Comparing Adult Smokers who Switched to JUUL vs Continuing Smokers: Biomarkers of Exposure and of Potential Harm and Respiratory Symptoms

doi:10.21203/rs.3.rs-2539616/v1

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Comparing Adult Smokers who Switched to JUUL vs Continuing Smokers: Biomarkers of Exposure and of Potential Harm and Respiratory Symptoms

https://doi.org/10.21203/rs.3.rs-2539616/v1

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Introduction: More real-world evidence on exposure to harmful and potentially harmful constituents (HPHCs) and on biological effects when cigarette smokers switch to e-cigarettes would be useful.

Methods: This cross-sectional, observational study assessed adults who had smoked ≥ 10 cigarettes/day for ≥ 10 years, comparing 124 continuing cigarette smokers (Smokers) to 140 former smokers who switched to JUUL-brand e-cigarettes exclusively for ≥ 6 months (Switchers). Assessments included biomarkers of exposure (BOEs) to select HPHCs, biomarkers of potential harm (BOPHs) related to smoking-related diseases, and psychometric assessments of tobacco dependence and respiratory symptoms. Planned analyses compared geometric means, adjusted for covariates; exploratory analyses adjusted for additional covariates.

Results: Nicotine was higher in Switchers, who were heavy users of JUUL. All other BOEs, including NNAL and HPMA3 (primary endpoints), were significantly lower in Switchers than Smokers. Most BOPHs (sICAM-1 [primary], and e.g., white blood cell count, MCP1, HbA1c) were significantly lower in Switchers than Smokers; HDL was significantly higher. Switchers reported significantly lower tobacco dependence and respiratory symptoms than Smokers.

Conclusions: Compared to continuing smokers, smokers who switched to JUUL had substantially lower exposures to multiple HPHCs, favorable differences in markers of inflammation, endothelial function, oxidative stress, and cardiovascular risk, and less respiratory symptoms.

Health sciences/Biomarkers

Biological sciences/Drug discovery/Biomarkers

Cigarette smoking is the leading cause of preventable death in much of the world [1–3], being responsible for approximately 7.7 million deaths annually [4]. Cigarette smoking causes cardiovascular disease, cancer and respiratory disease, as well as adverse outcomes in pregnancy [1, 2]. These adverse health effects are primarily caused by exposure to the byproducts of combustion [1, 2]. Cigarette smoke contains multiple carcinogens, as well as toxicants associated with cardiovascular disease, respiratory disease, and adverse reproductive and developmental effects [5]. The US FDA has identified many harmful and potentially harmful constituents (HPHCs) in cigarette smoke and tobacco products that are associated with smoking-related disease [5].

While chemical analyses indicate the presence of such toxins in cigarette smoke, linking these analyses to cigarette smokers’ actual exposures requires assessment of levels of biomarkers of exposure (BOEs) in smokers. BOEs are molecules derived from the HPHCs (usually analogs, degradants, or metabolites of the parent compound) that can be measured in biological matrices such as urine or blood [6–8]. For example, NNAL is a urinary metabolite of the tobacco specific nitrosamine NNK, a potent carcinogen [9] linked to cancer mortality risk [5, 10]. As another example, acrolein, a highly reactive compound present in cigarette smoke that causes inflammation and irritation in many tissues, including the respiratory tract [11, 12], and is a respiratory and cardiovascular toxin [5]. Exposure to acrolein is measured by the biomarker HPMA3, its urinary metabolite. Unlike NNAL, which is specific to tobacco, acrolein exposures also occur from other sources, such as cooking, so HPMA3 is also found in the urine of non-smokers, but at considerably lower levels than those in smokers [13].

While BOEs assess smokers’ exposure to toxicants, they do not in themselves reflect the body’s pathological response to such exposures, which are more proximally related to subsequent disease [14]. In contrast, biomarkers of potential harm (BOPHs) reflect early biological effects associated with physiological harm. BOPHs more directly reflect the pathogenic mechanisms through which cigarette smoke toxin exposures lead to disease, and thus may foreshadow development of smoking-induced diseases.

For example, inflammatory processes are involved in various smoking-related diseases including chronic obstructive pulmonary disease (COPD), atherosclerosis, and cancer [15]. Several molecules are biomarkers of inflammation and can serve as indicators of associated health risk. Soluble intercellular adhesion molecule-1 (sICAM-1) stimulates proinflammatory cascades. sICAM-1 is elevated in smokers [16], and higher levels are associated with atherosclerosis, cardiovascular disease, and COPD [17–20]. Because BOPHs reflect pathological states in the body, rather than exposure to a specific toxicant, they can reflect exposure to toxicants that may not be identifiable or measurable through BOEs.

The best way for smokers to reduce their risk of disease and premature death is to eliminate all tobacco-related exposures by quitting use of all tobacco products. However, many continue to smoke. While nicotine is the addictive substance that maintains smoking, it is not a major cause of smoking-related harm [1, 21, 22]. Accordingly, for smokers who are not likely to stop using combusted tobacco in the near term, it has been suggested that switching completely to noncombustible nicotine products may reduce their exposure to harmful chemicals in cigarette smoke and consequently the risk of smoking-related disease [23, 24].

E-cigarettes, or electronic nicotine delivery systems (ENDS), are the most widely used non-combustible nicotine products, and are intended as an alternative for adult smokers. In contrast to combustible cigarettes, ENDS do not involve combustion, and thus deliver nicotine with substantially lower levels of toxicants associated with smoking. Comparisons of ENDS aerosols with cigarette smoke confirm that many of these toxicants are not present at detectable levels at all in ENDS aerosols, or are dramatically reduced, often by 90–99% [25].

This suggests that users’ exposures to such toxicants would be similarly reduced. Indeed, BOE studies confirm that smokers who switch to ENDS substantially reduce their exposure to the HPHCs in cigarette smoke. This has been demonstrated in randomized controlled studies [7, 8, 24, 26, 27], which have the benefit of making the strongest causal inferences, limiting exogenous influences (e.g., environmental exposure, diet), and, by confining participants, ensuring compliance with protocols. However, the external validity of these studies is limited by their artificial settings. Also, their typically brief durations are often not long enough for BOEs with long-half-lives to dissipate and may not be long enough for the body’s responses, as reflected in BOPHs, to change. Hence, there is a need to assess BOEs and BOPHs in longer-term naturalistic studies in real world settings.

Several observational studies have assessed BOEs and BOPHs, comparing smokers with ENDS users. Notably, using cross-sectional biomarker data collected in Wave 1 of the population-representative PATH study, analyses [28] found significantly lower exposures to acrolein and NNK, as well as to other toxicants in exclusive ENDS users compared to exclusive smokers, and lower markers of inflammation and oxidative stress, adjusting for demographic differences [29]. Additionally, Anic [30] and Dai [31] reported within-person reductions in BOEs such as NNAL and CEMA (a marker for acrylonitrile) when smokers switched to ENDS, although the duration of switching was brief and variable. Taken together, these analyses confirm the implications of the aerosol chemistry analyses and the expectation that ENDS use is less toxic than cigarette smoking.

A limitation of the PATH biomarker analyses is that they use data from 2013–2014, and thus reflect ENDS products of almost a decade ago, rather than those in common use today. The ENDS products analyzed were also notably heterogeneous [32], which could mask relevant effects. Further, the usage of the ENDS was very heterogenous, including individuals who were using ENDS intermittently or at very low levels. A more sensitive analysis would focus on individuals who are heavy users, where adverse exposures and biological reactions would be more likely to be detected.

In this study, we focus on a single ENDS product – JUUL, a contemporary pod-based ENDS product that was until recently the most-used ENDS in the US [33], and that has been the subject of considerable scientific attention (over 300 scientific papers; PubMed search using term "JUUL[Title/Abstract]" on Nov 15, 2022). In a randomized trial, Pulvers, et al., [34] reported reductions in NNAL after just 6 weeks of switching from smoking to JUUL. That study also reported reductions in respiratory symptoms, showing that such behavior changes can affect not only biomarkers but also clinical symptoms.

The current cross-sectional naturalistic observational study compared levels of select BOEs and BOPHs between two groups of adults with a substantial history of smoking: Former cigarette smokers who switched to JUUL for at least six months (“Switchers”) and cigarette smokers (“Smokers”) who continued to smoke. The Switchers were particularly heavy users of JUUL, providing a sensitive test of exposures to JUUL aerosols. The primary endpoints were the BOEs NNAL and HPMA3 and the BOPH sICAM-1. The study also assessed a panel of other BOEs and BOPHs, as well as assessing nicotine exposure. Additionally, the study assessed dependence, and measured respiratory symptoms using a scale designed for the purpose [35].

Participants

Participants were adult smokers or ex-smokers who had smoked ≥ 10 cigarettes per day for ≥ 10 years, recruited into two groups: “Switchers” were former smokers who switched completely to JUUL 5% nicotine ENDS for at least 6 months (i.e., no cigarette smoking or any other tobacco product use). “Smokers” were current smokers continuing to smoke ≥ 10 cigarettes per day, and had not used other tobacco or nicotine-containing products in the past 30 days. (The study was intended to include dual users, but the numbers enrolled were insufficient for analysis.) Participants were recruited between September 2021 and April 2022 from 30 states in the US. An attempt was made to balance the groups by strata of age, sex, and smoking rate (historical smoking rate for the Switchers), as these variables were likely to influence biomarker levels [36, 37].

Participants included males or females, 31–65 years of age, who qualified as Switchers or Smokers. Participants were required to be in self-reported good health and have a BMI between 18 and 40 kg/m². Participants had to reside within reasonable driving distance of a local laboratory site (LabCorp Patient Service Centers) and have internet access, an e-mail address, and a phone number. Participants were excluded if they were pregnant or planning to become pregnant, had smoked marijuana in the past 30 days, or were engaged in ENDS litigation.

The study was conducted in accordance with Good Clinical Practice (GCP) based on the International Conference on Harmonization guidelines, the corresponding US Code of Federal Regulations and the Basic Principles of the Declaration of Helsinki. The study was approved by an Institutional Review Board (Advarra IRB, Columbia, MD), and participants provided informed consent.

Procedure

Switchers were largely recruited from among smokers who had subscribed to regular shipments of JUUL (which generally average 31 pods per month) for the preceding 6 months. Adult smokers were recruited by vendor from proprietary databases of adult smokers and social media. After being pre-screened online, participants were screened and enrolled online, sent a laboratory kit, and scheduled for a visit to a local clinical laboratory (LabCorp, Inc.). On the morning of the visit, participants collected a first-void morning urine sample (≥ 4 hours of retention in the bladder), which is optimum for estimating exposure to cigarette smoke constituents from a spot urine sample [38]. Blood (8-h fasting) was drawn and all specimens shipped to a central lab for processing. Participants were free to use their normal tobacco product as they wished.

Outcome Measures

Biomarkers

Table 1 lists the BOEs assessed in the study, including the parent compounds they represent and the organ systems they affect, indicating their likely relationship to particular diseases, as determined by FDA [5]. Table 2 lists the BOPHs and the physiological processes and diseases to which they are related [14]. The pre-designated primary end-points were levels of NNAL, HPMA3, and sICAM-1. Following standard practice [38], urine creatinine was used to adjust urinary biomarkers for urinary dilution.

Biomarker analyses was performed by Analytisch-Biologisches Forshunglabor (Planegg, Bayern, Germany) and Covance (Indianapolis, Indiana). Values below the limit of detection were set to the limit/√2.

Self-report data

Participants completed brief questionnaires (See Supplemental Materials) regarding their demographics, use of cigarettes or JUUL, respectively, tobacco use history, and current diet and exercise practices.

Participants also completed the Tobacco Dependence Index (TDI, [39]), a 16-item measure of dependence on tobacco and nicotine products developed by the PATH investigators. Smokers completed the TDI with respect to cigarette smoking; Switchers with regard to JUUL use. The TDI has uniquely been validated for assessing dependence on both cigarette smoking and ENDS use, and for statistical comparison of scores between products [39, 40]. Participants also completed the Respiratory Symptoms Questionnaire (RSQ), developed and validated specifically for assessing respiratory symptoms in smokers who have not yet been diagnosed with respiratory diseases [35].

Statistical Analysis

A target sample size of 128 participant per group was set based on achieving 90% power to detect the group difference reported in a previous study [41], based on adjusting for multiplicity in testing the three primary end-points at an adjusted alpha level of 0.016 (0.05/3). Other tests were at alpha = 0.05. Supplemental Fig. 1 shows the flow of recruitment into the study. The final analytic dataset included 140 Switchers and 124 Smokers who completed the study. Assays of particular analytes were sometimes unavailable due to errors in sample processing, so sample sizes differ across analyses.

As biomarker levels were not normally distributed, a natural log-transformation was applied, and the geometric LS mean was used; self-report measures were not transformed. In the pre-specified analyses, Switchers’ vs Smokers’ levels were compared using analysis of variance, with age group (31–44, ≥ 45), sex, BMI (< 25, ≥ 25 kg/m2), and CPD (10–15, > 15) as covariates. The analysis was robust against the influence of outliers: results were the same when the analyses were re-run excluding observations with studentized residuals >|3|. To adjust for differences observed in other variables, particularly demographics, exercise practices, and total smoking exposure (pack-years), these were entered as additional covariates in exploratory analyses. PROC GLM (SAS v9.4; SAS Institute Inc., Cary, NC) was used for analyses.

Participants description

Table 3 shows the participant demographics and tobacco use histories. The sample was predominantly White and female. Switchers did not differ from Smokers in BMI or sex, but were younger (45.9 vs 50.4 years of age). Additionally, it was observed that Switchers were more likely to be White; had higher educational achievement; higher income; reported more days per week of vigorous exercise. Exploratory analyses additionally adjusted for these variables.

Tobacco Use History and Current Use

Almost all Smokers (98.4%) and Switchers (98.6%) had used their respective product on each of the previous 30 days. Smokers reported slightly lower daily cigarette consumption than Switchers’ historical consumption but smoked for longer, resulting in greater overall lifetime smoking exposure (pack-years). This was adjusted in the exploratory analyses.

Switchers’ median time since stopping smoking and duration of JUUL use was 3.0 years. The majority of Switchers (61.4%) reported using JUUL least 15 times per day (a time was defined as 15 puffs or 10 minutes of use), and a similar majority (61.4%) reported using 20 + pods per month. To put this level usage into context, Supplement 2 reports the levels of JUUL usage reported in two samples: (1) a longitudinal cohort study of adult smokers who had purchased a JUUL Starter Kit two years earlier [42] and (2) a sample of adult JUUL users from the population-representative PATH data [43]. The comparisons show that the participants in the Switcher sample were more likely to use daily, and twice as likely to use 15 or more times per day and to use 20 + pods per month than the more representative samples.

Exposure to Select HPHCs

The model estimates for the BOE and BOPH are shown in Tables 5 and 6. Consistent with their heavy usage of JUUL, the Switchers showed significantly higher intake of nicotine than did the Smokers, as indicated by total nicotine (TNE3).

Otherwise, Switchers had statistically significantly lower levels of all BOEs than Smokers (Table 5, Fig. 1, and Supplemental Fig. 2). On the primary biomarker NNAL, the Switchers showed > 99% less NNAL than the Smokers (2.03 vs 326.49 ng/g creatinine, p < .0001). On the primary endpoint of HPMA3, differences were also substantial (269.69 vs 1145.34 ug/g creatinine, p < .0001); complete elimination was not expected, as acrolein exposure also occurs from the environment even for non-smokers [44]. Switchers were also significantly lower on BOEs to carbon monoxide (COHB), 1,3 butadiene (MHBMA), crotonaldehyde (HMPMA), acrylonitrile (CEMA), and benzene (SPMA).

As shown in Fig. 1, after adjusting for additional demographics, smoking history, and exercise practices, all the significant group differences remained, including those on the primary end-points NNAL and HPMA3.

Biomarkers of Potential Harm

As shown in Table 6 (and Figs. 2 and 3 and supplemental Figs. 3 and 4), Switchers had statistically significantly lower levels than Smokers on most BOPHs, and statistically significantly higher levels of HDL-C. This included the primary end-point of sICAM-1, where the Switchers had significantly lower levels (240.4 vs 306.1 ng/mL, p < 0.0001). Switchers had significantly lower levels of markers of systemic inflammation (sICAM-1, White blood cell count, interleukin-6, MCP1, and C-Reactive Protein), oxidative stress (PF2A8EP), blood coagulation (fibrinogen), and insulin resistance (HbA1c), and favorable differences in lipid metabolism (lower triglycerides and higher HDL). Other markers of lipid metabolism (LDL and total cholesterol) and coagulation (11-dehydrothromboxane B2) showed no significant differences.

As shown in Figs. 2 and 3, after adjusting for additional demographics, smoking history, and exercise practices, differences in CRP, Interleukin-6, and fibrinogen were rendered non-significant; in each case, the adjustment increased the variance of the estimates as well as narrowing the group differences. Other significant differences remained, including on the primary end-point sICAM-1.

Psychometric assessments

Switchers had statistically significantly lower tobacco dependence scores, and also had statistically significantly lower respiratory symptoms scores (Table 7, Fig. 4).

In this cross-sectional real-world observational study, heavy long-term smokers who switched to ENDS, specifically to heavy use of JUUL, had substantially and significantly lower exposures to HPHCs than smokers who continued smoking. Additionally, compared to Smokers, Switchers demonstrated favorable differences in several BOPHs relevant to the development of smoking-related diseases, and reported experiencing less severe or frequent respiratory symptomatology. Consistent with their unusually heavy use of JUUL, the smokers who switched to JUUL showed higher nicotine levels, but also showed lower dependence than did the smokers. With a few exceptions, these differences remained significant even after adjusting for demographic, smoking history, and lifestyle differences between the groups.

The findings of this study regarding BOEs are consistent with prior controlled confinement studies [7, 8] and a 6-week randomized clinical trial [34] that found that adult smokers who switch completely to JUUL experience substantial reductions in exposure to many smoking-related toxicants. This study extends those findings to longer-periods of switching to JUUL (almost 3 years on average) under actual use conditions in real-world settings. The findings are also consistent with observational studies comparing smokers to users of ENDS as a category [29, 41, 45]. This study differs from those prior studies in that it examined a single current ENDS product in a sample with substantial smoking histories and very high levels of exposure to ENDS, rather than a heterogeneous group of early ENDS products with a broad range of ENDS use. These differences render precise comparisons between studies difficult, but the studies are consistent in showing reduced exposures to toxicants and improved BOPH profiles in ENDS users.

In the three prior studies of smokers randomized to switch to JUUL, smokers typically maintained their baseline nicotine intake while showing reduced BOEs [7, 34]. That is, JUUL substituted for the nicotine smokers had been getting from cigarettes, while reducing their exposure to smoking-related toxicants. In this study, the Switchers’ ‘baseline’ nicotine intake – i.e., their levels when they were smoking – is not known, but at the time of study they demonstrated about one third greater nicotine intake than the Smokers. This likely reflects the pattern of unusually heavy use of JUUL in the Switcher sample. Their JUUL use was considerably higher than that seen in a large cohort of real-world purchasers of JUUL [42]. Compared to the broader JUUL purchaser sample, the Switcher sample was 40% more likely to use JUUL daily, twice as likely to use 15 + times per day, and > 2.5 times more likely to consume 20 + pods monthly. They were also 2.5 times as likely to be using JUUL daily as a population-representative sample of JUUL users (Supplement 2). The recruitment of Switchers from among smokers who had subscribed to regular shipments of JUUL likely selected for heavier users. The heavy use in this switcher sample helps strengthen the findings, as lower BOEs and BOPHs were observed even in the context of very heavy exposure to the JUUL aerosol.

As FDA has identified nicotine as a risk factor for reproductive and developmental toxicity and dependence, the higher nicotine levels in the Switchers could raise concerns. The finding reinforces that women who are pregnant should not use ENDS, including JUUL, unless they cannot otherwise stop smoking [46]. Additionally, nicotine has sometimes been thought to confer some cardiovascular risk [47], partly because its acute effects include increased heart rate and blood pressure [47]. However, epidemiological data on users of snus – a smokeless tobacco product that delivers high levels of nicotine but without combustion – showed no increase in primary CV risk, though there was an indication of risk to those with pre-existing CV disease [47]. Reflecting this, FDA has authorized a snus product to inform smokers that switching to snus would lower their risk of heart disease [48]. Notably, even with heavy use and high nicotine levels, the Switchers group showed a broadly more favorable cardiovascular risk profile, both in reduced exposures to cardiovascular toxicants and in a more favorable profile of biomarkers related to cardiovascular risk. However, the present study did not examine other cardiovascular parameters such as arterial stiffness, which has been attributed to ENDS use in some studies [49].

The Switchers’ higher levels of nicotine might also raise concern about dependence, but Switchers actually had significantly lower dependence than the Smokers on the TDI (which is validated for such comparisons), despite heavy use and higher nicotine levels. Moreover, the observed decrease in dependence was meaningful: it exceeded the minimally important difference for the scale [50]). The present finding is consistent with several studies showing lower dependence on ENDS [24, 51, 52], and on JUUL [53], compared to dependence on cigarettes. Given their higher nicotine levels, Switchers’ lower dependence may seem surprising, but in fact the correlation between overall nicotine intake and dependence is limited. Analyzing nationally representative PATH data, Strong et al [54] note “low to moderate” relationships between the two, particularly among ENDS users, where the correlation was 0.13. Observing lower dependence in the presence of higher nicotine exposure has precedent: users of smokeless tobacco show significantly higher nicotine levels than smokers, but significantly lower levels of dependence [54]. This may be due to the dynamics of intake: Cigarette smoking, wherein a whole cigarette is consumed in a short time, creates sharp peaks and troughs in blood nicotine levels [2, 55]; these are less prominent in smokeless tobacco use (and in nicotine gum and patch use [2, 55]). Yingst et al [56], who also observed lower dependence in ENDS users than in smokers, suggested that ENDS users’ pattern of “grazing,” taking a few puffs at a time at frequent intervals, may similarly avoid dependence-inducing peaks and troughs. In any case, the Switchers showed lower dependence on JUUL than the Smokers did on cigarettes, consistent will other studies [40, 51, 52].

The current findings extend the literature on ENDS by showing not only reduced exposure to toxicants, but also favorable differences in physiological markers of inflammation, oxidative stress, coagulation, lipid metabolism, and insulin resistance among smokers who switched to a contemporary ENDS product for several years, after a long history of smoking. Taken together with the data on biomarkers of exposure, the data suggest that smokers who switch to JUUL are likely to reduce their risk of diseases associated with smoking, including cancer, cardiovascular disease, and reparatory disease.

With regard to respiratory disease, the data went beyond biomarkers to demonstrate that clinically meaningful differences were already evident among the smokers who switched to JUUL. The Switchers reported lesser respiratory symptoms on a scale developed to be sensitive to such smoking-related symptoms [35, 57]. Moreover, the magnitude of the observed difference was meaningful, exceeding the difference seen between individuals with and without diagnosed respiratory conditions. This finding is consistent with observations by Pulvers [34] showing improvements in respiratory symptoms after only 6 weeks of switching to JUUL in a randomized longitudinal study with African American and Latinx smokers. Such findings are important, since ENDS are inhaled, and thus could conceivably irritate the airways, yet the data from both studies shows that respiratory symptoms are lessened with JUUL use compared to cigarette smoking. As respiratory disease develops slowly, longer follow-up of smokers who switch to JUUL or other ENDS may be important to observe longer-term effects, but the results already available are promising.

The study showed favorable results comparing smokers who switched to JUUL to continuing smokers. But the study did not include comparison to never-smokers, or former smokers who were not using ENDS. Thus, the findings of lower exposures and biological reactions do not imply absolute absence of exposures or absolute safety. The potential role of ENDS such as JUUL in tobacco harm reduction is based on their reduction in exposure and likely reduction in risk relative to cigarette smoking [24, 58]. A tobacco harm reduction approach sees ENDS as a less hazardous alternative to smoking, not as a safe behavior in itself.

The study was subject to several limitations, largely due to its cross-sectional observational design. It is an unavoidable limitation of real-world comparisons between groups that have and have not made a behavior change that one cannot rule out the influence of unmeasured differences. In this case, the consistency between the present findings and those based on randomized studies suggest that the findings are unlikely to be accounted for by such confounds. Switchers were more likely to be White, younger, better-educated, and higher-income, consistent with what is known about smokers who adopt ENDS [59]. However, while controlling for these factors rendered some differences non-significant, it did not change the fundamental pattern of results. In any case, modest lifestyle differences seem unlikely to account for the large observed differences in BOPHs, and are not likely to be important for the BOEs, which reflect exposures not much affected by lifestyle factors other than smoking.

The BOEs analyzed in this analysis reflect major toxicants in cigarette smoke, some of which have also been cited in ENDS aerosols [24, 60], but they do not capture all potential toxicants that could be present in the aerosols, though chemical analyses of the aerosols have indicated dramatic reductions in major classes of potential toxicants [61]. Similarly, the BOPHs studied do not necessarily capture all the biological processes by which cigarette smoke or ENDS aerosols could adversely impact health. However, the observed favorable changes in a broad range of disease-relevant physiological processes do suggest that an overall reduction in disease risk is reasonably likely.

The Switchers in the study were a select group of heavy users demonstrating prolonged switching away from smoking after a substantial smoking career; they are not necessarily representative of typical JUUL or ENDS users. The Switchers were all using JUUL ENDS exclusively; it is not known how much the findings would generalize to other ENDS. Although the Switchers uniformly reported no smoking for at least six months, it is possible some were still occasionally smoking; this would have narrowed the difference between Smokers and Switchers.

The study also had substantial strengths, notably the real-world setting. The Switchers in the study had switched away from smoking for a long period, averaging 3 years, which likely allowed time for recovery from some lingering after-effects of smoking, though some effects may be very long-lasting [16]. The study included a broad array of BOEs and BOPHs relevant to multiple smoking-related diseases. Because the heterogeneity of ENDS products [32] may make biomarker findings variable, and obscure underlying effects, focusing on a single, uniform, ENDS product may have helped yield clearer findings. Finally, the fact that the Switchers were using JUUL particularly heavily, with substantial exposure to JUUL aerosol also indicated by the observed higher nicotine levels, strengthens the findings, as it indicates that exposures to toxicants are reduced and physiological processes improved even in the face of heavy exposures to the aerosol.

In summary, the study showed that long-time smokers who switched to JUUL and used it heavily had much lower exposures to a range of harmful chemicals than smokers who continued smoking. The Switchers also showed significantly lower indicators of adverse biological processes such as inflammation, adverse lipid metabolism, and insulin resistance that are associated with the development of diseases associated with smoking, and also reported less severe respiratory symptomatology. The data suggest that switching completely from smoking to JUUL is reasonably likely to result in reduced risk of smoking-related disease.

Funding

This work was funded by JUUL Labs

Competing Interests Statement

Funding for this study was provided by Juul Labs, Inc. Through Pinney Associates, SS provides consulting on tobacco harm reduction to JUUL Labs, Inc., on an exclusive basis. DRO provides consulting on tobacco harm reduction to JUUL Labs, Inc. Other authors are or were employees of JUUL Labs, Inc.

Acknowledgments

The authors acknowledge the valuable contributions to this work by Joey Chen, Lisa Cowell, Jeff Edmiston, Madhu Gokul, Gail Johnson, Sandra Miller, Scott Rensch, Lonnie Rimmer, Mark Sembower, and Jingzhu Wang

Author Contributions

All authors were involved in design of the study. SM and DRO oversaw study conduct. QL developed the statistical analysis plan, and QL and SS oversaw the analyses. SS drafted the manuscript, and all authors reviewed and made critical intellectual contributions to the manuscript.

Data Availability Statement

The datasets analyzed during the current study available from the corresponding author on reasonable request.

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Table 1

Biomarkers of Exposure [5]
Biomarker	Acronym	Parent compound	Associated diseases†
4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol ‡	NNAL	NNK	CA
3-hydroxypropylmercapturic acid ‡	HPMA3	acrolein	RT, CT
Blood carboxyhemoglobin	COHB	CO	RDT
Monohydroxybutenylmercapturic acid *	MHBMA	1,3-butadiene	CA, RT, RDT
S-phenyl mercapturic acid *	SPMA	Benzene	CA, CT, RDT
3-hydroxy-1-methylpropylmercapturic acid *	HMPMA	Crotonaldehyde	CA
2-cyanoethyl-mercapturic acid *	CEMA	Acrylonitrile	CA, RT
Total nicotine †† *	TNE3	Nicotine	RDT, AD
† CA = carcinogen; RT = respiratory toxicant; CT = cardiovascular toxicant; RDT = reproductive or developmental toxicant; AD = addictive
* Urinary biomarker, analyzed with correction for creatinine concentrations
‡ Primary endpoint

†† Designated TNE3, based on the molar concentrations of nicotine itself plus two metabolites (cotinine and 3-hydroxy-cotinine), each including their glucuronides.

Table 2

Biomarkers of Potential Harm [14, 62]
Biomarker	Associated Health-Related Processes	Associated diseases†
Soluble intercellular adhesion molecule1 (sICAM-1) ‡	Endothelial function, Vascular inflammation	CV, RS
White blood cells (WBC)	Systemic inflammation	CV, RS, CA
Interleukin 6 (IL6)	Systemic inflammation	CV, RS
Monocyte Chemoattractant Protein-1 (MCP1)	Systemic inflammation	CV, RS
C-reactive protein (CRP)	Systemic inflammation	CV, RS, CA
Triglycerides	Atherosclerosis	CV
Total cholesterol	Atherosclerosis	CV
Low-density lipoprotein cholesterol (LDL)	Atherosclerosis	CV
High-density lipoprotein cholesterol (HDL)	Atherosclerosis	CV
8-epi-prostaglandin F2α * (PF2A8EP)	Oxidative stress	CV, RS, CA
11-dehydrothromboxane B2 * (TXB2_D11)	Oxidative stress, Platelet activation	CV
Fibrinogen	Coagulation, Inflammation	CV, RS, CA
Hemoglobin A1c (HbA1c)	Blood glucose, insulin resistance	CV, DM
† CV = cardiovascular; RS = respiratory; CA = cancer; DM = diabetes mellitus
* Urinary biomarker, analyzed with correction for creatinine concentrations
‡ Primary endpoint

Table 3

Demographics
	Statistic	Smokers (N = 124)	Switchers (N = 140)	p-Value^a Smokers vs. Switchers
Demographics
Age (years)	Mean SD	50.4 9.71	45.9 8.70	<0.0001
Age group† 31to < 45 years	n (%)	40 (32.3)	72 (51.4)	0.0017
45 to ≤ 65 years	n (%)	84 (67.7)	68 (48.6)
Sex† Male	n (%)	52 (41.9)	50 (35.7)	0.3753
Female	n (%)	72 (58.1)	90 (64.3)
Race‡ White	n (%)	103 (83.1)	129 (92.2)	0.0003
Black/African American	n (%)	18 (14.5)	3 (2.1)
Other	n (%)	3 (2.4)	8 (5.7)
Ethnicity Hispanic or Latino	n (%)	4 (3.2)	5 (3.6)	1.0000
Not Hispanic or Latino	n (%)	120 (96.8)	135 (96.4)
BMI† <25 kg/m²	n (%)	38 (30.6)	43 (30.7)	0.9903
≥25 kg/m²	n (%)	86 (69.4)	97 (69.3)
Annual Household Income‡
Under $30,000	n (%)	53 (42.7)	8 (5.7)	<0.0001
$30,000-$59,999	n (%)	35 (28.2)	26 (18.6)
$60,000-$99,999	n (%)	19 (15.3)	44 (31.4)
$100,000 and over	n (%)	13 (10.5)	62 (44.3)
I do not wish to answer	n (%)	4 (3.2)	1 (0.7)
Highest Education‡
High school or less	n (%)	35 (28.2)	10 (7.1)	< 0.0001
Some college	n (%)	65 (52.4)	52 (37.1)
College graduate	n (%)	16 (12.9)	48 (34.3)
Post-graduate	n (%)	8 (6.5)	29 (20.7)
I do not wish to answer	n (%)	0 (0.0)	1 (0.8)
Diet and Exercise
Specialized diet?	n (%)	5 (4.0)	10 (7.4)	0.3015
Typical days/week physical activity least moderate intensity	Mean SD	3.2 1.98	3.5 2.05	0.1965
Number of past 7 days ≥ 20 minutes vigorous physical activity (sweat and breathing hard)‡	Mean SD	1.8 1.83	2.5 2.10	0.0062

N = number of participants; SD = standard deviation.

^aBased on the Chi-square test or Fisher exact test (where the expected frequency is five or less) for categorical data and t-test for continuous data.

† Adjusted in a priori specified analyses

‡ Adjusted for in post-hoc analyses with additional covariates

Table 4

Tobacco Product Use History
Assessment	Statistic	Smokers (N = 124)	Switchers (N = 140)
Years smoked regularly	Mean	30.3	22.8
Years smoked regularly	SD	10.9	8.8
Past 30 days cigarettes/day	Mean	17.3	NA
Past 30 days cigarettes/day	SD	5.7
Cigarettes/day prior to switching	Mean	NA	18.4
Cigarettes/day prior to switching	SD		6.5
Cigarettes/day category†
10–15	n (%)	62 (50.0)	52 (37.1)
> 15	n (%)	62 (50.0)	88 (62.9)
Pack-years of smoking‡	Mean	27.0	21.9
	SD	14.4	13.8
Years since quit smoking	Mean	NA	3.5
	SD		3.4
	Median		3.0
JUUL use among Switchers^a
Years using JUUL exclusively	Mean	NA	2.9
	SD		1.1
	Median		3.0
Number JUUL pods used in past 30 days
≤ 12 pods	n (%)	NA	16 (11.4)
13 to 19 pods	n (%)	NA	38 (27.1)
20 or more pods	n (%)	NA	86 (61.4)
Time per day used JUUL
0–9 times	n (%)	NA	22 (15.7)
10–19 times	n (%)	NA	59 (42.1)
20–29 times	n (%)	NA	23 (16.4)
30 + times	n (%)	NA	36 (25.7)

N = number of participants; SD = standard deviation.

^a Supplement 1 compares these JUUL usage figures to those from a larger and less selected sample of smokers who switched to JUUL

† Adjusted in a prori specified analyses

‡ Adjusted for in post-hoc analyses with additional covariates

Table 5

Biomarkers of Exposure
	Pre-Planned Adjusted Analysis			Analyses with Additional Adjustments
Outcome Study Group	N^a	Geometric-LS mean (95% CI)^b	Difference p-Value^b	N^a	Geometric-LS mean (95% CI)^c		Difference p-Value^c
Total NNAL (ng/g creatinine)
Smokers	117	333.1 (280.1, 396.1)	< 0.0001	113	315.0 (258.2, 384.4)		< 0.0001
Switchers	133	2.0 (1.7, 2.3)	< 0.0001	131	2.1 (1.7, 2.5)		< 0.0001
3-HPMA(µg/g creatinine)
Smokers	116	1145.7 (999.1, 1313.8)	< 0.0001	112		1214.8 (1037.4, 1422.5)	< 0.0001
Switchers	133	269.6 (237.3, 306.3)	< 0.0001	131		253.5 (218.9, 293.7)	< 0.0001
COHb (% saturation)
Smokers	104	6.3 (5.8, 6.8)	< 0.0001	100		6.0 (5.5, 6.6)	< 0.0001
Switchers	103	2.0 (1.9, 2.2)	< 0.0001	101		2.1 (1.9, 2.3)	< 0.0001
MHBMA (ug/g creatinine)
Smokers	116	2.7 (2.2, 3.4)	< 0.0001	112		3.0 (2.4, 3.8)	< 0.0001
Switchers	133	0.08 (0.06, 0.10)	< 0.0001	131		0.07 (0.05, 0.08)	< 0.0001
S-PMA (ug/g creatinine)
Smokers	116	4.0 (3.5, 4.7)	< 0.0001	112		4.0 (3.4, 4.8)	< 0.0001
Switchers	133	0.10 (0.08, 0.11)	< 0.0001	131		0.09 (0.08, 0.11)	< 0.0001
HMPMA (ug/g creatinine)
Smokers	116	281.4 (248.3, 319.1)	< 0.0001	112		293.7 (254.6, 338.9)	< 0.0001
Switchers	133	99.7 (88.7, 112.1)	< 0.0001	131		95.2 (83.4, 108.8)	< 0.0001
CEMA (ug/g creatinine)
Smokers	116	173.1 (152.8, 196.1)	< 0.0001	112		178.5 (155.1, 205.5)	< 0.0001
Switchers	133	1.9 (1.7, 2.2)	< 0.0001	131		1.8 (1.6, 2.1)	< 0.0001
NE (mg/g creatinine)
Smokers	117	12.1 (10.9, 13.5)	0.0011	113		11.7 (10.4, 13.3)	0.0012
Switchers	133	15.6 (14.1, 17.3)	0.0011	131		16.0 (14.3, 17.9)	0.0012

CI = confidence interval; LS = least squares; N = number of participants.

^aN Sample size in the analyses. For adjusted analyses, sample size was smaller than for the pre-planned analyses due to missing data on income (5 cases) or education (1case).

^b From the ANOVA model with study group, age (< 45 or > = 45), BMI (< 25 or > = 25 kg/m2), gender (Female or male) and CPD (>-10-15 or > 15) as model terms. ^c From the ANCOVA model with study group, age (< 45 or > = 45, BMI (< 25 or > = 25 kg/m²), gender (female or male), race (White vs. African American vs. Others), education class (HS or less vs. some college vs. college graduate vs. Postgraduate), income ($30,000 vs. $30,000-$59,999 vs. $60,000-$99,999 vs. $100,000 or more) and days of physical activity last week and pack years as the model terms. ^d Least squares (LS) mean, not geometric LS mean,

Table 6

Biomarkers of Potential Harm
	Pre-Planned Adjusted Analysis			Analyses with Additional Adjustments
Outcome Study Group	N^a	Geometric-LS mean (95% CI)^b	Difference p-Value^b	N^a	Geometric-LS mean (95% CI)^c	Difference p-Value^c
sICAM-1 (ng/mL)
Smokers	123	306.1 (289.3, 323.9)	< 0.0001	119	299.6 (281.8, 318.5)	< 0.0001
Switchers	138	240.6 (228.1, 253.7)	< 0.0001	136	243.3 (230.0, 257.5)	< 0.0001
WBC (×10³/µL)
Smokers	118	7.4 (7.0, 7.8)	< 0.0001	114	7.4 (7.0, 7.9)	< 0.0001
Switchers	127	6.0 (5.7, 6.2)	< 0.0001	125	5.9 (5.6, 6.2)	< 0.0001
IL-6 (pg/mL)
Smokers	117	3.5 (3.2, 3.9)	0.0047	114	3.4 (3.0, 3.8)	0.1225
Switchers	131	2.9 (2.6, 3.1)	0.0047	129	3.0 (2.7, 3.3)	0.1225
MCP-1 (pg/mL)
Smokers	123	445.0 (419.1, 472.5)	0.0018	119	444.6 (415.8, 475.4)	0.0075
Switchers	136	388.8 (367.3, 411.6)	0.0018	134	387.0 (363.7, 411.8)	0.0075
Triglycerides (mg/dL)
Smokers	120	119.8 (109.1, 131.4)	0.0466	116	120.0 (108.2, 133.0)	0.0306
Switchers	133	104.9 (96.0, 114.6)	0.0466	131	101.1 (91.9, 111.2)	0.0306
HDL (mg/dL)
Smokers	119	45.4 (43.3, 47.6)	< 0.0001	115	46.2 (43.8, 48.7)	< 0.0001
Switchers	134	55.7 (53.3, 58.2)	< 0.0001	132	55.6 (53.0, 58.4)	< 0.0001
LDL (mg/dL)
Smokers	115	105.4 (99.4, 111.8)	0.6347	111	106.6 (99.7, 114.0)	0.9230
Switchers	132	107.5 (101.8, 113.6)	0.6347	131	106.1 (99.8, 112.7)	0.9230
Total cholesterol (mg/dL)
Smokers	120	183.0 (176.0, 190.3)	0.1460	116	184.8 (176.9, 193.1)	0.6477
Switchers	136	190.6 (183.7, 197.7)	0.1460	134	187.7 (180.3, 195.4)	0.6477
11-Dehydrothromboxane B2 (ug/g creatinine)
Smokers	117	0.27 (0.21, 0.34)	0.4938	113	0.26 (0.19, 0.34)	0.8126
Switchers	133	0.24 (0.19, 0.30)	0.4938	131	0.24 (0.19, 0.32)	0.8126
8-epi-PGF2α (ug/g creatinine)
Smokers	117	0.42 (0.36, 0.49)	0.0077	113	0.42 (0.35, 0.51)	0.0217
Switchers	133	0.31 (0.27, 0.36)	0.0077	131	0.31 (0.26, 0.36)	0.0217
CRP (mg/dL)
Smokers	120	0.26 (0.21, 0.32)	0.0001	116	0.22 (0.17, 0.28)	0.0765
Switchers	136	0.14 (0.12, 0.17)	0.0001	134	0.16 (0.12, 0.19)	0.0765
Fibrinogen (mg/dL)
Smokers	113	389.7 (372.4, 407.7)	0.0003	110	374.0 (355.2, 393.8)	0.1642
Switchers	132	346.4 (332.2, 361.2)	0.0003	130	354.2 (338.1, 371.1)	0.1642
HbA1c (%)
Smokers	122	5.9 (5.7, 6.1)	0.0001	118	5.8 (5.6, 6.0)	0.0050
Switchers	132	5.4 (5.3, 5.6)	0.0001	130	5.4 (5.3, 5.6)	0.0050

CI = confidence interval; LS = least squares; N = number of participants.

^aN Sample size in the analyses. For adjusted analyses, sample size was smaller than for the pre-planned analyses due to missing data on income (5 cases) or education (1case).

Table 7

Psychometric Measures
	Pre-Planned Analysis				Analyses with Additional Adjustments
Outcome Study Group	N^a	Geometric-LS mean (95% CI)^b	Difference p-Value^b		N^a	Geometric-LS mean (95% CI)^c		Difference p-Value^c
Tobacco Dependence Index (Range: 1–5)
Smokers	124	3.60 (3.46, 3.74)	< 0.0001	120			3.61 (3.45, 3.77)		< 0.0001
Switchers	140	2.94 (2.80, 3.07)	< 0.0001	138			2.95 (2.80, 3.10)		< 0.0001
Respiratory Symptom Questionnaire (Range: 1–5)
Smokers	124	1.92 (1.81, 2.02)	< 0.0001	120			1.89 (1.78, 2.01)		< 0.0001
Switchers	140	1.29 (1.19, 1.39)	< 0.0001	138			1.29 (1.18, 1.39)		< 0.0001

CI = confidence interval; LS = least squares; N = number of participants.

^aN Sample size in the analyses. For adjusted analyses, sample size was smaller than for the pre-planned analyses due to missing data on income (5 cases) or education (1case).

Competing interest reported. Funding for this study was provided by Juul Labs, Inc. Through Pinney Associates, SS provides consulting on tobacco harm reduction to JUUL Labs, Inc., on an exclusive basis. DRO provides consulting on tobacco harm reduction to JUUL Labs, Inc. Other authors are or were employees of JUUL Labs, Inc.

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Comparing Adult Smokers who Switched to JUUL vs Continuing Smokers: Biomarkers of Exposure and of Potential Harm and Respiratory Symptoms

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