ 101 ]. Indeed, a significant reduction in exacerbations was observed and, consequently, the ability to perform physical activities was improved when data was compared with those non-vapers COPD smokers. Nevertheless, a longer follow-up of these COPD patients is required to find out whether they have quitted conventional smoking or even vaping, since the final goal under these circumstances is to quit both habits.

Based on the current literature, it seems that several factors have led to the success of e-cigarette use as a smoking cessation tool. First, some e-cigarette flavours positively affect smoking cessation outcomes among smokers [ 102 ]. Second, e-cigarettes have been described to improve smoking cessation rate only among highly-dependent smokers and not among conventional smokers, suggesting that the individual degree of nicotine dependence plays an important role in this process [ 97 ]. Third, the general belief of their relative harmfulness to consumers' health compared with conventional combustible tobacco [ 103 ]. And finally, the exposure to point-of-sale marketing of e-cigarette has also been identified to affect the smoking cessation success [ 96 ].

Implication of e-cigarette consumption in COVID-19 time

Different reports have pointed out that smokers and vapers are more vulnerable to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infections or more prone to adverse outcomes if they suffer COVID-19 [ 104 ]. However, while a systematic review indicated that cigarette smoking is probably associated with enhanced damage from COVID-19, a meta-analysis did not, yet the latter had several limitations due to the small sample sizes [ 105 ].

Interestingly, most of these reports linking COVID-19 harmful effects with smoking or vaping, are based on their capability of increasing the expression of angiotensin-converting enzyme 2 (ACE2) in the lung. It is well known that ACE2 is the gate for SARS-CoV-2 entrance to the airways [ 106 ] and it is mainly expressed in type 2 alveolar epithelial cells and alveolar macrophages [ 107 ]. To date, most of the studies in this field indicate that current smokers have higher expression of ACE2 in the airways (reviewed by [ 108 ]) than healthy non-smokers [ 109 , 110 ]. However, while a recent report indicated that e-cigarette vaping also caused nicotine-dependent ACE2 up-regulation [ 42 ], others have revealed that neither acute inhalation of e-cigarette vapour nor e-cigarette users had increased lung ACE2 expression regardless nicotine presence in the e-liquid [ 43 , 110 ].

In regard to these contentions, current knowledge suggests that increased ACE2 expression is not necessarily linked to enhanced susceptibility to SARS-CoV-2 infection and adverse outcome. Indeed, elderly population express lower levels of ACE2 than young people and SARS-CoV-2/ACE2 interaction further decreases ACE2 expression. In fact, most of the deaths provoked by COVID-19 took place in people over 60 years old of age [ 111 ]. Therefore, it is plausible that the increased susceptibility to disease progression and the subsequent fatal outcome in this population is related to poor angiotensin 1-7 (Ang-1-7) generation, the main peptide generated by ACE2, and probably to their inaccessibility to its anti-inflammatory effects. Furthermore, it seems that all the efforts towards increasing ACE2 expression may result in a better resolution of the pneumonic process associated to this pandemic disease.

Nevertheless, additional complications associated to COVID-19 are increased thrombotic events and cytokine storm. In the lungs, e-cigarette consumption has been correlated to toxicity, oxidative stress, and inflammatory response [ 32 , 112 ]. More recently, a study revealed that while the use of nicotine/flavour-containing e-cigarettes led to significant cytokine dysregulation and potential inflammasome activation, none of these effects were detected in non-flavoured and non-nicotine-containing e-cigarettes [ 43 ]. Therefore, taken together these observations, e-cigarette use may still be a potent risk factor for severe COVID-19 development depending on the flavour and nicotine content.

In summary, it seems that either smoking or nicotine vaping may adversely impact on COVID-19 outcome. However, additional follow up studies are required in COVID-19 pandemic to clarify the effect of e-cigarette use on lung and cardiovascular complications derived from SARS-CoV-2 infection.

Conclusions

The harmful effects of CS and their deleterious consequences are both well recognised and widely investigated. However, and based on the studies carried out so far, it seems that e-cigarette consumption is less toxic than tobacco smoking. This does not necessarily mean, however, that e-cigarettes are free from hazardous effects. Indeed, studies investigating their long-term effects on human health are urgently required. In this regard, the main additional studies needed in this field are summarized in Table 3 .

The composition of e-liquids requires stricter regulation, as they can be easily bought online and many incidences of mislabelling have been detected, which can seriously affect consumers’ health. Beyond their unknown long-term effects on human health, the extended list of appealing flavours available seems to attract new “never-smokers”, which is especially worrying among young users. Additionally, there is still a lack of evidence of e-cigarette consumption as a smoking cessation method. Indeed, e-cigarettes containing nicotine may relieve the craving for smoking, but not the conventional cigarette smoking habit.

Interestingly, there is a strong difference of opinion on e-cigarettes between countries. Whereas countries such as Brazil, Uruguay and India have banned the sale of e-cigarettes , others such as the United Kingdom support this device to quit smoking. The increasing number of adolescent users and reported deaths in the United States prompted the government to ban the sale of flavoured e-cigarettes in 2020. The difference in opinion worldwide may be due to different restrictions imposed. For example, while no more than 20 ng/mL of nicotine is allowed in the EU, e-liquids with 59 mg/dL are currently available in the United States. Nevertheless, despite the national restrictions, users can easily access foreign or even counterfeit products online.

In regard to COVID-19 pandemic, the actual literature suggests that nicotine vaping may display adverse outcomes. Therefore, follow up studies are necessary to clarify the impact of e-cigarette consumption on human health in SARS-CoV-2 infection.

In conclusion, e-cigarettes could be a good alternative to conventional tobacco cigarettes, with less side effects; however, a stricter sale control, a proper regulation of the industry including flavour restriction, as well as further toxicological studies, including their chronic effects, are warranted.

Availability of data and materials

Not applicable.

Abbreviations

Angiotensin-converting enzyme 2

Angiotensin 1-7

Bronchoalveolar lavage fluid

Cinnamaldehyde

US Centers for Disease Control and Prevention

Carbon monoxide

Chronic obstructive pulmonary disease

Coronavirus disease 2019

Cigarette smoke

Electronic nicotine dispensing systems

e-cigarette or vaping product use-associated lung injury

Food and Drug Administration

Gas chromatography with a flame ionisation detector

Gas chromatography-mass spectrometry

Granulocyte–macrophage colony-stimulating factor

High performance liquid chromatography

Human umbilical vein endothelial cells

Interleukin

Interferon γ

Liquid chromatography-mass spectrometry

Monocyte chemoattractant protein-1

Matrix metallopeptidase 9

α7 Nicotinic acetylcholine receptor

Nickel‐chromium alloy

Nitric oxide

Propylene glycol

Regulated on activation, normal T cell expressed and secreted

Reactive oxygen species

Severe acute respiratory syndrome coronavirus 2

Stainless‐steel atomizer

Tetrahydrocannabinol

Tumour necrosis factor-α

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Acknowledgements

The authors gratefully acknowledge Dr. Cruz González, Pulmonologist at University Clinic Hospital of Valencia (Valencia, Spain) for her thoughtful suggestions and support.

This work was supported by the Spanish Ministry of Science and Innovation [Grant Number SAF2017-89714-R]; Carlos III Health Institute [Grant Numbers PIE15/00013, PI18/00209]; Generalitat Valenciana [Grant Number PROMETEO/2019/032, Gent T CDEI-04/20-A and AICO/2019/250], and the European Regional Development Fund.

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Marques, P., Piqueras, L. & Sanz, MJ. An updated overview of e-cigarette impact on human health. Respir Res 22 , 151 (2021). https://doi.org/10.1186/s12931-021-01737-5

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• Volume 32, Issue 2
• Exploring the gateway hypothesis of e-cigarettes and tobacco: a prospective replication study among adolescents in the Netherlands and Flanders
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• http://orcid.org/0000-0003-0055-5631 Thomas Martinelli 1 , 2 ,
• Math J J M Candel 3 ,
• Hein de Vries 4 ,
• Reinskje Talhout 5 ,
• Vera Knapen 4 ,
• Constant P van Schayck 6 ,
• Gera E Nagelhout 1 , 4
• 1 IVO , The Hague , The Netherlands
• 2 Tranzo, Tilburg School of Social and Behavioral Sciences , Tilburg University , Tilburg , The Netherlands
• 3 Department of Methodology and Statistics , Maastricht University , Maastricht , The Netherlands
• 4 Department of Health Promotion , Maastricht University , Maastricht , The Netherlands
• 5 Laboratory for Health Protection Research , National Institute for Public Health and the Environment (RIVM) , Bilthoven , The Netherlands
• 6 Department of Family Medicine , Maastricht University , Maastricht , The Netherlands
• Correspondence to Thomas Martinelli, IVO, The Hague, The Netherlands; martinelli{at}ivo.nl

Background Studies demonstrated that adolescent e-cigarette use is associated with subsequent tobacco smoking, commonly referred to as the gateway effect . However, most studies only investigated gateways from e-cigarettes to tobacco smoking. This study replicates a cornerstone study revealing a positive association between both adolescent e-cigarette use and subsequent tobacco use; and tobacco and subsequent e-cigarette use in the Netherlands and Flanders.

Design The longitudinal design included baseline (n=2839) and 6-month (n=1276) and 12-month (n=1025) follow-up surveys among a school-based cohort (mean age: 13.62). Ten high schools were recruited as a convenience sample. The analyses involved (1) associations of baseline e-cigarette use and subsequent tobacco smoking among never smokers; (2) associations of e-cigarette use frequency at baseline and tobacco smoking frequency at follow-up; and (3) the association of baseline tobacco smoking and subsequent e-cigarette use among non-users of e-cigarettes.

Findings Consistent with prior findings, baseline e-cigarette use was associated with higher odds of tobacco smoking at 6-month (OR=1.89; 95% CI 1.05 to 3.37) and 12-month (OR=5.63; 95% CI 3.04 to 10.42) follow-ups. More frequent use of e-cigarettes at baseline was associated with more frequent smoking at follow-ups. Baseline tobacco smoking was associated with subsequent e-cigarette use (OR=3.10; 95% CI 1.58 to 6.06 at both follow-ups).

Conclusion Our study replicated the positive relation between e-cigarette use and tobacco smoking in both directions for adolescents. This may mean that the gateway works in two directions, that e-cigarette and tobacco use share common risk factors, or that both mechanisms apply.

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Data availability statement

Data are available upon reasonable request. Anonymised data and protocols are available upon reasonable request from the corresponding author ([email protected]).

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/tobaccocontrol-2021-056528

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Introduction

Debates about electronic cigarettes are dividing many of those concerned with tobacco control. Proponents of e-cigarettes point at evidence which suggests that e-cigarettes are an effective smoking cessation aid 1 and that e-cigarette use is substantially less harmful than tobacco smoking. 2 Therefore, e-cigarettes could dramatically reduce disease and death caused by smoking. 3 4 However, opponents are concerned that e-cigarettes attract new generations of youth into nicotine addiction, 5 that most e-cigarette users simultaneously use tobacco 6 7 and that it acts as a gateway to smoking tobacco. 8 9

The gateway or stepping stones theory originates from the 1970s as a mix of academic and popular explanations of the observed sequence from cannabis use to other illicit drug use. 10–12 While initially descriptive, the theory was also used to explain causal relationships of substance use. 13 However, the theory was and remains controversial as it fails to exclude alternative explanations, particularly the notion that all substance use is associated with shared characteristics of individuals, especially a propensity to use drugs. 14 More recently, the gateway theory is applied to e-cigarettes in both policy and research. 15 The European Tobacco Products Directive, for example, states that ‘Electronic cigarettes can develop into a gateway to nicotine addiction and ultimately traditional tobacco consumption’. 16

Various cohort studies show that in the last decade e-cigarette use among adolescents increased while tobacco use decreased. 17–20 Simultaneously, longitudinal studies show that e-cigarette use is associated with initiation of tobacco smoking. The first study that revealed such association among adolescents was published in 2015. 21 22 These findings generated ample media attention and led to restrictions for e-cigarettes in the USA, prohibiting the sale to persons under 18 years. 23 Since then, cohort studies 3 17 20 24–28 have followed finding similar associations, including in the Netherlands. 29 Despite the above research into the relation between e-cigarette use and tobacco smoking, only few studies examined the reverse relation between tobacco smoking and initiation of e-cigarette use, 21 30 31 which has also been found for tobacco and alcohol use, for example. 32

In this paper, we present a replication study with new data using the same protocol as the cornerstone study by Leventhal et al . 21 22 Replication is crucial to the scientific method as it enables one to build on demonstrated and confirmed findings. 33 We collected data among adolescents in the Netherlands and Flanders, the Dutch-speaking region of Belgium. Both regions have similar legislation in which e-cigarettes (with and without nicotine) are treated as tobacco products, meaning that sales to minors (under 18 years) and advertising are prohibited. 34 35 This is different in the original study setting in the USA, where few federal restrictions on e-cigarette marketing existed at the time of the study and some leading e-cigarette brands have been investigated for particularly targeting youth. 36–39 In the Netherlands, in 2019, 25% of youth between 12 and 16 years old had ever tried an e-cigarette and 17% had ever tried smoking tobacco 19 (see figure 1 ). Currently, more youth experiment with e-cigarettes than with tobacco. With this paper, we aim to answer the following research questions:

Is e-cigarette use associated with subsequent combustible tobacco use?

Is e-cigarette use frequency associated with subsequent tobacco smoking frequency?

Is combustible tobacco use associated with subsequent e-cigarette use?

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Ever use of tobacco and e-cigarettes in Dutch youth. Source: Health Behaviour in School-aged Children (HBSC 2001, 2005, 2009, 2013, 2017) and Peilstationsonderzoek (Peil 2003, 2007, 2011, 2015, 2019).

Participants and procedure

Data were collected with online surveys between September 2018 and December 2019 throughout the Netherlands and Flanders. Since high schools are very hard to recruit for research, due to the large number of study requests and ongoing studies, we used multiple recruitment strategies. The research was presented as a study on smoking, alcohol, drugs and other risk behaviours. Different national and regional organisations (including addiction services, school health promotion and youth organisations) were approached to help contact schools. A representative selection of 82 schools were approached by telephone, of which 50 received recruitment packages. At 14 schools we reached the right contacts by phone. Another 10 schools were approached through informal networks of the research group. Lastly, a mass recruitment e-mail with reminder was sent to 580 schools in the Netherlands and 1343 in Flanders (Belgium). In total, 10 schools responded with interest in our study, and of those, all agreed to participate, including eight schools in the Netherlands and two in Flanders. The schools were provided with informed consent forms about the study, which they disseminated among parents and students. Students enrolled through passive consent and were excluded if they or their parents refused participation. Data collection involved three waves that took place 6 months apart. We provided participating schools with links to online surveys at each wave and the schools administered the surveys in the classes. Since recruitment of schools was difficult, some schools enrolled later. This meant that some schools only participated 6 months in the study and did not complete all three surveys. This partly explains the attrition rates across follow-ups (see online supplemental figure 1 ). Other reasons for attrition were that some students left the school (either to change schools or because they graduated), some schools had shop classes (such as wood or metal shop class, which meant they were at other locations), illness at the day of the survey and, lastly, one school could not be reached to conduct a follow-up despite many reminders.

Supplemental material

All measures are the same as in the replicated study 21 and possess adequate psychometric properties in youth samples. 40–44

E-cigarette and combustible tobacco use

Youth Risk Behavior Surveillance (YRBS) 40 and Monitoring the Future Surveys (MTF) 41 based items measured lifetime (ever) and past 6-month use (yes/no) of e-cigarettes and combustible tobacco products, which included: combustible cigarettes (including ‘even a few puffs’), full-size cigars, little cigars/cigarillos and hookah waterpipes. Lifetime (ever vs never) e-cigarette use at baseline was the primary exposure variable. Outcome variables were any use in the last 6 months (yes/no) of: (1) any combustible tobacco product; (2) combustible cigarettes; (3) cigars (full-size cigars or little cigars); (4) hookah; and (5) the number of different combustible tobacco products (range: 0–3). The terms ‘ever-smokers’ and ‘never-smokers’ refer to participants who either have ever or never used any of the three combustible tobacco products, respectively. The four-level continuous e-cigarette use frequency variable was categorised as never, prior (ever use with no past 30-day use), infrequent (1–2 days during the past 30 days) or frequent (≥3 days during the past 30 days). The cigarette use frequency variable consisted of non-smokers (smoked 0 day in the past 30 days); infrequent smokers (smoked 1–2 days in the past 30 days); and frequent smokers (smoked ≥3 days in the past 30 days).

Variables that are potentially associated with risk of combustible tobacco use initiation based on previous literature 45–50 were selected a priori as covariates, as they potentially overlap with both e-cigarette use and tobacco use. Covariate categories are described below.

Sociodemographics

Age, gender, ethnicity and highest parental education represented sociodemographics ( table 1 contains response categories). In the main analysis, Belgian participants from Belgian (Flanders) schools were coded as Dutch students, as they belonged to the ethnic majority (native) group of their school.

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Sample characteristics by baseline ever e-cigarette use status among baseline never smokers

Environmental variables

Environment indicators included family living situation, assessed by asking: ‘Who do you live with most of the time?’ (both biological parents/other). 46 Family history of smoking was assessed by asking: ‘Does anyone in your immediate family (brothers, sisters, parents, or grandparents) have a history of smoking cigarettes?’ (yes/no). Peer smoking was measured by asking: ‘In the last 30 days, how many of your five closest friends have smoked cigarettes?’ (range: 0–5). 51

Intrapersonal factors

Personality traits, mental health and psychological processes that are linked to risky behaviour, experimentation and smoking were assessed. Depressive symptoms were assessed through the 20-item Center for Epidemiologic Studies Depression Scale 43 composite sum of past week frequency ratings (eg, 0=Rarely or none of the time (0–1 day) to 3=Most or all of the time (5–7 days)). Impulsivity was assessed with Temperament and Character Inventory 52 Impulsivity subscale sum score (eg, ‘I often do things based on how I feel at the moment’; range: 0–5). Lifetime use of other (non-nicotine or tobacco) substances was assessed with the MTF and YRBS items on ever use (yes/no) of alcohol and 13 other substances. Delinquent behaviour 53 was assessed by calculating a mean of frequency ratings for engaging in 11 different behaviours (eg, stealing, lying to parents; 1=Never to 6=Ten or more times) in the past 6 months. Susceptibility to smoking was measured by using a mean of a three-item index 44 (eg, ‘Would you try smoking a cigarette if one of your best friends offered it to you?’ (1=Definitely not, 4=Definitely yes)). Smoking outcome expectancies were assessed by a mean of the two responses for ‘I think I might enjoy (…) smoking’ and (reversed) ‘I think I might feel bad (…) from smoking’ (1=Strongly disagree, 4=Strongly agree). 54

Data analysis

Following the protocol of Leventhal et al , 21 prevalence and associations of lifetime (ever vs never) e-cigarette use and lifetime (ever vs never) combustible tobacco use in the overall baseline sample were first analysed. Subsequently, study attrition and descriptive statistics in the sample of baseline never smokers were reported. For the primary analyses, separate generalised linear mixed models 55 for each outcome were used with the follow-up data at two time points (6 and 12 months) as repeated measurements. For research question 1, each binary outcome (eg, any lifetime combustible tobacco product, cigarettes, cigars, hookah) was analysed with logistic mixed regression, whereas the ‘number of combustible products’ outcome was analysed with ordinal mixed regression. All models included baseline e-cigarette ever use, school and time (6-month vs 12-month follow-up) as fixed effects and were fit with and without adjustment for all covariates. To examine whether the association between baseline e-cigarette use and combustible tobacco use differed across follow-ups, the baseline ‘e-cigarette × time’ interaction term was added to each model. If this interaction was significant, the association between e-cigarette use and the outcome was examined separately for the 6 and 12-month follow-ups, including differences in outcome for ever and never use of e-cigarettes at baseline. If not significant, the interaction term was removed from the model, and associations between baseline e-cigarette use and the outcome were averaged across both follow-ups. Participants with missing data on baseline e-cigarette use or baseline measurements of the outcome variable were not included. For research question 2, an ordinal logistic mixed regression model was used to determine the association between baseline frequency of e-cigarette use and follow-up frequency of smoking, fit with and without adjustment for covariates, but always adjusting for time, school and baseline smoking frequency. 22 Finally, for research question 3, on the association between baseline combustible tobacco ever use and ever use of e-cigarettes at follow-ups among never users of e-cigarettes at baseline, a similar analysis protocol as research question 1 was used: a binary logistic mixed regression model predicting e-cigarette use from baseline ever tobacco use status (with and without adjustment for covariates).

To account for missing data, multiple imputation was done with an imputation model that was identical to the analysis model including the covariates. The fully conditional specification method was used, 56 which has been shown to yield unbiased parameter estimates and SEs. 57 58 To obtain unbiased estimates of the relations between the predictor variables and the outcomes, missing data on baseline covariates and on the outcome variables were imputed. 59 An analysis done on imputed data sets introduces power loss and also some uncertainty concerning the p value. By taking the number of imputations at least as large as the proportion of incomplete cases, 80 imputations in the present analysis, this is estimated to yield an acceptable power loss 59 and a Monte Carlo SE of the p value 0.05 less than 0.01. 55 Although analysis with multiply imputed data is valid under the assumption of missingness at random, 56 this assumption cannot be tested. Therefore, for research question 1, a sensitivity analysis was performed using imputations according to a scenario violating this assumption. We chose to impute according to a pessimistic scenario, in that it decreased the association between baseline e-cigarette use and an outcome, by imputing the smoking outcome at follow-up in the same way for both baseline ever and never e-cigarette users. At each follow-up, the highest school-specific incidences of each outcome were taken as smoking probabilities for the imputation. Significance was set to 0.05 and all tests were two tailed. All primary analyses were conducted in Mplus V.7.2.

Role of funding source

The funder had no role in the study design, the collection or analysis of the data, the interpretation of data, the writing of the report or the decision to submit the article for publication.

Study sample

All students in the recruited schools were eligible to participate and enrolled (n=2845). Data were collected for 2839 participants at baseline, including 2185 participants who never smoked tobacco at baseline, of which 1276 (58%) and 1025 (47%) participants completed the 6 and 12-month follow-ups, respectively. The analytical sample available for analyses across waves for research question 1 is depicted in online supplemental figure 1 .

Descriptive analyses

Table 1 shows that boys were more likely than girls to have ever used e-cigarettes. Furthermore, age, highest parental education and all environmental and intrapersonal factors showed significant associations with e-cigarette use at baseline.

Table 2 shows that in the combined sample of ever and never smokers, baseline e-cigarette use was positively associated with baseline use (ever) of combustible tobacco products. Prevalence of combustible tobacco products was between 5.6% (cigars) and 17.8% (cigarettes). 21.6% (n=603) of participants reported ever use of e-cigarettes; 14.7% (n=412) ever use of e-cigarettes as well as combustible tobacco; 7.1% (n=197) combustible tobacco only; and 71.4% (n=1994) never used e-cigarettes nor combustible tobacco.

Prevalence and cross-sectional association of baseline e-cigarette use and combustible tobacco use in combined sample of baseline ever smokers and never smokers

Associations between baseline e-cigarette ever use and combustible tobacco ever use at follow-ups in baseline never smokers

Table 3 shows that, among never smokers, baseline ever e-cigarette users were more likely than never users to have used combustible tobacco at 6-month (23.2% vs 5.5%; % difference=17.7; 95% CI 9.8 to 25.6) and 12-month (44.4% vs 10.8%; % difference=33.6; 95% CI 23.1 to 44.1) follow-ups. Table 4 displays the results after imputation. Interaction of e-cigarette use (ever) with time was significant (OR=3.03; 95% CI 1.39 to 6.63) in the unadjusted analysis. Separate analyses at both follow-ups indicated a stronger association for baseline e-cigarette use and combustible tobacco use at the 12-month follow-up (OR=11.39; 95% CI 6.04 to 21.50), compared with the 6-month follow-up (OR=3.76; 95% CI 2.12 to 6.65). Analysis for time (of follow-up) was significant for both e-cigarette ever and never users, indicating an increase in the use of combustible tobacco across time in both groups. In the adjusted model, the interaction between baseline e-cigarette use and time was also significant for any combustible tobacco product use (OR=2.99; 95% CI 1.37 to 6.50). Furthermore, baseline e-cigarette use was associated more with any combustible tobacco use at 12-month follow-up (OR=5.63; 95% CI 3.04 to 10.42), compared with the 6-month follow-up (OR=1.89; 95% CI 1.05 to 3.37).

Prevalence of past 6-month combustible tobacco use at 6 and 12-month follow-ups by baseline e-cigarette ever use among baseline never smokers

Association of baseline e-cigarette ever use and covariates to combustible tobacco use outcomes at 6 and 12-month follow-ups among baseline never smokers

Table 3 also shows that, among never smokers, e-cigarette use (ever vs never) at baseline was positively associated with higher odds of smoking cigarettes, cigars and hookah. In the unadjusted analyses in table 4 , e-cigarette use (ever vs never) at baseline was also positively associated with each of these outcomes at both follow-ups. In the adjusted model, baseline ever e-cigarette use was associated with cigarette use at the 12-month follow-up and with number of tobacco products at both follow-ups. Interaction with time was not significant for hookah use and cigar use, thus associations of baseline ever use of e-cigarettes with these outcomes averaged across time were examined. The relation between e-cigarette use at baseline and hookah use (averaged over 6 and 12-month follow-ups) was significant (OR=3.69; 95% CI 1.75 to 7.77). Of the intrapersonal factors, only smoking susceptibility was positively associated with combustible tobacco use at both follow-ups (see table 4 ).

Additional sensitivity analyses were performed (see online supplemental table 3 ). Findings were consistent in the adjusted models for any tobacco use, combustible cigarette use, cigar use and number of tobacco products. For hookah use, there was a significant interaction of e-cigarette use and time. Only the association between e-cigarette use and hookah use at 6-month follow-up remained significant.

Association between baseline e-cigarette use frequency and tobacco use frequency at follow-ups

Online supplemental table 1 shows that, for the unadjusted model, higher scores on the four-level baseline e-cigarette use frequency variable were associated with greater odds of higher smoking frequency averaged across both follow-ups (OR=2.11; 95% CI 1.69 to 2.64). After adjusting for covariates, this association remained significant (OR=1.63; 95% CI 1.29 to 2.06). In the unadjusted analysis, the positive association between baseline e-cigarette use and follow-up smoking frequency differed between the different baseline smoking groups (OR=0.70; 95% CI 0.52 to 0.94, p=0.02), the association being stronger among baseline non-smokers (n=2180; OR=2.53; 95% CI 1.99 to 3.23) than baseline infrequent (smoked 1–2 in the past 30 days; n=41; OR=1.84; 95% CI 1.46 to 2.31) and frequent (smoked ≥3 in the past 30 days; n=127; OR=1.33; 95% CI 0.89 to 1.99) smokers (not shown in online supplemental table 1 ).

Association between baseline ever smoking and follow-up ever e-cigarette use at follow-ups in baseline never e-cigarette users

Online supplemental table 2 shows that among never users of e-cigarettes, smoking at baseline was positively associated with higher odds of using e-cigarettes averaged across both follow-ups for the unadjusted (OR=5.22; 95% CI 3.06 to 8.92) and adjusted (OR=3.10; 95% CI 1.58 to 6.06) analyses.

The current replication study confirms that e-cigarette use by non-smoking adolescents is associated with increased odds of subsequent combustible tobacco smoking initiation; that more frequent e-cigarette use is associated with more frequent subsequent tobacco smoking; and that the ‘reverse’ association applies, namely that tobacco smoking among never users of e-cigarettes is associated with greater odds of later e-cigarette use. These findings are consistent with previous studies, 21 22 30 31 employ various adjusted analyses and sensitivity analyses and extend the findings from the US to a European context.

Collectively, this suggests that e-cigarettes may indeed act as a gateway to tobacco smoking for youth, as we see that youth who used e-cigarettes are more likely to try smoking tobacco later. We also found a dose-related relation, namely that the higher the frequency of e-cigarette use was at baseline, the higher the frequency of subsequent tobacco use was at follow-up. This may indicate a causal relation for using both products. Additionally, our analyses suggest a ‘reversed’ gateway of tobacco smoking to e-cigarette use. Yet, these gateways may operate through different mechanisms. E-cigarettes may be a smoother introduction to tobacco smoking 60 ; and reversely, the transition from tobacco smoking to e-cigarette use could point at transitioning to a less harmful behaviour. However, both behaviours may also stem from common risk factors, such as increased propensity to experiment with substances, 14 61–63 where final choices on which specific substance is used first may depend on personal preferences, circumstances or cultural norms. 32 Dual users of e-cigarettes and cigarettes, for example, share similar characteristics (eg, impulsivity) regardless of which product is used first. 64

It remains uncertain whether the found associations are causal, which the gateway hypothesis suggests, or an indication of shared risk factors for e-cigarette and tobacco use. While multiple predictors linked to tobacco smoking were addressed in our study, it remains uncertain whether all common liability risks were controlled for. Studies show that at least some youth with low propensity for tobacco smoking, nevertheless, used e-cigarettes. 24 65 66 Another study found that relative to a propensity-matched control group without initial e-cigarette use, non-smoking adolescent e-cigarette users were less likely to become established smokers (30-day use and 100+ lifetime cigarettes). 67 This suggests that e-cigarettes do not function as a gateway to tobacco for everyone. Additionally, a study found that the relationship between e-cigarette use and subsequent smoking among adolescents may be weakened through interventions. 68 Likely, multiple mechanisms are complementary and the relation between causes and outcomes is complex and multidirectional. 69

A critique on quantitative studies on the gateway from e-cigarette to tobacco smoking is that the key question of ‘why’ is not addressed. 63 A popular explanation is that the nicotine in e-cigarettes makes individuals dependent and this may cause them to try combustible cigarettes. 70 71 The authors of the original gateway theory even describe nicotine as a gateway drug that primes the brain for other substance use, ‘whether the exposure is from smoking tobacco, passive tobacco smoke, or e-cigarettes.’ 72 A qualitative study of youth who use(d) e-cigarettes found that e-cigarettes can be a ‘smoother’ introduction to the concept of smoking and that they appear to remove boundaries to smoking: ‘There used to be a barrier that said either you’re a smoker or a non-smoker, now I can smoke without smoking’. 60 Consequently, e-cigarette users get used to the acts and gestures of smoking which facilitates the transition to tobacco smoking. However, information in this area is still limited and more investigation is needed, including studies among tobacco smokers who started with e-cigarettes to assess reasons for taking up smoking.

Evidence and understanding of associations between e-cigarettes and tobacco and the long-term consequences of e-cigarette use among youth are still limited, 28 partly because of the relative novelty of e-cigarettes and still evolving technologies. Until this has been resolved, policymakers should carefully consider whether to act on the dangers (eg, a gateway to tobacco) or rather the benefits (eg, a potentially less harmful alternative to tobacco smoking) of e-cigarettes for smokers. Given the wide availability and marketing of e-cigarettes 73 74 and that available evidence provides reasons for caution, prevention of e-cigarette use among non-smoking youth is recommended.

Limitations

The original and current study used binary and categorical outcome measures of smoking. These measures are limited, as smoking intensity (how many cigarettes per day) was not assessed and a fairly low cut-off point for frequent smoking (≥3 days during the past 30 days) was used because of the limited number of participants. Also, the majority (68.3%) of ever users of e-cigarettes had already smoked tobacco at baseline, which may mean that the current study sample is already too old for the main research question focusing on never users of tobacco. Furthermore, substance use was included as a dichotomous variable, meaning, for example, that students who drank alcohol and used cannabis on a weekly basis got the same score as students who had one drink in their lifetime. Additionally, the response of participating (vs invited) schools was low, which may have resulted in selection bias. However, relevant differences between students from participating schools and students at non-participating schools are not expected, as the response rate of students who were present at the time of the survey in participating school classes was 100%. Furthermore, the dropout rate from baseline to follow-up was high and may have affected the relations between e-cigarette use and tobacco use. However, the imputation of missing outcomes for research question 1 according to a pessimistic scenario (see the Data analysis section) yielded the same relations, indicating robustness of the results. Lastly, we coded Flemish (Belgian) students as Dutch in the main analyses because they are part of the (native) ethnic majority of their schools. Thus, differences between Dutch and Belgian students were not assessed, even though (cultural) differences may exist. However, preliminary analyses, in which Flemish students were coded as ‘not Dutch’, yielded similar results in the main analyses.

For this study, we replicated an American cornerstone study 21 22 on the association between e-cigarette use and tobacco smoking in the Netherlands and Flanders and found similar results. High school students in the Netherlands and Flanders who used e-cigarettes at baseline were more likely to report initiation of combustible tobacco use over the next year compared with never users of e-cigarettes. These findings add to a growing body of studies that indicate a link between e-cigarette use and tobacco smoking in youth. The gateway hypothesis was further explored by also analysing the ‘reverse’ relation between baseline tobacco smoking and subsequent e-cigarette use. A similar association was found which may indicate that the gateway works in two directions, that e-cigarette use and tobacco smoking share common risk factors, or that both mechanisms apply. Different types of studies are needed to better understand why there are such associations and whether these may be causal relationships.

What is already known on this subject

E-cigarette use is associated with subsequent tobacco smoking among youth.

A popular explanation of this relation is that e-cigarettes function as a gateway to tobacco smoking for youth.

What important gaps in knowledge exist on this topic

Most studies only investigate the relation between e-cigarette use and tobacco smoking in one direction. Less is known about the opposite relation between tobacco smoking and subsequent e-cigarette use.

It is not yet determined whether the gateway hypothesis or the common liability hypothesis best explains the relation between e-cigarette use and tobacco smoking.

What this paper adds

This study replicated the findings from an American cornerstone study on the relationship between e-cigarette use and tobacco use. Findings indicate both the relationship between e-cigarette use and subsequent tobacco smoking and the opposite relation.

The association between e-cigarette use and tobacco smoking is likely bidirectional for adolescents.

Ethics statements

Patient consent for publication.

Not required.

Ethics approval

The Maastricht University Ethical Review Board approved the study (2018-0418).

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors TM: writing–original draft, validation, formal analysis, data curation. MJJMC: formal analysis, validation, writing–original draft. HdV, RT, CPvS: conceptualisation, methodology, writing–review and editing. VK: investigation, project administration, resources. GEN: conceptualisation, methodology, funding acquisition, writing–review and editing, supervision.

Funding This study was funded by NWO (401.16.012).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Published: 08 May 2024

Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions

• Akihiro Kishimoto 1 ,
• Dan Wu 2 &
• Donal F. O’Shea 2  

Scientific Reports volume  14 , Article number:  9591 ( 2024 ) Cite this article

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• Public health

Vaping involves the heating of chemical solutions (e-liquids) to high temperatures prior to lung inhalation. A risk exists that these chemicals undergo thermal decomposition to new chemical entities, the composition and health implications of which are largely unknown. To address this concern, a graph-convolutional neural network (NN) model was used to predict pyrolysis reactivity of 180 e-liquid chemical flavours. The output of this supervised machine learning approach was a dataset of probability ranked pyrolysis transformations and their associated 7307 products. To refine this dataset, the molecular weight of each NN predicted product was automatically correlated with experimental mass spectrometry (MS) fragmentation data for each flavour chemical. This blending of deep learning methods with experimental MS data identified 1169 molecular weight matches that prioritized these compounds for further analysis. The average number of discrete matches per flavour between NN predictions and MS fragmentation was 6.4 with 92.8% of flavours having at least one match. Globally harmonized system classifications for NN/MS matches were extracted from PubChem, revealing that 127 acute toxic, 153 health hazard and 225 irritant classifications were predicted. This approach may reveal the longer-term health risks of vaping in advance of clinical diseases emerging in the general population.

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Introduction.

The delivery of nicotine to the lungs through the inhalation of tobacco smoke has been practiced by mankind for centuries with devastating impacts on public health 1 . Relatively recently, vaping of e-liquids has emerged as a modern variant of this ancient practice. In their original construction, the constituents of e-liquids contained only four chemical entities, nicotine, propane-1,2-diol, propane-1,2,3-triol and water, with the goal of providing a less hazardous means of nicotine delivery than tobacco leaf 2 . Their use as an aid for tobacco smoking cessation has evolved as a cornerstone of some national public health policies, though others have restricted or prohibited their use 3 .

Soon after their first commercialization in the mid 2000’s, the number of chemical entities used in vaping e-liquids dramatically increased as an array of flavours were added. Currently, at least 180 discrete chemicals are known to be in use in e-liquids, blended in various amounts to produce a specific flavour branded product 4 . A European based study identified a mean and range of 6 (±) 4 chemical flavours used per specific e-liquid product, whereas a comparable US study found a range of 22 to 47 chemical flavours per e-liquid 5 , 6 . In both studies it was found that the total flavour chemical concentration in the majority of e-liquids exceeded that of nicotine. Furthermore, several studies have shown that flavoured e-liquids are linked to a lowering of the vaping age demographic 7 . Their appeal to non-smoking teenagers and young adults has led to a divergence of opinion on their use as an aid for tobacco smoking cessation in the established smoking population 8 . Concerns are growing that vaping in younger generations jeopardizes the decline in nicotine use and also risks the emergence of future vaping induced diseases 9 . Yet, while the strongly polarized debates about the pros and cons of vaping are ongoing, the implications for long-term effects on public health, morbidity and mortality are simply unknown 10 , 11 , 12 , 13 . While the health risks from exposure to the carcinogenic chemicals in tobacco smoke are known, it can take decades of accumulative damage before clinical manifestation of disease occurs.

Intuitively for vaping, it would be reasonable to anticipate that lung exposure to a large number of chemical entities can only increase health risks. In 2019, the potential for vaping health risks became apparent when cases of acute lung injury emerged attributable to tetrahydrocannabinol vaping products. E-cigarette or vaping use-associated lung injury (EVALI) statistics from the CDC document 2807 hospitalizations and 68 deaths over one year in the United States. A single chemical additive, vitamin E acetate (VEA), has been strongly linked to the outbreak that ended once its use stopped 14 . Our previous research showed that the action of pyrolysis heating within a vaping device could transform VEA into more than ten different substances including the highly toxic gas ketene which could account for the severe lung injuries 15 . While the chemicals used for nicotine vaping are different from tetrahydrocannabinol products 16 , 17 , the number of chemicals is considerably higher. Prolonged exposure to these chemicals and their pyrolysis products makes it plausible that we are standing at the starting line of a new wave of chronic diseases that will only emerge in 15 to 20 years from now.

The chemicals used as e-liquid flavours are not specifically developed for vaping and are adopted from the food industry 18 . Much like VEA, which is also widely used in foodstuffs and cosmetics, these compounds have a good safety record for these specific uses. However, it was not envisaged that they would be used in a significantly different manner that involves heating to high temperatures with inhalation into the lungs. Remarkably, there are a myriad of different vaping devices whose operating temperature ranges are often unknowingly determined by user preferences. Studies have measured typical temperatures ranging from 100 to 400 °C depending upon factors such as power, heating coil materials, puff size and e-liquid quantity, with dry coil temperature measured above 1000 °C 19 , 20 . Pyrolysis decomposition of flavours at these temperatures could produce large numbers of unknown secondary chemical entities, thereby hugely amplifying the health risks from each flavour. In contrast to tobacco smoking, combustion products are minor in vaping so were not included in this initial study.

As hundreds of chemicals are used in tens of thousands of commercial e-liquid products, the experimental analysis of all their vaping induced chemistries and associated products could take decades of research. In this study, a holistic research strategy employing artificial intelligence (AI) was adopted to simultaneously investigate all flavours in e-liquids. AI is increasingly being used to perform chemistry tasks such as retrosynthetic route planning, the prediction of reaction outcomes and the acceleration of drug discovery 21 , 22 , 23 . Currently, a unique opportunity exists to exploit AI to anticipate vaping risks in advance of their public health impact, which may take years to emerge.

Overview of 180 e-liquid flavour chemicals

While the exact number of flavour chemicals in current worldwide e-liquid use is unclear, 180 representative chemicals known to be used as flavourings in e-liquids were chosen for this study based on literature reports 4 , 5 , 6 , 18 . Structural inspection of the chemical functional groups within the 180 flavours revealed 66 esters, 46 ketones/aldehydes, 27 alcohols/acetals, 26 aromatics/heterocycles/carbocycles and 15 carboxylic acids/amides, clearly indicating the potential for a wide range of pyrolysis reactions (Supplementary Table S1 ). Flavour structural diversity was analyzed by comparing their molecular weight, hydrogen bond donors/acceptors, topological polar surface area, number of rotatable bonds, and octanol–water partition coefficient properties 24 . A 3D visualization of the chemical space reflecting these six properties shows that compounds are clustered in a similar area, indicating moderate diversity with 85.5% of the variance accounted for by molecular weight, surface area and number of rotatable bonds (Fig.  1 A, red circles, Supplementary Dataset S1 ). Their mean molecular weight was 146.2 signifying a relative volatile set of molecules (Fig.  1 B, red distribution profile).

Chemistry diversity analysis of 180 flavour chemicals and their predicted pyrolysis products. ( A ) 3D representation of the chemical space occupied by 180 e-liquid compounds (red circles) and their discrete 4524 NN predicted pyrolysis products (grey circles). Principal component (PC) scale refers to normalized projections of the six molecular properties. ( B ) Molecular weight distribution of 180 e-liquid compounds (red distribution profile) and their discrete 4524 NN predicted pyrolysis products (grey distribution profile).

Workflow for e-liquid flavour risk identification and classification

All 180 flavours were subjected to a common workflow that blended NN pyrolysis prediction with experimental electron-impact mass spectrometry (EI-MS) data. An overview of each stage is shown in Fig.  2 , which started with transcribing the 180 chemical structures into their simplified molecular-input line-entry system (SMILES) format. A graph-convolutional neural network model was used to predict pyrolysis chemical transformations and their associated products for each flavour. Experimental MS data containing the molecular ion, associated fragmentation masses and their relative abundances were sourced for each flavour. As both pyrolysis reactions and MS fragmentations involved energy induced bond breaking; a correlation between both was anticipated. Using specifically written script, the molecular weight of each NN predicted product from each flavour was correlated against the MS fragmentation masses for that flavour. A data subset was formed containing NN-reactions with a predicted product which matched a MS fragmentation mass. Next, the GHS classification was identified for each NN/MS matched product. A second NN was used to predict activation energies (AE) for reactions producing products with the most significant health implications. Data collation generated an enumerated list of NN predicted products, MS matched products and their associated GHS hazard classification for each of the 180 flavours (Fig.  2 ). Each step was automated and could accept new compound inputs as required.

Workflow chart for the pyrolysis risk identification of vaping e-liquid components (solid arrows). Dashed arrows indicate future scope for an informative feedback into NN pyrolysis predictor.

Graph-convolutional neural network model for pyrolysis products prediction

To date, reaction prediction methods have primarily focused on synthetic transformations in which at least two reactants generate a product and a byproduct under varying experimental conditions 25 , 26 , 27 . Pyrolysis reactions differ in that a single reactant produces an array of lower molecular weight products by different transformation pathways with heat being the driving force of the reactions (Fig.  3 ).

Synthetic and pyrolysis transformations.

It was found that the previously described Weisfeiler–Lehman neural network (W–L NN) model suited our requirements as it operates by prediction of reaction centers based on bond changes for every pair of atoms in a molecule 26 . As a graph convolutional network, it can predict unimolecular pyrolysis transformations without any training data specific to pyrolysis reactions. Supervised learning of the W–L NN was achieved using US patent literature as a source of data, with pyrolysis predictions based on a training set of 354,937 reactions 26 , 28 , 29 . For this study, only first phase pyrolysis products were considered with further pyrolysis of initial pyrolysis products not included. All reactions that included flavour molecules were removed from the training data to ensure that no characteristics of these flavour molecules were leaked before their pyrolysis predictions were performed. Prevention of such data leakage allows the performance assessment of the trained W–L NN model without bias, even if a new flavour molecule is passed to the trained model. As designed, the W–L NN architecture embedded the inherent computations in the W–L graph kernel to learn atomic representations. This starts by converting chemical SMILES (notation to describe a chemical structure that can be understood by computer software) to attributed graph representations of molecules. For example, the SMILES for flavour 2,3-pentanedione being CCC(=O)C(=O)C converts to a labelled form of atoms 1 to 7 as shown in Fig.  4 A,B. Each atom representation was computed by including contributions from adjacent atoms such as atom 3 with atom 2, 4, and 5. Specifically, each atom was initialized with a feature vector f atom indicating its key properties such as atomic number, connectivity, valence, formal charge, and aromaticity. Representation of the bond order (number of chemical bonds between a pair of atoms) and connectivity of each bond was through the feature vectors f bond (Fig.  4 C). Local feature vectors were calculated for each atom based on its representation and those of other atoms directly bonded to it. Next, global atom features were produced for each atom to account for the influence of atoms not directly bonded to it. Finally, a combination of local and global feature vectors was used to predict the likelihood of bond changes for each pair of atoms (Fig.  4 D).

W–L neural network for predicting bond changes between every pair of atoms in 2,3-pentandione. (i) Molecular SMILES converted to attributed graph. (ii) Atom descriptors generated by incorporating information from neighboring atoms. (iii) Updated new atom features after iterations, calculation of atom local and global features vector and final prediction of reactivity for each pair of atoms. (iv) Calculated scores for each likelihood bond change by W–L neural network. (v) Potential products enumerated after removal of those failing chemical valence rules. (vi): W–L difference network model for ranking pyrolysis reactions enumerated based on the most probable bond changes. (vii) Predicted pyrolysis reactions ranked from 1 to 25 and their associated products (P).

In the representative input example of 2,3-pentandione, all atom pairs were “tested” to identify high probability bond breaking positions (Fig.  4 E). Up to 16 likely bond-breaking positions were identified to enumerate their possible pyrolysis transformations and associate output products. Up to five simultaneous chemically feasible bond changes per pyrolysis reaction were allowed. Any predicted products that did not comply with chemical valence rules (correct number of bonds from each atom) were removed (Fig.  4 F). Next, a W–L difference network (W–L DN) generated a probability score for each predicted pyrolysis transformation based on the differences in atom representations between the products and the original molecule (Fig.  4 G). The W–L DN then selected and ranked the twenty-five most likely transformations based on their probability scores (Fig.  4 H). Analysis of the NN output of 4500 pyrolysis predictions for the 180 flavours showed 7307 products (Supplementary Dataset S2 ). When duplicate products from the same flavour are not included, the total number was 4524. The average number of discrete products per flavour was 25.1 with a greater number predicted for compounds of larger molecular size and complexity. The top 20 predicted pyrolysis products (excluding duplicates arising from the same flavour) included alkanes, alkene, alcohols, aldehydes, acids, and aromatics, as shown in Table 1 .

Structural diversity of the 4524 predicted products was determined using the same molecular parameters used for the 180 flavours (23). The 3D chemical space visualization showed the NN-predicted pyrolysis products clustered in a similar space as their originating flavours (Fig.  1 A, grey circles, Supplementary Dataset S1 ). The expected difference was a significant shift to lower molecular weight compounds, with a mean molecular weight of 111.7 indicating the production of highly volatile organic compounds (Fig.  1 B, grey distribution profile).

Sourcing experimental EI-MS data for each e-liquid flavour

Mass spectrometry fragmentation identifies intramolecular bond breaking positions that occur as a result of molecular interaction with the applied energy from the instrument source. As pyrolysis is a heat induced bond breaking process, a correlation between both can exist 30 . Experimental EI-MS mass data was retrieved, using Python script, from the online National Institute of Standards and Technology (NIST) database for each of the 180 e-liquid flavours 31 , 32 . Data obtained included the molecular weight of all fragmentations from the parent ion and their relative abundance. Representative flavour examples for 2,3-pentandione, linalool, 2-acetyl pyridine and α-methylbenzyl acetate in Fig.  5 , show their MS fragmentation patterns and corresponding molecular weights. Specifically for 2,3-pentandione, the series of fragmentation masses (% relative abundance) of 100 (11), 57 (32), 43 (100), 42 (12), 29 (60), 27 (25) and 15 (14) can be seen which correspond to the molecular ion and the most likely bond breaking positions of the molecule (Fig.  5 ). In this way, the MS fragmentation data for each e-liquid component can act as a minable dataset to identify molecular weight alignments with the products from the NN predicted pyrolysis reactions. A 5% relative abundance threshold for each mass peak was applied to the MS data to eliminate the possibility of instrument noise or isotope contributions. The average number of mass fragmentation peaks per e-liquid component was 16.5 with the maximum at 54 and the minimum at 2. As expected, larger molecular weight compounds typically have more fragmentation mass peaks than those of lower weight.

Representative flavour EI-MS data of ( A ) 2,3-pentandione; ( B ) linalool; ( C ) 2-acetyl pyridine; ( D ) α-methylbenzyl acetate from the NIST database. Threshold (T) set at 5% relative abundance indicated by blue dotted line. Green asterisk indicates the molecular weight matches with W–L NN predicted products. Insets show structures of NN-predicted products that are molecular weight matched with an MS fragmentation.

Amalgamation of W–L NN and EI-MS data

Next, automated amalgamation of in silico NN with experimental MS data was carried out 32 . The goal of merging these two information sources was to identify the most likely pyrolysis products for each flavour, from which their health risks could be assigned. Correlation of molecular weights of NN predicted products with their experimental MS fragmentation masses identified 1169 discrete matches between the two datasets (not counting repeat matches for a flavour) (Supplementary Dataset S2 ). The average number of NN/MS matches per flavour was 6.4 with 92.8% having at least one match and 86% having more than one match. Examples of specific NN/MS matches are shown in Fig.  5 for four structurally different flavours. Green asterisks indicate the molecular weight matches in the mass spectral data with W–L NN predicted products and the insets show structures of the matched compounds. It is noteworthy that this data amalgamation was successful for a wide variety of different molecular structures and functional groups.

Encouragingly, plotting the number of MS matches against the NN rank position for each predicted product shows a clear bias towards higher rank positions, with the highest NN-rank 1 accounting for 8.7% of all matches (Fig.  6 , rank 1). Comparison of the cumulative number of matches for the top (1–5) and bottom (21–25) rank positions show that the higher positions accounted for 29% of all matches whereas the lower ranks accounted for only 15% (Fig.  6 ). These correlations indicate that NN predicted products could be substantiated through experimental MS fragmentations and that in future work MS data could be used in a hybrid supervised and reinforcement learning model. The non-matched W–L NN predicted products were not used further in this work but may serve as an informative feedback allowing future refinement of the NN pyrolysis predictor (Fig.  2 , dashed arrow).

Comparative plot of W–L NN rank position from 1 to 25 for predicted products matched with experimental EI-MS data.

Examining the most commonly matched compounds it was encouraging to find that a broad distribution of molecular classes was matched (saturated, unsaturated and aromatic hydrocarbons, aliphatic alcohols and carboxylic acids) (Table 2 ). Seventeen of the top twenty W–L NN predicted products (Table 1 ) were also in the top 20 matched compounds, further increasing confidence in the NN/MS matched predictions. Next, the health risk of each NN/MS matched product was identified.

Acquisition of risk assessment data for W–L NN and EI-MS matched products

Using specifically written Python script, the GHS classifications for each NN/MS matched product was obtained from the open-source PubChem database 32 , 33 . The script used the SMILES string of each compound as a query keyword to identify matching URLs within the site. Within each URL, the hazard statements in the GHS classification section were downloaded in JSON format. Three different classification categories were used to build each flavour risk profile (i) acute toxic; (ii) health hazard; or (iii) irritant. In addition, a category (iv) was used to group compounds not classified as either (i), (ii) or (iii) but that may have other hazard warnings, and category (v) was compounds for which a search query did not produce a result, indicating they were not in the database (Supplementary Dataset S3 ). The GHS classifications of acute toxic (127 compounds), health hazard (153 compounds) and irritant (225 compounds) accounted for 11%, 13% and 19% of classifications attributed to the dataset respectively (Fig.  7 A). Only 49% of products were not included in the categories (i) to (iii) and 8% had no classification information available. Mining classification data specifically for inhalation health hazards revealed further insights into their health risks with a representative selection of these results for a structurally diverse set of the functional compounds shown in Fig.  7 B (Supplementary Dataset S3 ). It is noteworthy that while some similarities to compounds produced by tobacco smoke exist (e.g. formaldehyde, ethylene oxide, aromatic amines), many others differ such as α,β-unsaturated carbonyl compounds (aldehydes, ketones, esters), heterocycles and phenols. This is due to the diverse chemical makeup of the individual vaping flavours, which differ from the natural products found within tobacco leaf. This indicates that, while related, vaping biomarkers and their clinical disease manifestations could differ significantly from those of tobacco smoking 34 . Furthermore, vaping biomarkers are likely to differ based on commercial e-liquid product, as the spectrum of pyrolysis products differ for each chemical flavour 35 .

( A ) Distribution of globally harmonized system classifications of W–L NN/MS matched products (for compounds with more than one classification only the most serious classification is included). ( B ) Representative examples of the structure for W–L NN/MS predicted compounds with acute toxic GHS classification and their specific inhalation hazard warning. GHS hazard statements: H330 fatal if inhaled; H331 toxic if inhaled; H335 may cause respiratory irritation; H340 may cause genetic defects; H341 suspected of causing genetic defects; H350 may cause cancer; H373 causes damage to organs through prolonged or repeated exposure. ( C ) Distribution of Cramer classifications of W–L NN/MS matched products.

Additionally, NN/MS matched products were grouped using the three Cramer classes (Supplementary Dataset S4 ) 36 . Cramer classification is a commonly used predictive approach for classifying chemicals on the basis of their expected level of oral toxicity. Cramer Class III, which represents the most severe potential toxic hazard, accounted for 35% of these compounds with Class II (moderate risk) and Class I (low risk) accounting for 8 and 57% respectively (Fig.  7 C). It was noteworthy that many of the more commonly used flavour chemicals (e.g. cis -3-hexenol, isoamyl acetate, benzaldehyde, ethyl hexanoate, cinnamaldehyde, benzyl acetate, hexyl acetate) had one or more predicted products identified as Class III. Across all 180 flavours, the most common Cramer III classifications were for benzene, ketene and ethylene oxide predicted by 16, 10 and 8 different flavours respectively (Supplementary Dataset S4 ).

W–L NN prediction of pyrolysis activation energies

The activation energy (AE) of a chemical reaction is the minimum energy required for a reaction to proceed. With respect to vaping, AEs are an excellent means of obtaining a first approximation of the thermal conditions required for pyrolysis to occur. Yet, determination of AEs is experimentally very laborious and computationally expensive, as it requires quantum chemical calculations. As such, the use of NN methods to obtain quantitative values for flavour pyrolysis reactions would be of significant value. To address this goal, a recently reported directed message passing neural network (D-MPNN) for AE predictions has been employed 37 , 38 . D-MPNN is a graph convolutional neural network, similar to that used for pyrolysis predictions described earlier, though it should be noted that other NN methods have been employed for AE predictions 39 , 40 . The training data used consisted of published gas phase energy activation data of 16,264 transformations determined by quantum chemistry calculations using B97-D3/def2-mSVP theory following the exclusion of flavour compounds to prevent data leakage of the test set 38 . AEs for 482 NN predicted reactions were determined. The reactions were chosen to reflect different transformation types and reactions that generated products classified as high health risk were prioritized (Supplementary Dataset S5 ). The outcome gave a wide range of AE values from 45 to 121 kcal/mol indicating that comparisons could be made between different degradation pathways for each flavour.

Fruit flavoured products are the most popular commercial brands for the younger vaping demographic so warrant particular attention. These compounds commonly have an ester functional group which are known to undergo thermal decomposition by different elimination and free radical β-scission reactions, both of which are plausible under vaping conditions 41 , 42 . To illustrate use of AE values, ten acetate esters with substituent containing β-hydrogens were selected for comparative data analysis (Fig.  8 ). Previously reported experimental and computational studies have mostly focused on the simplest derivatives such as ethyl acetate with others as of yet unstudied 42 , 43 , 44 , 45 . These results show that three different elimination pathways are possible to generate either acetic acid and substituted alkenes (pathway A); ketene with substituted alcohols (pathway B); or C–O cleavage resulting in the formation of two carbonyls (pathway C) 42 . Analysis of the NN predicted reactions for each of these acetates showed that these transformation pathways were common to all. Comparison of the D-MPNN derived AE values for these reactions showed that pathway A consistently predicted the lowest energy requirement for most of the acetates (Fig.  8 , table). The identification of pathway A as most favorable is consistent with literature reports and thus identifies inhalation of acetic acid and substituted alkenes as the likely health hazards 46 . It is noteworthy that of the ten different alkenes producible via AE favored pathway A, eight are GHS classified as either irritant or health hazard (ethene, hexene, 1,3-hexadiene, 2-methylpropene, 3-methyl-1-butene, 2-methyl-1-butene, 3,7-dimethylocta-1,6-diene, styrene). Additionally, it is important to recognize that due to the complex reacting conditions within a vaping device, pyrolysis would not be expected to follow a single pathway 47 . In the case of acetates, products could also occur via free radical β-scission reactions as vaping conditions have been shown to promote radical type reactions 48 , 49 .

D-MPNN derived activation energies applied to three different NN predicted pyrolysis pathways of acetate fruit flavours (ethyl acetate, butyl acetate, amyl acetate, hexyl acetate, cis -3-hexenyl acetate, isobutyl acetate, isoamyl acetate, 2-methylbutyl acetate, citronellyl acetate, 2-phenylethyl acetate). a kcal/mol, # not predicted by NN.

While these AE values are, as yet, a first approximation, taking no account of the conditions under which reactions are taking place, their importance will grow as the accuracy in predicting these values improves. It could be envisaged that they play a future role in reinforcement learning models in conjunction with MS fragmentation data (Fig.  2 , dashed arrows).

E-Liquid flavour reports

Collation of all data generated an output for each of the 180 flavours with an enumerated list of NN predicted reactions and their associated products, EI-MS matched products identified and their associated GHS hazard classifications (Supplementary Datasets S2 , S3 ). Taken together, these constitute a minable reference source that encompasses the complex and interconnected facets of vaping with the potential to be refined and adapted in the future.

The e-liquid marketplace is vast and growing, driven by increased investment by tobacco companies into vaping products 50 . The original source of flavours in e-liquids stems from food flavouring compounds so it could be anticipated that the number of compounds being used will increase over time 4 , 5 , 6 . Since their inception, an incorrect assumption has grown that the flavour ingredients used in e-liquids are designated “generally recognized as safe” (GRAS) under health regulations. However, this GRAS status only relates to human consumption via ingestion (compatible with their use in foodstuffs) and not inhalation following thermal activation 18 . While the health concerns for lung exposure to the flavours themselves are serious, what is even more concerning is the array of thermal degradation products which they generate as a consequence of their heating immediately prior to inhalation 51 . The vast majority of these degradation products remain unknown as do their health consequences from long-term exposure. From a public health perspective, the use of flavours in e-liquids can be viewed as a double-edged sword. The role for vaping flavours is cited as a support for smoking cessation for those already addicted to nicotine tobacco products, but the same flavours are the main attractant for a non-smoking younger demographic 8 , 52 .

Experimental research into the heat-induced breakdown of organic compounds has its origins in the early twentieth century with the seminal work of Hurd and others 53 . Such research was conducted to gain fundamental scientific insights into the nature of chemical bond dissociations and formations. Vaping devices can be considered as crude versions of a laboratory pyrolysis apparatus 54 . Both are designed to rapidly heat organic molecules to high temperatures, although when using an experimental apparatus the products are safely trapped, quantified and characterized whereas in vaping they are drawn into the lungs. A laboratory pyrolysis apparatus has rigorous control over temperature, is made from materials to limit radical formation and is used to study test molecules individually. In contrast, a vaping device has poor temperature control, is constructed using metal materials that induce radical reactions and simultaneously heats an array of chemical entities in an e-liquid. By its nature, experimental pyrolysis chemistry is highly complex, but within vaping this complexity is magnified due to e-liquid, device and user variabilities making it a daunting task to map all possible chemical outcomes from a vaping “experiment”.

To date, experimental studies on the thermal decomposition products from vaping flavours have focused on detecting and quantifying volatile carbonyls (VC) as they have known negative health implications 55 , 56 , 57 , 58 , 59 , 60 . Several research teams have conclusively shown that VCs such as formaldehyde, acetalaldehyde and propanaldehyde are produced in concerning quantities as a result of the vaping decomposition of flavours. The quantity of aldehydes produced is proportional to the specific commercial brands, flavour and nicotine quantity in the e-liquid, the vaping device power, and users’ puff topography 61 , 62 , 63 , 64 . Establishing which flavours produced which VCs is challenging, as the e-liquids tested were comprised of mixtures of several flavour chemicals, propane-1,2-diol, propane-1,2,3-triol (which also produce aldehydes) and nicotine. Mining our dataset allows mapping of VC producers back to specific flavour chemicals while also identifying the other chemicals co-produced with these VCs. For example, the results for acetaldehyde revealed over forty flavours as having the potential to produce it with co-products including heterocycles, aromatics, aldehydes, alkenes and alkanes (Fig.  9 , Supplementary Dataset S6 ). Sources were mostly fruit, candy and dessert flavoured products containing ester, ketone, di-ketone, aldehyde and carboxylic acid functional groups. Cross-referencing this list with the most commonly used fruit and candy flavours 4 , 5 , 6 implicates ethyl acetate, ethyl butyrate, ethyl 2-methylbutryate and 2,3-pentanedione as the more common sources of pyrolysis produced acetaldehyde. The rapid identification of chemicals of concern is an advantageous feature of this dataset which could be of assistance in focusing experimental work to confirm their generation which could in turn inform regulatory agencies.

Map showing structural classes of e-liquid flavour chemicals identified as having the potential to produce acetaldehyde (blue box) with a representative selection of co-products (red circle) and the names of the chemical flavours from which they could be produced (see Supplementary Dataset S6 ). *For ethyl esters the prediction of CH 3 CH 2 O˙ (blue box) indicates the first step in formation of acetaldehyde 47 .

Reflecting on the limitations of this study, it is important to identify areas that warrant further development. In this initial iteration of our AI-vape forecast, only the first phase of pyrolysis products has been explored. It would be expected that some pyrolysis products would themselves undergo further pyrolysis reactions and that intermolecular reactions between pyrolysis products could occur 65 . The framework we have put in place lends itself to building a second layer of prediction using the predicted products described in this work as new starting points. Generation of combustion products has not been included in this study as they are considered minor to pyrolysis products, though ambient oxygen reactions could be investigated by inclusion of O 2 as a reagent in the NN predictions 66 . The cross correlation of predicted pyrolysis products with experimental MS fragmentation data does not distinguish between different structural isomers with the same molecular weights, though in the future more elaborate MS experiments may do so if needed. A limitation of the NN-predictions is the current size of the training sets, though it is anticipated that these will continue to grow in the near future. Additionally, improvements in NN ranking of reaction predictions could be guided by predicted AEs acting as an informative feedback loop (Fig.  2 , dashed arrows). A merit of the scientific approach adopted in this work is its openness to continual refinement as chemistry related NNs evolve and that it can serve as a benchmark for other AI methods to achieve similar aims. We hope that this work motivates further research in these areas.

The aerosols produced by e-cigarette vaping contain immensely complex uncharacterized mixtures of pyrolysis products, the health implications of which are, as yet, mostly unidentified. In advance of health effects of vaping becoming apparent in the general population, AI can be exploited to give guidance to the public, policy makers and health care professionals. It was envisaged that this could be achieved through a strategy that utilizes a combination of innovative NN prediction of pyrolysis transformations and freely accessible experimental EI-MS data to construct an in silico dataset of pyrolysis products from e-liquid chemical flavours. Screening of predicted pyrolysis products against databases of chemical hazard classifications identified those of highest health risk, allowing individual flavour risk profiles to be constructed. Results show that relatively low molecular weight volatile compounds can be produced of which 24% are categorized as either acute toxic or health hazard using the GHS classification system. Collated flavour risk reports may act as an informative public health resource and assist experimental vaping research. Results show that while similarities do exist with conventional tobacco smoking, a significantly different profile of hazardous compounds emerges from vaping. As such, using tobacco smoking as the sole comparison for gauging vaping health risks is likely to give a false sense of security, especially for younger non-tobacco smokers. Regulations could be employed such that attempts to remedy nicotine addictions of older tobacco smokers does not risk the transferal of new health issues to younger generations. A protective balance needs to be struck for both cohorts rather than pitching one against the other. AI methods appear ideally suited to address the complex and multifaceted health concerns that vaping raises. As vaping is a new and unprecedented stress to the human body, with the ability to generate pyrolysis products more toxic that their parent compounds, it seems prudent to strictly limit the number of chemical entities in e-liquids.

Diversity analysis of flavours and WL-NN predicted pyrolysis products

Chemical diversity analysis was carried out using PUMA 1.0 ( http://132.248.103.152:3838/PUMA/ ) 24 . The SMILES structure of 180 flavours and 4524 NN predicted products (duplicate predicted products from the same flavour removed) were used as inputs. This platform computed six molecular properties of pharmaceutical relevance including molecular weight (MW), hydrogen bond donors (nHBDon), hydrogen bond acceptors (nHBAcc), topological polar surface area (TopoPSA), number of rotatable bonds (nRotB), and the octanol–water partition coefficient (ALogP). Then six principal components (PCs) were computed based on these molecular properties. The 3D representation of the chemical space was plotted by using Veusz 3.3.1 software using the three PCs that contributed the most proportion of the variance.

WL–NN pyrolysis reaction predictions

Supervised learning of Weisfeiler–Lehman network was achieved utilizing the US patent literature as a source of data. The starting dataset which consists of 409,035 reactions is available at: https://github.com/connorcoley/rexgen_direct/tree/master/rexgen_direct/data/ 26 . All reactions within the training data that included a flavour molecule were removed to prevent the trained W–L network from data leakage of the test set. In total, a training set of 354,937 reactions was used, on which the pyrolysis predictions were based. The Python script to remove flavour molecules from the original dataset is available at https://github.com/IBM/pyrolysis-prediction . SMILES of the 180 flavour chemicals were used as inputs for the W–L NN using the published protocols. The computational training and prediction were run using a machine with eight CPU cores (Intel Xeon E5-2690 at 2.60 GHz), one GPU (Tesla V100) and 60 GB memory. The number of iterations to train the W–L network and W-LDN was set to 140,000 mini-batches of size 20 and 1,000,000 mini-batches of a single reaction and its candidate outcomes, respectively. Accuracy of prediction tasks was determined using 40,000 test examples 26 (not in the training set) which gave 0.924 for the model to identify reaction mode when the top 25 predictions are considered and 0.9341 for ranking reactions when the top 5 predictions are considered. The total training time was 2.5 days to train both models (reaction centre and ranking) for reaction prediction. Results from 4500 pyrolysis predictions for 180 flavours gave 7307 products of which 4524 were discrete products (when duplicate products from the same flavour are not included). Average reaction prediction times were 40 ms per reaction for reaction core identification and 127 ms per reaction for ranking.

Experimental EI-MS data retrieval

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , the SMILES representations for each flavour were converted to their corresponding InChIKey and the EI-mass spectra data associated with each InChIKey was extracted from the online NIST database at https://webbook.nist.gov/chemistry/ 31 . EI-mass data including fragmentation molecular weights and relative abundance were retrieved in JCAMP format. The data for each flavour was checked manually to ensure that the correct data had been acquired for each flavour and errors corrected. Data for some flavours (2-ethyl-3-methyl pyrazine, 2-methoxy-3-methylpyrazine, acetoin, α-damascone, benzaldehyde propylene glycol acetal, benzyl alcohol, β-damascenone, cedrol, citral, ethyl lactate, ethyl vanillin propylene glycol acetal, γ-dodecalactone, γ-octalactone, menthone, neral, propenyl guaethol, tabanone, thio-menthone, trans-2-hexenylacetate, vanillin propylene glycol acetal) were either not available in the NIST database or not accessible and data was manually extracted from the NIST or from Spectrabase ( https://spectrabase.com/ ).

Correlation of EI-MS fragmentation molecular weights with W–L NN predicted products

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , the molecular weight of each W–L NN predicted product was calculated using the Descriptors.ExactMolWt method in RDKit. The value of 1 was subtracted from that weight, followed by a rounding to the nearest whole number. NN predictions with the same molecular weight as the flavour molecular ion were not included. The values obtained for each product were correlated with the EI-mass spectrum fragmentation mass data for the relevant flavour molecule available in JCAMP format. Correlation results identified 1169 discrete matches between NN predicted products with EI-MS fragmentations.

GHS classification data retrieval and cramer classifications

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , a NN/MS matched product represented as SMILES was converted to its InChIKey and all compounds matching the InChIKey in the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) were retrieved. For each compound identifier, the script retrieved the hazard keywords in the pictographs that appeared in the GHS Classification subsection. Specific inhalation hazards were searched and compiled manually. Cramer classifications were obtained by using compound SMILES inputs into the available prediction software 36 . Results of GHS classifications identified 127 NN/MS matched product predictions as acute toxic, 153 as health hazard, 225 as irritant, 566 as neither acute, health hazard nor irritant and 95 were not identified in the database.

Mapping reactions for activation energy predictions

The SMILES of reactant and NN-predicted products were used as inputs for the automated mapping algorithm available at http://mapper.grzybowskigroup.pl/marvinjs/ 67 . The full atoms reaction maps were completed by using the “map the drawing” command after adding explicit hydrogen atoms.

D-MPNN pyrolysis activation energy predictions

The original training dataset for activation energy prediction consisting of 16,365 reactions is available at https://zenodo.org/record/3715478#.Yich5BPP2Wj 37 , 38 . To prevent from data leakage of the test set, reactions involving the 180 flavour chemicals were removed using specific Python script available at https://github.com/IBM/pyrolysis-prediction . The resulting dataset consisted of default hyper-parameters and the b97d3 theory data consisting of 16,264 reactions. Accuracy for AE predictions was determined by performing a tenfold cross validation to train the model with the data split into 85% training, 5% validation and 10% test data 38 . The performance/accuracy metric for the AE is the rooted mean square error defined as \(\sqrt {\frac{1}{N}\backslash_{i = 1}^{N} \left( {y_{i} - z_{i} } \right)^{2} }\) where N is the number of examples, y i is an AE value in the i-th example, and z i is a predicted AE value in the i-the example. The average accuracy for AE prediction was determined as the root mean square error (RMSE) which was 7.53 mol −1 with standard deviation of 0.74 mol −1 . The average AE value calculated by the 10 models was used to predict AE for pyrolysis with standard deviations included.

Data availability

All data are available in the main text, Supplementary Information or GitHub ( https://github.com/IBM/pyrolysis-prediction ). Raw data files are available from the corresponding author upon request.

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Acknowledgements

D.W. acknowledges the Synthesis and Solid State Pharmaceutical Centre (SSPC) and Science foundation Ireland for funding support, Grant Number 12/RC/2275_P2.

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D.F.O. designed research; A.K. and D.W. performed research; D.F.O., A.K. and D.W. analyzed data; D.F.O., A.K. and D.W. wrote the paper.

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Kishimoto, A., Wu, D. & O’Shea, D.F. Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions. Sci Rep 14 , 9591 (2024). https://doi.org/10.1038/s41598-024-59619-x

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• Systematic Review
• Open access
• Published: 21 November 2022

The prevalence of electronic cigarettes vaping globally: a systematic review and meta-analysis

• Hadi Tehrani 1 , 2   na1 ,
• Abdolhalim Rajabi 3   na1 ,
• Mousa Ghelichi- Ghojogh 4 ,
• Mahbobeh Nejatian 5 &
• Alireza Jafari 6  

Archives of Public Health volume  80 , Article number:  240 ( 2022 ) Cite this article

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Metrics details

The purpose of this systematic review study was to determine the national, regional, and global prevalence of electronic cigarettes (e-cigarettes) vaping.

The articles were searched in July 2020 without a time limit in Web of Science (ISI), Scopus, PubMed, and Ovid-MEDLINE. At first, the titles and abstracts of the articles were reviewed, and if they were appropriate, they entered the second stage of screening. In the second stage, the whole articles were reviewed and articles that met the inclusion criteria were selected. In this study, search, selection of studies, qualitative evaluation, and data extraction were performed by two authors independently, and any disagreement between the two authors was reviewed and corrected by a third author.

In this study, the lifetime and current prevalence of e-cigarettes vaping globally were 23% and 11%, respectively. Lifetime and current prevalence of e-cigarettes vaping in women were 16% and 8%, respectively. Also, lifetime and current prevalence of e-cigarettes vaping in men were 22% and 12%, respectively. In this study, the current prevalence of e-cigarettes vaping in who had lifetime smoked conventional cigarette was 39%, and in current smokers was 43%. The lifetime prevalence of e-cigarettes vaping in the Continents of America, Europe, Asia, and Oceania were 24%, 26%, 16%, and 25%, respectively. The current prevalence of e-cigarettes vaping in the Continents of America, Europe, Asia, and Oceania were 10%, 14%, 11%, and 6%, respectively.

Conclusions

Based on the results of this study, it can be concluded that the popularity of e-cigarettes is increasing globally. Therefore, it is necessary for countries to have more control over the consumption and distribution of e-cigarettes, as well as to formulate the laws prohibiting about the e-cigarettes vaping in public places. There is also a need to design and conduct information campaigns to increase community awareness about e-cigarettes vaping.

Peer Review reports

Electronic cigarettes (e-cigarettes) are another type of tobacco that has become popular in the world in recent years. These cigarettes have batteries and heat the liquid and usually contain nicotine and other toxins [ 1 ]. In recent years, the prevalence of e-cigarettes has increased.

The results of a study by Brożek and et al. in several European countries showed that the overall prevalence of lifetime e-cigarette vaping was 43.7%, with 51.3% in men and 40.5% in women [ 2 ]. According to the results of various studies, the prevalence of e-cigarettes vaping in different countries such as France, Mexico, China, Australia, and in the United States were 25.46%, 42.42%, 24.44%, 12.52%, and 13.47%, respectively [ 3 , 4 , 5 , 6 , 7 ].

A systematic review by Pisinger and Dossing in 2014 showed that e-cigarettes can have an adverse effect on the health of individuals due to materials such as fine/ultrafine particles, volatile organic compounds, carcinogenic carbonyls, carcinogenic tobacco-specific nitrosamines, and cytotoxicity. Additionally, another major concern is the availability of novel compounds, such as propylene glycol, which are not found in conventional cigarettes with unknown impact on health [ 8 ]. The results of studies showed that using e-cigarettes may increase the risk of cardiovascular disease and respiratory disease [ 9 , 10 ].

People usually e-cigarettes vaping to quit conventional cigarettes, while some people using both types of cigarettes and are at higher risk [ 11 ]. The e-cigarettes vaping can also encourage people to initial use of conventional cigarettes and other substances [ 12 , 13 ]. The results of a systematic review study have shown that adolescents whose parents and friends vaping of e-cigarettes are more likely to be inclined towards e-cigarettes vaping in the future [ 14 ]. Therefore, this systematic and meta-analysis review study was conducted with the aims of (1) Investigating an updated estimate of the prevalence of lifetime and current e-cigarettes vaping in around the world based on countries, and (2) also demonstrate a trend of the prevalence of lifetime and current e-cigarettes vaping.

Search strategy and selection of articles

This study was a systematic review and meta-analysis to determine the national, regional and global prevalence of e-cigarettes vaping. In this study, articles were searched in July 2020 without a time limit and only in articles published in English in Web of Science (ISI), Scopus, PubMed, and Ovid-MEDLINE. Contrary to what is mentioned in the protocol, we did not use Google Scholar to search for articles. Also, the reference sections of relevant systematic review articles were checked. In this study, the phrase of “lifetime prevalence” referred to e-cigarette vaping by a person during his/her lifetime, and the phrase of “current prevalence” referred to e-cigarette vaping during the last 12 months. The search strategy was performed with the keywords of “Electronic Cigarette” OR “Electronic Nicotine” OR “E-Cigarette” OR “Vaping” OR “E-Cig” (Additional file 1 ). This study was based on the PRISMA guideline (Fig.  1 ). The protocol of this study has been registered in the PROSPERO system (registration number: CRD42020183032).

Flowchart of the systematic review process using PRISMA checklist

To select articles, first, all search results were entered into Endnote software and then reviewed by two authors separately and any disagreement was reviewed by the third author. At this stage, first, the titles and abstracts of the articles were reviewed, and if they were appropriate, they entered in the second stage of screening. In the second stage, the all articles were reviewed and articles that met the inclusion criteria were selected. The process of reviewing the selection of articles is shown in Fig.  1 .

Inclusion and exclusion criteria

Inclusion criteria included (1) papers published in English language, (2) cross-sectional, cohort, case–control, and intervention articles, (3) papers that reported the prevalence of e-cigarette vaping, and (4) papers that were published in full text. Exclusion criteria included qualitative papers, and papers that were published as review study, editorials comments, presentations or conference abstracts.

Quality assessment

Methodological quality was assessed using the Joanna Briggs Institute’s critical appraisal tool [ 15 ] for prevalence studies. This tool evaluates the extent to which a study has addressed the potential biases in its design, conduct, and analysis. Studies were examined for representativeness, sample size, recruitment, description of study participants and setting, data coverage of the identified sample, reliability of the measured condition, statistical analysis, and confounding factors. Scores ranged from 0–9 with ≤ 5 as “low/moderate quality” and > 5 as “fair quality.” All studies selected for this meta-analysis were independently assessed by two authors (A.R. and A.J), and any disagreements between the two authors were reviewed and corrected by a third author.

Data extraction

All final papers entered into the study process were extracted from a pre-prepared checklist. The checklist included the surname of the first author, year of data collection, year of paper publication, target group, age of target group, place of study, type of study, the data gathering instrument, sample size, current and lifetime prevalence of e-cigarettes vaping, the prevalence of current e-cigarettes vaping in who had lifetime smoked conventional cigarettes, or currently smoking conventional cigarettes (Table S 1 ). In this study, search, study selection, qualitative evaluation, and data extraction were conducted independently by two authors, and any disagreements between the two authors were reviewed and corrected by a third author.

Data analysis

The pooled prevalence of e-cigarettes and a 95% confidence interval (CI) was calculated with raw data in STATA version 16 (Stata Corp LP, College Station, TX, USA). A random effects models (Der-Simonian Laird method) were used to combine data from individual studies. Q test and I2 statistic were used to calculate the heterogeneity between studies. I2 describes the percentage of total variation because of between-study heterogeneity [ 16 ]. Subgroup analysis was conducted according to the continent, study design, population study, and tools of assessment of e-cigarettes. Meta-regression was performed to explore the possible sources of heterogeneity. A p -value less than 0.05 was considered to be statistically significant.

In brief, a total of 146 eligible studies were identified and included in in the final analysis from 4026 potentially relevant articles with 5,495,495 participants. A flowchart of the inclusion and exclusion criteria of articles are shown in Fig.  1 . The included studies were published between 2010 and 2020. The studies were conducted on four continents, with 67 studies in North America, 28 studies in Asia, 43 studies in Europe, and 8 studies in Australia/Oceania. Of the total studies included in this systematic review, 137 studies were cross-sectional and 9 studies were cohort studies (Table S 1 ) [ 3 , 4 , 5 , 6 , 7 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ].

The prevalence of lifetime and current e-cigarettes vaping

The results of this study showed that the lifetime and current prevalence of e-cigarettes vaping were 23% (with a confidence interval (CI) of 95%: 21–25%) and 11% (95% CI: 10–11%), respectively (Fig.  2 ). The lifetime and current prevalence of e-cigarettes vaping among women were 16% (95% CI: 15–18%) and 8% (95% CI: 0.07–0.08%), respectively (Fig.  3 ). Also, the lifetime and current prevalence of e-cigarettes vaping among men were 22% (95% CI: 20–25%) and 12% (95% CI: 11–13%), respectively (Fig.  4 ).

Pooled lifetime and current prevalence of e-cigarettes vaping in all subjects

Pooled lifetime and current prevalence of e-cigarettes vaping in women

Pooled lifetime and current prevalence of e-cigarettes vaping in men

In this study, the lifetime prevalence of e-cigarettes vaping among adolescents and school students, adults, college students, and patients were 25% (95% CI: 21–30%), 19% (95% CI: 17–21%), 26% (95% CI: 15–37%), and 29% (95% CI: 16–43%), respectively (Fig.  5 ). Also, the current prevalence of e-cigarettes vaping in adolescent and school students, adults, college students, and patients were 11% (95% CI: 10–12%), 11% (95% CI: 10–12%), 14% (95% CI: 7–22%), and 10% (95% CI: 8–11%), respectively (Fig.  5 ). The lifetime and current prevalence of e-cigarettes by subgroups in women and men can be seen in Fig S 1 and Fig S 2 .

Pooled lifetime and current prevalence of e-cigarettes vaping in all subjects by study population, continent, type of study, and tools assessment

The lifetime prevalence of e-cigarettes vaping in the continents of America, Europe, Asia, and Oceania were 24% (95% CI: 21–27%), 26% (95% CI: 21–31%), 16% (95% CI: 11–20%), and 25% (95% CI: 18–33%), respectively (Fig.  5 ). The current prevalence of e-cigarettes vaping in the continents of America, Europe, Asia, and Oceania were 10% (95% CI: 9–10%), 14% (95% CI: 10–17%), 11% (95% CI: 10–11%), and 6% (95% CI: 4–8%), respectively (Fig.  5 ).

According to the type of study, the lifetime prevalence of e-cigarettes vaping in cohort studies and cross-sectional studies were 28% (95% CI: 11–45%) and 23% (95% CI: 21–25%), respectively (Fig.  5 ). Also, based on the type of study, the current prevalence of e-cigarettes vaping in cohort studies and cross-sectional studies were 13% (95% CI: 11–16%) and 11% (95% CI: 10–11%), respectively (Fig.  5 ).

In terms of assessment tools, the lifetime prevalence of e-cigarettes vaping in studies conducted by self-report and standard questionnaire were 23% (95% CI: 21–26%) and 20% (95% CI: 15–25%), respectively (Fig.  5 ). Also, in terms of assessment tools, the current prevalence of e-cigarettes vaping in studies conducted by self-report and the standard questionnaire were 12% (95% CI: 11–12%) and 5% (95% CI: 4–6%), respectively (Fig.  5 ). In this study, the current prevalence of e-cigarettes vaping in people who had lifetime used conventional cigarettes, and in current smokers (conventional cigarettes) were 39% (95% CI: 36–42%) and 43% (95% CI: 39–47%), respectively (Fig.  6 ).

Pooled current prevalence of e-cigarettes vaping in ex-smokers and current smokers

The trend of current e-cigarettes vaping

The cumulative meta-analysis examined current e-cigarette vaping trends, which showed an upward trend from 2011 to 2014 and then a constant trend from 2014 to 2019 (Figure S 3 , Part A). The current prevalence of e-cigarettes among women first showed an upward and then a downward trends (Figure S 4 , part A). However, the current prevalence of e-cigarettes among men first showed an upward trend and then showed a constant trend (Fig S 5 , part A). The current prevalence of e-cigarettes vaping among adolescents and school students showed an upward trend. However, results of current e-cigarettes vaping among adolescents and school students showed an upward trend and among adults showed a downward trend (Fig S 6 , part A). The current prevalence of e-cigarettes vaping in continents of Americas and Asia first showed an upward trend and then showed an almost constant trend. The current prevalence of e-cigarettes vaping in Europe continent showed an upward trend (Fig S 7 , part A). The current prevalence of e-cigarettes vaping among people who had lifetime used conventional cigarettes and among current smokers (conventional cigarettes) first showed an upward trend and then showed an almost constant trend (Fig S 8 ). The current prevalence of e-cigarettes vaping by subgroups among women and men can be seen in Fig S 9 (part A) and Fig S 10 (part A). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men in each continent can be seen in Fig S 11 (part A) and Fig S 12 (part A).

The trend of lifetime e-cigarettes vaping

The cumulative meta-analysis examined the lifetime e-cigarettes vaping, which showed an upward trend from 2011 to 2019 (Fig S 3 , part B). The trend of lifetime e-cigarettes vaping among women first showed an upward trend and then showed a constant trend (Fig S 4 , part B). Also, the trend of lifetime e-cigarettes vaping among men showed an upward trend and then showed a constant trend (Fig S 5 , part B). According to the results, the lifetime e-cigarettes vaping among adolescents and school students showed an upward trend (Fig S 6 , part B). The lifetime e-cigarettes vaping in the continents of the Americas, Asia, Europe, and Oceania showed an upward trend (Fig S 7 , part B). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men can be seen in Fig S 9 (part B) and Fig S 10 (part B). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men in every continent can be seen in Fig S 11 (part B) and Fig S 12 (part B).

Quality of included studies

The risk of bias and the quality of the included articles is illustrated in Table S 2 . All studies used an adequate sample size (100%) to determine the prevalence of e-cigarettes vaping. All studies (100%) used appropriate statistical analysis to measure the prevalence of e-cigarettes vaping. According to the Joanna Briggs Institute's Quality Assessment Checklist; the included articles had a score ranging from five to nine (Total nine-scored scale). Four studies scored nine out of nine (2.74%), fifty-seven studies scored seven to eight out of nine (39.04%) and the remaining eighty-five studies scored five to six out of nine score (58.22%) (Table S 2 ) [ 3 , 4 , 5 , 6 , 7 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ].

Meta-regression analyses

Exploratory univariate meta-regression was conducted with the introduction of sample size, year of publication, tools of assessment, study design, continent, and population study for lifetime vaping and current vaping prevalence. The meta-regression coefficients for lifetime e-cigarettes vaping, 95% CI and P -value for these variables were, year of publication: β = 0.013, (95% CI: 0.0024, 0.0254, p  = 0.01), sample size: β = -1.42e −6 (95% CI: -2.05e −6 , -7.82e −7 , p  < 0.001), tools of assessment: β = -0.029, (95% CI: -0.098, 0.039, p  = 0.39), continent: β = 0.010, (95% CI: -0.011, 0.032, p  = 0.34), study design: β = -0.049, (95% CI: -0.170, 0.072, p  = 0.42), study population: β = -0.0012, (95% CI: -0.028, 0.025, p  = 0.92). The meta-regression coefficients for current e-cigarettes vaping showed that th95% CI and P -value for follow variables were, year of publication: β = 0.0065, (95% CI: 0.0037, 0.0092, p  < 0.001), sample size: β = -1.88e −6 (95% CI: -2.30e −6 , -1.46e −7 , p  < 0.001), tools of assessment: β = -0.059, (95% CI: -0.076, -0.043, p  < 0.001), continent: β = 0.005, (95% CI: -0.0013, 0.010, p  = 0.05), study design: β = -0.025, (95% CI: -0.042, -0.0075, p  = 0.005), study population: β = 0.0037, (95% CI: -0.0022, 0.0097, p  = 0.22).

This systematic review and meta-analysis study was conducted to determine the global prevalence of e-cigarettes vaping. In this study, the lifetime and current prevalence of e-cigarettes vaping in both sexes were 23% and 11%, respectively. The Europe continent had the high prevalence of e-cigarettes vaping and the lifetime and current of e-cigarettes vaping were 26 and 14 respectively. According to the results of this study, the overall trend of e-cigarettes vaping from 2011 to 2019 showed an upward trend. The current e-cigarettes vaping trend has been increasing from 2011 to 2014, and then there is a steady trend from 2014 to 2019.

Prevalence in men and women

The lifetime prevalence of e-cigarettes vaping among men and women were 22% and 16%, respectively. Also, the current prevalence of e-cigarettes vaping among men and women were 12% and 8%. In a study conducted in South Korea, the lifetime and current prevalence of e-cigarettes were 11.2% and 2% in men and 2.1% and 0.4% in women, respectively [ 117 ]. In a study conducted in Spain, the lifetime prevalence of e-cigarettes vaping among men and women were 8% and 5.3%, respectively [ 121 ]. The current prevalence of e-cigarettes vaping among Japanese men and women were 6.7% and 3.1%, respectively [ 136 ]. The lifetime prevalence of e-cigarettes vaping among Germany men and women were 9.2% and 6.7%, respectively, and the current prevalence of e-cigarettes vaping were 2.6% and 1.3%, respectively [ 115 ]. Among American men and women, the lifetime prevalence of e-cigarettes vaping were 9.6% and 7.4%, respectively, and current prevalence of e-cigarettes vaping were reported 2.6% and 2.1%, respectively [ 133 ]. Men and women use e-cigarettes for a variety of reasons. Men will start using e-cigarettes for reasons such as quitting smoking, health concerns related to conventional cigarette, and curiosity about e-cigarettes. In women, the recommendation to use e-cigarettes by family or friends is one of the important reasons for e-cigarettes vaping [ 157 ].

According to the results, the current prevalence of e-cigarettes among men first showed an upward trend and then showed a constant trend. Also, the current prevalence of e-cigarettes among women first showed an upward and then a downward trends. One of the reasons for the increasing trend of the current prevalence is the positive expectations of e-cigarettes including good taste, good social performance, and increased energy in men compared to women, while the only positive expectation of women to use e-cigarettes is weight loss due to e-cigarettes vaping [ 157 ]. The findings suggest that young women are more likely to use e-cigarettes, while pregnant women are less likely to use e-cigarettes due to the adverse effects of e-cigarettes [ 158 ]. The reason for the decrease of e-cigarettes vaping among women may be the failure of smoking consumption to help with weight loss and fitness. Also, women are generally more concerned about their health than men, and the reason for the reduced consumption may be due to greater awareness of the complications of e-cigarettes vaping.

Prevalence in adolescent’s and school students

In this study, the lifetime and current prevalence of e-cigarettes vaping among adolescents and school students were 25% and 11%, respectively. In a study conducted in Russia, the lifetime and current prevalence of e-cigarettes vaping were 28.6% and 2.2%, respectively [ 114 ]. The current prevalence of e-cigarettes vaping among adolescents and school students is very wide in different countries, such as 1% in Mexico [ 159 ] and 9.9% in the United States [ 122 ]. In other countries such as China, the United Kingdom, Canada, and Poland, the current prevalence of e-cigarettes vaping were reported 1.2%, 2.2%, 3.6% and 3.5%, respectively [ 148 , 150 , 160 ]. According to the results, the trend of lifetime and current prevalence of e-cigarettes vaping in adolescents has been increasing, for example, the lifetime prevalence rate in the UK has increased from 22% in 2014 to 25% in 2016 [ 161 ], also the current prevalence rate in the United States has increased rapidly from 1.5% in 2011 to 20.8% in 2018 [ 162 ]. In various studies, a positive relationship has been found between the amounts of monthly allowance given by parents to their adolescent children, so as much as the amount of money is higher, the probability of e-cigarettes vaping is also higher by children [ 144 , 163 , 164 , 165 ] and this factor could have been a reason to increase e-cigarettes vaping. Another reason for increasing the prevalence of e-cigarettes vaping could be the use of e-cigarettes to quitting conventional cigarette by adolescents. Therefore, this results indicate that families should pay more attention to their adolescent and children about e-cigarettes vaping. Also, as an important channel for e-cigarettes vaping education, health professionals could play an important role, especially for adolescents and school students. Additionally, banning the sale of e-cigarettes to people under 18 years may help reduce e-cigarettes vaping rates among adolescents and school students.

Prevalence in adults

In this study, the lifetime and current prevalence of e-cigarettes vaping among adults were 19% and 11%, respectively. In a study in South Korea, the lifetime and current prevalence of e-cigarettes were 6.6% and 1.1%, respectively [ 117 ]. In a study conducted in Spain, the lifetime prevalence of e-cigarettes vaping among adult men was 6.5% [ 121 ]. The current prevalence of e-cigarettes vaping in Japan has been reported to be 4.3% [ 136 ]. The current prevalence of e-cigarettes among adults varies from country to country, which can be influenced by various factors such as availability of these products and regulatory rules. For example, in China, the current prevalence of e-cigarettes vaping was 1.2%, while in the United States has been reported to be 5.5% [ 148 , 159 , 166 ]. However, the lack of laws on the sale of e-cigarettes and widespread access to tobacco in Chinese stores is a cause for concern about the increasing use of e-cigarettes, as in other countries [ 148 ]. In various studies conducted in different countries around the world, including Mexico, Australia, New Zealand, and Canada, the current prevalence of e-cigarettes has been reported to be 1.1%, 1.2%, 2.1%, and 2.9%, respectively [ 37 , 167 , 168 ]. Based on the results of this study, current prevalence of e-cigarettes vaping among adults showed decreasing trend. The downward trend in current prevalence may be due to increased awareness among adults about the harms and dangers of e-cigarettes, and the creation of regulatory laws that prohibit e-cigarette use.

Prevalence in college students

In this study, the lifetime and current prevalence of e-cigarettes in college students were 26% and 14%. In a study conducted in five European countries including Slovakia, Belarus, Poland, Russia and Lithuania, the lifetime prevalence of e-cigarettes among college students were 34.4%, 42.7%, 45%, 33.4%, and 42.7%, respectively, and the current prevalence of e-cigarettes in these five countries were 2.3%, 2.7%, 2.8%, 4%, and 3.5%, respectively [ 2 ]. In a study conducted in the United States, the lifetime and current prevalence of e-cigarettes vaping among college students were 9% and 30%, respectively [ 130 ]. In another study among health science students in Saudi Arabia, the lifetime prevalence of e-cigarettes vaping has been reported to be 27.7% [ 137 ]. In a study conducted in Pakistan on medical students, the prevalence of e-cigarettes vaping was 13.9% [ 139 ], while in another study, the current prevalence of e-cigarettes vaping was 4.4% on medical students and 12.4% on non-medical students [ 2 ]. It has been reported that the reason for the low prevalence among medical students maybe their high awareness of the dangers of e-cigarettes vaping during the period of their education course [ 2 ]. The lifetime prevalence of e-cigarettes in Malaysian college students has been reported to be 20.4% [ 143 ]. Differences prevalence of e-cigarettes vaping in studies can be due to the different target groups, differences in age groups, and method of conducted the studies. According to the results of this study, the lifetime prevalence of e-cigarettes among college students showed increasing trend and the current prevalence of consumption has been decreasing. The reasons for the declining trend of the current prevalence of e-cigarettes can be cultural differences and the creation of laws to monitor and prohibit the use of e-cigarettes. Also, the prohibition of e-cigarettes vaping in the college can be effective in reducing e-cigarettes vaping.

Prevalence by continent

In this study, the lifetime prevalence of e-cigarettes vaping was 24% in the Americas, 26% in Europe, 16% in Asia and 25% in Oceania. Also, in this study the current prevalence of e-cigarettes vaping was 10% in the Americas, 14% in Europe, 11% in Asia, and 6% in Oceania. In a study conducted in 27 European countries, the lifetime prevalence of e-cigarettes increased from 7.2% in 2012 to 11.6% in 2016 [ 169 ]. One of the reasons for the increase in consumption in this continent may be because people usually use e-cigarettes to reduce or quit conventional cigarettes, but after a period of time, they start to use e-cigarettes continuously.

In this study, the lifetime prevalence of e-cigarettes vaping in the continents of Americas, Asia, Europe, and Oceania showed an upward trend. Also, the current e-cigarettes vaping in the continents of Americas and Asia first showed an upward trend and then showed an almost constant trend, but in Europe continent, it was showed an upward trend. In general, the use of e-cigarettes is increasing across different continents, possibly due to insufficient taxation of e-cigarettes. Also, given the increase in e-cigarettes in recent years, the law may not have been enacted yet. The reason for the differences in the prevalence of e-cigarettes in different continents may be due to the enactment of laws to reduce publicity, ban sales, increase taxes and conduct information campaigns in the field.

Prevalence of e-cigarettes vaping among ex-smokers and current smokers

In this study, the current prevalence of e-cigarettes vaping in people who had lifetime used conventional cigarettes, and among current smokers were 39% and 43%, respectively. In a study conducted in Malaysia, the current prevalence of e-cigarettes vaping in who had lifetime smoked conventional cigarettes, and in current smokers conventional cigarettes were 4.3% and 8%, respectively [ 146 ]. In a study in the USA, the current prevalence of e-cigarettes vaping among current smokers has been reported to be 24.1% [ 128 ]. One of the reasons for e-cigarettes vaping among current smoker’s conventional cigarettes is the curiosity to try it, helping to quit and reduce conventional cigarette smoking. In a study conducted in Serbia, 12.8% of respondents reported that e-cigarettes vaping helped reduce their conventional cigarette smoking [ 153 ].

The current prevalence of e-cigarettes among people who had lifetime smoked conventional cigarettes or among current smokers first showed an upward trend and then t showed an almost constant trend. The reason for the increasing trend of e-cigarettes vaping may be the tendency of more smokers to quit or reduce conventional cigarettes, which can also be seen as both a threat and an opportunity. The threat aspect of this approach may be that a greater tendency to use e-cigarettes can lead to addiction to e-cigarettes. The opportunity aspect of this approach is that since most people have a tendency to quit smoking, e-cigarette can be a good option for quit or reducing conventional cigarettes.

According to the results of this study, it can be concluded that the prevalence of e-cigarettes is increasing worldwide. Therefore, it is necessary for countries to have more control over the consumption and distribution of e-cigarettes, as well as to formulate laws prohibiting the consumption of e-cigarettes in public places. Due to the increase in the prevalence of e-cigarettes among adolescents and school students, it is necessary that parents pay more attention to their children and also schools should also design and implement various educational programs to increase the awareness of adolescents and school students in this field. A broad program of behavioral, communications, and educational research is crucial to assess how youth perceive e-cigarettes and associated marketing messages, and to determine what kinds of tobacco control communication strategies and channels are most effective.

Besides, due to the high prevalence of e-cigarettes among current smokers, to quit or reduce their conventional cigarette smoking, more evidence is require in this regard and Clinical trial studies are also recommended to evaluate the benefits and harms of e-cigarettes vaping. The increase in e-cigarettes consumption in continental Europe compared to other continents indicates more detailed studies to identify the use of e-cigarettes, survey, and enact laws to ban e-cigarettes in this continent.

Limitations and strengths

This study also had its limitations. Due to the use of studies whose data are collected through self-reporting data, the results of the study may be distorted due to measurement errors such as reporting bias and reminder bias. This self-reporting can lead to the misclassification of people that applies to smoke behavior in women, who are often underreported. Another limitation of this study was that due to the smaller number of studies that reported the lifetime prevalence of e-cigarettes vaping in pregnant women (2 studies) than the studies that reported the current prevalence of e-cigarettes vaping in this group (3 studies), the lifetime prevalence rate was lower than the current prevalence rate. One of the strengths of the study is that it includes cross-sectional, cohort, case–control, and intervention studies, and examines the prevalence of e-cigarettes in worldwide, and examines both the lifetime and current prevalence of e-cigarettes. It has also examined the prevalence of e-cigarettes in different subgroups including men, women, adults, adolescents, university students, by continents, and conventional cigarette users.

Availability of data and materials

Not applicable.

Abbreviations

Electronic cigarettes

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Acknowledgements

Social Development and Health Promotion Research Center funded this project.

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Hadi Tehrani and Abdolhalim Rajabi contributed equally as first author.

Authors and Affiliations

Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Hadi Tehrani

Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

Environmental Health Research Center, Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran

Abdolhalim Rajabi

Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran

Mousa Ghelichi- Ghojogh

Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

Mahbobeh Nejatian

Department of Health Education and Health Promotion, School of Health, Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

Alireza Jafari

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AJ and HT conceptualized the study and led the project and writing. All authors contributed to the development of the coding scheme. AJ, MGh and AR conducted the coding and analyses and drafted the methods. AR, MN, AJ and HT reviewed the codes and results. All authors contributed to the writing and revision and approved the final version of the manuscript.

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Supplementary Information

Additional file 1..

Search Strategy.

Additional file 2: Table S1.

Population characteristics of the studies reported the lifetime and current prevalence of electronic cigarette (e-cigarettes) vaping among women and men.

Additional file 3: Table S2.

Qualities of studies included in the systematic review and meta-analysis.

Additional file 4: Fig S1.

Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 5: Fig S2.

Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in men.

Additional file 6: Fig S3.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping in all subjects.

Additional file 7: Fig S4.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes smoking among women.

Additional file 8: Fig S5.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping among men.

Additional file 9: Fig S6.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by study population.

Additional file 10: Fig S7.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by continent.

Additional file 11: Fig S8.

Cumulative meta-analysis of current prevalence of e-cigarettes vaping in ex-smokers and current smokers. Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 12: Fig S9.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 13: Fig S10.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in men.

Additional file 14: Fig S11.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in women in every continent.

Additional file 15: Fig S12.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in men in every continent.

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Tehrani, H., Rajabi, A., Ghelichi- Ghojogh, M. et al. The prevalence of electronic cigarettes vaping globally: a systematic review and meta-analysis. Arch Public Health 80 , 240 (2022). https://doi.org/10.1186/s13690-022-00998-w

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Health Risks Of Vaping: Let's Stick To The Science And Speculate Less

Related articles.

Despite increasing evidence that vaping is safer than smoking, uncertainty surrounds the long-term effects of electronic cigarette use. Many in the tobacco control field have used the lack of data to speculate about these unknown risks. Here's a better way to deal with the uncertainty.

A growing body of evidence gathered over the last 15 years has shown that using an electronic cigarette ("vaping") is probably far safer than smoking and likely to help smokers quit their deadly habit forever. Certain segments of the public health establishment have reacted oddly to these results—they've ignored them and treated vaping as a serious threat. The American Heart Association, for example, has even called for e-cigarettes to be taxed and regulated as stringently as tobacco products are. [1]

Fortunately, this view doesn't seem to be as predominant as it once was; we're beginning to see more physicians, scientists, and public health organizations make statements based on the available science instead of what they think the evidence might show one day.

Consider this May 24 review article published in Prescriber : E-cigarettes: informing the conversation with patients  by Anna Kate Barton. The author, a clinical research fellow at the University of Edinburgh in the UK, helpfully outlined the history, anatomy, and science of e-cigarettes with the aim of helping doctors more knowledgeably discuss vaping with their patients. Compared to the typical news report about vaping —"Vaping is not better than smoking, and it still causes long-term lung damage"—Barton's article illustrated how we should talk about scientific issues when the evidence surrounding them is evolving. Let's consider a few examples.

Smoking cessation

What does the current evidence say about vaping and smoking cessation? ACSH has previously reported that vaping very likely helps smokers quit cigarettes, and maybe even nicotine , for good. Citing some of the same literature we have, Barton reached a similar conclusion about smoking cessation. While acknowledging the limitations of these studies ( discussed here ), she explained:

Patients using e-cigarettes also often report greater satisfaction and greater reduction in smoking than those using nicotine patches, and e-cigarettes are regarded as the most popular form of smoking cessation aid with smokers wishing to quit. Current position statements and the existing evidence base advocate their combination with stop smoking counseling, the most effective smoking cessation tool.

This comes down to a concept known as “ harm reduction .” Ideally, people would never take up smoking. But since they do, the goal should be to help them mitigate the risks when abstinence isn't feasible. More experts are beginning to embrace this approach in order to enhance smoking cessation campaigns, as Barton noted:

E-cigarettes as aids to smoking cessation are advocated by several organizations including Public Health England. This is based on the principle of risk-reduction – simply, e-cigarettes provide nicotine in a much safer form that traditional cigarettes. Although neither are entirely risk-free, e-cigarettes are generally accepted to confer less risk to both the user and passive smokers than traditional cigarettes.

Health risks of vaping

After contrasting the overall risk of vaping with smoking, Barton added that some preliminary studies have indeed associated e-cigarette use with various negative outcomes. For example, an onslaught of headlines in mid-2019 warned the public about an outbreak of “e-cigarette or vaping product use-associated lung injury” (EVALI). Under-reported at the time was the fact that the injury-causing devices were typically purchased illegally and contained THC or certain dangerous additives, which made them far more harmful than the nicotine-containing devices adult customers can legally purchase in licensed vape shops in the US and UK. Surveying the literature nearly two years later, we get a better sense of the problem:

Interestingly, 82% [of EVALI cases] reported use of THC-containing [vape] products. Vitamin E acetate is sometimes added as a condensing agent in e-liquid, particularly in those containing THC, and this has been associated with EVALI. As such, the CDC discourages use of THC-containing [vape products], particularly those sourced informally from family or friends.

The point, then, is that proper regulation and vigilance by individual users can minimize these types of injuries. The UK, where vaping has proven to be a similarly popular smoking cessation approach, “has not thus far experienced a similar epidemic of EVALI as the USA,” Barton added, though she mentioned two severe cases that apparently weren't related to THC or Vitamin E acetate .

About those long-term effects

Opponents of vaping often point to the dearth of research on its chronic health effects as a first line of criticism. This is a fair enough point, but I hasten to add that it cuts both ways. If we don't know the long-term effects of vaping, we don't know the long-term effects of vaping. “At present,” Barton observed in reference to chronic lung disease, “we can only reflect on potential consequences of 10–15 years of widespread e-cigarette use.”

But that's often not what tobacco control advocates do. “The long-term risks of exclusive use of e-cigarettes are not fully known,” The American Cancer Society claims, “but evidence is accumulating that e-cigarette use has negative effects on the cardiovascular system and lungs. Without immediate measures to stop epidemic use of these products, the long-term adverse health effects will increase.” Retired University of California, San Francisco tobacco researcher Stanton Glantz has even suggested that  e-cig users would “be better off just smoking.”

The impulse to reject anything tobacco-related is understandable. But we have to stick with the data we have, which suggests vaping is far safer than smoking, and wait for the long-term results to come in. What we can't do is minimize the existing evidence while simultaneously making statements about the future. Uncertainty is acceptable when we don't have enough evidence, as Barton wrote:

It seems unlikely that e-cigarettes will be without pathological consequences within the human lung and elsewhere, though when we will be able to prove or disprove this is less clear … Regular monitoring of suspected adverse events arising from e-cigarettes … will aid recognition of new complications in [the] future, though it is unlikely we will appreciate the full picture of any long-term harms until well into this century.

[1] The FDA classifies e-cigarettes as tobacco products, but this designation makes little sense. According to Nicotine and Tobacco Research , "If all products containing nicotine derived from tobacco were labeled as 'tobacco products' internationally, then nicotine-replacement therapies would be classified as tobacco products, which they are clearly not."

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Nicotine Addiction From Vaping Is a Bigger Problem Than Teens Realize

March 19, 2019

Data show clearly that young people are vaping in record numbers. And despite the onslaught of reports and articles highlighting not only its dangers but the marketing tactics seemingly aimed to hook teens and young adults, the number of vaping users continues to climb. 

These teens may be overlooking (or underestimating) a key ingredient in the vapors they inhale: nicotine. Though it’s possible to buy liquid or pod refills without nicotine, the truth is you have to look much harder to find them. Teens may not realize that nicotine is deeply addictive. What’s more, studies show that young people who vape are far likelier to move on to cigarettes, which cause cancer and other diseases.

So, why is nicotine so addictive for teens?

Nicotine can spell trouble at any life stage, but it is particularly dangerous before the brain is fully developed, which happens around age 25.

“Adolescents don’t think they will get addicted to nicotine, but when they do want to stop, they find it’s very difficult,” says Yale neuroscientist Marina Picciotto, PhD, who has studied the basic science behind nicotine addiction for decades. A key reason for this is that “the adolescent brain is more sensitive to rewards,” she explains. 

The reward system, called the mesolimbic dopamine system, is one of the more primitive parts of the brain. It developed as a positive reinforcement for behavior we need to survive, like eating. Because the mechanism is so engrained in the brain, it is especially hard to resist. 

When a teen inhales vapor laced with nicotine, the drug is quickly absorbed through the blood vessels lining the lungs. It reaches the brain in about 10 seconds. There, nicotine particles fit lock-and-key into a type of acetylcholine receptor located on neurons (nerve cells) throughout the brain.   

The unique attributes that make nicotine cravings persist

“Nicotine, alcohol, heroin, or any drug of abuse works by hijacking the brain’s reward system,” says Yale researcher Nii Addy, PhD, who specializes in the neurobiology of addiction. The reward system wasn’t meant for drugs—it evolved to interact with natural neurotransmitters already present in the body, like acetylcholine. This neurotransmitter is used to activate muscles in our body. The reason nicotine fits into a receptor meant for acetylcholine is because the two have very similar shapes, biochemically speaking, Addy explains. 

Once nicotine binds to that receptor, it sends a signal to the brain to release a well-known neurotransmitter—dopamine—which helps create a ‘feel-good’ feeling. Dopamine is part of the brain’s feedback system that says “whatever just happened felt good” and trains the brain to repeat the action. But nicotine, unlike other drugs such as alcohol, quickly leaves the body once it is broken down by the liver. Once it’s gone, the brain craves nicotine again. 

When an addicted teen tries to quit nicotine, the problem of cravings is of course tied to the drug that causes the dopamine rush, Addy says. What’s more, recent animal study research and human brain imaging studies have shown that “environmental cues, especially those associated with drug use, can change dopamine concentrations in the brain,” he says. This means that simply seeing a person you vape with, or visiting a school restroom—where teens say they vape during the school day—can unleash intense cravings. “In the presence of these cues, it’s difficult not to relapse,” Addy says.  

Physical changes caused by nicotine

Nicotine can also cause physical changes in the brain, some temporary, and others that some researchers, like Picciotto, worry could be long-lasting. 

Decades of cigarette smoking research have shown that, in the short term, the number of acetylcholine receptors in the brain increases as the brain is continuously exposed to nicotine. The fact that there are more of these receptors may make nicotine cravings all the more intense. However, those same studies found that the number of receptors decreases after the brain is no longer exposed to nicotine, meaning that these changes can be reversed. 

But animal studies show nicotine also can cause issues with brain function, leading to problems with focus, memory, and learning—and these may be long-lasting. In animals, nicotine can cause a developing brain to have an increased number of connections between cells in the cerebral cortex region, says Picciotto. “If this is also true for humans, the increased connections would interfere with a person’s cognitive abilities,” Picciotto says. 

To illustrate how this might work, Picciotto gives an example. A student sitting in a noisy classroom, with traffic passing by the window, needs to be able to focus her attention away from the distracting sounds so she can understand what the teacher says. “Brains not exposed to nicotine learn to decrease connections, and refinement within the brain can happen efficiently,” Picciotto says. “But when you flood the system with nicotine, this refinement doesn’t happen as efficiently.” 

“There’s hope that the current vaping epidemic won’t lead to major health problems like lung cancer or pulmonary disease,” Picciotto says. “But we may still see an epidemic of cognitive function problems and attention problems. The changes made in the brain could persist.” 

Vaping vs. regular cigarettes

Weighing the pros and cons of vaping versus smoking is difficult to do. On the one hand, e-cigarettes likely do not produce 7,000 chemicals—some of which cause cancer—when they are activated, like regular combustible cigarettes do. However, the aerosol from a vape device has not been proven safe. Studies have found that it contains lead and volatile organic compounds, some of which are linked to cancer. Researchers are still gathering data on the possible long-term health effects from vaping. It’s notable that e-cigarettes have not been approved by the Food and Drug Administration (FDA) as smoking cessation devices. However, e-cigarettes may be a better choice for adult smokers if they completely replace smoking, according to the Centers for Disease Control and Prevention (CDC). 

But where nicotine levels are concerned, a newer and popular type of vape device, called a “pod mod,” outcompetes many other e-cigarette devices. The form of nicotine in these pods is estimated to be 2 to 10 times more concentrated than most free-base nicotine found in other vape liquids. A single pod from one vape manufacturer contains 0.7 mL of nicotine, which is about the same as 20 regular cigarettes.

Despite its extremely addictive nature, people can successfully quit using nicotine with personalized approaches, especially under the guidance of physicians who understand addiction. 

For young people, intervening early in a vaping habit could make an important difference in the quality of life they have throughout their adult years. It could also mean they won’t become part of next year’s statistics.

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More than 2.5 Million Youth Reported E-Cigarette Use in 2022

Flavored products, disposable devices, and a wide variety of brands threaten the health of our nation’s youth

Embargoed Until: Thursday, October 6, 2022, 1:00 p.m. ET Contact: Media Relations (404) 639-3286

A  study  released today from the U.S. Food and Drug Administration and the U.S. Centers for Disease Control and Prevention (CDC) found that 2.55 million U.S. middle and high school students reported current (past 30-day) e-cigarette use in 2022, which includes 14.1% of high school students and 3.3% of middle school students. Nearly 85% of those youth used flavored e-cigarettes and more than half used disposable e-cigarettes.

Among youth who currently used e-cigarettes, 14.5% reported their usual brand was Puff Bar, followed by Vuse (12.5%), Hyde (5.5%), and SMOK (4.0%); more than one fifth (21.8%) reported their usual brand was a brand other than the 13 listed in the survey.

The findings, published in the  Morbidity and Mortality Weekly Report , are based on data from the 2022 National Youth Tobacco Survey (NYTS), a cross-sectional, self-administered survey of U.S. middle (grades 6–8) and high (grades 9–12) school students, which was administered January 18–May 31, 2022. The study assessed current use (on one or more of the past 30 days) of e-cigarettes; frequency; and use by device type, flavors, and usual brand.

“This study shows that our nation’s youth continue to be enticed and hooked by an expanding variety of e-cigarette brands delivering flavored nicotine,” said Deirdre Lawrence Kittner, Ph.D., M.P.H., director of CDC’s Office on Smoking and Health. “Our work is far from over. It’s critical that we work together to prevent youth from starting to use any tobacco product – including e-cigarettes – and help all youth who do use them, to quit.”

Other Key Findings

• Frequency of Use: Among youth who currently used e-cigarettes, more than one in four (27.6%) used them daily and more than four in 10 (42.3%) used them on 20 or more of the past 30 days.
• Device Type: Among youth who currently used e-cigarettes, the most commonly used e-cigarette device type was disposable (55.3%), followed by prefilled or refillable pods or cartridges (25.2%), and tanks or mod systems (6.7%). Additionally, 12.8% reported not knowing the type of device used.
• Flavored E-cigarettes: Among youth who currently used e-cigarettes, 84.9% used flavored e-cigarettes, i.e., with flavors other than tobacco, including 85.5% of high school and 81.5% of middle school students reporting use. Overall, the most used flavors were fruit (69.1%); candy, desserts, or other sweets (38.3%); mint (29.4%); and menthol (26.6%).

“Adolescent e-cigarette use in the United States remains at concerning levels, and poses a serious public health risk to our nation’s youth,” said Brian King, Ph.D., M.P.H., director of the FDA’s Center for Tobacco Products. “Together with the CDC, protecting our nation’s youth from the dangers of tobacco products—including e-cigarettes—remains among the FDA’s highest priorities, and we are committed to combatting this issue with the breadth of our regulatory authorities.”

Due to changes in methodology, including differences in survey administration and data collection procedures in recent years due to the COVID-19 pandemic, the ability to compare estimates from 2022 with those from prior NYTS waves is limited; differences between estimates might be due to changes in methodology, actual behavior, or both.

Addressing Youth Tobacco Product Use

Youth use of tobacco products in any form, including e-cigarettes—is unsafe. Such products contain nicotine, which is highly addictive and can harm the developing adolescent brain. Using nicotine during adolescence might also increase risk for future addiction to other drugs.

Since 2014, e-cigarettes have been the most used tobacco product among U.S. youth. As the tobacco product landscape continues to change, the sustained implementation of comprehensive tobacco prevention and control strategies at the national, state, and local levels, coupled with FDA regulations, is critical to prevent and reduce youth access to and use of e-cigarettes.

For additional information, including quit resources, visit:

• Quick Facts on the Risks of E-cigarettes for Kids, Teens, and Young Adults | CDC
• Smokefree.gov

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Teen Vaping Is a Public Health Crisis: What You Need to Know

Published on Feb 04, 2020

Children's Hospital of Philadelphia

Parents should be concerned because:

• Vaping increases the risk of teens developing an addiction to nicotine.
• Vaping exposes children and teens to harmful metals and toxic chemicals found in e-cigarettes.
• A mysterious, vaping-related illness is on the rise: e-cigarette or vaping product use-associated lung injury (EVALI).

To get an update on the latest data and the dangers associated with vaping and e-cigarettes, we spoke with Brian Jenssen, MD, MSHP , a primary care pediatrician at Children's Hospital of Philadelphia (CHOP) and a researcher at CHOP’s PolicyLab who has worked with the American Academy of Pediatrics (AAP) to shape tobacco policy.

In short: The scope of the problem has widened, and there are many misconceptions out there. Here we share the latest data, and dispel some common myths about vaping.

Vaping is causing an epidemic of nicotine addiction in teens

The 2019 survey shows the rate of vaping among high school and middle school students continues to rise. With more than 1 in 4 high schoolers and 1 in 10 middle schoolers reporting vaping use, the need to educate families about the risks of vaping is critical. 

Tobacco is the leading cause of disease and death in the United States, and its use is entirely preventable. Repeated vaping can lead to the same risk of addiction to nicotine that comes with smoking. 

“There is remarkably clear data showing that teens who try vaping are much more likely to go on to smoke regular cigarettes,” says Jenssen. “Kids who were at low risk for smoking can be drawn to traditional tobacco products through their use of e-cigarettes.”

Children and teenagers younger than 18 years old are especially vulnerable to addiction. “Nicotine can change the biochemical pathways in the body, making paying attention more difficult and priming the brain for addiction,” says Dr. Jenssen.

Harmful chemicals and a new mysterious lung illness

We’ve learned more about the hazards of vaping by identifying some of the chemicals found in the solvents of vapes, sweeteners and flavorings, and how they change when they are heated into an aerosol. We’ve also learned what is in the bodies of teens who vape: heavy metals, volatile organic compounds (VOCs) and nicotine. 

“We are seeing the direct health harms from e-cigarette use, and prevention is the most effective tool,” says Dr. Jenssen.

Those direct health harms include a dramatic rise in acute lung injuries associated with vaping, known as EVALI. It’s unclear what is causing the condition, but the common denominator is e-cigarette use. Across the United States, more than 2,660 cases of EVALI hospitalization or deaths were reported to the CDC, as of Jan. 14, 2020. Sixty deaths related to EVALI were confirmed in 27 states and the District of Columbia during the same time. “These are lung injuries that look like the person worked in a chemical plant for years,” says Dr. Jenssen. 

A public health crisis

As smoking was decades ago, vaping is promoted with enticing advertising but little information about the very real health risks. Even more dangerous: Vaping products are designed to appeal to young people — they come in flavors like cotton candy and sour gummy worms, and with devices styled to appeal to tech-savvy teens.

In the fall of 2019, the U.S. Food and Drug Administration (FDA) issued a warning letter to JUUL Labs Inc. for misleading claims and for marketing specifically to school-age kids. The company is facing several lawsuits for deceptive marketing practices, including one from the New York Attorney General’s Office.

While several states and cities have banned or are considering bans on vaping products, the federal government has stepped in to offer its guidance. In December 2019, a national law was passed restricting all cigarettes, e-cigarette and tobacco products from being sold to anyone under the age of 21.

While this new law is a public health victory, other recent federal action falls short. We know that flavors have fueled use among teens. That’s why the new policy pulling only some flavored e-cigarettes from the market does not adequately protect youth . Allowing menthol-flavored pod devices in traditional retail settings and all flavors in refillable tank-based products sold at vape shops will continue to draw teens to vaping.

What you can do: Dispel the myths

The misleading and unsubstantiated claims made by vaping companies have left lingering myths about the dangers and use of e-cigarette products.

“Many teens — and adults for that matter — don’t know there is nicotine in this product,” says Dr. Jenssen. “They think it’s just flavored water, and that it’s completely harmless. It’s like where we were with smoking 70 years ago.”

Myth: Vaping is a “healthier” alternative to smoking. 

Fact: There is no evidence that supports the claim that vaping is a healthier alternative to smoking. These products are not regulated by the FDA and do not disclose their ingredients.

Myth : Vapes don’t contain nicotine.

Fact: “Because it’s a tobacco product that is not regulated, you can’t tell what’s in the liquid you’re buying or what goes into your body when you use it,” says Dr. Jenssen. “We know that most of the products contain nicotine, which is addictive. It’s particularly addictive and damaging in young people whose brains are still developing.” (This is an issue PolicyLab researchers are watching closely .)

On top of that, recent research on actual e-cigarette users has shown that they are taking in heavy metals like nickel, tin and lead, as well as chemicals known to cause cancer. Some of that is coming from the flavorings and the heating devices, and some is from the tobacco from which the liquids are made.

Myth: Vapes can help smokers quit smoking.

Fact: For adult smokers, Dr. Jenssen explains, there may be some benefit to e-cigarettes as a means of quitting smoking. But the evidence for that is inconclusive. Other means of quitting have been found to be more effective for adult smokers.

Myth : There are no secondhand smoke risks from vaping.

Fact: Like secondhand vapor, secondhand vapor is harmful. When kids are vaping in a school bathroom, others who enter the room inhale the nicotine and the harmful metals and chemicals.

Be informed and talk with your teen

Whether or not you think your child is vaping, Dr. Jenssen encourages parents to open the conversation and create an environment where their teens feel comfortable disclosing their vaping use, asking questions and sharing their worries.

More information and resources to help teens quit vaping

• MyLifeMyQuit. Free help for teens
• This is Quitting by Truth Initiative
• 2018 NYTS Data: A Startling Rise in Youth E-cigarette Use
• Quick Facts on the Risks of E-cigarettes for Kids, Teens, and Young Adults
• FDA warns JUUL Labs for marketing unauthorized modified risk tobacco products, including in outreach to youth
• Vaping Illness Tracker by the New York Times

Contributed by: Brian Jenssen, MD, MSHP

Categories: Health Tip of the Week

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Study shows alarming rise of electronic vaping use in US adolescents

by Florida Atlantic University

Electronic vapor products (EVPs), also known as e-cigarettes or vaping devices, have an allure because of their marketed image as a safer alternative to traditional cigarette smoking and for their variety of appealing flavors.

Yet, they contain many substances beyond nicotine, including propylene glycol , glycerin, flavorings and potentially harmful chemicals such as formaldehyde and metals, which could pose significant health risks such as respiratory disease, cardiovascular disease and cancer. Vaping also is strongly linked with a serious medical condition that damages the lungs due to the vitamin E acetate, an additive used in tetrahydrocannabinol-containing e-cigarettes.

In 2022, 6% of adults in the U.S. reported current vaping device use. Widespread use by adults has raised concerns about EVP use among adolescents.

Researchers from Florida Atlantic University's Schmidt College of Medicine explored temporal trends in EVP use from the Youth Risk Behavior Survey for ninth through 12th grades among 57,006 subjects from 2015 (earliest available data) to 2021 (most recently available data) from the U.S. Centers for Disease Control and Prevention.

Results of the study, published online ahead of print in Ochsner Journal , show alarming statistically significant and clinically important increases of the daily use of EVPs in U.S. adolescents.

Daily use of EVPs increased from 2% in 2015 to 7.2% in 2019, greater than three-and-one-half times increase. Although the percentage decreased to 5% in 2021, it was still more than a two-and-one-half increase since 2015. The researchers speculate that the effects of COVID-19, which included lockdowns and remote schooling, may have contributed to the decrease in 2021 but cautioned that further research is warranted.

Findings also show that in 2015, the percentage of EVP use was significantly higher in boys (2.8%) than girls (1.1%). By 2021, the percentage of EVP use was higher in girls (5.6%) than boys (4.5%), a one-and-one-quarter increase.

In addition, the percentage of EVP use in 2021 was higher in whites (6.5%) than Blacks (3.1%), Asians (1.2%), and Hispanic/Latinos (3.4%) compared to 2015. However, white and Black adolescents had the highest increases of about threefold between 2015 and 2021. In all four survey years, daily EVP use was highest in grade 12 where most students are ages 17 to 18.

"EVP use increases risks of nicotine addiction, drug-seeking behavior, mood disorders and long-term risks of avoidable premature morbidities and mortality," said Charles H. Hennekens, M.D., Dr.PH, first author, first Sir Richard Doll Professor of Medicine and senior academic advisor, FAU Schmidt College of Medicine.

"In addition, compared to nonusers, adolescents and young adults who use EVPs are more likely to switch to cigarette smoking, which, despite remarkable declines in the U.S., remains the leading avoidable cause of premature death in the U.S. and worldwide."

The researchers also raise concerns about risks of short- as well as long-term use of EVPs.

"Almost 100% of e-cigarettes sold in the U.S. contain nicotine, and the use of these products by adolescents may lead to future abuse of and addiction to additional substances," said Panagiota "Yiota" Kitsantas, Ph.D., senior author and professor and chair of the Department of Population Health and Social Medicine, FAU Schmidt College of Medicine.

"EVP use is not a safer alternative to smoking but may have contributed to the decline in regular tobacco product use. EVP use also raises concerns about new health risks, including nicotine addiction."

While data indicate a substantial decline in traditional cigarette smoking among U.S. adolescents, the introduction of EVP use and their alarming increases have presented new challenges. The researchers believe that the data create clinical and public health challenges.

"These alarming trends in the use of EVPs suggest the need for targeted interventions such as mass media campaigns and peer interventions to combat the influences of social norms that promote the adoption of risky health behaviors during adolescence," said Hennekens. "Clinical interventions could include routine screening for vaping and nicotine dependence during adolescent health assessments as well as counseling and tailored cessation programs."

Study co-authors are Adedamola Adele, Department of Biomedical Science; Maria C. Mejia, M.D., professor of population health and social medicine; and Robert S. Levine, M.D., affiliate professor of family medicine, all within the Schmidt College of Medicine.

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• Open access
• Published: 15 November 2016

Hypothesis: may e-cigarette smoking boost the allergic epidemic?

• Jean Bousquet 1 , 2 , 3 , 4 , 9 ,
• Claus Bachert 5 ,
• Laura Crotty Alexander 6 , 7 &
• Frank T. Leone 8  

Clinical and Translational Allergy volume  6 , Article number:  40 ( 2016 ) Cite this article

6506 Accesses

7 Citations

7 Altmetric

Metrics details

IgE-associated allergic diseases represent a global health problem increasing in prevalence and severity. An epidemic of IgE-associated allergic diseases has occurred over the past decades globally [ 1 , 2 ] and many factors driving this epidemic are not clear. The most common diseases (asthma, rhinitis and eczema) are linked, at least partly, to IgE immune response. These diseases are complex multifactorial disorders, with both genetic and environmental components. Reasons explaining the allergy epidemic are not clear. Many inhalants such as air pollution and diesel exhaust particulates are associated with a modulation of the IgE response [ 3 ]. On the other hand, tobacco smoking has a minimal effect on the increased prevalence or severity of allergic rhinitis [ 4 ].

Any new inhaled compound should be considered a potential adjuvant of the IgE immune response or non-allergic mechanisms leading to a boost in the allergy epidemic. E-cigarettes are largely used to replace conventional cigarette smoking with the intention to reduce known risks for smokers’ health; however, many side effects may still be unknown. Here we focus on the question of whether allergy may be theoretically associated with e-cigarette use.

E-cigarette vaping

The behaviour of smoking is a cardinal sign of a complex, biosocial compulsive disorder of the brain—a disorder induced by repeated exposure to nicotine [ 5 ]. As a result, the global tobacco epidemic claims nearly 6 million lives annually, despite a near-universal appreciation of the catastrophic health consequences of continued use [ 6 ]. Ambivalence, or the continuation of maladaptive behaviours in the face of a rational desire to stop, is the hallmark of nicotine dependence, and is frequently resolved by adopting compromise positions, including the use of “light” or filtered cigarettes [ 7 ].

The electronic cigarette, or “e-cigarette”, is the latest addition to the list of available compromise products introduced to western markets by the industry. A wide variety of devices are available, with an array of design features and constituent components that significantly influence the pharmacologic/toxicologic profile of each device [ 8 ]. Though the devices vary greatly in technical specifications, they most commonly deliver a nicotine-containing aerosol to the aeropharyngeal mucosa, and have been heavily marketed as healthier alternatives to tobacco smoking. In addition to varying amounts of nicotine, the aerosol also delivers propylene glycol and vegetable glycerin, humectants used as a stabilizing vehicle and to create the appearance of smoke plumes, and one or more flavourant additives to increase the appeal of the product [ 9 ].

Precisely because the consequences of conventional smoking are so serious, it has been easy for e-cigarette users [ 10 ], physicians [ 11 ], and at least one professional society [ 12 ] to explicitly judge these electronic nicotine delivery devices to be safer than cigarettes. In fact, e-cigarette aerosols do contain far lower concentrations of common cigarette carcinogens on average [ 13 , 14 ]. However, what is true of the average is not true of the individual products [ 15 ]. Neither is it true that carcinogenesis is the sole hazard presented by the aerosol. A growing body of evidence suggests that e-cigarette aerosol constituents may have their own unique hazard profile, distinct from that expected from smoke. For example, a number of disturbances in airway epithelial, endothelial, and inflammatory regulatory physiology have been identified, with uncertain implications on long term health [ 16 , 17 ].

In high school students in South Korea, e-cigarette users have an increased association with asthma and are more likely to have had days absent from school due to severe asthma symptoms [ 18 ].

Effects of e-cigarettes on human host defence and bacterial virulence

E-cigarette vapor inhaled into human airways interacts with several types of cells, including epithelial and macrophages. Exposure of human cells to e-cigarette vapor has been found to alter innate immune responses and inflammatory signaling [ 17 , 19 – 22 ]. In this way, e-cigarette use may induce inflammatory lung diseases or increase susceptibility to invasive bacterial pathogens by effects on mammalian airway cells. Acute e-cigarette vapor inhalation has been found to alter airway physiology: 5 min of e-cigarette use increased airways resistance and lowered the fractional exhalation of nitric oxide (NO), both of which suggest activation of pathways known to be important in asthma [ 23 ].

Mammalian cells are not the only residents of human airways. Bacterial pathogens such as Staphylococcus aureus ( S. aureus ) commonly colonize the airways and are exposed to inhalants. E-cigarette exposure imposes stress on S. aureus , inducing changes in the surface charge, hydrophobicity, and biofilm formation. Nicotine alone had subtle effects, while both aerosolized propylene glycol and vegetable glycerin had dramatic effects on bacterial pathogenicity, independently or when used together. Changes induced by e-cigarette vapor improved the ability of S. aureus to adhere to and invade epithelial cells, and increased resistance to human antimicrobial peptides. When unflavored e-cigarette vapor exposed S. aureus were introduced into mouse lungs, increased virulence was found via increased bacteria within lung parenchyma and increased mortality [ 17 , 21 ]. Thus, the use of e-cigarettes may increase the incidence and severity of bacterial lung infections by both direct effects on human cells of host defense as well as effects on bacterial cells. It is unknown whether flavorants will also have pro-virulent effects on bacteria.

Staphylococcus sensitization and allergic diseases

Staphylococcus aureus is a frequent colonizer of the upper and lower airways and the skin. In the nose, it may form biofilms and resides intramucosally, and has been associated with different types of T helper cell reactions; recently, arguments for a role also in Th2 immune diseases such as chronic rhinosinusitis and asthma accumulate [ 24 ]. S. aureus forms a rich immune proteome, with more than 1500 different proteins comprising virulence factors, enterotoxins including classical superantigens, and proteins with enzymatic properties. Whereas the classical enterotoxins may activate T cell populations unspecifically via the variable ß-chain of T cell receptors, other recently discovered molecules such as serine protease-like proteins (spls) obviously elicit a strong Th2-biased immune response and act as allergens or super-allergens, as they induce IgE formation also to inert proteins [ 25 ]. Spl-specific memory T cells elaborate Th2 cytokines including IL-4, IL-5 and IL-13, whereas small amounts of IFN-γ, IL-6, TNF and IL-17 are produced. IL-4 and IL-13 drive the immunoglobulin class switch to IgE, and IL-5 orchestrates activation and survival of eosinophils. Both protein families, enterotoxins and spls, have been found in human airway mucosa, and elicit, when given intra-tracheal in mice, an allergic lung inflammation. A typical hallmark of Th2 reactions is the formation of IgE; in the presence of staphylococcal superantigens, a polyclonal IgE formation is regularly found including several IgE antibodies directed towards classical and ECG-locus enterotoxins (SE-IgEs); the latter are indicators of a manifest immune reaction to S. aureus products. SE-IgE was significantly associated with asthma in 3000 Europeans [ 26 ] and was shown to be linked to severe asthma, atopic or non-atopic, both in European [ 27 ] and Asian populations [ 28 ]. MeDALL (Mechanisms of the Development of Allergy) proposed that S. aureus sensitization was associated with a re-occurrence of foetal Type 2 signalling leading to the onset of IgE and non-IgE dependent diseases [ 29 , 30 ].

From a hypothesis to public health strategies

The hypothesis that e-cigarette increases S. aureus colonisation and then induces sensitization is important to consider since S. aureus colonisation is needed for the development of an IgE immune response that is often associated with a polyclonal IgE response, allergic symptoms of the upper and lower airways including allergic rhinitis, severe asthma and/or chronic rhinosinusitis with nasal polyposis.

Although currently there is no confirmation that e-cigarette smoking may induce allergic diseases, there is sufficient background to seriously consider this hypothesis and to test it in appropriate cross-sectional and longitudinal epidemiologic studies. If the effect on S. aureus colonisation is important in e-cigarette users, the proof-of-concept should already be demonstrable, as e-cigarettes were introduced to the international market in 2007 and now are used by upwards of 10% of US, European and Asian populations [ 31 ]. One possible differentiating feature of e-cigarette-induced allergy would be that users may be prone to develop polysensitization whereas people developing allergic sensitization in adulthood are more often monosensitized or oligosensitized [ 32 ].

The next important step will be the identification of e-cigarette components which may induce allergic mechanisms: propylene glycol, glycerine, nicotine, flavorants or toxins produced by the aerosolization process. Animal studies may help guide human studies by narrowing the field of relevant e-cigarette components, defining molecular pathways affected by e-cigarette vapor, and assessing duration and extent of exposure needed to confer increased risk of allergic disease. Large human trials will then be needed to confirm findings with a sufficient power to discriminate between the different types of e-cigarettes.

The most difficult task will be to derive health promotion strategies from this hypothesis. Proponents of e-cigarettes will indicate that allergy and asthma are trivial diseases by comparison to putative cancer prevention [ 12 , 31 , 33 ]. Opponents to e-cigarettes will take the asthma example as a major adverse health consequence of e-cigarette vapor inhalation. In any case, removal of the IgE-promoting components will be needed.

In conclusion, it is urgent to confirm or refute this hypothesis using appropriate studies.

Abbreviations

interleukin

nitric oxide

Staphylococcus aureus

serine protease-like proteins

tumor necrosis factor

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Bousquet, J., Bachert, C., Alexander, L.C. et al. Hypothesis: may e-cigarette smoking boost the allergic epidemic?. Clin Transl Allergy 6 , 40 (2016). https://doi.org/10.1186/s13601-016-0130-y

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Chemicals in vapes could be highly toxic when heated, research finds

AI analysis of 180 vape flavours finds that products contain 127 ‘acutely toxic’ chemicals, 153 ‘health hazards’ and 225 ‘irritants’

Chemicals used to produce vapes could be acutely toxic when heated and inhaled, according to research .

Vaping devices heat the liquid flavouring to high temperatures to form an aerosol that is then inhaled. They contain chemicals including vegetable glycerin, propylene glycol, nicotine and flavourings, blended in various amounts.

Previous experiments have shown that some fruit-flavoured vapes – such as strawberry, melon and blueberry – produce dangerous compounds called volatile carbonyls due to this heating process.

These compounds are known to have health implications for chronic obstructive pulmonary disease (COPD), cardiovascular disease and cancers.

With so many chemicals used in tens of thousands of different vape products, conducting experiments to test every brand and flavour for toxicity could take decades of research.

Instead, the study used AI to analyse the chemical composition of 180 vape flavours and simulate how they decompose when heated. The research, published in Scientific Reports , predicted that vapes produce 127 “acutely toxic” chemicals, 153 “health hazards” and 225 “irritants”.

Nearly every flavour put through the AI predictor showed at least one product that was classified as a health hazard, with many predicting several. The toxins were associated with vapes containing no nicotine, as well as those with.

The research team at RCSI University of Medicine and Health Sciences, Dublin , conclude there is a “potential public health threat facing the 4.5 million vapers in the UK” and an urgent need for “enhanced restrictions” on flavours and regulations that are reflective of the health risks of vaping, especially for young people.

In January, the government announced that it would ban disposable vapes and restrict sweet and fruity flavours . Lead author Donal O’Shea, professor of chemistry at RCSI, said that the UK government should go further and remove all flavours from vapes.

It is crucial to understand the impact of flavoured vapes on health “before it’s too late”, he added.

“It is plausible that we are on the cusp of a new wave of chronic diseases that will emerge 15 to 20 years from now due to these exposures.”

Given the popularity of flavoured vapes among non-smoking teenagers and young adults, understanding the long-term effects of these products on public health, morbidity and mortality is crucial, the study concludes.

“Without comprehensive regulation, as we try to treat the nicotine addictions of older tobacco smokers, there is a substantial risk of transferring new health issues to younger generations.”

Responding to the findings, a Department of Health and Social Care spokesperson said: “The health advice is clear – if you don’t smoke, don’t vape and children should never vape.

“That’s why we are banning disposable vapes and our tobacco and vapes bill includes powers to limit flavours, packaging and displays of vapes to reduce the appeal to children.

“It is clear that flavours like cotton candy and cherry cola are deliberately being targeted at children, not adult smokers trying to quit, which is completely unacceptable. That is why we are taking decisive action and will be restricting vape flavours.”

Prof Sanjay Agrawal, the Royal College of Physicians’ special adviser on tobacco, said that while vaping can be a very effective way to break the addiction to tobacco, it should only be used for this purpose.

“Vaping is not risk-free, so those who don’t smoke, including children and young people, should not vape either,” he said.

John Dunne, director general at the trade body the UK Vaping Industry Association, said: “The science on vaping is very clear, it is the most effective way for smokers to quit and is at least 95% less harmful than smoking. Every chemical used in vaping e-liquid in the UK is stringently tested, including analysing chemicals when heated, and is only approved for use by the UK government if it is deemed safe.”

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Vaping Rates Fall Among Teens, But Still Too High

Medically reviewed by Carmen Pope, BPharm . Last updated on May 14, 2024.

By Ernie Mundell HealthDay Reporter

TUESDAY, May 14, 2024 -- Vaping rates among U.S. kids in grades 9 through 12 fell to 5% in 2021, the latest year for which data is available.

That's down from a peak of 7.2% of teens who vaped in 2019, a new report finds.

However, the 5% vaping rate observed in 2021 is still more than double the 2% rate observed among teens in 2015, the study authors noted.

It's also only slightly less than the 6% of adults who vaped in 2022.

All of this doesn't bode well for teens' long-term health, said study senior author Panagiota Kitsantas .

“Almost 100% of e-cigarettes sold in the U.S. contain nicotine , and the use of these products by adolescents may lead to future abuse of and addiction to additional substances,” said Kitsantas, chair of population health and social medicine at Florida Atlantic University in Boca Raton.

The new study is based on a look at data from an ongoing database of youth behaviors compiled by the U.S. Centers for Disease Control and Prevention.

The data on vaping involved over 57,000 people and began in 2015.

The data also showed a pronounced switch in which teens are more prone to vape. In 2015, boys were more prone to the habit than girls were (2.8% vs 1.1%, respectively). However, by 2021 girls were more likely to vape than boys (5.6% vs 4.5%, respectively), the study found.

Between 2015 and 2021, 12th graders consistently had the highest rate of e-cigarette use, compared to lower grades, the study found.

Vaping "increases risks of nicotine addiction, drug-seeking behavior [and] mood disorders," all of which raise a person's odds for illness and death over time, warned study lead author Dr. Charles Hennekens , professor of medicine at Florida Atlantic.

And even though some may view vaping as an alternative to tobacco smoking , research has shown that people who vape "are more likely to switch to cigarette smoking, which, despite remarkable declines in the U.S., remains the leading avoidable cause of premature death in the U.S. and worldwide," Hennekens said in a university news release.

According to Kitsantas, the continued high uptake of vaping by youth “suggest the need for targeted interventions such as mass media campaigns and peer interventions to combat the influences of social norms." She also believes that doctors should routinely screen young patients about whether or not they vape.

The findings were published in the May issue of the Oschner Journal .

• Florida Atlantic University, news release, May 13, 2024

Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.

© 2024 HealthDay. All rights reserved.

Posted May 2024

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The Influence of Friends on Teen Vaping: A Mixed-Methods Approach

Allison l. groom.

1 American Heart Association Tobacco Regulation and Addiction Center, Dallas, TX 75231, USA; [email protected] (R.L.L.); [email protected] (A.K.); [email protected] (R.M.R.)

Thanh-Huyen T. Vu

2 Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; ude.nretsewhtron@uvneyuh

Robyn L. Landry

Anshula kesh, joy l. hart.

3 Department of Communication, University of Louisville, Louisville, KY 40292, USA; [email protected] (J.L.H.); [email protected] (K.L.W.); [email protected] (L.A.W.)

Kandi L. Walker

Lindsey a. wood, rose marie robertson.

4 Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA

Thomas J. Payne

5 Department of Otolaryngology, Head and Neck Surgery, University of Mississippi Medical Center, Jackson, MS 39216, USA; moc.liamg@321pjtrd

6 Department of Psychiatry, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

Associated Data

Data available upon request.

Vaping is popular among adolescents. Previous research has explored sources of information and influence on youth vaping, including marketing, ads, family, peers, social media, and the internet. This research endeavors to expand understanding of peer influence. Our hypothesis is that friends’ influence on teen vapers’ first electronic nicotine delivery systems (ENDS) use varies by demographic variables and awareness of ENDS advertising. In August–October 2017, youth ( n = 3174) aged 13–18 completed an online survey to quantify ENDS behaviors and attitudes and were invited to participate in follow-up online research in November-December 2017 to probe qualitative context around perceptions and motivations ( n = 76). This analysis focused on the ENDS users, defined as having ever tried any ENDS product, from the survey ( n = 1549) and the follow-up research ( n = 39). Among survey respondents, friends were the most common source of vapers’ first ENDS product (60%). Most survey respondents tried their first ENDS product while “hanging out with friends” (54%). Among follow-up research participants, the theme of socializing was also prominent. ENDS advertising and marketing through social media had a strong association with friend networks; in fact, the odds of friends as source of the first vaping experience were 2 times higher for those who had seen ENDS ads on social media compared with other types of media. The influence of friends is particularly evident among non-Hispanic Whites, Hispanics/Latinos, those living in urban areas, those living in high-income households, those with higher self-esteem, and those who experiment with vaping. These findings support the premise that peer influence is a primary social influencer and reinforcer for vaping. Being included in a popular activity appears to be a strong driving force.

1. Introduction

Awareness and use of electronic cigarettes (e-cigarettes) and other electronic nicotine delivery systems (ENDS) has become increasingly pervasive among U.S. teens in recent years. High rates of e-cigarette awareness have been observed among middle school (84.3%) and high school (92.0%) students [ 1 , 2 ]. Further, in the 2019 National Youth Tobacco Survey, 27.5% of high school students and 10.5% of middle school students reported current e-cigarette use. More than five million students had used e-cigarettes in the past 30 days; nearly one million used them daily [ 3 , 4 ]. More than half (51.2%) of middle school e-cigarette users identified e-cigarettes as the first tobacco product tried [ 1 ].

Teen vaping can be influenced by several sources of information, including marketing, family, peers, and the internet. Exposure to e-cigarette marketing and advertising is associated with openness to using e-cigarettes and curiosity about trying e-cigarettes [ 5 ] and predicts subsequent e-cigarette experimentation among teens who have never used tobacco [ 6 ]. Furthermore, exposure to e-cigarette marketing is associated with use of other tobacco products, including cigarettes, hookah and cigars, as well as polytobacco product use [ 7 ]. Exposure to tobacco product promotions, including advertising and social media, is significantly associated with ever and current smoking and vaping, and susceptibility to vaping among never-users [ 8 ].

Vaping is popular among teens. Both teens who had used and those who had never used e-cigarettes acknowledged their popularity and acceptance among their peers [ 9 ]. The literature on teen vaping identifies peer influence (having peers who use tobacco) as one of the most common drivers of teen e-cigarette use, with demographics (male gender identity, older age, higher amount of pocket money) and other tobacco use behavior (such as regular and heavier use) also associated with use [ 10 ]. Peer influence (friends use) is cited as a reason for using ENDS [ 11 ] and, in particular, for liking JUUL pods [ 12 ]. Social acceptance (cool social image, fitting in) plays a role in appeal [ 13 ]. The most common source for getting e-cigarettes is a friend, followed by a family member. A quarter of youth live with someone who uses e-cigarettes, which plays a role in vaping: a third of youth who live with an e-cigarette user reported receiving or buying e-cigarettes from a family member, a higher proportion compared to those not living with an e-cigarette user [ 14 ].

This research endeavors to expand understanding of influences on teen vaping. Our hypothesis is that friends’ influence on teen vapers’ first ENDS use varies by demographic variables and awareness of ENDS advertising.

2. Materials and Methods

A mixed-methods approach was used, including online quantitative and qualitative analyses. This approach allowed us to quantify vaping behavior, knowledge, and attitudes, as well as explore motivations and barriers through in-depth responses.

2.1. Quantitative Online Survey

2.1.1. recruitment.

From August to October 2017, researchers conducted a quantitative online survey with a U.S. sample of teens aged 13–18. The online approach provided access to a diverse nationwide sample, recruited by an established marketing research vendor that manages an online panel of 65,000 U.S. teens and young adults. Members were recruited via buzz campaigns, newspaper ads, and social networks. Panelists earn points for each completed survey that can be redeemed for prizes. Panel management procedures complied with marketing research industry standards set by professional marketing research associations. Procedures for obtaining proper online consent were implemented. No identifying information was collected, and guidelines established by the Children’s Online Privacy Protection Act (COPPA) were followed. Teen participants were given assent forms and could elect not to participate. Parental consent was obtained for panelists under the age of 18; parents and children were informed that parents would have no access to study data. The study team had no direct contact with recruited individuals. The Chesapeake/Advarra Institutional Review Board reviewed and approved this study [ 15 , 16 , 17 , 18 ].

2.1.2. Sample

The study sample consisted of 3174 participants. The inclusion criteria were based on ENDS use status (users and non-users), age, sex, race/ethnicity, and nationwide geographic representation. Two groups of U.S. youth aged 13 to 18 years were recruited: (a) ENDS users, defined as teens who have ever tried e-cigarettes or other ENDS and (b) a control group, defined as teens who have never tried ENDS. This analysis focused on ENDS users ( n = 1549). Although respondents were asked about their current ENDS use, the focus of this research was on initiation; therefore, all respondents who had ever tried ENDS were included in the analysis. Quotas were set for key demographics, ensuring sufficient numbers of participants to examine or control for the following factors: age, sex, and race/ethnicity. Non-Hispanic Black and Hispanic/Latino respondents were oversampled to ensure sufficient sample sizes for comparison by race and ethnicity. Age, sex, and race/ethnicity data were employed to accurately weight the results. The data were weighted to be representative of the overall U.S. population in terms of age, sex, race, ethnicity, and region, based on U.S. Census data [ 15 , 16 , 17 , 18 ].

2.1.3. Measures

Demographic variables included age group based on birth year and month, sex, sexual orientation (straight or lesbian/gay/bisexual/transgender/queer), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic/Latino, and non-Hispanic Other--including more than one race, Asian or Pacific Islander, Native American/Alaska Native), place of residence (urban, suburban, or rural), and household income. Household income status was categorized as low vs. high, with low-income status defined as the respondent participating in a free/reduced cost lunch program at school or family receiving government/public assistance (Medicaid, Section 8 housing, Obama phone, food stamps, the link card/SNAP, or other government financial help). To determine ever-ENDS use, we asked “Which of the following types of tobacco have you ever tried (even one time or two times)?” and listed a choice of 10 tobacco product types with corresponding images: (1) electronic nicotine products, (2) traditional cigarettes, (3) traditional cigars, (4) cigarillos, (5) smokeless tobacco, (6) hookahs to smoke tobacco, (7) little or filtered cigars, (8) dissolvable tobacco products, (9) bidis and/or kreteks, and (10) others [ 15 , 16 , 17 , 18 ]. Vaping status was categorized as current (within the last 30 days), experimental (occasionally, but less than monthly), and former (in the past, but not now). We asked two questions regarding exposure to ENDS advertising: “In the past 3 months, have you heard, seen, or read advertising for electronic nicotine products?” and “Where have you heard, read, or seen advertising or marketing for electronic nicotine products? Choose all that apply.” Respondents were asked to choose from a list of 25 items and eight of these 25 items (i.e., Facebook, Instagram, Snapchat, Twitter, Tumblr, Pinterest, Periscope, and Bumble) were categorized as social media vs. non-social media. To examine influencers, we asked “Where did you get your first electronic nicotine product? Choose one.” Respondents were shown a list of options, the order of which was randomized, and were asked to choose one of the following: (1) a friend, (2) a family member or relative, (3) a neighbor, (4) someone else, but not a friend or relative, (5) I bought it at a store, (6) other, and (7) I don’t remember. Respondents were then asked “Where were you when you first used electronic nicotine products? Choose one.” They were asked to choose one of the following randomized answers: (1) hanging out with friends, (2) at parties, (3) by myself, (4) with my family, (5) school, (6) other, and (7) I don’t remember. Finally, we assessed self-esteem by asking “Self-esteem is defined as how much you like yourself. Please respond to the following statement: “I have high self-esteem.” Respondents were asked to rate their self-esteem on a 7-point Likert scale where 1 = not very true of me and 7 = very true of me. The responses were grouped into two categories: low (1–4) and high (5–7) [ 19 , 20 ].

2.1.4. Statistical Analysis

Descriptive analyses were used to show the distribution of where respondents got their first ENDS product and where they first used ENDS. Differences in demographic characteristics related to influences were compared using Chi-square tests. In multivariable analyses, logistic regression models were used to estimate the odds of reporting a friend as the source of first ENDS product as well as reporting hanging out with friends as the location of first ENDS use by age group, sex, race/ethnicity categories, place of residence, household income status, sexual orientation, awareness of ENDS advertising or marketing, vaping status, and self-esteem. Sampling weight was generated and applied in the analysis. Analyses were conducted with SAS statistical software (version 9.4 with SAS/STAT 14.1, SAS Institute Inc., Cary, NC, USA).

2.2. Qualitative Online Community Research

2.2.1. recruitment.

Survey respondents were invited to participate in a follow-up online community. During two weeks in November and December 2017, participants were asked to visit the online community each day to answer questions and conduct interactive activities.

2.2.2. Sample

A total of 76 survey respondents participated in the online community, including 39 ENDS users and 37 never ENDS users. This analysis focused on the 39 ENDS users.

2.2.3. Measures

The participants were asked to “Finish the following sentences to help us learn more about vaping: I vape because_____; The best things about vaping are _____; The worst things about vaping are_____.” They were allowed to enter multiple responses.

2.2.4. Analysis

The data were analyzed using inductive qualitative content analysis to identify themes that emerged. Using an open coding technique, codes were assigned to participant responses using their words and uploaded images to establish the coding scheme.

3.1. Quantitative Online Survey

3.1.1. sample characteristics.

The weighted sample of 1549 ENDS users included teens across three age groups: 13–14 (14.1%), 15–16 (34.7%), and 17–18 (51.3%). Of the sample, 56.9% were male and 43.1% were female; 64.4% were non-Hispanic White, 10.1% were non-Hispanic Black, and 3.7% were non-Hispanic Other; 21.7% were Hispanic/Latino; 22.7% identified as LGBTQ; 52.8% were from low-income households; 36% lived in urban areas, 40.2% suburban, and 23.8% rural; and 24.5% rated their self-esteem as low. Additionally, in terms of vaping status, 35% were current users, 21.3% experimenters, and 43.7% former users. We observed significant differences by sex with regard to age (females skewed older than males), race/ethnicity (females were more apt than males to be non-Hispanic White), sexual orientation (females were more apt than males to identify as LGBTQ), vaping status (females were more likely to be experimenters or former users), and self-esteem (females were more likely to rate their self-esteem as low). ( Table 1 )

Participant Characteristics, Overall and by Sex—Quantitative Survey.

Data are weighted. * p -Value for comparisons between male and female participants based on Rao Scott χ 2 tests. ** Low-income status defined as participating in a free/reduced cost lunch program at school or family receiving government public assistance (Medicaid, Section 8 housing, Obama phone, food stamps, the link card/SNAP, or other government financial help). The bold numbers represent statistical significance.

3.1.2. Awareness of ENDS Advertising

Slightly more than one-half of the ENDS users ( n = 830, 53.6%) in the online survey said they had heard, seen, or read advertising for electronic nicotine products in the past three months, and the majority ( n = 508) received information from social media.

The most common sources of advertising were point-of-purchase outlets: vape stores (48.6%) and convenience stores or gas stations (41.0%). Other common sources were TV (38.1%), Facebook (33.0%), Instagram (31.9%), YouTube video (31.4%), and website (25.2%) ( Table 2 ).

Awareness of ENDS advertising or marketing—Quantitative Survey.

Data are weighted. * Social media sources. ** Other types of media sources/locations.

3.1.3. Sources of First ENDS Product

Among survey respondents, friends were the most common source of the first ENDS product (59.7%). Less frequently cited sources were a family member or relative (16.0%), store (8.0%), someone else/other (9.0%), and don’t remember (7.3%). Significant differences were observed by age, sex, race, awareness of ENDS advertising/marketing, and self-esteem. Older teens were significantly more likely than younger teens to identify a friend as the source of their first ENDS product (62.3% of 17–18 vs. 57.5% of 13–14 and 56.8% of 15–16, p -Value < 0.016). Older teens were significantly less likely than younger teens to identify a family member as the source (12.9% of 17–18 vs. 18.8% of 13–14 and 19.5% of 15–16, p -Value < 0.016). Although friends were the most common source for both females and males, females were significantly more likely than males to indicate a family member was the source (20.9% vs. 12.3%, p -Value < 0.001) ( Table 3 ).

Source of First ENDS Product, Overall and by Sex, Age, and Race/Ethnicity—Quantitative Survey.

* p -Value for comparisons across source of first ENDS product based on Rao Scott χ 2 tests. Data are weighted.

The odds of ENDS users identifying a friend as the source of their first ENDS product were significantly higher for non-Hispanic Whites compared with non-Hispanic Blacks (OR: 1.78, 95% CI: 1.19, 2.67; p = 0.005) and Hispanics/Latinos compared with non-Hispanic Blacks (OR: 1.95, 95% CI: 1.19, 3.20; p = 0.008); users who had seen, heard, or read ENDS advertising on social media compared with other types of media channels or locations (OR: 2.04, 95% CI: 1.41, 2.96; p < 0.001); users with high self-esteem compared with users with low self-esteem (OR: 1.35, 95% CI: 1.02, 1.78; p = 0.038); urban compared with rural (OR: 1.50, 95% CI: 1.04, 2.15; p = 0.028); high income compared with low income (OR: 1.97, 95% CI: 1.49, 2.61; p < 0.001); and vaping experimenters (OR: 1.80, 95% CI: 1.25, 2.61; p < 0.002) ( Table 4 ).

Multivariable Adjusted Odds Ratios (95% CI) of Friends as Source of First ENDS Product – Quantitative Survey.

* Low-income status defined as participating in a free/reduced cost lunch program at school or family receiving government public assistance (Medicaid, Section 8 housing, Obama phone, food stamps, the link card/SNAP, or other government financial help); ** Social media sources—Facebook, Instagram, Snapchat, Twitter, Tumblr, Pinterest, Periscope, and Bumble; *** High self-esteem, rated 5–7; low self-esteem, rated 1–4.

3.1.4. Location of First ENDS Product Use

Most online respondents tried their first ENDS product while “hanging out with friends” (54.0%). Less frequent locations reported were by myself (13.5%), with my family (10.1%), at school (7.8%), at parties (7.1%), I don’t remember (5.5%), and other (2.1%). Significant differences were observed by sex and race/ethnicity. Although friends were mentioned most often by both males and females (55.4% of females; 52.9% of males), females were significantly more likely than males to have tried their first ENDS product with family (13.9% of females vs. 7.3% of males, p -value < 0.001). Non-Hispanic White and Hispanic/Latino respondents were significantly more likely than non-Hispanic Black respondents to have tried their first ENDS product with friends (56.7% of non-Hispanic White and 54.6% of Hispanic/Latino vs. 37.7% of non-Hispanic Black, p -value = 0.042). ( Table 5 ) The odds of ENDS users trying their first ENDS product while “hanging out with friends” were significantly higher for non-Hispanic Whites compared with non-Hispanic Blacks (OR: 2.04, 95% CI: 1.35, 3.06; p < 0.001) and Hispanics/Latinos compared with non-Hispanic Blacks (OR: 1.91, 95% CI: 1.17, 3.10; p = 0.010). The odds were significantly lower for the 13–14 age group compared with the 17–18 age group (OR: 0.67, 95% DI: 0.44, 1.02; p = 0.062). The odds were significantly higher for high income compared with low income respondents (OR: 1.60, 95% CI: 1.22, 2.11; p < 0.001). And, the odds were signficantly higher for those who had seen ENDS ads on social media compared with other types of media (OR: 1.51, 95% CI: 1.04, 2.18; p = 0.031) and for vaping experimenters (OR: 1.56, 95% CI: 1.08, 2.27; p = 0.019) ( Table 6 ).

Location of First ENDS Product Use, Overall and by Sex, Age, and Race/Ethnicity—Quantitative Survey.

* p -Value for comparisons across location of first ENDS use based on Rao Scott χ 2 tests; Data are weighted.

Multivariable Adjusted Odds Ratios (95% CI) of Hanging Out with Friends as Location of First ENDS Product Use—Quantitative Survey.

3.2. Qualitative Online Community Research

3.2.1. sample characteristics.

The demographic subgroups in the sample of 39 ENDS users were collapsed due to the smaller sample size. The subgroups included two age groups: 13–15 (17.9%) and 16–18 (82.1%); 41.0% were male, 56.4% were female; and one participant identified as non-binary who had not done so in the online survey; 51.3% were non-Hispanic White and 48.7% were Other (non-Hispanic Black, non-Hispanic Other, Hispanic/Latino) ( Table 7 ).

Participant Characteristics—Qualitative Research.

* One online community participant identified as non-binary who had not done so in the online survey.

3.2.2. Sources of First ENDS Product

Many of the online community participants reported that, shortly after they first tried vaping, they made vaping purchases of their own for the first time. A majority bought products through a friend, a friend’s older sibling, or a friend of a friend, who guided them through the process. These more experienced vapers advised them about flavors and equipment, and sometimes made the purchase for them.

3.2.3. Associations with Vaping

In the Qualitative Online Community, the theme of socializing was prominent: one of the reasons they vaped the first time was because “my friends and I do it together” and one of the best things about vaping was “socializing with friends.” ( Figure 1 ) Most of the time ENDS users vaped with friends or other people their age. Together they shared flavors and exchanged liquids to experiment. Most vapers indicated they would vape less if their friends didn’t vape. Many participants conveyed the importance of peer influence with quotes such as the following:

Associations with Vaping. Note: Numbers in parentheses represent the number of participant comments related to the theme.

“I know a lot of people who vape. It’s pretty popular with most of my friends. If my friends didn’t vape, I doubt I would have ever started.” (Male, 16–18)

“My friends and I vape together almost every time we are together. If they did it less I probably would too.” (Female, 13–15)

Although teens tend to be open with friends about their vaping, some acknowledged the opinions of their non-vaping friends and reported a stigma against vaping that prevents them from vaping openly. ( Figure 2 ) One of the worst things about vaping, according to some ENDS users, is the negative stigma:

Openness About Vaping. Note: The size of the bubbles corresponds with the proportion of participant comments related to the theme.

“People are quick to judge you if you do it.” (Female, 13–15) ( Figure 1 )

4. Discussion

Our findings provide evidence of the important role friends play in the lives of most teens and their decision to start vaping. Most teens get their first ENDS product from a friend and recount that their first vaping experience was with friends. Older friends and acquaintances play an advisory role to the newly initiated vaper. The influence of friends is particularly evident among non-Hispanic Whites, Hispanics/Latinos, those living in urban areas, those living in high-income households, those with high self-esteem, and those who experiment with vaping. Although friends are also the most common influence among non-Hispanic Blacks, family played a more substantial role relative to other race groups. The greater influence of family was also observed for females compared with males.

ENDS advertising and marketing through social media has a strong association with friend networks, reflected by the fact that ENDS users who were initially influenced by a friend were more likely to have been exposed to ENDS messaging on social media. Acknowleding the prominent role that social media play in many teens’ social networks, anti-vaping ads placed in social media channels used by teens may have the potential to offset peer influence.

The insights from the qualitative research highlight the role of friends in the vaping experience, with some saying they might not have started vaping if their friends didn’t vape, or they might vape less if their friends vaped less. It is possible that greater time spent on social media and physical distancing practices during the COVID-19 pandemic may influence vaping behavior; these are important topics for future research.

Vaping may have a negative social stigma among friends who do not vape. Some ENDS users mentioned hiding their vaping habit from friends, although most felt comfortable with vaping openly and did not convey a sense of shame. Exploring the influence that non-vapers might have on their friends could be useful in understanding how to dissuade teen vaping initiation and uptake or encourage and support cessation.

The research had some limitations. Survey respondents who opted into the follow-up community may not represent the full survey sample or general population of teens who vape. Also, this research was conducted prior to the COVID-19 pandemic, so behavior reported does not reflect the physical distancing practiced by some teens during the pandemic or potential changes in access to e-cigarette products. Additionally, new vaping products have appeared in the marketplace since this research was conducted. Finally, this analysis focused on social aspects of vaping rather than the adverse health effects of vaping [ 21 , 22 , 23 ] and perceptions of health consequences [ 16 ], an important topic for future analysis.

5. Conclusions

These findings support the premise that peers are a primary social influencer and reinforcer for vaping. Inclusion in a popular activity appears to be a strong driving force among teens in general, but particularly among older teens (17–18), males, and non-Hispanic White and Hispanic/Latino teens. These findings support previous research indicating that certain demographics are more susceptible to peer influence than others and add new insight about the impact of social media and friends as the source of initial product use. Educational and social media strategies should consider the importance of peer influence, as well as that of the family. The retail environment is also a notable source, indicating the need for increased enforcement of purchase age restrictions.

Future research on peer influence could expand knowledge by examining: (1) non-vapers’ influence on preventing friends from vaping, (2) the potential of substituting alternative popular activities, and (3) the impact of physical distancing, stay-at-home policies and remote learning during the COVID-19 pandemic.

Acknowledgments

We used the services of Ypulse, a market research company with expertise in the teen market.

Author Contributions

Conceptualization, A.L.G., T.-H.T.V., R.L.L., A.K., J.L.H., K.L.W., R.M.R. and T.J.P.; Formal analysis, T.-H.T.V.; Funding acquisition, R.M.R.; Methodology, A.L.G., T.-H.T.V., J.L.H., K.L.W., L.A.W., R.M.R. and T.J.P.; Project administration, A.K.; Supervision, R.M.R. and T.J.P.; Writing—original draft, A.L.G. and T.-H.T.V.; Writing—review & editing, R.L.L., A.K., J.L.H., K.L.W., L.A.W., R.M.R. and T.J.P. All authors have read and agreed to the published version of the manuscript.

This research was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) and the FDA Center for Tobacco Products (CTP) under Awards P50HL120163 and U54HL120163. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the Food and Drug Administration, or the American Heart Association.

Institutional Review Board Statement

The study was approved by the Advarra Institutional Review Board (protocol code 00021530) on 4-11-17.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study or their parents. Teen participants were given assent forms and could elect not to participate. Parental consent was obtained for panelists under the age of 18; parents and children were informed that parents would have no access to study data.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

12 Things That Happen to Your Body When You Stop Vaping

What is vaping.

W hen e-cigarettes first hit the market in late 2000, they were believed to be a safer alternative to tobacco cigarettes, but now there is evidence to the contrary. As of 2020, the Centers for Disease Control and Prevention (CDC) counted close to 3,000 cases of the vaping related lung disease known as EVALI (e-cigarette, or vaping, product use-associated lung injury). In statistics gathered by 29 states, the agency has recorded 68 deaths. And then there’s the potential for the habit to aggravate the symptoms of COVID-19 , potentially leading to severe cases and increasing the risk of death from COVID.

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Vaping is deadly. It’s also addictive. Vaping with a JUUL can be as dangerous as smoking a pack of cigarettes a day. When you vape, you inhale liquid (or e-juice) from a cartridge attached to the vaping device. In addition to nicotine, that liquid can contain dozens of other chemical ingredients and flavorings.

Kids and teenagers have been especially attracted to vaping , thanks in part to attractive flavors like bubble gum, mango, and mint. In November of 2023, the CDC reported that among high school students in the US, overall tobacco use declined during 2022-2023 (16.5% to 12.6%), primarily because of a drop in e-cigarette use.

That may be in part be because in June 2022, the FDA issued marketing denial orders (MDOs) to JUUL Labs Inc., forcing the company to stop selling and distributing its JUUL device and four types of its “JUULpods.” The move was part of a largescale effort by the FDA to put the vaping industry under a microscope, requiring companies to prove that their e-cigarette products benefit the public health by helping people cut back on or quit smoking. Though the agency later decided to temporarily suspend the order to conduct more research, there is still a federal push to regulate vaping and encourage the public to stop using e-cigarettes.

Quitting vaping can be difficult though, just like trying to stop smoking. And while quitting can be hard on the body, you’ll mostly start to benefit as soon as you make the decision to kick the habit. Read on to learn exactly what happens in your body the minute you stop vaping.

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20 minutes later: Cardiovascular improvements

In as little as 20 minutes, “your heart rate returns to normal, your blood pressure drops, and your circulation starts to normalize,” says Nikola Djordjevic, MD, project manager of Med Alert Help .

Your breathing may improve, too: The two key ingredients in an e-cigarette—propylene glycol and vegetable glycerin— produce chemicals when heated that are detrimental to your respiratory tract, according to research published in 2018 in the  International Journal of Environmental Research and Public Health. “When you quit vaping, you should find that your breathing becomes less labored and your airflow is clearer,” says Caleb Backe, a certified health and wellness expert for Maple Holistics .

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A few hours later: Nicotine withdrawals

Nicotine is addictive, and you may experience some minor and temporary symptoms. “Acute nicotine withdrawal symptoms can be psychological and physical,” says Dr. Djordjevic. The psychological symptoms can include cravings for nicotine, mood swings, trouble concentrating, irritability , and anxiety, he says. Physical symptoms include “headaches, sweating, tremors, insomnia, increased appetite, abdominal cramps, and constipation,” Dr. Djordjevic says.

These are the first effects you’re likely to feel, often within four to 24 hours after quitting. These effects will peak around day three, Dr. Djordjevic says, “and gradually decrease during the following three to four weeks. So it will take around a month to break the habit.” If you think smoking e-cigarettes is healthy, these  silent ways vaping impact your body may surprise you.

“I Got Lung Disease from Vaping and Almost Died”

One day later: Heart attack risk falls

According to a 2018 study published in the American Journal of Preventive Medicine, daily e-cigarette use doubles a person’s risk for a heart attack . If you quit, however, the risk begins to fall very quickly. “After just one day, your heart attack risk starts to decrease thanks to the lowering of blood pressure, rising blood oxygen levels, and reducing the negative influence on cholesterol levels and the formation of blood clots ,” Dr. Djordjevic says.

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Two days later: Senses begin to improve

Vaping, like cigarette smoking, can blunt your senses, reducing your ability to smell and taste. After just 48 hours without a puff, you may begin to notice your ability to taste and smell food has improved. Nicotine affects more than your brain; new research suggests nicotine can raise your blood sugar, too.

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Three days later: Nicotine is out of the body

If you haven’t had nicotine withdrawals yet, you may start experiencing them by day three. “Nicotine leaves your body on day three, which is why withdrawal symptoms peak then,” Dr. Djordjevic says.

“You can have withdrawal symptoms of nicotine in the form of a headache, sweating, abdominal cramping, or nicotine cravings,” says Osita Onugha, MD , thoracic surgeon and director of thoracic surgery research and surgical innovation lab at John Wayne Cancer Institute at Providence Saint John’s Health Center in Santa Monica, California.

One month later: Lungs begin to show how much healthier they are

Smokers often have a nagging cough or make a wheezing sound when they breathe that many refer to as a smoker’s cough. Smoking even e-cigarettes can badly impair your lung health and make fighting off infections difficult. Quitting, however, will help your lungs rebound. “After one month, your lung capacity improves; there’s noticeably less shortness of breath and coughing,” Dr. Djordjevic says.

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After three months: Blood circulation has improved

Nicotine in cigarettes constricts the blood vessels in your skin and around your heart, 2016 research published in the journal Trends in Cardiovascular Medicine . The nicotine in an e-cigarette may be just as harmful. However, after you quit, your blood circulation will begin to improve, as blood vessels return to their normal diameter.

After nine months: Your lungs can fight infections again

“After nine months, lung health improves significantly thanks to the renewal of microscopic hair-like structures inside the lungs that help push out mucus and fight infections,” Dr. Djordjevic says. This could significantly reduce your likelihood of some infections and complications from illnesses like the flu and pneumonia.

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After one year: Your risk of a heart attack is cut in half

Now that your blood vessels are returning to normal size, your heart rate is back to a safe pace, and your blood pressure is lowered, your risk for a heart attack is much lower than while you were still vaping. “After one year, cardiovascular risk reduces by 50%,” Dr. Djordjevic says.

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After five years: Stroke risk is significantly lower

The long-term effects of better heart health and lower blood pressure grants you another significant benefit: Lower stroke risk. Compared to nonsmokers, e-cigarette users have a 71% higher risk of stroke , according to research presented at the 2019 International Stroke Conference. Quitting can lower that risk almost immediately, but the risks continue to fall with each passing calendar month.

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A decade later: Lower cancer risks

A 2017 study published in  Scientific Reports  suggests e-cigarettes and vaping may lead to DNA changes and genetic mutations that can increase the risk of cancer. So the longer you avoid e-cigarettes, the healthier your body will be. “After a decade, lung cancer risk is reduced by 50%, as well as the risk of pancreatic, mouth, and throat cancer,” Dr. Djordjevic says. “After 15 years, your risk of developing coronary heart disease becomes the same as a nonsmoker’s. The same goes for the risk of developing pancreatic cancer.”

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20 years later: It’s like you never vaped

There will come a day that the bad habit of vaping won’t have any lasting impact on your body and your health. “After 20 years, your risk factors will be similar to those who have never smoked or vaped,” says Dr. Djordjevic. If you’re ready to kick the habit, ex-smokers offer their best advice for quitting cigarettes .

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