Biomarkers of Exposure among Adult Smokeless Tobacco Users in the Population Assessment of Tobacco and Health Study (Wave 1, 2013-2014)

Non-cigarette Tobacco Use and Stroke Among West Africans: Evidence From the SIREN Study

INTRODUCTION

Non-cigarette tobacco (NCT) represents a form of tobacco use with a misperceived significance in chronic disease events. Whether NCT use is sufficient to promote stroke events, especially among Africans, is yet to be understood. This study assessed the relationship between NCT use and stroke among indigenous Africans.

METHODS

A total of 7617 respondents (NCT users: 41 vs. non-NCT: 7576) from the Stroke Investigative Research and Educational Network (SIREN) study were included in the current analysis. NCT use was defined as self-reported use of smoked (cigars or piper) or smokeless (snuff or chewed) tobacco in the past year preceding stroke events. Stroke was defined based on clinical presentation and confirmed with a cranial computed tomography/magnetic resonance imaging. Multivariable-adjusted logistic regression was applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for the relationship of NCT with stroke at a two-sided p < .05.

RESULTS

Out of the 41 (0.54%) who reported NCT use, 27 (65.9%) reported using smokeless NCT. NCT users were older than non-NCT users (62.8 ± 15.7 vs. 57.7 ± 14.8 years). Overall, NCT use was associated with first-ever stroke (OR: 2.08; 95% CI: 1.02, 4.23) in the entire sample. Notably, smokeless NCT use was independently associated with higher odds of stroke (OR: 2.74; 95% CI: 1.15, 6.54), but smoked NCT use (OR: 0.16; 95% CI: 0.02, 1.63) presented a statistically insignificant association after adjusting for hypertension and other covariates.

CONCLUSIONS

NCT use was associated with higher odds of stroke, and public health interventions targeting NCT use might be promising in reducing the burden of stroke among indigenous Africans.

IMPLICATIONS

A detailed understanding of the relationship between NCT use and stroke will likely inform well-articulated policy guidance and evidence-based recommendations for public health prevention and management of stroke on the African continent.

Associations between biomarkers of nicotine/tobacco exposure and respiratory symptoms among adults who exclusively smoke cigarettes in the U.S.: Findings from the PATH Study Waves 1-4 (2013-2017)

Significance

Determining if tobacco-related biomarkers of exposure (BOE) are associated with respiratory symptoms is an important public health tool that can be used to evaluate the potential harm of different tobacco products.

Methods

Adult data from people who exclusively smoked cigarettes (N = 2,438) in Waves 1-4 (2013-2017) of the Population Assessment of Tobacco and Health Study were stacked to examine associations between baseline and follow-up within wave pairs (W1-W2, W2-W3, W3-W4). Weighted generalized estimating equation models were used to evaluate associations between biomarkers of nicotine, tobacco-specific nitrosamines, acrolein, acrylonitrile, cadmium, and lead at baseline/follow-up and respiratory symptom(s) (wheezing/whistling in the chest, wheezing during exercise, and/or dry cough in the past 12 months) at follow-up.

Results

Higher acrolein metabolite (CEMA) levels at follow-up were associated with increased odds of respiratory symptoms at follow-up for people who exclusively smoked cigarettes (aOR = 1.34; 95% CI = 1.06, 1.70), including when limited to those without a diagnosed respiratory disease (aOR = 1.46; 95% CI = 1.12, 1.90) and those who smoked daily (aOR = 1.40; 95% CI = 1.06, 1.84). Higher cadmium levels at baseline (while controlling for follow-up levels) were associated with reduced odds of respiratory symptoms at follow-up (aOR = 0.80; 95% CI = 0.65, 0.98) among people who exclusively smoked cigarettes without a respiratory disease. There were no significant associations between baseline/follow-up BOE and follow-up respiratory symptoms for people who smoked cigarettes non-daily.

Conclusions

This research supports measuring biomarkers of acrolein, such as CEMA, as a potential intermediate measurement for increased respiratory symptom development. Measuring these biomarkers could help alleviate the clinical burden of respiratory disease.

Smoking is associated with elevated blood level of volatile organic compounds: a population-based analysis of NHANES 2017-2018

BACKGROUND

The study aims to explore the association between cigarette smoking with blood exposure to volatile organic compounds using population data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018.

METHODS

Based on the data of NHANES 2017-2018, we identified 1117 participants aged 18 to 65 years, who had complete VOCs testing data and filled out the Smoking-Cigarette Use and Volatile Toxicant questionnaires. The participants consisted of 214 dual-smoking persons, 41 E-cigarette smokers, 293 combustible-cigarette smokers and 569 non-smokers. We used One-way ANOVA and Welch's ANOVA to compare differences of VOCs concentration among 4 groups and multivariable regression model to confirm the factors associated with VOCs concentration.

RESULTS

In dual-smoking and combustible-cigarette smokers, blood concentration of 2,5-Dimethylfuran, Benzene, Benzonitrile, Furan, Isobutyronitrile were higher than non-smokers. When compared with people who never smoked, E-cigarette smokers had similar blood concentrations of VOCs. Blood concentrations of Benzene, Furan, and Isobutyronitrile were significant higher in combustible-cigarette smokers than in E-cigarette smokers. In the multivariable regression model, dual-smoking and combustible-cigarette smoking were associated with elevated blood concentrations of several VOCs except 1,4-Dichlorobenzene, while E-cigarette smoking was only associated with elevated 2,5-Dimethylfuran concentration.

CONCLUSIONS

Smoking, mainly dual-smoking and combustible-cigarette smoking, is associated with elevated blood concentration of VOCs, while the effect is weak in E-cigarette smoking.

Binary classification of users of electronic cigarettes and smokeless tobacco through biomarkers to assess similarity with current and former smokers: machine learning applied to the population assessment of tobacco and health study

BACKGROUND

Exposure to harmful and potentially harmful constituents in cigarette smoke is a risk factor for cardiovascular and respiratory diseases. Tobacco products that could reduce exposure to these constituents have been developed. However, the long-term effects of their use on health remain unclear. The Population Assessment of Tobacco and Health (PATH) study is a population-based study examining the health effects of smoking and cigarette smoking habits in the U.S.

POPULATION

Participants include users of tobacco products, including electronic cigarettes and smokeless tobacco. In this study, we attempted to evaluate the population-wide effects of these products, using machine learning techniques and data from the PATH study.

METHODS

Biomarkers of exposure (BoE) and potential harm (BoPH) in cigarette smokers and former smokers in wave 1 of PATH were used to create binary classification machine-learning models that classified participants as either current (BoE: N = 102, BoPH: N = 428) or former smokers (BoE: N = 102, BoPH: N = 428). Data on the BoE and BoPH of users of electronic cigarettes (BoE: N = 210, BoPH: N = 258) and smokeless tobacco (BoE: N = 206, BoPH: N = 242) were input into the models to investigate whether these product users were classified as current or former smokers. The disease status of individuals classified as either current or former smokers was investigated.

RESULTS

The classification models for BoE and BoPH both had high model accuracy. More than 60% of participants who used either one of electronic cigarettes or smokeless tobacco were classified as former smokers in the classification model for BoE. Fewer than 15% of current smokers and dual users were classified as former smokers. A similar trend was found in the classification model for BoPH. Compared with those classified as former smokers, a higher percentage of those classified as current smokers had cardiovascular disease (9.9-10.9% vs. 6.3-6.4%) and respiratory diseases (19.4-22.2% vs. 14.2-16.7%).

CONCLUSIONS

Users of electronic cigarettes or smokeless tobacco are likely to be similar to former smokers in their biomarkers of exposure and potential harm. This suggests that using these products helps to reduce exposure to the harmful constituents of cigarettes, and they are potentially less harmful than conventional cigarettes.

Polycyclic aromatic hydrocarbons (PAHs) may explain the paradoxical effects of cigarette use on preeclampsia (PE)

Tobacco smoking and use of snus (smokeless tobacco) are associated with adverse effects on pregnancy and neonatal outcomes. Nicotine is considered a key toxicant involved in effects caused by both smoking and snus, while pyrolysis products including polycyclic aromatic hydrocarbons (PAHs) in cigarette smoke represents the constituents most unequally divided between these two groups of tobacco products. The aim of this review was: i) to compare the impact, in terms of relative effect estimates, of cigarette smoking and use of Swedish snus on pregnancy outcomes using similar non-tobacco user controls, and ii) to examine whether exposure to PAHs from smoking could explain possible differences in impact on pregnancy outcomes. We systematically searched MEDLINE, Embase, PsycInfo, Web of Science and the Cochrane Database of Systematic Reviews up to October 2021 and identified studies reporting risks for adverse pregnancy and neonatal outcomes associated with snus use and with smoking relative to pregnant women with no use of tobacco. Both snus use and smoking were associated with increased risk of stillbirth, preterm birth, and oral cleft malformation, with comparable point estimates. These effects were likely due to comparable nicotine exposure. We also found striking differences. While both smoking and snus increased the risk of having small for gestational age (SGA) infants, risk from maternal smoking was markedly higher as was the reduction in birthweight. In contrast, the risk of preeclampsia (PE) was markedly lower in smokers than in controls, while snus use was associated with a slightly increased risk. We suggest that PAHs acting via AhR may explain the stronger effects of tobacco smoking on SGA and also to the apparent protective effect of cigarette smoking on PE. Possible mechanisms involved include: i) disrupted endocrine control of fetal development as well as placental development and function, and ii) stress adaption and immune suppression in placenta and mother.

Nicotine Exposure in the U.S. Population: Total Urinary Nicotine Biomarkers in NHANES 2015-2016

We characterize nicotine exposure in the U.S. population by measuring urinary nicotine and its major (cotinine, trans-3′-hydroxycotinine) and minor (nicotine 1′-oxide, cotinine N-oxide, and 1-(3-pyridyl)-1-butanol-4-carboxylic acid, nornicotine) metabolites in participants from the 2015−2016 National Health and Nutrition Examination Survey. This is one of the first U.S. population-based urinary nicotine biomarker reports using the derived total nicotine equivalents (i.e., TNEs) to characterize exposure. Serum cotinine data is used to stratify tobacco non-users with no detectable serum cotinine (−sCOT), non-users with detectable serum cotinine (+sCOT), and individuals who use tobacco (users). The molar concentration sum of cotinine and trans-3′-hydroxycotinine was calculated to derive the TNE2 for non-users. Additionally, for users, the molar concentration sum of nicotine and TNE2 was calculated to derive the TNE3, and the molar concentration sum of the minor metabolites and TNE3 was calculated to derive the TNE7. Sample-weighted summary statistics are reported. We also generated multiple linear regression models to analyze the association between biomarker concentrations and tobacco use status, after adjusting for select demographic factors. We found TNE7 is positively correlated with TNE3 and TNE2 (r = 0.99 and 0.98, respectively), and TNE3 is positively correlated with TNE2 (r = 0.98). The mean TNE2 concentration was elevated for the +sCOT compared with the −sCOT group (0.0143 [0.0120, 0.0172] µmol/g creatinine and 0.00188 [0.00172, 0.00205] µmol/g creatinine, respectively), and highest among users (33.5 [29.6, 37.9] µmol/g creatinine). Non-daily tobacco use was associated with 50% lower TNE7 concentrations (p < 0.0001) compared with daily use. In this report, we show tobacco use frequency and passive exposure to nicotine are important sources of nicotine exposure. Furthermore, this report provides more information on non-users than a serum biomarker report, which underscores the value of urinary nicotine biomarkers in extending the range of trace-level exposures that can be characterized.

Smokeless Tobacco Use and Prevalence of Cardiovascular Disease Among Males in the Population Assessment of Tobacco and Health (PATH) Study, Waves 1–4

•

Oral smokeless tobacco (SLT) products are non-combusted forms of tobacco that can be dependence producing.

•

SLT use may pose health risks to users such as cardiovascular disease (CVD) through various pathways including influencing hemodynamics, endothelial dysfunction, inflammation, insulin resistance, hyperlipidemia, and arrhythmogenesis.

•

Past studies have suggested a small, elevated risk of CVD among SLT users compared to never tobacco users.

•

This study advances the literature by exploring how the duration of regular SLT use relates to CVD prevalence.

•

In this study of ≥ 40-year-old men only, we did not find a consistent dose–response trend for years of SLT use and prevalence of CVD.

The purpose of this period prevalence study is to compare the prevalence of cardiovascular disease (CVD) in current/former established smokeless tobacco (SLT) users (ever SLT users who have used the product fairly regularly) to those who were: 1) never established cigarette smokers and SLT users, and 2) current/former established exclusive cigarette smokers (have smoked at least a 100 or more cigarettes in lifetime) only, adjusting for known risk factors for CVD. Analyses included 4,703 men ≥ 40 years of age who participated in the Population Assessment of Tobacco and Health (PATH) Study, Waves: 1–4, conducted between 2013 and 2017. Current users were those using SLT products daily or on some days, whereas former users had not used SLT and/or cigarettes in the past 12 months. CVD prevalence was defined as a self-reported diagnosis of congestive heart failure, stroke, or myocardial infarction. Among current/former established SLT users, years of use defined exposure history, while pack-years defined exposure history for smokers. Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were reported with trend tests to examine dose–response associations. Current/former established exclusive SLT users were not significantly more likely to have had any CVD compared to never established cigarette and SLT users (OR = 1.7 [0.8–3.7]), or current/former established exclusive cigarette smokers (OR = 0.9 [0.5–1.8]). Current/former established exclusive cigarette smokers were more likely to have had any CVD compared to those who were never established cigarette and SLT users (OR = 1.6 [1.1–2.3]).

Biomarkers of Potential Harm among Adult Cigarette and Smokeless Tobacco Users in the PATH Study Wave 1 (2013-2014): A Cross-sectional Analysis

BACKGROUND

While smokeless tobacco (ST) causes oral cancer and is associated with cardiovascular diseases, less is known about how its effects differ from other tobacco use. Biomarkers of potential harm (BOPH) can measure short-term health effects such as inflammation and oxidative stress.

METHODS

We compared BOPH concentrations [IL6, high-sensitivity C-reactive protein, fibrinogen, soluble intercellular adhesion molecule-1 (sICAM-1), and F2-isoprostane] across 3,460 adults in wave 1 of the Population Assessment of Tobacco and Health study (2013-2014) by tobacco use groups: primary ST users (current exclusive ST use among never smokers), secondary ST users (current exclusive ST use among former smokers), exclusive cigarette smokers, dual users of ST and cigarettes, former smokers, and never tobacco users. We estimated geometric mean ratios using never tobacco users, cigarette smokers, and former smokers as referents, adjusting for demographic and health conditions, creatinine (for F2-isoprostane), and pack-years in smoker referent models.

RESULTS

BOPH levels among primary ST users were similar to both never tobacco users and former smokers. Most BOPH levels were lower among ST users compared with current smokers. Compared with never tobacco users, dual users had significantly higher sICAM-1, IL6, and F2-isoprostane. However, compared with smokers, dual users had similar biomarker levels. Former smokers and secondary ST users had similar levels of all five biomarkers.

CONCLUSIONS

ST users have lower levels of inflammatory and oxidative stress biomarkers than smokers.

IMPACT

ST use alone and in combination with smoking may result in different levels of inflammatory and oxidative stress levels.

Tobacco-Specific Nitrosamines (NNAL, NNN, NAT, and NAB) Exposures in the US Population Assessment of Tobacco and Health (PATH) Study Wave 1 (2013-2014)

INTRODUCTION

The tobacco-specific nitrosamines (TSNAs) are an important group of carcinogens found in tobacco and tobacco smoke. To describe and characterize the levels of TSNAs in the Population Assessment of Tobacco and Health (PATH) Study Wave 1 (2013-2014), we present four biomarkers of TSNA exposure: N'-nitrosonornicotine, N'-nitrosoanabasine, N'-nitrosoanatabine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) which is the primary urinary metabolite of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone.

METHODS

We measured total TSNAs in 11 522 adults who provided urine using automated solid-phase extraction coupled to isotope dilution liquid chromatography-tandem mass spectrometry. After exclusions in this current analysis, we selected 11 004 NNAL results, 10 753 N'-nitrosonornicotine results, 10 919 N'-nitrosoanatabine results, and 10 996 N'-nitrosoanabasine results for data analysis. Geometric means and correlations were calculated using SAS and SUDAAN.

RESULTS

TSNA concentrations were associated with choice of tobacco product and frequency of use. Among established, every day, exclusive tobacco product users, the geometric mean urinary NNAL concentration was highest for smokeless tobacco users (993.3; 95% confidence interval [CI: 839.2, 1147.3] ng/g creatinine), followed by all types of combustible tobacco product users (285.4; 95% CI: [267.9, 303.0] ng/g creatinine), poly tobacco users (278.6; 95% CI: [254.9, 302.2] ng/g creatinine), and e-cigarette product users (6.3; 95% CI: [4.7, 7.9] ng/g creatinine). TSNA concentrations were higher in every day users than in intermittent users for all the tobacco product groups. Among single product users, exposure to TSNAs differed by sex, age, race/ethnicity, and education. Urinary TSNAs and nicotine metabolite biomarkers were also highly correlated.

CONCLUSIONS

We have provided PATH Study estimates of TSNA exposure among US adult users of a variety of tobacco products. These data can inform future tobacco product and human exposure evaluations and related regulatory activities.

Urinary Cyanoethyl Mercapturic Acid, a Biomarker of the Smoke Toxicant Acrylonitrile, Clearly Distinguishes Smokers From Nonsmokers

INTRODUCTION

Cyanoethyl mercapturic acid (CEMA) is a urinary metabolite of acrylonitrile, a toxicant found in substantial quantities in cigarette smoke, but not in non-combusted products such as e-cigarettes or smokeless tobacco and rarely in the diet or in the general human environment. Thus, we hypothesized that CEMA is an excellent biomarker of combusted tobacco product use.

AIMS AND METHODS

We tested this hypothesis by analyzing CEMA in the urine of 1259 cigarette smokers (urinary cotinine ≥25 ng/mL) and 1191 nonsmokers. The analyses of CEMA and cotinine were performed by validated liquid chromatography-tandem mass spectrometry methods. Logistic regression was fit for log-transformed CEMA to construct the receiver operating characteristic curve.

RESULTS

We found that a CEMA cutpoint of 27 pmol/mL urine differentiated cigarette smokers from nonsmokers with sensitivity and specificity greater than 99%. The use of different cotinine cutpoints to define smokers (10-30 ng/mL) had little effect on the results.

CONCLUSIONS

CEMA is a highly reliable urinary biomarker to identify users of combusted tobacco products such as cigarettes as opposed to users of non-combusted products, medicinal nicotine, or nonusers of tobacco products.

IMPLICATIONS

CEMA can be used to distinguish users of combusted tobacco products from non-combusted products such as e-cigarettes, smokeless tobacco, and medicinal nicotine. Levels of CEMA in the urine of people who use these non-combusted products are extremely low, in contrast to cotinine.

Cluster analysis of urinary tobacco biomarkers among U.S. adults: Population Assessment of Tobacco and Health (PATH) biomarker study (2013-2014)

Tobacco use delivers nicotine, tobacco-specific nitrosamines (TSNAs), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs), which are metabolized and excreted in urine offering useful biomarkers of exposure. Previous studies compared individual toxicants across tobacco users. Based on a group of biomarkers, cluster analysis was used to define tobacco toxicant exposure profiles. Clusters with distinct exposure profiles, were determined and described, based on levels of urinary biomarkers of exposure to nicotine, TSNAs, VOCs, and PAHs among a national sample of current, established, adult tobacco users, and examine the association of use behavior and cluster membership. The PATH Biomarker Wave 1 data were analyzed. Current established tobacco users with complete urinary biomarker data were included (N = 6724). User groups included cigarette smokers, users of electronic cigarette (ECIG), smokeless tobacco (SLT), and dual and poly tobacco users. Cluster analysis, pairwise comparisons, and multinomial logistic regression were conducted. Cigarette smokers were primarily in clusters with high biomarker concentrations across all groups, but actual concentrations were associated with smoking quantity. A cluster with high TSNAs but low levels of PAHs and VOCs was heavily populated by SLT users. Exclusive ECIG users, depending on use frequency, were predominantly in clusters with low biomarker concentrations, except for one cluster that had relatively high TSNAs. Clusters heavily populated by dual and poly tobacco users were the same as those heavily populated by cigarette smokers. Ten exposure profiles (clusters) were determined and linked to tobacco use behavior. Findings could inform future research and policy initiatives.

U.S. Military Veteran Versus Nonveteran Use of Licit and Illicit Substances

INTRODUCTION

To provide up-to-date substance use surveillance data among U.S. military veterans versus nonveterans, this study assesses current use of tobacco products, alcohol, marijuana, prescription pain relievers, tranquilizers, sedatives, stimulants, cocaine, heroin, methamphetamines, inhalants, and hallucinogens.

METHODS

Pooled data were from the 2015-2017 National Survey on Drug Use and Health, a nationally representative, self-reported survey of the U.S. adult non-institutionalized population. Military veterans were those who had "ever been in the United States Armed Forces" and were "now separated/retired from reserves/active duty" (n=7,301). Nonveterans were those who had never been in the U.S. Armed forces (n=121,366). Age- and gender-stratified weighted prevalence estimates were calculated and compared with chi-square tests. All analyses were conducted in 2019.

RESULTS

Illicit substance use, including marijuana and cocaine, was generally lower among veterans than nonveterans, whereas use of licit substances such as tobacco and alcohol was higher among veterans than nonveterans. The most commonly used substances among veterans were tobacco and alcohol. Among male participants aged 18-25 years, 59.8% of veterans reported past-12-month cigarette/cigar smoking (vs 46.6% of nonveterans), whereas 17.6% reported heavy drinking (vs 12.2% of nonveterans). For both cigarette/cigar smoking and binge drinking, there was a marked narrowing of the male-female gap in prevalence with increasing age among veterans. Female veterans aged 18-25 years reported significantly higher opioid use than their nonveteran counterparts (54.7% vs 35.0%); they also had the highest prevalence of opioid misuse (15.3%) than any other group.

CONCLUSIONS

Intensified efforts are needed to reduce substance use among veterans and provide cessation and mental health services.

Urinary Acrylonitrile Metabolite Concentrations Before and after Smoked, Vaporized, and Oral Cannabis in Frequent and Occasional Cannabis Users

Cannabis use through smoking, vaping, or ingestion is increasing, but only limited studies have investigated the resulting exposure to harmful chemicals. N-acetyl-S-(2-cyanoethyl)-L-cysteine (2CYEMA), a urinary metabolite of acrylonitrile, a possible carcinogen, is elevated in the urine of past-30-day cannabis users compared to non-cannabis users. Five frequent and five occasional cannabis users smoked and vaped cannabis on separate days; one also consumed cannabis orally. Urine samples were collected before and up to 72 h post dose and urinary 2CYEMA was quantified. We compared 2CYEMA pre-exposure levels, maximum concentration, time at maximum concentration for occasional versus frequent users following different exposure routes, and measured half-life of elimination. Smoking cannabis joints rapidly (within 10 min) increased 2CYEMA in the urine of occasional cannabis users, but not in frequent users. Urine 2CYEMA did not consistently increase following vaping or ingestion in either study group. Cigarette smokers had high pre-exposure concentrations of 2CYEMA. Following cannabis smoking, the half-lives of 2CYEMA ranged from 2.5 to 9.0 h. 2CYEMA is an effective biomarker of cannabis smoke exposure, including smoke from a single cannabis joint, however, not from vaping or when consumed orally. When using 2CYEMA to evaluate exposure in cannabis users, investigators should collect the details about tobacco smoking, route of consumption, and time since last use as possible covariates.