BACKGROUND

Despite the increasing prevalence of vaping e-cigarettes among adolescents, there remains a lack of population-level assessments regarding the objective measurement of nicotine exposure.

METHODS

This study analyzed a nationally representative sample of adolescents aged 13 to 17 years from Wave 5 of the Population Assessment of Tobacco and Health Study conducted between 2018 and 2019. Urinary nicotine metabolites, including cotinine and trans-3′-hydroxycotinine (3-HC), were assessed among exclusive nonnicotine e-cigarette users (n = 56), exclusive nicotine e-cigarette users (n = 200), and nonusers (n = 1059). We further examined nicotine exposure by past 30-day vaping frequency (ie, occasional [1–5 days], intermittent [6–19 days], and frequent [20+ days]) and flavor types among nicotine e-cigarette users. Multivariable linear regressions tested pairwise group effects, and biomarkers were normalized by the log transformation.

RESULTS

Compared with nonusers, both nonnicotine and nicotine e-cigarette users exhibited higher levels of cotinine and 3-HC. Nicotine e-cigarette users had mean cotinine concentrations (61.3; 95% confidence interval, 23.8–158.0, ng/mg creatinine) approximately 146 times higher (P < .0001) than nonusers (0.4; 0.3–0.5), whereas nonnicotine users (4.9; 1.0–23.2) exhibited cotinine concentrations ∼12 times higher (P = .02). Among nicotine e-cigarette users, the levels of cotinine and 3-HC increased by vaping frequency, with cotinine increasing from 10.1 (2.5–40.1) among occasional users to 73.6 (31.8–170.6) among intermittent users and 949.1 (482.5–1866.9) among frequent users. Nicotine exposure was not significantly different by flavor type.

CONCLUSIONS

E-cigarette use poses health-related risks resulting from nicotine exposure among adolescents. Comprehensive regulations of e-cigarette products and marketing, vaping prevention, cessation, and public policies are needed to prevent youth from developing nicotine addiction.

What’s Known on This Subject:

Adolescent brain development can be adversely affected by nicotine exposure. Youth e-cigarette use continues to be a public health concern, with more adolescents initiating vaping at an early age.

What This Study Adds:

Adolescents who use e-cigarettes exhibit substantially higher levels of nicotine metabolites than those who do not use e-cigarettes. Individuals who engage in frequent vaping may experience high levels of nicotine exposure.

Adolescent e-cigarette use is a public health concern in the United States, with e-cigarette use increasing dramatically from 2017 to 2019 and remaining at a high level.1  The 2022 National Youth Tobacco Survey estimated that 9.4% (2.55 million) of middle and high school students reported e-cigarette use in the past 30 days.2  E-cigarette products often contain nicotine,3  which is highly addictive, and tobacco use during adolescence can be harmful to brain development.2  The latest generation of vaping products has a sleek design with increased nicotine concentration, nicotine salt formulations, and a variety of flavors, such as menthol, fruit, and candy, that are appealing to youth.4  Meanwhile, the percentage of e-cigarette sales with products containing ≥5% nicotine strength increased from 5.1% in 2017% to 80.9% in 2022.3  Yet, there is a lack of population-level objective assessment of nicotine exposure among adolescent e-cigarette users.

Biomarkers can significantly contribute to the objective assessment of harmful components resulting from nicotine exposure and serve as early indicators of potential biological effects, thereby predicting the health outcomes of individuals who vape. Nicotine, extracted from the tobacco plant or chemically produced in a laboratory, is a powerful alkaloid that acts as a potent stimulant for the central nervous system.5  Nicotine biomarkers (or nicotine metabolites), such as cotinine and trans 3′-hydroxycotinine (3-HC), play a crucial role in assessing nicotine exposure and its impact on health. Nicotine is metabolized by the enzyme CYP2A5, resulting in the formation of 3-HC, 5′-hydroxycotinine, and cotinine N-oxide.6  Recent studies have shown that nicotine exposure during adolescence can negatively affect cognitive behavior, including memory and learning deficits.7  The collective evidence suggests that adolescence is a critical period for preventing vaping because nicotine consumption at an early age could lead to a progression toward regular usage, difficulty in cessation, and long-lasting detrimental effects.2,8  Adolescents also exhibit unique responses to nicotine compared with adults,9  emphasizing the need for further research on the effects of nicotine in this population. This study examined nicotine metabolites by answering 3 research questions: (1) How much does nicotine exposure vary by vaping status? (2) Are there any dose-response effects on biomarker concentration by the frequency of e-cigarette use? and (3) Do nicotine metabolite levels vary by e-cigarette flavor types?

Data were obtained from Wave 5 of the Population Assessment of Tobacco and Health (PATH) study, a longitudinal cohort study among a nationally representative sample of US civilian, noninstitutionalized individuals.10  The PATH Wave 5 survey was conducted from December 2018 to November 2019 with a weighted response rate of 88.0% using a 4-stage, stratified probability sample design. The PATH data collection was approved by Westat’s institutional review board, and participants provided written informed consent.11 

Study participants completed a survey in an interview and provided urine samples in person voluntarily. Notably, the Wave 5 urine samples were obtained from participants in the Wave 4 Biomarker Core, which included a diverse sample of youth aged 12 to 17 years who had previously completed the Wave 4 youth interview and supplied adequate urine for the planned laboratory analyses. The selection encompassed a diverse mix of individuals from 5 different groups, representing various patterns of tobacco product use and nonuse.11  Isotope dilution high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry was used to measure cotinine and 3-HC, with 0.015 ng/mL as the lower detection limit for both biomarkers. Liquid chromatography tandem mass spectrometry was used to measure creatinine, and the lower limit for detecting creatine is 3.17 mg/dL.11  Survey data and biomarker assay results from the PATH-restricted files were linked using a deidentified participant ID.

As illustrated in Fig 1, the PATH Wave 5 Youth interview survey included 12 098 participants and the Wave 5 urine panel recruited from the Wave 4 Biomarker Core included 2958 participants. After excluding adults (n = 1351) from the Wave 5 urine panel file and participants who reported using nicotine replacement therapies in the past 3 days or those with creatinine levels ≤10 mg/dL or >370 mg/dL12  (n = 62), the combined dataset included 1545 participants. We further excluded 203 participants who reported current use of other tobacco products and 27 participants who reported “I don’t know” about whether their e-cigarettes contain nicotine, resulting in 1315 participants in the final analytical sample.

FIGURE 1

Flowchart for participants included in the analytical sample.

FIGURE 1

Flowchart for participants included in the analytical sample.

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Biomarkers of Exposure (BOE) to Toxicants

The outcomes comprised 2 urinary nicotine metabolites: cotinine and 3-HC. Biomarker concentrations below the limit of detection (LOD) were imputed using a standard substitution formula: the LOD divided by the square root of 2.13 

E-Cigarette and Tobacco Use Status

Participants were asked, “In the past 30 days, how many days did you use an electronic nicotine product?” Individuals who responded 0 days were classified as “nonusers.” Those who reported ≥1 day were classified as current e-cigarette users. Similarly, those who reported using ≥1 day of other tobacco products (ie, cigarettes, cigars, pipe, hookah, smokeless tobacco, bidi, kretek) in the past 30 days were classified as other tobacco users. Other tobacco users were excluded in this study to avoid confounding effects.14 

Current e-cigarette users were further asked whether the electronic nicotine product used most often contains nicotine with response options of “Yes,” “No,” and “I don’t know.” Those who responded “Yes” were defined as exclusive nicotine containing e-cigarette users and those who responded “No” were defined as exclusive nonnicotine containing e-cigarette users. Those who responded, “I don’t know” were excluded from the study.

E-Cigarette Use Behaviors

(1) Vaping frequency: exclusive e-cigarette users were further classified as occasional (1–5 days), intermittent (6–19 days), and frequent (20+ days) based on the number of days they reported e-cigarette use in the past 30 days.15  (2) Types of vaping devices used: current e-cigarette users were assessed with the type of electronic nicotine product used most often with response options of “A disposable device,” “A device that uses replaceable prefilled cartridge,” “A device with a tank that you refill with liquids,” “A mod system,” and “Something else.” To ensure a sufficient sample size for each group, we classified respondents into 3 mutually exclusive groups: “a device with replaceable prefilled cartridge,” “a device with a tank,” and “others” that include “a disposable device,” “a mod system,” and “something else.” (3) Types of e-cigarette flavors used: current e-cigarette users were asked, “Which flavors of (electronic nicotine products/electronic nicotine cartridges/e-liquid) have you used in the past 30 days? Choose all that apply.” with response options of “Tobacco-flavored,” “Menthol or mint,” “Clove or spice,” “Fruit,” “Chocolate,” “An alcoholic drink (such as wine, cognac, margarita, or other cocktails),” “A nonalcoholic drink (such as coffee, soda, energy drinks, or other beverages),” “Candy, desserts, or other sweets,” and “Some other flavor.” Those who reported using 2 or more flavors in the past 30 days or did not know or refused to report which flavors they used in the past 30 days were further asked which flavors they have used most often. Based on the response options and to ensure a sufficient sample size for each group, participants were classified into 4 mutually exclusive groups of “Menthol or mint,” “Fruit,” “Sweet,” (ie, “Chocolate,” “Candy, desserts, or other sweets”), and “Others.” (4) The number of vaping episodes, a continuous variable, was based on the response to the question, “On average, on the days that you use, how many times each day do you pick up your electronic nicotine product to use it, whether you take one puff or several?” (5) The number of vaping puffs, a continuous variable, was based on the response to the question, “Each time you pick up your electronic nicotine product to use it, about how many puffs do you take?”

Exposure to Secondhand Smoke and Current Marijuana Use

Participants were asked, “Not including yourself, does anyone who lives with you now do any of the following?” Those who marked “Smoke cigarettes” were categorized as exposed to secondhand smoke, whereas the rest were classified as having no secondhand smoke. Current marijuana use was assessed by the question, “Have you used marijuana, hash, THC, grass, pot, or weed in the past 30 days?” Those who responded “Yes” were classified as current marijuana users, as opposed to nonmarijuana users.

Demographic covariates were included to reflect the epidemiologic aspects of youth vaping and to control for potential confounders in the regression analyses, encompassing age (continuous, ranged from 13 to 17 years old), sex (male or female), self-reported race and ethnicity (non-Hispanic [NH] White, NH Black, Hispanic, and NH-Others [ie, Asian, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, other races and multiracial group]) as social constructs instead of as generic or biological categories, parental education (less than high school, high school graduates, and some college or college graduates), and annual household income (<$50 000 and ≥$50 000).

Weighted sample characteristics were reported overall and by e-cigarette use status (ie, nonusers, nonnicotine users, and nicotine users). Group differences were detected using the Rao-Scott χ2 test for categorical variables and the general linear regression for continuous variables. Urinary biomarkers (cotinine and 3-HC) were calculated as a normalized ratio (fold change) to urinary creatinine concentration to control for variations in urine volume. Because of the skewness in the distribution, biomarker data were transformed using a natural log before multivariable regression analyses. Analyses applied urinary sample single-wave weight, 100 replicated weights, and the balanced repeated replication method with the Fay adjustment = 0.3 to account for the PATH study’s complex design.

Multivariable linear regressions tested pairwise group effects across e-cigarette use status, vaping frequency, and e-cigarette flavors for biomarker levels. Covariates included age, sex, exposure to secondhand smoke, and current marijuana use. In subgroup analyses, for regression models to compare the effects of vaping frequency among e-cigarette users, the types of e-cigarette devices used were also included as a covariate. Geometric mean and 95% confidence interval of creatinine-corrected biomarkers along with ratios (fold change) of means between groups were reported. Analysis was conducted in SAS 9.4, with P < .05 (2-tailed) for statistical significance.

Among 1315 participants with urine samples, the median age was 14.9 years, 49.3% were female, 47.4% were NH White (14.4% NH Black and 28.5% Hispanic), 6.6% reported exclusive nicotine vaping, and 1.3% were exclusive nonnicotine e-cigarette users. Compared with nonnicotine e-cigarette users, nicotine e-cigarette users were more likely to be older (median age, 15.5 vs 15.0; P = .003), NH White individuals (77.7% vs 43.3%; P < .0001), and have higher annual household incomes of ≥$50 000 (72.4% vs 43.1%; P = .01). See Table 1.

TABLE 1

Sample Characteristics by E-cigarette Use Status Among Adolescents, 2018–2019a

E-cigarette Use StatusOverallbNonusersbNonnicotine E-cigarette Usersb,cNicotine E-cigarette UsersbPd
n (weighted %) 1315 1059 (92.1) 56 (1.3) 200 (6.6)  
Sociodemographics 
 Age, median (IQR) 14.9 (13.9–15.9) 14.9 (13.9–15.9) 15.0 (14.0–16.1) 15.5 (14.5–16.3) .003 
 Sex     .06 
  Male 714 (50.7%) 576 (51.7%) 33 (47.3%) 105 (37.1%)  
  Female 599 (49.3%) 483 (48.3%) 22 (52.7%) 94 (62.9%)  
 Race/ethnicity     <.0001 
  Non-Hispanic White 542 (47.4%) 401 (45.2%) 22 (43.3%) 119 (77.7%)  
  Non-Hispanic Black 157 (14.4%) 143 (15.2%) 5 (3.8%) 9 (5.4%)  
  Hispanic 428 (28.5%) 374 (29.7%) 14 (36.2%) 40 (10.7%)  
  Others 126 (9.8%) 93 (9.9%) 9 (16.7%) 24 (6.2%)  
 Parental education .46 
  Less than high school 177 (10.2%) 152 (10.6%) 9 (15.1%) 16 (3.6%)  
  High school 320 (22.4%) 271 (22.3%) 16 (27.0%) 33 (22.4%)  
  Some college/college graduate 809 (67.4%) 627 (67.0%) 31 (57.9%) 151 (74.0%)  
 Annual household income     .01 
  <$50 000 608 (42.1%) 506 (42.9%) 31 (56.9%) 71 (27.6%)  
  ≥$50 000 657 (57.9%) 509 (57.1%) 25 (43.1%) 123 (72.4%)  
Tobacco and e-cigarette use 
 Exposure to secondhand smokee     .30 
  No 993 (82.9) 823 (83.3) 36 (71.6) 134 (79.1)  
  Yes 322 (17.1) 236 (16.7) 20 (28.4) 66 (20.9)  
 Current marijuana use     <.0001 
  No 1149 (93.5) 974 (95.9) 47 (93.1) 128 (60.0)  
  Yes 166 (6.5) 85 (4.1) 9 (6.9) 72 (40.0)  
 Past 30-day e-cigarette use     <.0001 
  1–5 d (occasional) NA NA 41 (88.8%) 65 (51.9%)  
  6–19 d (intermittent) NA NA 8 (9.0%) 45 (20.9%)  
  ≥20 d (frequent) NA NA 4 (2.2%) 81 (27.2%)  
 E-cigarette device typee     .06 
  Replaceable prefilled cartridge NA NA 20 (44.6%) 125 (54.8%)  
  A tank NA NA 20 (27.6%) 58 (36.2%)  
  Others NA NA 14 (27.7%) 17 (9.0%)  
 E-cigarette flavors     .01 
  Menthol/mint NA NA 9 (22.9) 80 (33.0)  
  Fruit NA NA 23 (47.0) 87 (49.8)  
  Sweet NA NA 14 (12.5) 26 (15.3)  
  Others NA NA 10 (17.6) 7 (2.0)  
Average number of vaping episodes, mean (SE) NA NA 2.1 (0.4) 8.8 (1.7) .0002 
Average number of vaping puffs, mean (SE) NA NA 1.7 (0.3) 4.5 (0.6) <.0001 
E-cigarette Use StatusOverallbNonusersbNonnicotine E-cigarette Usersb,cNicotine E-cigarette UsersbPd
n (weighted %) 1315 1059 (92.1) 56 (1.3) 200 (6.6)  
Sociodemographics 
 Age, median (IQR) 14.9 (13.9–15.9) 14.9 (13.9–15.9) 15.0 (14.0–16.1) 15.5 (14.5–16.3) .003 
 Sex     .06 
  Male 714 (50.7%) 576 (51.7%) 33 (47.3%) 105 (37.1%)  
  Female 599 (49.3%) 483 (48.3%) 22 (52.7%) 94 (62.9%)  
 Race/ethnicity     <.0001 
  Non-Hispanic White 542 (47.4%) 401 (45.2%) 22 (43.3%) 119 (77.7%)  
  Non-Hispanic Black 157 (14.4%) 143 (15.2%) 5 (3.8%) 9 (5.4%)  
  Hispanic 428 (28.5%) 374 (29.7%) 14 (36.2%) 40 (10.7%)  
  Others 126 (9.8%) 93 (9.9%) 9 (16.7%) 24 (6.2%)  
 Parental education .46 
  Less than high school 177 (10.2%) 152 (10.6%) 9 (15.1%) 16 (3.6%)  
  High school 320 (22.4%) 271 (22.3%) 16 (27.0%) 33 (22.4%)  
  Some college/college graduate 809 (67.4%) 627 (67.0%) 31 (57.9%) 151 (74.0%)  
 Annual household income     .01 
  <$50 000 608 (42.1%) 506 (42.9%) 31 (56.9%) 71 (27.6%)  
  ≥$50 000 657 (57.9%) 509 (57.1%) 25 (43.1%) 123 (72.4%)  
Tobacco and e-cigarette use 
 Exposure to secondhand smokee     .30 
  No 993 (82.9) 823 (83.3) 36 (71.6) 134 (79.1)  
  Yes 322 (17.1) 236 (16.7) 20 (28.4) 66 (20.9)  
 Current marijuana use     <.0001 
  No 1149 (93.5) 974 (95.9) 47 (93.1) 128 (60.0)  
  Yes 166 (6.5) 85 (4.1) 9 (6.9) 72 (40.0)  
 Past 30-day e-cigarette use     <.0001 
  1–5 d (occasional) NA NA 41 (88.8%) 65 (51.9%)  
  6–19 d (intermittent) NA NA 8 (9.0%) 45 (20.9%)  
  ≥20 d (frequent) NA NA 4 (2.2%) 81 (27.2%)  
 E-cigarette device typee     .06 
  Replaceable prefilled cartridge NA NA 20 (44.6%) 125 (54.8%)  
  A tank NA NA 20 (27.6%) 58 (36.2%)  
  Others NA NA 14 (27.7%) 17 (9.0%)  
 E-cigarette flavors     .01 
  Menthol/mint NA NA 9 (22.9) 80 (33.0)  
  Fruit NA NA 23 (47.0) 87 (49.8)  
  Sweet NA NA 14 (12.5) 26 (15.3)  
  Others NA NA 10 (17.6) 7 (2.0)  
Average number of vaping episodes, mean (SE) NA NA 2.1 (0.4) 8.8 (1.7) .0002 
Average number of vaping puffs, mean (SE) NA NA 1.7 (0.3) 4.5 (0.6) <.0001 

IQR, Interquartile range; NA, not applicable; SE, standard error.

a

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. Those who reported current use of other tobacco products (i.e., cigarettes, cigars, hookah, pipe, smokeless tobacco, bidi, and kretek) were excluded from the analytical sample to avoid confounding effects. We also excluded individuals who used nicotine replacement therapies in the past 3 days or had creatinine values outside the normal range of 10–370 mg/dL.

b

n (column %). We reported unweighted sample n and weighted % in Table 1. Since the PATH study oversampled tobacco users and some demographic groups, the weighted % might be different from the calculation based on the unweighted n.

c

Participants were asked whether electronic nicotine products used most often contain nicotine. Current (past 30-d) e-cigarette users who responded to “No” were classified as non-nicotine e-cigarette users and those who responded “Yes” were classified as nicotine e-cigarette users. Those who reported “I don’t know” were excluded in the analyses.

d

P values were from Rao-Scott χ2 test for categorical variables and linear regression for continuous variables to compare differences among all groups.

e

Participants were asked the type of electronic nicotine product used most often with response options “A disposable device,” “A device that uses replaceable prefilled cartridge,” “A device with a tank that you refill with liquids,” “A mod system,” and “Something else.” Those who responded, “a disposable device,” “a mod system,” and “something else” were classified as “other.”

Adolescents who used nicotine e-cigarettes were more inclined to report current marijuana use compared with nonnicotine e-cigarette users or individuals who did not use e-cigarettes (P < .0001). Nicotine (vs nonnicotine) users were more likely to be frequent e-cigarette users (27.2% vs 2.2%; P < .0001) with higher numbers of vaping episodes (8.8 vs 2.1; P = .0002) and puffs (4.5 vs 1.7; P < .0001). Compared with nonnicotine e-cigarette users, a higher proportion of nicotine e-cigarette users reported vaping menthol/mint flavor (33.0% vs 22.9%; P = .01), whereas a lower proportion reported vaping other flavors (2.0% vs 17.6%; P = .01).

As shown in Table 2, both nonnicotine and nicotine e-cigarette users had higher cotinine and 3-HC than nonusers. Notably, nicotine e-cigarette users had mean concentrations of cotinine and 3-HC concentrations approximately 146 and 156 times higher than nonusers, respectively. In contrast, these levels were around 12 and 14 times higher than nonnicotine users. Additionally, compared with nonusers, nonnicotine users had about 12 and 11 times higher cotinine and 3-HC levels.

TABLE 2

Comparison of Biomarker Levels of Exposure to Nicotine by Vaping Statusa

Nicotine MetabolitesNonusers (n = 1059)Nonnicotine E-Cigarette Users (n = 54)Nicotine E-Cigarette Users (n = 200)
Cotinine (ng/mg creatinine)b 0.4 (0.3–0.5) 4.9 (1.0–23.2) 61.3 (23.8–158.0) 
Fold change (vs nonusers)c  11.7 (2.4–56.2) 145.8 (57.4–370.6) 
Pd  .02 <.0001 
Ratio (vs nonnicotine users)c   12.4 (2.3–66.8) 
Pd   .02 
3-HC (ng/mg creatinine)b 0.8 (0.6–0.9) 8.6 (1.8–42.3) 118.1 (46.0–303.0) 
Fold change (vs nonusers)c  11.3 (2.3–57.0) 155.7 (61.0–397.5) 
Pd  .02 <.0001 
Fold change (vs nonnicotine users)c   13.7 (2.4–77.5) 
Pd   .007 
Nicotine MetabolitesNonusers (n = 1059)Nonnicotine E-Cigarette Users (n = 54)Nicotine E-Cigarette Users (n = 200)
Cotinine (ng/mg creatinine)b 0.4 (0.3–0.5) 4.9 (1.0–23.2) 61.3 (23.8–158.0) 
Fold change (vs nonusers)c  11.7 (2.4–56.2) 145.8 (57.4–370.6) 
Pd  .02 <.0001 
Ratio (vs nonnicotine users)c   12.4 (2.3–66.8) 
Pd   .02 
3-HC (ng/mg creatinine)b 0.8 (0.6–0.9) 8.6 (1.8–42.3) 118.1 (46.0–303.0) 
Fold change (vs nonusers)c  11.3 (2.3–57.0) 155.7 (61.0–397.5) 
Pd  .02 <.0001 
Fold change (vs nonnicotine users)c   13.7 (2.4–77.5) 
Pd   .007 

3-HC, trans-3′-hydroxycotinine.

a

Biomarker concentrations below the limit of detection (LOD) were imputed using a standard substitution formula (the LOD divided by the square root of 2).

b

The geometric mean concentration level and 95% confidence interval for creatinine-corrected cotinine and 3-HC.

c

Fold change and 95% confidence interval pairwise comparisons between groups were reported.

d

P values were from multivariable regressions adjusted by age, sex, and exposure to secondhand smoke and current marijuana use. For comparisons of nonnicotine and nicotine e-cigarettes, we further adjusted e-cigarette device types.

Among nicotine e-cigarette users, the levels of cotinine and 3-HC increased with vaping frequency. The geometric mean of cotinine levels for occasional, intermittent, and frequent users was 10.1, 73.6, and 949.1 ng/mg creatinine, whereas the corresponding 3-HC levels were 19.1, 114.8, and 1866.6 ng/mg creatinine, respectively. Comparatively, frequent users exhibited cotinine levels approximately 94 and 13 times higher than occasional and intermittent users, whereas the corresponding values for 3-HC were about 98 and 16 times higher. See Table 3.

TABLE 3

Comparison of Biomarkers of Exposure to Nicotine Among Exclusive Nicotine Vapers by Vaping Frequency, 2018–2019a

Nicotine MetabolitesOccasional (1–5 d, n = 65)Intermittent (6–19 d, n = 45)Frequent (20+ days, n = 81)
Cotinine (ng/mg creatinine)b 10.1 (2.5–40.1) 73.6 (31.8–170.6) 949.1 (482.5–1866.9) 
Fold change (vs occasional vapers)c  7.3 (1.5–35.7) 94.3 (24.5–363.2) 
Pd  .008 <.0001 
Fold change (vs intermittent vapers)c   12.9 (4.4–37.6) 
Pd   <.0001 
3-HC (ng/mg creatinine)b 19.1 (5.0–72.9) 114.8 (55.4–237.8) 1866.6 (989.8–3520.1) 
Fold change (vs occasional vapers)c  6 (1.4–25.5) 97.7 (28.0–340.6) 
Pd  .007 <.0001 
Fold change (vs intermittent vapers)c   16.3 (6.3–41.9) 
Pd   <.0001 
Nicotine MetabolitesOccasional (1–5 d, n = 65)Intermittent (6–19 d, n = 45)Frequent (20+ days, n = 81)
Cotinine (ng/mg creatinine)b 10.1 (2.5–40.1) 73.6 (31.8–170.6) 949.1 (482.5–1866.9) 
Fold change (vs occasional vapers)c  7.3 (1.5–35.7) 94.3 (24.5–363.2) 
Pd  .008 <.0001 
Fold change (vs intermittent vapers)c   12.9 (4.4–37.6) 
Pd   <.0001 
3-HC (ng/mg creatinine)b 19.1 (5.0–72.9) 114.8 (55.4–237.8) 1866.6 (989.8–3520.1) 
Fold change (vs occasional vapers)c  6 (1.4–25.5) 97.7 (28.0–340.6) 
Pd  .007 <.0001 
Fold change (vs intermittent vapers)c   16.3 (6.3–41.9) 
Pd   <.0001 

3-HC, trans-3′-hydroxycotinine.

a

Biomarker concentrations below the limit of detection (LOD) were imputed using a standard substitution formula (the LOD divided by the square root of 2).

b

The geometric mean and 95% confidence interval for creatinine-corrected cotinine and 3-HC.

c

Fold change and 95% confidence interval of pairwise comparisons between groups were reported.

d

P values were from multivariable regressions adjusted by age, sex, exposure to secondhand smoke, current marijuana use, and e-cigarette device types.

Results for the levels of BOEs by vaping frequency among nonnicotine e-cigarette users are presented in Supplemental Table 4. Directionally, intermittent and frequent nonnicotine e-cigarette users had higher concentrations of cotinine and 3-HC than occasional users. However, the sample sizes for intermittent and frequent nonnicotine users are relatively small (n = 8 and n = 4, respectively), and the pairwise comparisons are not statistically significant.

Supplemental Table 5 presents the comparison of BOEs by e-cigarette flavors among nicotine vapers. Overall, the mean concentrations of cotinine and 3-HC were highest among fruit users, followed by menthol/mint, sweet, and other flavor users. However, the pairwise comparisons are not statistically significant.

Through a nationally representative youth sample, this study provided timely evidence to gauge elevated nicotine exposure among adolescent e-cigarette users as compared with nonusers. E-cigarette aerosol contains varying levels of nicotine and a number of potentially toxic substances.16  Nicotine exposure during adolescence can be harmful to brain development, causing addiction and resulting in other adverse health outcomes.2  Although the impact of e-cigarettes on long-term health effects remains to be determined, a growing body of evidence has shown that early initiation of e-cigarette use during adolescence is associated with increased risk of subsequent combustible cigarette smoking and other substance use.17,18  Meanwhile, the intensity of e-cigarette use has also increased dramatically among teenagers, with many using these products on a regular basis.19  E-cigarette addiction, as measured by the number of vaping days per month and the proportion of users reporting use of their first tobacco products within 5 minutes of waking, has surpassed the addiction rates for all other forms of tobacco products combined since 2019.19  All of these findings raise concerns about nicotine addiction of e-cigarette use among this vulnerable subpopulation.

Previous studies have reported that frequent e-cigarette users among adolescents are more likely to be NH Whites than NH Blacks and Hispanics/Latinos20  and males than females.21  This study represents a pioneering effort in using objective biomarker data to illuminate a significant increase in the mean concentration level of nicotine metabolites with vaping frequency. Our findings demonstrated that individuals who vape nicotine frequently exhibited cotinine levels more than 90 times higher than occasional users and more than 10 times higher than intermittent users. The substantial nicotine exposure observed among frequent users is particularly alarming, especially considering that they composed 42.3% of current adolescent e-cigarette users in 2022,1  a significant increase from 14.8% in 2014.20  The high nicotine exposure might also provide a plausible explanation for the heightened nicotine dependence observed among individuals who engage in frequent e-cigarette use.22 

Nonnicotine e-cigarette users represent a small, but appreciable number of adolescent individuals. Nearly 17% of adolescent e-cigarette users in this study reported exclusively vaping nonnicotine-containing products. Nonnicotine e-cigarette users tend to be younger and more likely to be Hispanics and other races than nicotine e-cigarette users. Although a higher number of nonnicotine e-cigarette users report occasional vaping than nicotine users (88.8% vs 51.9%), some of these occasional users might transition to regular nicotine users because of nicotine dependence. A recent study found that a significant portion of young adults and adults who use nonnicotine e-cigarettes also simultaneously use nicotine e-cigarettes.23  Our research also noted that exclusive nonnicotine users had significantly higher levels of cotinine and 3-HC in their urine samples compared with nonusers, indicating potential nicotine exposure, even after adjusting for secondhand smoke. This phenomenon may arise from various factors, including adolescents' lack of awareness regarding the presence of nicotine in their e-cigarette products24  or the mislabeling of nicotine content.25,26  Notably, adolescents often engage in vaping activities close to one another while socializing and product-sharing is prevalent among them.27  Secondhand exposure from their peers or family members who use nicotine products could also contribute to these elevated biomarker levels reported in this study. Additional research is required to explore this phenomenon and gain insight into the potential public health implications associated with nicotine exposure among nonnicotine e-cigarette users.

This study is subject to limitations. First, the cross-sectional study and short half-life of biomarkers might not capture nicotine intake longitudinally and accurately. Second, the sample size was unbalanced between nonusers, nonnicotine e-cigarette users, and nicotine e-cigarette users. However, we think the strength of separating nonnicotine and nicotine use outweighed the weakness of smaller sample sizes because the 2 subgroups had distinct vaping characteristics and behaviors and large effect sizes of difference. To our knowledge, this is by far the largest biomarker assessment among youth e-cigarette users. Third, because of the limited sample size, we aggregated e-cigarette disposable products into a miscellaneous category. Rechargeable products dominated the e-cigarette market during the study period (2018–2019).28  However, there has been a notable shift in usage patterns since 2019, with disposable e-cigarette products emerging as the most popular form.29  Future studies should assess whether nicotine exposure and e-cigarette use patterns differ by rechargeable versus disposable products. Finally, “non-nicotine e-cigarette users” in this study refer to adolescents who reported using electronic products that do not contain nicotine. However, it is possible that some of these individuals might use the same e-cigarette devices to vape marijuana, cannabidiol, or other compounds,30  which can increase the risk of short-term anxiety, paranoia, memory loss, and distraction.31  Given that the focus of the study is primarily on tobacco and nicotine-related behaviors, additional research is needed to assess other substances used by these nonnicotine e-cigarette users.

By leveraging nationally representative data, This study found that both nicotine and nonnicotine e-cigarette users had higher levels of nicotine metabolites compared with nonusers. The frequency of e-cigarette use strongly correlates with higher levels of nicotine metabolites among nicotine users. Comprehensive e-liquid nicotine regulation, youth vaping prevention, and cessation programs are needed to prevent youth from nicotine addiction.

Dr Dai conceptualized the study, performed analyses, drafted the initial manuscript, and critically revised the manuscript; Dr Michaud assisted in result interpretation and critically reviewed and revised the manuscript; Dr Guenzel assisted in result interpretation and critically reviewed and revised the manuscript; Ms Morgan assisted in result interpretation and critically reviewed and revised the manuscript; and Dr Cohen assisted in result interpretation and critically reviewed and revised the manuscript.

FUNDING: Research was partially supported by the National Institute on Drug Abuse under award numbers R21DA054818 and R21DA058328. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 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.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated that they have no potential conflicts of interest to disclose.

Data Sharing Statement: Population Assessment of Tobacco and Health can be accessed at https://www.icpsr.umich.edu/web/NAHDAP/studies/36840.

3-HC

trans-3′-hydroxycotinine

BOE

biomarker of exposure

LOD

level of detection

NH

non-Hispanic

PATH

Population Assessment of Tobacco and Health study

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Supplementary data