Biomarkers of Exposure to Tobacco-Related Toxicants and Oxidative Stress Among Cancer Survivors

Abstract

Introduction

Cancer survivors who continue to use tobacco products may have negative impacts on their long-term health outcomes.

Aims and Methods

Data were drawn from the Population Assessment of Tobacco and Health Study Wave 5 (n = 6946). Geometric mean concentrations of biomarkers from four classes of harmful and potentially harmful constituents, along with a biomarker of oxidative stress, were compared between cancer survivors and non-cancer adults using weighted linear regressions adjusted for demographic factors. Among cancer survivors, comparisons were also made across current cigarette smoking and e-cigarette use statuses.

Results

Compared to non-cancer adults (n = 6441), cancer survivors (n = 505) exhibited higher mean concentrations of 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanol (NNAL, 10.9 vs. 6.7 pg/mg creatinine), cadmium (0.3 vs. 0.2 ng/mg creatinine), lead (0.4 vs. 0.3 ng/mg creatinine), n-Acetyl-s-(2-carboxyethyl)-l-cysteine (CEMA, 156.6 vs. 128.3 ng/mg creatinine), and 8-isoprostane (521.6 vs. 441.5 pg/mg creatinine) in the unadjusted analyses. Among cancer survivors, both exclusive cigarette smokers and dual users had higher mean concentrations of urine nicotine metabolites (ie, nicotine equivalents [TNE2], and cotinine), NNAL, cadmium, volatile organic compounds, and 8-isoprostane than non-tobacco users. Exclusive e-cigarette users also had higher mean concentrations of nicotine metabolites (TNE2, 4.1 vs. 0.01, p < .0001; cotinine, 288.1 vs. 0.4, p < .0001), NNAL (5.6 vs. 1.7, p = .03), cadmium (0.4 vs. 0.3, p = .01), and 8-isoprostane (594.2 vs. 482.5, p = .03) than non-tobacco users.

Conclusions

Both exclusive smoking and dual-use pose elevated risks of exposure to tobacco-related toxicants to cancer survivors. Exclusive e-cigarette use also increases the risk of nicotine addiction and oxidative stress. Abstinence from all tobacco use among cancer survivors is needed to improve health outcomes.

Implications

Cancer survivors represent a high-risk group for tobacco use. This study offers important insights into differences in biomarkers of tobacco-related toxicant exposure between cancer survivors and non-cancer adults and provides evidence of the health impacts associated with cigarette smoking and e-cigarette use. The findings emphasize the need for clinical monitoring and targeted public health interventions to encourage tobacco cessation, including addressing dual-use of cigarettes and e-cigarettes, in order to enhance long-term health outcomes and quality of life for cancer survivors.

An estimated 16.9 million cancer survivors lived in the United States as of January 2019, and the population of cancer survivors will increase to more than 22.1 million by 2030 due to the growth and aging of the population alone.¹ Tobacco use is the leading cause of preventable death worldwide.² Tobacco consumption in any form, especially when smoked, is carcinogenic. Tobacco smoke contains a deadly mix of more than 7000 chemicals, including over 60 recognized carcinogens and a variety of toxicants, increasing the risk of health problems, including coronary heart disease, stroke, cancer, and many other disorders.² Tobacco use causes at least 12 types of cancer (eg, lung, mouth and throat, esophagus, stomach, kidney, pancreas, liver, bladder, colon, and rectum), where tobacco-related cancers constitute 40% of all cancer cases identified.^3,4 Moreover, tobacco smoking is linked to 92% of lung, trachea, and bronchus cancers in males and 62% in females.⁵

Quitting smoking can greatly reduce the risk of tobacco-related diseases and associated premature death,⁶ but a sizable number of cancer survivors continue to smoke cigarettes regularly, including 19.8% of smoking-related cancer survivors and 10.6% of nonsmoking-related cancer survivors.⁷ Smoking elevates the likelihood of having a second primary cancer among cancer survivors and is strongly linked to increased toxicity from treatment for cancer.⁸ Almost half of individuals diagnosed with cancers who smoke may continue to smoke throughout treatment or relapse after successfully quitting, which can complicate cancer care.⁹ Furthermore, cigarette smoking in cancer patients is associated with a higher likelihood of treatment failure and a higher incidence of secondary tobacco-related malignancies, both resulting in a shorter survival time.¹⁰ Enyioha. et al.⁹ reported that smoking at the time of a cancer diagnosis increased the risk of all-cause mortality by around 50% and cancer-related mortality by nearly 60% across cancer sites and treatment options. Existing evidence underlines the need for programs that promote long-term smoking cessation among cancer patients and survivors while also improving overall health outcomes.¹⁰

Unlike combustible cigarettes, e-cigarettes do not contain carcinogenic incomplete combustibles and tobacco nitrosamines,¹¹ so they are generally considered less toxic than regular cigarette smoking in the general population.¹² The transition from cigarette smoking to exclusive e-cigarette use is associated with reduced exposure to tobacco-related toxicants.¹³ However, knowledge is scarce about the health effects of emerging tobacco use, such as e-cigarette use among cancer survivors, given that they are relatively new to the market. Human exposure to harmful and potentially harmful constituents that arise from the use of tobacco products can be measured with biomarkers of exposure (BOE), which can serve as intermediate endpoints for assessing the health risk of tobacco products in the absence of long-term epidemiological evidence.^14,15 In addition to nicotine, tobacco users are exposed to carcinogenic tobacco-specific nitrosamines (TSNAs), volatile organic compounds, and polycyclic aromatic hydrocarbons.¹⁶

The landscape of U.S. adult tobacco use has been changing. Although the prevalence of adult smokers in the United States had decreased from 42.4% in 1965 to 11.5% in 2021,¹⁷ 4.5% of U.S. adults reported current use of e-cigarettes, with a higher prevalence among younger adults aged 18–24 and current and former smokers.^18,19 A previous study reported that e-cigarette ever-use among cancer survivors increased from 8.5% in 2014 to 10.7% in 2017,²⁰ and Salloum et al. estimated that 3.8% of cancer survivors were current e-cigarette users as compared to 5.7% of non-cancer participants by analyzing Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) study.²¹ Among cancer survivors, individuals who currently smoke are more likely to become e-cigarette users than nonsmokers, younger patients are more likely to use e-cigarettes than older patients, and female patients are more likely to use e-cigarettes than male patients.²¹ It is estimated that about 23.1% of current smokers among cancer survivors also reported current use of e-cigarettes.²²

Research has shown that BOE to harmful and potentially harmful constituents and biomarkers of health effects (eg, oxidative stress) that can play an important role in assessing the harmful effects of tobacco use and can further be used to confirm the link between tobacco exposure and cancer risk.¹¹ Understanding the BOE difference between cancer survivors and non-cancer adults is particularly important, with several health implications. First, this knowledge can provide clinicians with valuable insights into the potential health risks associated with different tobacco use in this population, supporting the creation of tailored interventions and informed clinical recommendations. Second, the BOE comparisons can offer cancer survivors concrete evidence about the benefits of smoking cessation or completely switching to less harmful alternatives, such as e-cigarettes, ultimately helping to reduce the tobacco-related disease burden among this vulnerable group.

This study analyzes biospecimen data from the cross-sectional PATH Wave 5 Adult study to examine the differences in BOE to tobacco-related toxicants between cancer survivors and non-cancer adults. We further compare the BOE differences by current cigarette smoking and e-cigarette use status (ie, no use, exclusive cigarette smoke, exclusive e-cigarette use, and dual-use) among cancer survivors. We hypothesize that cancer survivors might exhibit higher concentrations of certain BOEs (eg, NNAL), and cigarette smoking and dual-use of cigarettes and e-cigarettes are associated with elevated risks of exposure to tobacco-related toxicants and oxidative stress among cancer survivors.

Methods

Data

The PATH study is a cohort study of tobacco use behaviors, attitudes, beliefs, and health outcomes among a nationally representative sample of U.S. civilian, non-institutionalized individuals 12 years old and older. Wave 5 of the PATH study was collected between December 2018 and November 2019 with a weighted response rate of 88.0%.²³ A four-stage, stratified probability sampling design was used in the PATH study, which intentionally oversampled adult tobacco users, young adults, and African Americans. The PATH study was conducted by Westat and approved by Westat’s Institutional Review Board. Further details regarding the data collection, study design, and methods can be found in the study user guide.²³

Adult respondents who completed the Wave 1 interview were invited to voluntarily provide urine and blood samples. Of the 21 807 adults who provided a urine sample at baseline, a stratified subset of 11 522 individuals representing diverse tobacco use groups and with sufficient urine volume were selected for laboratory analysis, forming the W1 Biomarker Core. These subjects were asked to provide biospecimens at subsequent waves, along with answering interview questions. At Wave 5, 7988 Wave 1 Biomarker Core participants provided urine biospecimens, and 7868 were analyzed. Details of biospecimen collection and laboratory procedures were provided in the biospecimen urine collection procedures.²⁴ Respondents reported their use of all nicotine-containing products within a 3-day period of biospecimen collection, and nicotine exposure questions were incorporated into the adult interview.²⁴ The PATH biomarker and adult survey data at Wave 5 were linked through their unique personal ID.

Measures

Cancer Survivors

At Wave 1, all participants were asked the question, “Have you ever been told by a doctor, nurse, or other health professional that you had cancer?” with the response options of “yes” and “no.” Those who responded “yes” were classified as “cancer survivors.” In the following waves (W2-5), only respondents who had not answered the question in the previous survey (eg, aged-up adults) were asked this question again. Similarly, those who responded “yes” at Wave 5 to the question of ever having a diagnosis or a past-year diagnosis were classified as cancer survivors. In the following waves (Waves 2–5), those who responded “no” at Wave 1 (or previous waves) were asked the question “In the past 12 months, have you been told by a doctor, nurse, or other health professionals that you had cancer?” with the response options of “yes” and “no.” Those who responded “yes” were classified as “cancer survivors.”

Biomarkers of Exposure

we selected nine biomarkers that are most relevant to the health effects of tobacco use from four groups of harmful and potentially harmful constituents classes: (1) nicotine metabolites (nicotine equivalents [TNE2], cotinine), (2) Tobacco-Specific Nitrosamines (TSNAs, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol [NNAL, a metabolite of NNK]), (3) metals (cadmium and lead), and (4) volatile organic compounds (n-Acetyl-s-(2-carbamoylethyl)-l-cysteine [AAMA, a metabolite of acrylamide], n-Acetyl-s-(2-carboxyethyl)-l-cysteine [CEMA, a metabolite of acrolein], n-Acetyl-s-(2-cyanoethyl)-l-cysteine [CYMA, a metabolite of acrylonitrile]) as well as a biomarker of health effect measured by 8-isoprostane for oxidative stress. These select BOEs are clinically relevant to addiction (eg, TNE2, cotinine), carcinogens (eg, NNAL, cadmium, lead, CYMA), cardiovascular diseases (eg, lead, AAMA), reproductive or developmental health (eg, TNE2, cadmium, lead), and respiratory diseases (eg, cadmium, CEMA, CYMA).^25,26

Individuals who reported using nicotine replacement therapies in the past 3 days or had creatinine values outside the normal range of 10–370 mg/dl (n = 214) were excluded from this analysis. Biomarker concentrations below the limit of detection were imputed using a common substitution formula (the limit of detection divided by the square root of 2).²⁷

Tobacco Use Status at Wave 5

Those who reported having ever smoked ≥100 combustible cigarettes and currently smoke cigarettes some days or every day were classified as current cigarette smokers. Current e-cigarette users were defined as those who reported use of e-cigarettes regularly and currently use e-cigarettes some days or every day. A dual-use status of cigarettes and e-cigarettes was created with four levels (no use, exclusive cigarette smoking, exclusive e-cigarette use, and dual-use).

Similarly, current use (some days or every day) of other tobacco products (ie, traditional cigars, cigarillos, filtered cigars, pipe, hookahs, smokeless tobacco, snus, and dissolvable tobacco) was classified as “yes” versus “no.”

Sociodemographics and Other Covariates at Wave 5

Sociodemographic covariates included age (measured in a continuous scale), sex (male/female), race/ethnicity (non-Hispanic white, Non-Hispanic black, Hispanic, or other Non-Hispanic classifications), education (less than high school, high school graduate, some college, or Bachelor’s degree or above), and income level (<$10 000, $10 000–$24 999, $25 000–$49 999, $50 000–$99 999, $100 000, or missing). Exposure to secondhand smoke (SHS) was determined by the question, “Not including yourself, does anyone who lives with you now do any of the following?” Those who responded “Smoke cigarettes” were classified as exposed to SHS. Other substance use included the past 12 months of marijuana use (yes/no) and the past 12 months of alcohol use (yes/no).

Statistical Analysis

All analyses were performed using the Wave 5 person-level urinary specimen sampling weight and 100 replicate weights. Variances were estimated through balanced repeated replication with a Fay coefficient of 0.3 for population-level inference.^28,29

Descriptive sample characteristics were reported overall and by cancer survivor status. Rao-Scott χ2 test for categorical variables and general linear model for continuous variables were conducted to detect significant group differences. Unadjusted geometric mean and 95% confidence intervals of creatinine-corrected biomarker concentration levels were reported, and both bivariate and multivariable linear regression models were performed to compare BOE differences between cancer survivors and non-cancer adults, yielding unadjusted and adjusted results. Age, sex, race/ethnicity, education, income, exposure to secondhand smoke, current cigarette use, and current use of other tobacco products were adjusted in the multivariable regression models.

Among cancer survivors, a stratified analysis was conducted by cigarette and e-cigarette use status (ie, non-tobacco use, exclusive cigarette smoking, exclusive e-cigarette use, and dual-use) to understand the BOE differences by tobacco use status. Multivariable regressions controlled for age, sex, race/ethnicity, education, and exposure to secondhand smoke. Cigarette smoking status (someday vs. daily) was additionally adjusted for when comparing exclusive cigarette smoking versus dual-use, as well as e-cigarette use status (someday vs. daily) when comparing exclusive e-cigarette use versus dual-use.

To address the skewness in the distribution, BOE data were transformed using the natural logarithm. In the main analysis, urinary biomarkers were calculated as normalized ratios to urinary creatinine concentration to account for the hydration of participants. In the supplemental analysis, raw BOE data were analyzed with creatinine concentration additionally adjusted in the multivariable regressions. All analyses were performed using SAS 9.4 (Cary, NC), with significance assessed using two-tailed tests with a p-value of .05.

Results

Figure 1 illustrates the analytic sample flowchart, which shows the linkage of adult urinary biomarker data (n = 7868) and adult interview data (n = 34 309) in the PATH study Wave 5, and participant selection criteria. The combined biomarker-adult interview data included 7654 participants with valid biomarker data. After excluding those with missing cancer information (n = 708), the final analytic sample consisted of 6946 participants (weighted N = 114 620 000), including 505 cancer survivors (weighted N = 9 301 000) and 6441 non-cancer adults (N = 105 319 000). In the subgroup analysis of cancer survivors, we further excluded those who reported use of other tobacco products than cigarettes or e-cigarettes (n = 94), resulting in 411 cancer survivors (ie, 175 non-tobacco users, 194 exclusive cigarette smokers, 16 exclusive e-cigarette users, and 26 dual users).

Figure 1.

A flow chart of the study design and subject selection criteria

As shown in Appendix Table 1, the mean (standard error) age of study participants was 47.0 (0.2), and the analytic sample included 52.0% females, 62.4% non-Hispanic Whites, 13.8% non-Hispanic Blacks, 16.6% Hispanics, and 7.3% identified as other races. Additionally, 19.6% had less than a high school diploma, 21.9% were high school graduates, 29.6% attended some college, and 28.9% were college graduates. About 19.3% of participants reported exposure to secondhand smoke. The percentage of exclusive cigarette smoking, exclusive e-cigarette use, and dual-use was 25.7%, 2.8%, and 3.5%, respectively. About 8.5% of participants also reported use of other tobacco products.

Overall, 8.1% of the analytic sample were cancer survivors. As compared to non-cancer adults, cancer survivors tended to be older (57.8 vs. 46.0) and more likely to be non-Hispanic white (73.7% vs. 61.4%). Other sociodemographic factors, tobacco, and substance use behaviors were not significantly different between cancer survivors and non-cancer adults.

Table 1 presents the differences in urine biomarkers by cancer survivor status. Cancer survivors generally had higher geometric mean concentrations of BOEs than non-cancer survivors. In the unadjusted analysis, as compared to non-cancer adults, cancer survivors showed higher mean concentrations of NNAL (10.9 vs. 6.7 pg/mg creatinine, p = .03), cadmium (0.3 vs. 0.2 ng/mg creatinine, p < .0001), lead (0.4 vs. 0.3 ng/mg creatinine, p < .0001), CEMA (156.6 vs. 128.3 ng/mg creatinine, p = .01), and 8-isoprostane (521.6 vs. 441.5 pg/mg creatinine, p = .005). No statistically significant differences were found in these BOEs after adjusting for covariates.

Table 1.

Differences in biomarkers of exposure by cancer survivor status, the PATH study Wave 5^a

Geometric mean (95% CI)	Cancer survivors No Yes		Unadjusted p-valuec	Adjusted p-valued
Urinary nicotine metabolites
TNE2b (nmol/mg creatinine)	0.12 (0.1–0.14)	0.18 (0.09–0.36)	.29	.45
Cotinine (ng/mg creatinine)	7.4 (6.1–9.0)	10.3 (5.0–21.0)	.39	.60
TSNA  (pg/mg creatinine)
NNAL	6.7 (6.0–7.5)	10.9 (7.0–16.8)	.03	.16
Heavy metals (ng/mg creatinine)
Cadmium	0.2 (0.2–0.2)	0.3 (0.3–0.4)	<.0001	.11
Lead	0.3 (0.3–0.3)	0.4 (0.4–0.5)	<.0001	.66
VOC (ng/mg creatinine)
AAMA (Acrylamide)	73.1 (70.4–76.0)	82.0 (73.7–91.3)	.06	.26
CEMA (Acrolein)	128.3 (123.9–132.9)	156.6 (136.9–179.1)	.01	.50
CYMA (Acrylonitrile)	5.8 (5.4–6.3)	7.5 (5.1–11.1)	.20	.22
8-isoprostane (pg/mg creatinine)	441.5 (423.9–459.8)	521.6 (467.6–581.9)	.005	.07

Abbreviation. CI = confidence interval; TNE = total nicotine equivalents; NNAL = 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol; VOCs = volatile organic compounds; AAMA = n-Acetyl-S-(2-carbamoylethyl)-l-cysteine; CEMA = n-Acetyl-s-(2-carboxyethyl)-l-cysteine; CYMA = n-Acetyl-s-(2-cyanoethyl)-l-cysteine.

^aAll 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.

^bTNE2 (nmol/mg creatinine): The molar sum of the imputed values of cotinine and trans-3′-Hydroxycotinine in the urine sample.

^cUnadjusted p-values from bivariate analysis.

^dAdjusted p-values from multivariable regressions adjusted for age, sex, race/ethnicity, education, exposure to secondhand smoke, current cigarette use (yes/no), and current use of other tobacco products (yes/no).

Appendix Table 2 presents the sample characteristics by cigarette and e-cigarette use status among cancer survivors. Exclusive e-cigarette users tended to be the youngest (mean[standard error] = 48.9[3.4]) and more likely to report past 12-month alcohol use (50.2%) than other groups (16.0% for non-tobacco users, 36.1% for exclusive cigarette smokers, and 35.3% for dual users), while non-tobacco users were less likely to report past 12-month marijuana use (5.8%) than exclusive cigarette smokers (26.8%), exclusive e-cigarette users (17.1%), and dual users (22.8%). Exclusive cigarette smokers and exclusive e-cigarette users were also more likely to report currently living with a cigarette smoker (38.7% and 40.2%, respectively) than non-tobacco users (7.5%).

Table 2 presents differences in BOEs across cigarette and e-cigarette use status among cancer survivors. As compared to non-tobacco users, exclusive cigarette smokers, and dual users exhibited higher concentrations of urine nicotine metabolites, NNAL, heavy metals, volatile organic compounds metabolites, and 8-isoprostane. For instance, the mean concentrations of cotinine were 1895.0 and 3917.2 ng/mg creatinine (p < .0001) for exclusive cigarette smokers and dual users, respectively, as compared to 0.4 ng/mg creatinine for non-tobacco users. The mean concentrations of NNAL were 249.8 and 242.7 pg/mg creatinine (p < .0001) for exclusive cigarette smokers and dual users, respectively, as compared to 1.7 pg/mg creatinine for non-tobacco users. Exclusive e-cigarette users had higher mean concentrations of TNE2 (4.1 nmol/mg creatinine), cotinine (288.1 ng/mg creatinine), NNAL (5.6 pg/mg creatinine), cadmium (0.4 ng/mg creatinine), and 8-isoprostane (594.2 pg/mg creatinine) than non-tobacco users.

Table 2.

Differences in biomarkers of exposure among cancer survivors by tobacco product use, the PATH study Wave 5^a

Geometric mean (95% CI)	Non-tobacco use (n = 175)	Exclusive cigarette smoking (n = 194)	p-valuec	Exclusive e-cigarette use (n = 16)	p-valuec	Dual use (n = 26)	p-valuec
Urinary nicotine metabolites
TNE2b (nmol/mg creatinine)	0.01 (0.01–0.01)	31.7 (21.2–47.4)1,2	<.0001	4.1 (0.6–26.3)1	<.0001	53.5 (35.6–80.6)2	<.0001
Cotinine (ng/mg creatinine)	0.4 (0.2–0.8)	1895.0 (1242–2891.3)1	<.0001	288.1 (46.6–1783.1)1	<.0001	3917.2 (2369.3–6476.4)2	<.0001
TSNA (pg/mg creatinine)
NNAL	1.7 (1.1–2.5)	249.8 (179.2–348.2)1	<.0001	5.6 (2.6–12.1)2	.03	242.7 (62.8–936.9)1	<.0001
Heavy metals (ng/mg creatinine)
Cadmium	0.3 (0.2–0.3)	0.5 (0.4–0.6)1	<.0001	0.4 (0.3–0.6)1	.002	0.5 (0.4–0.6)1	.0002
Lead	0.4 (0.3–0.5)	0.5 (0.4–0.5)1	.005	0.5 (0.3–0.9)1	.11	0.4 (0.3–0.6)1	.72
VOC (ng/mg creatinine)
AAMA (Acrylamide)	62.3 (52.5–74)	141.6 (125.3–160.0)1	<.0001	78.6 (51.9–118.9)2	.97	118.3 (84.7–165.1)1,2	.02
CEMA (Acrolein)	120.4 (95–152.5)	258.2 (212.0–314.5)1	<.0001	95.7 (76.5–119.8)2	.11	339.5 (212.7–541.8)1	.0003
CYMA (Acrylonitrile)	1.7 (1.4–2)	117.2 (82.1–167.3)1	<.0001	3.4 (1.6–7.6)2	.36	109.9 (30.0–403.2)1	<.0001
8-isoprostane (pg/mg creatinine)	482.5 (405.4–574.3)	617.5 (550.6–692.5)1	.03	594.2 (507.8–695.4)1	.03	727.1 (566.3–933.6)1	.02

Abbreviation. Geo Mean = geometric mean of concentration; CI = confidence interval; TNE = total nicotine equivalents; NNAL = 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol; VOCs = Volatile Organic Compounds; AAMA = n-Acetyl-s-(2-carbamoylethyl)-l-cysteine; CEMA = n-Acetyl-s-(2-carboxyethyl)-l-cysteine; CYMA = n-Acetyl-s-(2-cyanoethyl)-l-cysteine.

^aAll 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. The analytical sample included cancer survivors who did not currently use other tobacco products.

^bTNE2 (nmol/mg creatinine): The molar sum of the imputed values of cotinine and trans-3’-Hydroxycotinine in the urine sample.

^cMultivariable regressions tested the biomarker differences with non-tobacco use as the reference group and covariates included age, sex, race, and exposure to secondhand smoke. Pairwise comparisons of exclusive cigarette smoking, exclusive e-cigarette use, and dual-use were conducted using multivariable regressions, including age, sex, race, and exposure to secondhand smoke as covariates. Additionally, cigarette smoking status (someday vs. daily) was included when comparing exclusive cigarette smoking versus dual-use; e-cigarette use status (someday vs. daily) was included when comparing exclusive e-cigarette use versus dual-use. Groups that are significantly different in pairwise contrasts are denoted by non-shared superscript numbers.

The pairwise comparisons showed that the mean concentrations of BOEs were generally similar between exclusive cigarette smokers and dual users, except for cotinine being higher among dual users (3917.2 ng/mg creatinine) than exclusive cigarette smokers (1895.0 ng/mg creatinine). In contrast, exclusive e-cigarette users generally had lower nicotine metabolites, NNAL, and some volatile organic compounds metabolites than exclusive cigarette smokers and dual users. For instance, exclusive e-cigarette users had mean concentrations of TNE2 significantly lower than dual users (4.1 vs. 53.5 nmol/mg creatinine) and NNAL and CYMA significantly lower than both exclusive cigarette smokers and dual users (5.6 vs. 249.8 vs. 242.7 pg/mg creatinine for NNAL and 3.4 vs. 117.2 vs. 109.9 ng/mg creatinine for CYMA).

Appendix Tables 3 and 4 present the raw BOE comparisons with urinary creatinine concentration included as an additional adjustment in the model. The adjusted p-values were similar to the main analyses.

Discussion

Previous studies have reported that tobacco use is prevalent among cancer survivors⁷ and continuous smoking post-cancer diagnosis poses an elevated risk to cancer survivors, including ongoing exposure to tobacco-related toxicants.⁸ To the best of our knowledge, this is the first study leveraging a nationally representative dataset to examine the differences in nine BOEs between cancer survivors and non-cancer adults and further investigate the BOE differences by cigarette and e-cigarette use status among cancer survivors. Findings from this study have important public health implications to inform potential harm-reduction strategies and improve health outcomes among cancer survivors.

This study reported that cancer survivors might have higher tobacco-related BOEs than non-cancer adults, especially for NNAL, a carcinogen metabolite, heavy metals such as cadmium and lead, as well as acrolein (CEMA) and urine 8-isoprostane, a biomarker of oxidative stress. However, after adjusting for covariates (eg, age), these differences attenuated to be insignificant, indicating that elevated BOEs in cancer survivors might be due to their long-term exposure to tobacco-related toxicants as cancer survivors were found to be, on average, 12 years older than non-cancer adults in this study, and they were also more likely to be former smokers or recent quitters.³⁰

Among cancer survivors, non-tobacco users had significantly lower mean concentrations of all BOEs than cigarette smokers or dual users. For instance, NNAL was nearly 99% lower in non-tobacco users than smokers, and 8-isoprostane was 22% lower than exclusive smokers and 37% lower than dual users. These differences highlighted the importance of smoking cessation in cancer survivors, as quitting smoking even after a cancer diagnosis can improve cancer treatment outcomes and overall survival.³¹ However, the utilization of FDA-approved smoking cessation medications (eg, nicotine replacement therapies) and evidence-based behavioral smoking cessation methods (eg, quitline) remains suboptimal among U.S. smokers.³² The development and implementation of tailored interventions to increase smoking cessation among cancer survivors is critically needed.

There is an ongoing debate about balancing the benefits and risks of e-cigarettes.³³ Consistent with prior literature^13,25 that showed substantially less toxicity of e-cigarette use than combustible cigarette smoking in the general population, this study found that exclusive e-cigarette users among cancer survivors tended to have lower levels of nicotine metabolites, NNAL, and some volatile organic compounds metabolites (eg, CYMA) than cigarette smokers. Thus, completely switching from cigarette smoking to e-cigarette use may serve as a potential harm-reduction alternative for some cancer survivors who are unable to quit smoking. Notably, unlike other cessation aids, e-cigarettes provide sensations that mirror combustible cigarettes, such as stimulation of the respiratory tract and the inhalation and exhalation sequence,³⁴ and may aid in quitting.

The dual use of cigarettes and e-cigarettes does not appear to reduce health risks for cancer survivors, as dual users exhibited similar or higher BOEs than exclusive cigarette smokers. Our findings support earlier studies indicating that the continued use of both combustible cigarettes and e-cigarettes is an ineffective harm-reduction strategy.³⁵ Dual use might increase nicotine addiction and extend the negative health impacts associated with combustible cigarette smoking. Therefore, if e-cigarettes are utilized as a smoking cessation aid, they should fully replace combustible cigarettes.³⁶ Additionally, e-cigarette use is not harmless, and e-cigarette aerosol contains a number of potentially toxic substances (eg, carbonyl compounds, heavy metals, and flavorings such as diacetyl)³⁷ and e-cigarette use is associated with an increased risk of respiratory symptoms.^38,39 Glycerol and nicotine in e-cigarettes can induce a decrease in CD8 + T cells and thus induce immunosuppression while increasing the secretion of IL-6 and TNFα, promoting inflammatory responses, accelerating the metastasis of tumor cells, and obtaining more aggressive and metastatic tumors.⁴⁰ Therefore, the ultimate goal for cancer survivors should be cessation of e-cigarette use to minimize potential health risks.

This study is subject to several limitations. First, the cancer survivor status is self-reported and thus may be subject to social desirability bias. Second, the cross-sectional study design precluded causal inference. Third, certain biomarker outcomes with long half-lives (eg, metals) may originate from previous combustible tobacco use, passive tobacco exposure, or other sources.⁴¹ However, most biomarkers analyzed in the study have a short half-life,⁴¹ and we also excluded other tobacco users from the analysis of BOE differences among cancer survivors to prevent potential confounding effects. Finally, this study used data from Wave 5 of the PATH study, which was collected between December 2018 and November 2019. The e-cigarette market has rapidly evolved, with disposable e-cigarettes emerging as the fastest-growing segment following the FDA’s guidance in February 2020, which cleared cartridge-based e-cigarettes with non-tobacco flavors other than menthol.⁴² Meanwhile, sales of high-nicotine-strength vaping products (ie, ≥5% nicotine) surged from 5.1% in January 2017 to 80.9% in March 2022,⁴³ and biomarker outcomes may differ based on the types of e-cigarette devices and e-liquid solutions used. Therefore, continued monitoring of BOE among cancer survivors is essential to understand the health effects of evolving tobacco products.

Despite these limitations, the study provides valuable insights into biomarkers of tobacco-related toxicants among cancer survivors and offers evidence of health effects related to cigarette smoking and e-cigarette use. These findings highlight the need for clinical monitoring and targeted public health interventions to promote tobacco cessation, including the dual use of cigarettes and e-cigarettes, to improve long-term health outcomes and quality of life among cancer survivors.

Funding

Research of HD was partly supported by grant number [R21DA058328] from the National Institute on Drug Abuse (NIDA) and the FDA Center for Tobacco Products (CTP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration. 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.

Declaration of Interest

The authors have no conflicts of interest to disclose.

Author Contributions

Hongying Dai (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Funding acquisition [lead], Investigation [equal], Methodology [lead], Project administration [lead], Resources [lead], Software [lead], Supervision [lead], Validation [equal], Visualization [equal], Writing - original draft [equal], Writing - review & editing [equal]), Yi Hao (Investigation [equal], Validation [equal], Visualization [equal], Writing - original draft [equal], Writing - review & editing [equal]), Mona Periera (Investigation [equal], Resources [equal], Validation [equal], Writing - original draft [equal], Writing - review & editing [equal]), and Tzeyu L. Michaud (Investigation [equal], Resources [equal], Validation [equal], Writing - original draft [equal], Writing - review & editing [equal])

Data Availability

The PATH Study data are available at https://www.icpsr.umich.edu/web/NAHDAP/studies/36231