Abstract
Background:
The current e-cigarette market has been rapidly evolving with an increase in the share of high nicotine concentration vaping products. This study examined urinary biomarkers of exposure (BOEs) by nicotine concentration level among exclusive e-cigarette users.
Methods:
Data were drawn from Wave 5 (December 2018-November 2019) of the Population Assessment of Tobacco and Health (PATH) Study. Between-subject differences in BOEs of nicotine, metal, tobacco-specific nitrosamine (TSNA), and volatile organic compounds (VOC) were examined across e-cigarettes containing nicotine or not (yes [n=300] vs. no [n=31] vs. non-tobacco use [n=3021]) and different nicotine concentration levels (0.1-1.7%, 1.8-4.9%, and 5.0%+).
Results:
Among 3353 participants, exclusive e-cigarette users exhibited higher mean concentrations of nicotine metabolites than non-tobacco users. Nicotine e-cigarette users had higher concentrations of TNE2 (mean [95% CI]=21.8 [15.2-31.2] vs. 0.2 [0.1-0.6] nmol/mg creatinine, p<.0001) and cotinine (1418.2 [998.0-2015.4] vs. 12.2 [0.1-0.6], p<.0001) ng/mg creatinine, p<.0001) than non-nicotine e-cigarette users. Users of e-cigarette products with nicotine levels of 1.8-4.9% had higher TNE2 and cotinine levels than those using 0.1-1.7%, though differences were insignificant after adjusting for covariates. As compared to non-tobacco users, nicotine vapers had higher concentrations of lead (adjusted p=0.01).
Conclusions:
Nicotine containing e-cigarette users exhibited elevated levels of nicotine metabolites than non-nicotine containing vapers and non-tobacco users. Future research needs to investigate health effects of e-cigarette use across different nicotine levels
Impact:
Regulating the nicotine content in e-cigarettes could be crucial in managing nicotine exposure and potentially mitigating associated health risks.
Keywords: exclusive e-cigarette use, nicotine concentration, PATH, biomarkers of exposure (BOE)
Introduction
Although cigarette smoking has been declining among U.S. adults and adolescents in the last few decades, e-cigarette use (or vaping) has gained popularity (1,2). It is estimated that 4.5% of U.S. adults aged 18 years and older (approximately 11.1 million) reported current use of e-cigarettes in 2021 (3), and the use of e-cigarettes is particularly higher among young adults (14.5% in 2021) (4). Evidence has suggested that e-cigarette aerosol is generally less toxic than combustible cigarette smoking (5). A previous study that analyzed Wave 1 of the Population Assessment of Tobacco and Health (PATH) Study reported that exclusive e-cigarette users showed 10% to 98% significantly lower concentrations of biomarkers of exposure (BOE), and toxicants, including nicotine, TSNAs, and most volatile organic compounds (VOCs), compared with exclusive cigarette smokers (6). Longitudinal studies also found a reduction in exposure to toxicants when exclusive cigarette smokers or dual users of cigarettes and e-cigarettes transitioned to exclusive e-cigarette use (7,8).
The current e-cigarette market consists of multiple generations of vaping products that are rapidly evolving (9). Nicotine concentration has been a key characteristic of modern e-cigarette products, and there are a variety of nicotine concentration levels available in e-liquid (5). The introduction of nicotine salts has allowed for higher nicotine levels in e-cigarettes by improving the nicotine’s palatability and reducing the harshness of inhalation. In recent years, the average nicotine concentration in e-cigarettes increased significantly from 2.1% to 4.3% between 2013 and 2018 (10). There is emerging epidemiologic and human laboratory literature on the use of different nicotine levels in e-cigarettes (5,11–14). Dai et al. (14), examined the prevalence and correlates with the use of different nicotine concentration levels in U.S. adults and reported that the use of high-nicotine strength (≥5%) vaping products might be elevated in some subpopulations, including young adults and never smokers. Foulds et al. (11), analyzed a sample of 3609 ex-smoking e-cigarette users and provided suggestive evidence that higher nicotine concentration levels (zero vs. 1-12 mg/ml vs. ≥13 mg/ml) are associated with more severe nicotine dependence symptoms.
Urinary biomarkers of nicotine and toxicants are vital to understanding the health effects of vaping different nicotine concentrations in e-cigarette products. First, the nicotine concentration level in e-cigarettes is one of the primary product-level determinants of nicotine delivery and sensorimotor characteristics. High nicotine levels in salt formulations can reduce the harshness and bitterness of nicotine, potentially intensifying nicotine dependence (5), while e-cigarette users who use low (vs. high) nicotine level products might exhibit compensatory puff behaviors, thus resulting in similar nicotine absorption (15,16). Second, unlike many other countries (e.g., the European Union, Canada) that cap the nicotine concentration at ~2% for all e-cigarette products (17,18), the U.S. Food and Drug Administration (FDA) has granted marketing orders to several e-cigarette products across a range of nicotine levels, including as high as 6.0% (i.e., NJOY Daily Extra Rich) (19). Although the long-term effects of e-cigarette use remain unknown, biomarkers can provide an objective assessment of exposure to nicotine metabolites, carcinogens, metals, and volatile compounds that contribute to the toxicity and irritation in the human body (20,21). Collectively, understanding whether biomarkers of exposure to tobacco-related toxicants differ by nicotine concentration levels can provide evidence of potential health effects and inform future regulatory policies on different nicotine levels in vaping products.
This cross-sectional study analyzed the Wave 5 of the PATH Study to 1) examine differences in BOEs among non-tobacco users, exclusive non-nicotine e-cigarette users, and exclusive nicotine e-cigarette users; 2) assess between-subjects differences in BOE across different nicotine concentration levels (e.g., 0.1-1.7% vs. 1.8-4.9% vs. 5.0+%). We seek to examine whether nicotine e-cigarette users exhibit higher concentrations in select biomarkers than non-nicotine e-cigarette users and whether BOEs differ by varying nicotine levels.
Materials and Methods
Data
The PATH Study is a longitudinal cohort study of tobacco use among a nationally representative sample of U.S. civilian, non-institutionalized individuals. The PATH study uses a four-stage, stratified probability sampling design that intentionally oversampled adult tobacco users, young adults, and African Americans. Wave 5 of the PATH study was conducted during December 2018 and November 2019 with a weighted response rate of 88.0% from the W4 cohort (22).
Adult respondents who completed the adult interview were asked to provide urine samples voluntarily. A stratified sample of participants who provided sufficient urine was selected from a diverse mix of tobacco use groups for the biospecimen analyses. The PATH biomarker and adult survey data were linked through the unique personal ID (22,23). PATH data collection was conducted by Westat and approved by Westat’s Institutional Review Board. This secondary data analysis of the PATH study followed guidelines for reporting observational studies: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).
Measures
Biomarkers (n=8) were selected based on prior literature (6,7,24), and grouped in four classes of harmful and potentially harmful constituents: 1) nicotine metabolites, including TNE2 and cotinine; 2) tobacco-specific nitrosamines (TSNAs), including 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL); 3) heavy metals, including cadmium (UCD) and lead (UPB); and 4) VOCs, including N-Acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA), N-Acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA), and N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA). These BOEs are clinically relevant to addiction (e.g., TNE2, cotinine), carcinogen (e.g., NNAL, cadmium, lead, CYMA), cardiovascular diseases (e.g., lead, AAMA), and respiratory diseases (e.g., cadmium, CEMA, CYMA) (6,25). Nicotine metabolites were measured using two separate isotope dilution high-performance liquid chromatography/tandem mass spectrometric methods. TSNAs were measured using liquid chromatography linked to tandem mass spectrometry. Heavy metals were measured using mass spectrometry after a simple dilution. VOCs were measured usingultra performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (6,23). Biomarker concentrations below the limit of detection (LOD) were imputed using a standard substitution formula (the LOD divided by the square root of 2).
Tobacco use status:
Participants who reported regularly using e-cigarettes and currently using e-cigarettes every day or some days from the Adult Interview Survey were classified as current e-cigarette users (yes/no). Current use status (yes vs. no) was also created for the other 9 tobacco products, including cigarettes, traditional cigars, cigarillos, filtered cigars, pipe, hookahs, smokeless tobacco, snus, and dissolvable tobacco. Those who reported not current use of any other tobacco products were classified as non-current tobacco users, and those who reported current use of ≥ 1 other tobacco product were excluded from the current study to avoid confounding effects of other tobacco use on BOE (26). Participants who reported having ever smoked >100 cigarettes in their lifetime and responded ‘not at all’ to the question ‘Do you now smoke cigarettes?’ were classified as former smokers (27).
Nicotine concentration level:
E-cigarette users were asked, “Please think about the electronic nicotine product/electronic nicotine cartridge/e-liquid you [use/used] most often. [Does/Did] it contain nicotine?” Participants who responded “No” were classified as non-nicotine e-cigarette users, and those who answered “Yes” were classified as nicotine e-cigarette users. Nicotine concentration levels were assessed by the question “What concentration of nicotine [do/did] you usually use?” with response options ranging from “1- 3 mg/ml or 0.1 - 0.3%” to “60+ mg/ml or 6.0+%” and “I don’t know the concentration” (see survey options in Appendix Figure 1). We further grouped nicotine e-cigarette users into four groups based on their nicotine levels, “I don’t know,” “low: 0.1-1.7%,” “moderate: 1.8-4.9%”, and “high: 5.0%+” (14).
Vaping characteristics included the frequency of e-cigarette use (some days vs. daily), e-cigarette device type (i.e., replaceable prefilled cartridges, a refillable tank, others [e.g., a mod system, disposable or something else]), average number of vaping episodes (continuous) and average number of puffs per vaping episode (continuous).
Sociodemographics and other characteristics included age (continuous), sex (male, female), race/ethnicity (non-Hispanic [NH] White, NH non-White races [i.e., Black, Hispanic, and other race]), education (high school or less, some college or college graduates), income (<$49,999 or missing, $50,000+), currently live with a cigarette smoker (yes/no), past 12-month use of marijuana (yes/no), home rule for combustible tobacco use (not allowed, partially allowed or allowed), and home rule for non-combustible tobacco use (not allowed, partially allowed or allowed). Exposure to secondhand smoke was defined as those currently living with a cigarette smoker or those with a “partially allowed or allowed” home rule for combustible tobacco used.
Statistical Methods
All analyses were conducted using Wave 5 person-level urinary specimen sampling weight and 100 replicate weights. Variances were estimated using balanced repeated replication (BRR) with Fay coefficient=0.3 for inference at the population level (28,29). Urinary biomarkers were calculated as a normalized ratio to urinary creatinine concentration to control for variations in urine volume. Due to the skewness in the distribution, BOE data were transformed using a natural log.
First, we reported sample characteristics, overall and stratified by e-cigarette use status (i.e., non-tobacco use, exclusive non-nicotine e-cigarette use, and exclusive nicotine e-cigarette use) as well as nicotine concentration levels (i.e., 0.1-1.7%, 1.8-4.9%, and 5.0%+) among nicotine vapers. Second, geometric means of BOE levels corrected by creatinine were reported across e-cigarette use groups and nicotine levels. Bivariate and multivariable regressions tested group differences, yielding unadjusted and adjusted p-values. Age, sex, race/ethnicity, exposure to secondhand smoke, and former smoker status were adjusted in the multivariable model, and frequency of e-cigarette use (daily vs. some days) and types of e-cigarette devices were also adjusted to compare BOEs across nicotine concentration levels. Participants who reported ‘I don’t know’ about the nicotine concentration levels were analyzed in the Appendix, which includes a comparison of their sample characteristics and BOE assessments with those of exclusive nicotine and non-nicotine e-cigarette users. Statistical analyses were performed using SAS 9.4 (Cary, NC), and the significance was two-tailed with a p-value of 0.05.
Data availability
The data used in this study are available to approved researchers in NAHDAP Restricted Use Files at https://www.icpsr.umich.edu/web/NAHDAP/studies/36840.
Results
As illustrated in Supplementary Figure S1, the Wave 5 PATH adult survey file included 34309 participants, and the Wave 5 biomarker file included 7868 participants. After excluding 214 participants who reported use of nicotine replacement therapies in the three days before urine collection or creatinine values outside the normal range of 10-370 mg/dl, the biomarker dataset included 7654 participants. We further excluded 4302 current cigarette smokers or other current tobacco users, resulting in 3352 participants in the final analytical sample, including 3021 non-current tobacco users, 31 exclusive non-nicotine e-cigarette users, and 300 exclusive nicotine e-cigarette users. Among nicotine vapers, 24 reported unknown nicotine levels, 197 reported 0.1-1.7%, 46 reported 1.8-4.9%, and 33 reported 5.0+%.
Sample characteristics are presented in Table 1. The mean age of those in the sample was 47.3 years; 56.0% were female, 41.5% were non-Whites, 37.1% had high school or less than high school education, and 29.5% had an annual income of less than $50,000 or missing income. Exclusive nicotine e-cigarette users had the lowest average age (37.9), the highest proportion of males (59.7%), non-Hispanic whites (78.8%), former smokers (86.2%), and those reporting “partially allowed or allowed” home rules for non-combustible tobacco use (44.0%). Non-nicotine e-cigarette users had the highest proportion of reporting currently living with a cigarette smoker (25.2%), “partially allowed or allowed” home rules for combustible tobacco use (36.4%), and past-year marijuana use (62.2%). In comparison with non-nicotine e-cigarette users, exclusive nicotine e-cigarette users were more likely to be daily vapers (81.4% vs. 30.5%, p<.0001) and reported a higher number of vaping episodes (30.1 vs. 6.4, p<.0001) and puffs (4.8 vs. 2.9, p=.01).
Table 1.
Sample Characteristics of Exclusive E-cigarette Users and Exclusive Cigarette Smokers, 2013-2019a
| Current Exclusive E-cigarette Use | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Characteristics | Non-Current Tobacco Use | Non-nicotine Users | Nicotine Users | ||||||
| n | Weighted % (95% CI) | n | Weighted % (95% CI) | n | Weighted % (95% CI) | n | Weighted % (95% CI) | p-valueb | |
| Overall | 3353 | ||||||||
|
| |||||||||
| Age, Mean (SE) | 3353 | 47.3 (0.3) | 3021 | 47.6 (0.3) | 31 | 39.9 (3.8) | 300 | 37.9 (1.0) | <.0001 |
|
| |||||||||
| Sex | <.0001 | ||||||||
| Male | 1612 | 44.0 (42.2-45.9) | 1418 | 43.4 (41.5-45.5) | 16 | 40.6 (25.3-57.9) | 177 | 59.7 (52.2-66.9) | |
| Female | 1739 | 56.0 (54.1-57.8) | 1601 | 56.6 (54.5-58.5) | 15 | 59.4 (42.1-74.7) | 123 | 40.3 (33.1-47.8) | |
|
| |||||||||
| Race/Ethnicity | <.0001 | ||||||||
| NH White | 1774 | 58.5 (55.0-61.9) | 1531 | 57.8 (54.1-61.3) | 13 | 56.6 (37.9-73.6) | 230 | 78.8 (71.5-84.6) | |
| Non-White | 1541 | 41.5 (38.1-45.0) | 1453 | 42.2 (38.7-45.9) | 18 | 43.4 (26.4-62.1) | 69 | 21.2 (15.4-28.5) | |
|
| |||||||||
| Education | 0.11 | ||||||||
| High school or less | 1206 | 37.1 (33.6-40.7) | 1078 | 37.0 (33.3-40.9) | 19 | 63.8 (44.9-79.3) | 108 | 36.1 (29.2-43.7) | |
| Some college or college graduates | 2147 | 62.9 (59.3-66.4) | 1943 | 63.0 (59.1-66.7) | 12 | 36.2 (20.7-55.1) | 192 | 63.9 (56.3-70.8) | |
|
| |||||||||
| Income | 0.34 | ||||||||
| <$49,000 or missing | 1148 | 29.5 (26.4-32.9) | 1040 | 29.5 (26.3-33.0) | 16 | 44.6 (25.4-65.6) | 91 | 27.5 (21.6-34.2) | |
| $50,000+ | 2205 | 70.5 (67.1-73.6) | 1981 | 70.5 (67.0-73.7) | 15 | 55.4 (34.4-74.6) | 209 | 72.5 (65.8-78.4) | |
|
| |||||||||
| Currently live with a cigarette smoker | 0.007 | ||||||||
| No | 2861 | 89.1 (86.6-91.2) | 2591 | 89.3 (86.7-91.4) | 24 | 74.8 (52.1-89.0) | 246 | 83.7 (78.7-87.7) | |
| Yes | 491 | 10.9 (8.8-13.4) | 430 | 10.7 (8.6-13.3) | 7 | 25.2 (11.0-47.9) | 54 | 16.3 (12.3-21.3) | |
|
| |||||||||
| Former Smokers | <.0001 | ||||||||
| No | 1976 | 81.2 (79.7-82.6) | 1929 | 83.7 (82.3-85.0) | 14 | 41.7 (23.1-63.0) | 33 | 13.8 (9.1-20.2) | |
| Yes | 1376 | 18.8 (17.4-20.3) | 1092 | 16.3 (15.0-17.7) | 17 | 58.3 (37.0-76.9) | 267 | 86.2 (79.8-90.9) | |
|
| |||||||||
| Home rule for combustible tobacco use | <.0001 | ||||||||
| Not Allowed | 2901 | 90.9 (89.1-92.4) | 2635 | 91.3 (89.5-92.8) | 20 | 63.6 (43.2-80.1) | 246 | 82.1 (75.7-87.1) | |
| Partially Allowed or allowed | 439 | 9.1 (7.6-10.9) | 375 | 8.7 (7.2-10.5) | 11 | 36.4 (19.9-56.8) | 53 | 17.9 (12.9-24.3) | |
|
| |||||||||
| Home rule for non-combustible tobacco use | <.0001 | ||||||||
| Not Allowed | 2536 | 83.3 (80.8-85.6) | 2355 | 84.3 (81.6-86.7) | 21 | 69.9 (50.3-84.1) | 159 | 56.0 (49.0-62.7) | |
| Partially Allowed or allowed | 791 | 16.7 (14.4-19.2) | 646 | 15.7 (13.3-18.4) | 9 | 30.1 (15.9-49.7) | 136 | 44.0 (37.3-51.0) | |
|
| |||||||||
| Past 12-month marijuana use | <.0001 | ||||||||
| No | 2508 | 88.2 (86.6-89.7) | 2309 | 89.2 (87.5-90.6) | 11 | 37.8 (20.2-59.4) | 188 | 66.2 (59.3-72.5) | |
| Yes | 844 | 11.8 (10.3-13.4) | 712 | 10.8 (9.4-12.5) | 20 | 62.2 (40.6-79.8) | 112 | 33.8 (27.5-40.7) | |
|
| |||||||||
| Frequency of E-cigarette Use | <.0001 | ||||||||
| Some day | N/A | N/A | 23 | 69.5 (49.5-84.1) | 54 | 18.6 (13.8-24.6) | |||
| Daily | N/A | N/A | 8 | 30.5 (15.9-50.5) | 246 | 81.4 (75.4-86.2) | |||
|
| |||||||||
| E-cigarette Devices | 0.67 | ||||||||
| Cartridge | N/A | N/A | 8 | 29.2 (15.0-49.0) | 86 | 31.2 (25.0-38.1) | |||
| Tank | N/A | N/A | 17 | 55.0 (36.6-72.1) | 148 | 50.6 (43.5-57.7) | |||
| Others | N/A | N/A | 6 | 15.8 (6.9-32.3) | 66 | 18.2 (13.9-23.5) | |||
|
| |||||||||
| Avg # of vaping episodes, mean (SE) | N/A | N/A | 31 | 6.4(1.5) | 300 | 30.1(3.5) | <.0001 | ||
| Avg # of puffs, mean (SE) | N/A | N/A | 31 | 2.9(0.4) | 300 | 4.8(0.7) | 0.01 | ||
Abbreviations, NH: non-Hispanic. SE: standard error. NA: not applicable.
All analyses applied urinary sample weight, 100 replicated weights, and the balanced repeated replication method with Fay’s adjustment = 0.3 to account for the PATH study’s complex design.
p-values from Rao-Scott X2 test for categorical variables and linear regression for continuous variable.
Table 2 presents BOE differences across e-cigarette use groups and the LOD for select biomarkers (22). As compared to non-nicotine e-cigarette users and non-tobacco users, exclusive nicotine e-cigarette users exhibited the highest mean concentrations of nicotine metabolites in TNE2 (21.8 vs. 0.2 vs. 0.01 nmol/mg creatinine, adjusted p-values<.0001) and cotinine (1418.2 vs. 12.2 vs. 0.4 ng/mg creatinine, adjusted p<.0001). Both nicotine and non-nicotine e-cigarette users also exhibited higher levels of NNAL (4.0 and 4.9 vs. 1.2 ng/mg creatinine) than non-tobacco users. Some differences in metals and VOCs were observed across e-cigarette use groups. For instance, in the adjusted results, nicotine vapers had higher concentrations of lead than non-tobacco users and non-nicotine users (adjusted p=0.01); in the unadjusted model, non-nicotine vapers had higher concentrations of CYMA (4.7 vs. 1.6 ng/mg creatinine, p<.0001) than non-tobacco users.
Table 2.
Biomarker of Exposure Comparison by E-cigarette Use vs. Non-Tobacco Usersa
| Tobacco Use Status | Comparison | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||
| Geometric Mean (95% CI) | No nicotine vs. No Use | Nicotine vs. No Use | Nicotine Yes vs. No | |||||||
|
| ||||||||||
| (ng/mg creatinine) | Limit of Detection | No Tobacco Use (n=3021) | Non-Nicotine E-cigarette Use (n=31) | Nicotine E-cigarette Use (n=300) | Unadj p-valuec | Adj p-valued | Unadj p-valuec | Adj p-valued | Unadj p-valuec | Adj p-valuee |
| Urinary Nicotine Metabolites | ng/ml | |||||||||
| TNE2b | 0.03 | 0.01 (0.01-0.01) | 0.2 (0.1-0.6) | 21.8 (15.2-31.2) | <.0001 | <.0001 | <.0001 | <.0001 | <.0001 | <.0001 |
| Cotinine (COTT) | 0.03 | 0.4 (0.3-0.5) | 12.2 (4.2-35.8) | 1418.2 (998-2015.4) | <.0001 | <.0001 | <.0001 | <.0001 | <.0001 | <.0001 |
|
| ||||||||||
| TSNA | ng/L | |||||||||
| NNAL | 0.6 | 1.2 (1.1-1.4) | 4.9 (2.4-9.9) | 4.0 (3.2-5.0) | 0.0002 | 0.05 | <.0001 | 0.001 | 0.57 | 0.88 |
|
| ||||||||||
| Heavy Metals | μg/L | |||||||||
| Cadmium (UCD) | 0.055 | 0.2 (0.2-0.2) | 0.2 (0.1-0.3) | 0.2 (0.2-0.2) | 0.93 | 0.98 | 0.63 | 0.09 | 0.96 | 0.50 |
| Lead (UPB) | 0.022 | 0.3 (0.3-0.3) | 0.2 (0.2-0.3) | 0.33 (0.3-0.4) | 0.11 | 0.10 | 0.41 | 0.01 | 0.06 | 0.01 |
|
| ||||||||||
| VOC | μg/L | |||||||||
| AAMA (Acrylamide) | 2.2 | 56.2 (54-58.6) | 64.6 (47.3-88.2) | 65.3 (58.3-73.3) | 0.38 | 0.90 | 0.02 | 0.62 | 0.94 | 0.60 |
| CEMA (Acrolein) | 6.96 | 98.3 (94.02-102.78) | 108.6 (82.9-142.1) | 107.1 (95.2-120.4) | 0.48 | 0.63 | 0.17 | 0.28 | 0.93 | 0.96 |
| CYMA (Acrylonitrile) | 0.5 | 1.6 (1.5-1.7) | 4.7 (2.2-9.9) | 4.0 (3.1-5.1) | 0.01 | 0.18 | <.0001 | 0.10 | 0.66 | 0.83 |
All analyses applied urinary sample weight, 100 replicated weights, and the balanced repeated replication method with Fay’s adjustment = 0.3 to account for the PATH study’s complex design.
TNE2 (nmol/mg creatinine): The molar sum of the imputed values of cotinine and trans-3′-Hydroxycotinine in the urine sample. The limit of detection is 0.03 ng/ml for both cotinine and trans-3′-Hydroxycotinine.
Unadjusted p-values from bivariate analysis.
Adjusted p-values from multivariable regressions adjusted for age, sex, race/ethnicity, exposure to secondhand smoke, and former smoker status.
Adjusted p-values from multivariable regressions adjusted for age, sex, race/ethnicity, exposure to secondhand smoke, frequency of e-cigarette use (daily vs. some days), former smoker status, and e-cigarette devices
As shown in Table 3, nicotine e-cigarette users with high nicotine levels (5.0%+) had the lowest average age (29.1 vs. 43.1 vs. 39.0, p<.0001), were more likely to report past 12-month marijuana use (53.5% vs. 41.6%, and 27.9%, p=0.04) and use of the e-cigarette devices with a cartridge (72.7% vs. 50.9% vs. 17.6%, p<.0001) than those who reported use of nicotine levels 1.8-4.9% and those reporting use of 0.1-1.7%.
Table 3.
Sample Characteristics by Nicotine Concentration Levels among Exclusive Nicotine E-cigarette Usersa
| Nicotine Concentration Level | 0.1-1.7% (n=197) | 1.8-4.9% (n=46) | 5.0%+ (n=33) | ||||
|---|---|---|---|---|---|---|---|
| Characteristics | n | Weighted % (95% CI) | n | Weighted % (95% CI) | n | Weighted % (95% CI) | p-valueb |
| Overall | 197 | 72.5 (66.3-77.9) | 46 | 14.7 (10.9-19.5) | 33 | 12.8 (8.5-18.9) | |
|
| |||||||
| Age | 197 | 39.0(1.3) | 46 | 43.1(3.1) | 33 | 29.1(1.1) | <0.0001 |
|
| |||||||
| Sex | 0.05 | ||||||
| Male | 109 | 55.9 (46.5-65.0) | 29 | 62.5 (45.4-76.9) | 26 | 81.6 (63.9-91.7) | |
| Female | 88 | 44.1 (35.0-53.5) | 17 | 37.5 (23.1-54.6) | 7 | 18.4 (8.3-36.1) | |
|
| |||||||
| Race/Ethnicity | 0.35 | ||||||
| NH White | 151 | 79.9 (71.5-86.2) | 36 | 84.7 (71.5-92.4) | 25 | 67.7 (41.6-86.0) | |
| Non-White | 46 | 20.1 (13.8-28.5) | 9 | 15.3 (7.6-28.5) | 8 | 32.3 (14.0-58.4) | |
|
| |||||||
| Education | 0.06 | ||||||
| High school or less | 77 | 38.4 (30.8-46.6) | 16 | 39.7 (25.6-55.7) | 10 | 19.0 (9.1-35.3) | |
| Some college or college graduates | 120 | 61.6 (53.4-69.2) | 30 | 60.3 (44.3-74.4) | 23 | 81.0 (64.7-90.9) | |
|
| |||||||
| Income | 0.74 | ||||||
| <$49,000 or missing | 60 | 26.0 (19.3-34.1) | 13 | 31.6 (17.9-49.5) | 11 | 31.4 (16.7-51.1) | |
| $50,000+ | 137 | 74.0 (65.9-80.7) | 33 | 68.4 (50.5-82.1) | 22 | 68.6 (48.9-83.3) | |
|
| |||||||
| Currently Live with a cigarette smoker | 0.86 | ||||||
| No | 161 | 83.9 (77.0-89.0) | 39 | 86.6 (73.7-93.8) | 26 | 81.5 (62.0-92.2) | |
| Yes | 36 | 16.1 (11.0-23.0) | 7 | 13.4 (6.2-26.3) | 7 | 18.5 (7.8-38) | |
|
| |||||||
| Former Smokers | 0.74 | ||||||
| No | 18 | 11.2 (6.5-18.5) | 4 | 6.3 (2.1-17.2) | 3 | 10.0 (2.7-30.9) | |
| Yes | 179 | 88.8 (81.5-93.5) | 42 | 93.7 (82.8-97.9) | 30 | 90.0 (69.1-97.3) | |
|
| |||||||
| Home rule for combustible tobacco use | 0.66 | ||||||
| Not Allowed | 159 | 82.0 (74.4-87.7) | 42 | 89.4 (74.6-96.0) | 26 | 82.6 (61.1-93.5) | |
| Partially Allowed or allowed | 38 | 18.0 (12.3-25.6) | 4 | 10.6 (4.0-25.4) | 7 | 17.4 (6.5-38.9) | |
|
| |||||||
| Home rule for non-combustible tobacco use | 0.21 | ||||||
| Not Allowed | 106 | 57.6 (49.9-64.9) | 19 | 45.0 (30.0-61.0) | 20 | 66.0 (48.3-80.2) | |
| Partially Allowed or allowed | 88 | 42.4 (35.1-50.1) | 26 | 55.0 (39.0-70.0) | 13 | 34.0 (19.8-51.7) | |
|
| |||||||
| Past 12-month marijuana use | 0.04 | ||||||
| No | 135 | 72.1 (64.4-78.7) | 26 | 58.4 (42.9-72.4) | 13 | 46.5 (26.5-67.7) | |
| Yes | 62 | 27.9 (21.3-35.6) | 20 | 41.6 (27.6-57.1) | 20 | 53.5 (32.3-73.5) | |
|
| |||||||
| Frequency of E-cigarette Use | 0.06 | ||||||
| Some day | 33 | 18.8 (12.3-27.6) | 4 | 4.9 (1.4-15.3) | 5 | 12.4 (5.0-27.7) | |
| Daily | 164 | 81.2 (72.4-87.7) | 42 | 95.1 (84.7-98.6) | 28 | 87.6 (72.3-95.0) | |
|
| |||||||
| E-cigarette Devices | <.0001 | ||||||
| Cartridge | 29 | 17.6 (12.5-24.3) | 21 | 50.9 (35.0-66.6) | 23 | 72.7 (55.7-84.9) | |
| Tank | 116 | 59.7 (51.7-67.2) | 20 | 42.2 (28.0-57.7) | 6 | 18.2 (8.6-34.4) | |
| Others | 52 | 22.7 (16.8-29.9) | 5 | 7.0 (2.5-18.1) | 4 | 9.2 (3.2-23.6) | |
|
| |||||||
| Avg # of vaping episodes, mean(SE) | 191 | 33.3(5.0) | 46 | 30.0(6.3) | 33 | 22.6(4.1) | 0.23 |
| Avg # of puffs, mean(SE) | 191 | 5.2(1.0) | 46 | 3.7(0.7) | 33 | 4.0(0.5) | 0.49 |
Abbreviations, NH: non-Hispanic. SE: standard error.
All analyses applied urinary sample weight, 100 replicated weights, and the balanced repeated replication method with Fay’s adjustment = 0.3 to account for the PATH study’s complex design.
p-values from the Rao-Scott X2 test for categorical variables and linear regression for continuous variables.
Table 4 presents BOE comparisons by nicotine concentration levels. As compared to those reporting use of products with 0.1-1.7% nicotine levels, exclusive e-cigarette users who reported use of products with 1.8-4.9% had higher concentrations of TNE2 (42.8 vs. 22.2 nmol/mg creatinine, unadjusted p-values=0.04) and cotinine (2942.3 vs. 1449.0 ng/mg creatinine, unadjusted p=0.02), but the differences were insignificant after adjusting for covariates. E-cigarette users who reported use of products with nicotine levels of 5.0+% did not exhibit significantly higher nicotine metabolites than those using other nicotine levels (0.1-1.7% or 1.8-4.9%). Other BOEs were not significantly different in the fully adjusted model.
Table 4.
Biomarkers of Exposure by Nicotine Concentration Level among Exclusive Nicotine E-cigarette Users
| Nicotine Concentration Levels | Comparison | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Geometric Mean (95% CI) | 1.8-4.9% vs. 0.1-1.7% | 5.0%+ vs. 0.1-1.7% | 5.0%+ vs. 1.8-4.9% | ||||||
|
| |||||||||
| (ng/mg creatinine) | 0.1-1.7% (n=197) | 1.8-4.9% (n=46) | 5.0%+ (n=33) | Unadj p-valuec | p-valued | Unadj p-valuec | p-valued | Unadj p-valuec | p-valued |
| Urinary Nicotine Metabolites | |||||||||
| TNE2b | 22.2 (13.4-36.9) | 42.8 (31.2-58.8) | 34.7 (25.3-47.6) | 0.04 | 0.45 | 0.18 | 0.78 | 0.38 | 0.38 |
| Cotinine (COTT) | 1449.0 (884.9-2372.5) | 2942.3 (2140.3-4044.9) | 2198.0 (1636.8-2951.6) | 0.02 | 0.98 | 0.19 | 0.65 | 0.19 | 0.65 |
|
| |||||||||
| TSNA | |||||||||
| NNAL | 4.0 (3.0-5.4) | 3.5 (2.4-5.1) | 3.3 (2.0-5.4) | 0.57 | 0.20 | 0.48 | 0.50 | 0.88 | 0.70 |
|
| |||||||||
| Heavy Metals | |||||||||
| Cadmium (UCD) | 0.2 (0.17-0.23) | 0.2 (0.2-0.3) | 0.1 (0.09-0.13) | 0.56 | 0.93 | <.0001 | 0.20 | 0.001 | 0.68 |
| Lead (UPB) | 0.3 (0.3-0.4) | 0.3 (0.3-0.4) | 0.2 (0.2-0.3) | 0.45 | 0.23 | 0.01 | 0.10 | 0.07 | 0.57 |
|
| |||||||||
| VOC | |||||||||
| AAMA (Acrylamide) | 64.3 (55.9-73.9) | 67.8 (50.2-91.5) | 66.3 (47.1-93.4) | 0.72 | 0.41 | 0.88 | 0.69 | 0.93 | 0.17 |
| CEMA (Acrolein) | 109.4 (94.4-126.8) | 118.8 (97.8-144.3) | 91.3 (69.1-120.8) | 0.48 | 0.41 | 0.22 | 0.72 | 0.09 | 0.23 |
| CYMA (Acrylonitrile) | 3.6 (2.7-4.7) | 4.4 (2.5-7.7) | 5.5 (2.6-11.9) | 0.54 | 0.47 | 0.26 | 0.34 | 0.63 | 0.59 |
All analyses applied urinary sample weight, 100 replicated weights, and the balanced repeated replication method with Fay’s adjustment = 0.3 to account for the PATH study’s complex design.
TNE2: The molar sum of the imputed values of cotinine and trans-3′-Hydroxycotinine, urine.
Unadjusted p-values from bivariate analysis.
Adjusted p-values from multivariable regressions adjusted for age, sex, race/ethnicity, exposure to secondhand smoke, frequency of e-cigarette use (daily vs. some days), and former smoker status and e-cigarette devices.
Supplementary Tables S1 and S2 presented information regarding those who reported unknown nicotine levels in e-cigarettes (n=24). The characteristics of “unknown” users were generally similar to exclusive non-nicotine e-cigarette users. They were less likely to be former smokers and daily vapers compared to exclusive nicotine e-cigarette users (Supplementary Table S1). Users who reported “I don’t know” regarding nicotine levels tended to have lower concentrations of nicotine metabolites (i.e., TNE2 and Cotinine) than exclusive nicotine e-cigarette users, but BOEs were not significantly different in the multivariable analysis. As compared to non-nicotine e-cigarette users, those reporting unknown nicotine levels had higher mean concentrations of TNE2 (3.9 vs. 0.2 nmol/mg creatinine, adjusted p-values=0.01) and cotinine (245.0 vs. 12.2 ng/mg creatinine, adjusted p=0.01). Other BOEs were not significantly different between these two groups (Supplementary Table S2).
Discussion
The current U.S. e-cigarette market is dominated by high nicotine-strength vaping products with sales of ≥5% nicotine level e-cigarettes increasing from 5.1% in January 2017 to 80.9% in March 2022 (10). Meanwhile, biomarkers of nicotine metabolites among exclusive nicotine e-cigarette users also increased significantly, with mean concentrations of TNE2 and cotinine nearly doubling from 2013 to 2019 (30). By analyzing Wave 5 of the PATH adult survey and biomarker, this study found significant BOE differences in several tobacco-related toxicants (e.g., nicotine metabolites, TSNA, heavy metal) between nicotine and non-nicotine vapers as well as notable variations in nicotine metabolites across different nicotine levels. Nicotine level has been a key tobacco regulatory focus of the FDA, and understanding the health effects of BOEs across nicotine levels in e-cigarettes at the population level has important implications for regulations.
Consistent with prior adult and youth studies (30,31), this study found significantly higher nicotine metabolites among exclusive nicotine vapers than both non-nicotine vapers and non-tobacco users. Nicotine in tobacco is highly addictive, and acute nicotine use can increase brain reward function through its effects on dopamine release, as nicotine has a high affinity for the brain (32,33). Additionally, though non-nicotine vapers exhibited much lower nicotine exposure than nicotine vapers, their mean concentrations of TNE2 and cotinine were about 30~200 times higher than non-tobacco users. Nicotine exposure among non-nicotine e-cigarette users might be due to several potential factors. First, adult vapers might not be aware of nicotine presence in their e-cigarette products, and the unawareness was particularly higher among infrequent users (34), which constituted nearly 70% of non-nicotine vapers in this study. Second, labeling of nicotine contents in e-cigarettes is not sufficient to convey clear information to consumers. Studies have found unclear labeling or mislabeling in vaping products(35,36), with one study reporting that six e-liquid samples labeled as containing 0 mg/ml of nicotine were found to actually contain nicotine in amounts ranging from 5.7 to 23.9 mg/ml. Lastly, social vaping and product sharing are common among e-cigarette users, especially among young adults (37,38), which can contaminate nicotine exposure among non-nicotine e-cigarette users.
This study reported higher concentration levels of lead in nicotine e-cigarette users than non-tobacco users. E-cigarettes are battery-powered devices that generate aerosols by heating a liquid solution with a metal coil. This heating process might result in the release of metal particles, including lead, into the liquid inhaled by e-cigarette users (39). Although this study also reported higher NNAL, a carcinogen metabolite, among nicotine and non-nicotine vapers than non-tobacco users, it is worth noting that the NNAL level in e-cigarette users is about 98% lower than in cigarette smokers (6,30). E-cigarette products may contain trace amounts of TSNAs, including NNAL (40). Exposure to NNAL among nicotine and non-nicotine vapers might also be attributed to prior tobacco use history, misreporting errors, or secondhand smoke exposure. Secondhand smoke exposure was higher among e-cigarette users than non-tobacco users, though this study adjusted for former smoker status and secondhand smoke exposure in the multivariable regressions. Further research is needed to understand health effects of e-cigarette use and the potential public health implications of nicotine exposure among users of supposedly nicotine-free e-cigarettes.
This population-based study enhances the literature with biomarker assessments in a real-world setting and generalizability, using weighted analysis for population-level inference and capturing diverse vaping behaviors. Existing knowledge on the effects of nicotine levels is primarily based on randomized controlled trials (RCTs) or laboratory studies. A prior RCT study of adult cigarette smokers (41), reported that those in the 36mg/ml e-cigarette condition reported greater dependence on e-cigarettes at 6 months, compared with baseline and the 0mg/ml and 8mg/ml conditions. However, there were no differences in total nicotine exposure compared to baseline, nor between any conditions at the follow-up visits. In a laboratory study (42), that provided e-cigarette liquid with different levels of nicotine concentration (0, 8, 18, or 36 mg/ml) to e-cigarette naïve adult cigarette smokers, Lopez et al. found that higher liquid nicotine concentrations increase plasma nicotine concentration for a given e-cigarette device. In addition, the average puff duration in the 36 mg/ml condition was significantly shorter compared to the 0 mg/ml nicotine condition. However, none of the existing studies have evaluated e-cigarette products with nicotine concentration levels of 5.0% or higher, which are common in the current e-cigarette market. To the best of our knowledge, this is the first population study to analyze the BOE differences among e-cigarette users across nicotine levels. This study found some evidence of differences in nicotine metabolites across different nicotine levels, with the highest nicotine exposure among those reporting use of products containing nicotine levels of 1.8-4.9%. For instance, their mean concentrations of TNE2 (42.8 nmol/mg creatinine) and cotinine (2942.3 ng/mg creatinine) were nearly 93% and 103% higher than those reporting use of products containing nicotine levels of 0.1-1.7% (22.2 and 1449.0, respectively). However, these differences were not statistically significant after adjusting for covariates. Additionally, a small, but appreciable number of exclusive e-cigarette users reported unknown about their nicotine levels. This might be due to the mislabeling of nicotine contents in current vaping products or e-cigarette users’ lack of awareness or attention to nicotine levels (34,35). Notably, these users were more likely to be never-smokers and nondaily vapers than nicotine e-cigarette users.
Importantly, the mean concentrations of nicotine metabolites among those reporting use of products containing nicotine concentrations of 1.8-4.9% and 5.0+% were higher or comparable to those reported by cigarette smokers in a prior study (30.8 nmol/mg creatinine and 2002.0 ng/mg creatinine, respectively) (30). The non-linear relationship between exposure to nicotine metabolites and nicotine levels might result from the complexity of e-cigarette products, as factors like device types, settings, and usage behaviors can all influence nicotine exposure(43,44). For instance, e-cigarette users who chose different nicotine levels might engage in compensatory puffing behaviors, with higher puff numbers and longer durations associated with using low (vs. high) nicotine strength liquid, thus increasing overall nicotine absorption (15,16). Furthermore, as previous studies on cigarette smoking have shown that concentrations of certain biomarkers, like cotinine and NNAL, tend to level off at high CPD levels (45,46), it is possible that individuals reporting a 5.0+% nicotine level might have titrated their nicotine intake by meeting their nicotine needs.
This study has limitations. First, nicotine concentration and e-cigarette/tobacco use status are self-reported, and they are subject to recall bias and misreporting errors. Second, we focused on the select biomarker outcomes that are most relevant to e-cigarette use. Most biomarkers have a short half-life (40), and we further excluded other tobacco users at each wave to mitigate confounding effects. However, biomarker outcomes with long half-lives (e.g., cadmium, lead, and NNAL) may originate from prior tobacco use history and other sources such as food consumption and environmental exposure (40). This study adjusted the exposure to secondhand smoke and former smoker status to mitigate this impact. Third, this is a cross-sectional study, thus precluding causal inference. In addition, limited sample sizes for some sub-groups by nicotine level might reduce statistical power in detecting small effects.
This national study provided evidence of elevated nicotine metabolite levels among nicotine e-cigarette users as compared to non-nicotine vapers and non-tobacco users. There were some variances in BOEs by nicotine levels, and e-cigarette users might compensate for nicotine-strength levels by different puff behaviors. Continued monitoring of BOE at the population level and assessment of BOE differences by nicotine concentration levels are warranted.
Supplementary Material
Funding:
Research reported in this publication was supported by the National Institute on Drug Abuse under Award Number R21DA058328 (PI: Dai). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Role of Funder:
The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Conflicts of Interest: The authors have no conflicts of interest to disclose.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used in this study are available to approved researchers in NAHDAP Restricted Use Files at https://www.icpsr.umich.edu/web/NAHDAP/studies/36840.