Supplemental Figure 1 shows the analytic sample flow chart.

Supplemental Table 2 provides demographic and tobacco use characteristics of any tobacco users.

Supplemental Table 6 provides Wave 1 and Wave 4 TNE-2 cut-points for any tobacco use.

Supplemental Table 3 provides prevalence rates of any tobacco use using cotinine cut-points.

Introduction: The effect of prolonged storage (12-19 years) on selected laboratory test results is examined in the National Health and Nutrition Examination Survey biospecimen collection to determine whether biospecimens stored long term in vapor-phase liquid nitrogen provide valid results once remeasured.Methods: Biospecimens were selected for remeasurement using systematic random sampling for five analytes: cotinine, methylmalonic acid (MMA), vitamin A, vitamin E, and hepatitis C virus RNA (HCV-RNA). Measurements from the original specimens in 1999-2000 or 2005-2006 are compared with 2018-2019 measurements from the same survey participants and specimens. For quantitative analytes, measurement accuracy is assessed using standard method comparison procedures, precision is evaluated by comparing to quality control standards, and reproducibility is estimated by treating data like an incurred sample reanalysis. Qualitative measures are analyzed using concordance measures and exact binomial tests.Results: Observed proportional differences are 3%-12% for cotinine in people who do not smoke, 11% for cotinine in people who smoke, -8% to 1% for vitamin A, 8%-9% for vitamin E, and -6% to 8% for MMA. Precision estimates are within the standards established by quality control data and generally applicable quality goals. Differences between measurements are within 20% of the average value for at least 85% of all samples. For qualitative HCV-RNA and MMA results, we observe 99% concordance between measurements.Conclusions: Multipronged analysis showed that most differences are within acceptable ranges based on standard laboratory criteria for assessing accuracy, precision, and reproducibility. Results suggest future measurements and subsequent statistical analyses of stored serum specimens should be valid.

Cannabis use among breastfeeding women is increasing. While studies have shown that cannabinoids transfer into human milk, whether breastfed infants of mothers who use cannabis are exposed to volatile organic compounds (VOCs) is not known. Here, we characterized VOC metabolite concentrations in urine produced by exclusively breastfed infants whose mothers used cannabis. Relationships between infant urinary VOC metabolite concentrations and milk delta-9-tetrahydrocannabinol (∆9-THC) concentrations and cannabis use modality (smoking/vaping) were documented. Twenty mother-infant dyads (<6 months postpartum) living in Washington and Oregon were enrolled in the Lactation and Cannabis (LAC) Study, and nineteen were included in the current study. Mothers collected baseline infant urine and milk samples (following ≥12 h of cannabis abstention) and three infant urine and five milk samples over 8-12 h following cannabis use. Sixty-five urine samples were analyzed for VOC metabolite concentrations, and 111 milk samples were previously analyzed for ∆9-THC. Results indicate detectable levels of 24 VOC metabolites in infant urine. Non-baseline infant urine had greater concentrations of 15 VOC metabolites than matched baseline samples (p < 0.05). Urinary 2CyEMA (acrylonitrile metabolite) and 2CaEMA (acrylamide metabolite) concentrations were positively correlated with ∆9-THC concentrations in milk. Urinary 2CyEMA concentrations were higher in infants whose mothers primarily smoked (including those who smoked and vaped) compared to those who exclusively vaped (B = -0.637, SE = 0.317, p = 0.044). 2CaEMA concentrations were also greater in the urine produced by infants whose mothers reported any cannabis smoking versus only vaping on the study day (B = -4.06, SE = 1.42, p = 0.004). Results indicate that infants whose mothers use cannabis have measurable VOC exposure. Further research is needed to assess the potential health implications, if any.

Benzene exposure is associated with increased risk of cancer and may occur from environmental sources such as smoke and fossil fuels. As part of the National Health and Nutrition Examination Survey (NHANES), our laboratory measures concentrations of benzene exposure biomarkers including benzene in blood (BB) and the urinary benzene metabolites phenyl mercapturic acid (PhMA, N-acetyl-S-phenyl-L-cysteine) and muconic acid (MUCA). The goals of this study were to determine the association between benzene exposure biomarkers and benzene exposure sources, specifically tobacco smoking (i.e., recent smoking of cigarettes or cigars (including little cigars and cigarillos)) and non-tobacco exposure sources such as recently pumping gas and diet among NHANES participants aged 12 years and over from 2017 to March 2020. The associations between benzene exposure biomarkers and benzene exposure sources were analyzed using multiple linear regression models and multiple logistic regression models. The model parameters were estimated using SAS, and the analysis incorporated survey weights to account for the complex survey design to create nationally representative estimates, as well as controlling for relevant metabolic, demographic, and dietary factors. The models indicate that recently pumping gas was associated with higher odds of detecting BB, and smoking cigarettes and smoking cigars (including little cigars and cigarillos) were associated with higher concentrations of benzene exposure biomarkers. Additionally, we found that BB, PhMA, and MUCA were correlated among smokers. Thus, we conclude that smoking cigarettes and smoking cigars (including little cigars and cigarillos) are sources of benzene exposure among the U.S. population, and recently pumping gas may also be a source of benzene exposure.

Supplemental Table 5 provides Wave 1 and Wave 4 cotinine cut-points for any tobacco use.

Introduction Urinary biomarkers are useful in characterizing exposure to harmful and potentially harmful constituents of tobacco products and linking exposure to health outcomes. However, the consistency/reproducibility of many urinary biomarkers over long periods is unknown.Methods Among people who exclusively used cigarettes in the Population Assessment of Tobacco and Health Study Waves 1, 2, 4, and 5 (ranging from 746 to 1361 subjects), we used weighted models to estimate variance components and intra-class correlation coefficients (ICC) for 15 biomarkers of exposure for urine samples collected 3-5 years apart, creatinine-only-adjusted and also adjusted for demographic and behavioral predictors.Results In models adjusted only for creatinine, ICC values of biomarkers ranged from 0.41 (95% confidence interval (CI): 0.32, 0.49) (N-acetyl-S-(2-carbamoylethyl)-L-cysteine) to 0.73 (95% CI: 0.65, 0.81) (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol), varying within each chemical class. For models adjusted for predictors, associations between biomarkers and predictors were similar for samples collected 3-5 years and 1 year apart. Predictor-adjusted ICCs for samples collected 3-5 years apart ranged from 0.29 (95% CI: 0.17, 0.40) (N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) to 0.63 (95% CI: 0.56, 0.69) (N-Acetyl-S-(2-hydroxyethyl)-L-cysteine) and appeared not different from those for samples collected 1 year apart.Conclusions Even for 3 or 5 years between urine sample collection, unadjusted biomarkers of exposure showed fair to excellent reproducibility. Similar consistency between 1 year and 3-5 years between collections was found when including predictors in the model.

BACKGROUND:The constituents of tobacco smoke that specifically contribute to lung cancer risk have yet to be fully identified. We evaluated associations between biomarkers of potentially harmful constituents-polycyclic aromatic hydrocarbons, tobacco-specific nitrosamines, nicotine, and volatile organic compounds-and lung cancer incidence among US women. METHODS:In a case-cohort study nested within the Sister Study (women aged 35-74 years at baseline, enrolled 2003-2009), data were obtained for a random subcohort and all remaining incident lung cancers through September 2017 (median follow-up = 9.6 years), stratified by race and ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White, others) and smoking status (current, former, never). The analytic sample included 356 cases and 433 noncases. We quantified 30 biomarkers in baseline urine samples and calculated hazard ratios (HRs) for associations between 1-unit increase in biomarker concentrations (log-scale) and lung cancer incidence using weighted Cox regression models adjusted for urinary creatinine and demographic, health, and lifestyle factors. RESULTS:Among women who were currently smoking at enrollment, positive associations were observed for biomarkers of polycyclic aromatic hydrocarbons (naphthalene, phenanthrene, pyrene, fluorene; HRs = 1.4-5.3), tobacco-specific nitrosamines (particularly 4-[methylnitrosamino]-1-[3-pyridyl]-1-butanone [nicotine-derived nitrosamine ketone]; HRs = 1.3-2.2), and volatile organic compounds (xylene, acrylamide, acrylonitrile, 1,2-dibromoethane and/or vinyl-chloride and/or ethylene-oxide and/or acrylonitrile, acrolein, styrene and/or ethylbenzene, benzene, dimethylformamide and/or methylisocyanate, 1,3-butadiene, crotonaldehyde, isoprene; HRs = 1.6-4.4). Associations with biomarkers of most polycyclic aromatic hydrocarbons, nicotine-derived nitrosamine ketone, xylene, and dimethylformamide and/or methylisocyanate remained after additional adjustment for smoking frequency, duration, and nicotine metabolites. In women who did not smoke, positive associations were observed for styrene and/or ethylbenzene and dimethylformamide and/or methylisocyanate biomarkers. CONCLUSION:Exposure to polycyclic aromatic hydrocarbons, tobacco-specific nitrosamines, and several volatile organic compounds through tobacco smoking were associated with increased lung cancer risk among women.

Supplemental Table 4 provides prevalence rates of any tobacco use using TNE-2 cut-points.

Introduction Given the increasing usage of vaping during pregnancy and limited longitudinal health-related data, there is an urgent need to assess the potential risks of vaping. Aims and Methods A cross-sectional study was conducted among pregnant UK adults (n = 140). Five study groups were purposively recruited: exclusive-smokers (n = 38), exclusive-vapers (former smokers) (n = 35), dual users of smoking and vaping (n = 25), dual users of smoking and nicotine replacement therapy (n = 10), and “never-users” of nicotine or tobacco products (n = 32). Sociodemographic, smoking, and vaping characteristics were assessed. Participants’ urine samples were analyzed for biomarkers of exposure to tobacco alkaloids, and toxicants, including 14 volatile organic compounds (VOCs), tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), heavy metals (cadmium, lead, chromium, nickel, copper, and tin) and a polycyclic aromatic hydrocarbon (2-naphthol). Regression analysis was used to compare biomarkers by group. Results Nicotine levels varied across product users, but not significantly. After controlling for confounders, for most VOCs, biomarker levels were similar for exclusive-vapers and never-users and significantly lower than for exclusive-smokers and any dual users. There were generally no significant differences between groups for 2-naphthol or heavy metals. For NNAL, cadmium and chromium, a high percentage of values were below the limit of detection, making analyses unreliable. Conclusions During pregnancy, former smokers who are established exclusive vapers, but not dual users, had levels of selected VOCs that were substantially lower than those for exclusive smokers and comparable with those who have never used nicotine or tobacco products. Implications Based on the biomarkers assessed in this study, during pregnancy, on average, exclusive-vapers are likely to have similar levels of exposure to selected VOCs as never-users and far lower levels than exclusive-smokers or dual-users (although dual-vaping and smoking may result in less exposure than exclusive-smoking). This provides preliminary information about exposure to vaping during pregnancy and suggests that, for some biomarkers, exclusive vaping is likely to result in lower exposures than exclusive smoking or dual-use. There may be exposure to other vaping toxicants that were not explored in this study. Studies are needed to assess pregnancy and birth outcomes as well as early life effects.

Opium consumption is carcinogenic, but the impact of the route of use (smoking vs. ingestion) on exposure to potential proposed carcinogens is understudied. As a nested study within the Golestan Cohort Study, we gathered comprehensive histories of teriak (raw opium), shireh (refined opium sap), and tobacco use by validated questionnaires and selected 100 long-term opium users (50 exclusively ingesting and 50 exclusively smoking), 15 cigarette smokers, and a reference sample using neither. We analyzed spot urine samples for seven hydroxy polycyclic aromatic hydrocarbons (PAH) and cotinine. PAH biomarker concentrations were creatinine-corrected to account for urinary dilution and adjusted for demographic factors and opium use patterns using multivariable linear regression models to evaluate associations between the route of opium use and PAH biomarker concentrations. After excluding opium users who reported no tobacco use but had discordant cotinine concentrations, PAH biomarker concentrations were significantly higher in opium users than the reference sample. Smoking opium was associated with substantially elevated PAH biomarker concentrations compared with ingestion, particularly for Σ2,3-Hydroxyphenanthrene (five-fold increase) and 3-Hydroxyfluorene (4.5-fold increase). For Σ2,3-Hydroxyphenanthrene, concentrations exceeded those of cigarette smokers. No difference was observed between teriak and shireh use. Only among opium smokers, PAH biomarker concentrations decreased by time since last use but remained consistently higher than the reference sample. Opium consumption, regardless of type and route, exposes individuals to PAHs, with greater concentrations of select PAH biomarkers observed for smoking compared with ingestion. Considering the route of opium use in exposure and cancer risk assessments is crucial.

Tobacco cigarette smoking is the leading cause of preventable diseases and death in the USA. Exposure to secondhand smoke (SHS) can also cause heart disease, lung cancer, and respiratory illness. Cotinine (COT) and trans-3'-hydroxycotinine (HCT) are the primary metabolites of nicotine, the main addictive alkaloid in tobacco products. For many years, we have measured serum levels of COT and HCT in National Health and Nutritional Examination Survey (NHANES) participants to monitor exposure of the US population to active smoking and SHS. As exposure to SHS is decreasing, a more sensitive analytical method is needed to detect the lower levels of these biomarkers for SHS assessment. We developed and validated a new automated method for the detection of COT and HCT in human serum. We implemented a new liquid handling automation system to aliquot and prepare samples using supported liquid extraction. Samples were analyzed by liquid chromatography-tandem mass spectrometry. The new automated sample preparation method increases sample throughput by reducing sample cleanup time to 2 hours for preparing a 96-well plate. The method has excellent sensitivity, specificity, precision (<10%), and accuracy (±15%). We were able to lower the estimated limit of detection (LOD) for COT by 33% and HCT by 73% from our previous LOD. The new LODs for COT and HCT are 0.010 and 0.004 ng/mL, respectively. These lower LODs would enable better detection of SHS in future NHANES surveys.

Table S1. Urinary BOEs in PATH Study Wave 1. Table S2. Urinary BOEs of Nicotine Metabolites in PATH Study Wave 1. Table S3. Urinary BOEs of TSNAs in PATH Study Wave 1. Table S4. Urinary BOEs of PAHs in PATH Study Wave 1. Table S5. Urinary BOEs of VOCs in PATH Study Wave 1. Table S6. Urinary BOEs of Metals in PATH Study Wave 1. Table S7. Urinary BOEs of Arsenic in PATH Study Wave 1.

AbstractBackground:Sex and racial/ethnic identity-specific cut-points for validating tobacco use using Wave 1 (W1) of the Population Assessment of Tobacco and Health (PATH) Study were published in 2020. The current study establishes predictive validity of the W1 (2014) urinary cotinine and total nicotine equivalents-2 (TNE-2) cut-points on estimating Wave 4 (W4; 2017) tobacco use.Methods:For exclusive and polytobacco cigarette use, weighted prevalence estimates based on W4 self-report alone and with exceeding the W1 cut-point were calculated to identify the percentage missed without biochemical verification. Sensitivity and specificity of W1 cut-points on W4 self-reported tobacco use status were examined. ROC curves were used to determine the optimal W4 cut-points to distinguish past 30-day users from non-users, and evaluate whether the cut-points significantly differed from W1.Results:Agreement between W4 self-reported use and exceeding the W1 cut-points was high overall and when stratified by demographic subgroups (0.7%–4.4% of use was missed if relying on self-report alone). The predictive validity of using the W1 cut-points to classify exclusive cigarette and polytobacco cigarette use at W4 was high (>90% sensitivity and specificity, except among polytobacco Hispanic smokers). Cut-points derived using W4 data did not significantly differ from the W1-derived cut-points [e.g., W1 exclusive = 40.5 ng/mL cotinine (95% confidence interval, CI: 26.1–62.8), W4 exclusive = 29.9 ng/mL cotinine (95% CI: 13.5–66.4)], among most demographic subgroups.Conclusions:The W1 cut-points remain valid for biochemical verification of self-reported tobacco use in W4.Impact:Findings from can be used in clinical and epidemiologic studies to reduce misclassification of cigarette smoking status.

This table provides Receiver Operating Curve (ROC) characteristics and optimal cut-point to distinguish past 30-day daily and non-daily cigarette users from non-users*, overall and by sex and race/ethnicity.

Supplementary Table S2: Sample-weighted multiple regression results with dietary categories for urinary 1AMN, 2AMN, 4ABP (n = 1,845) among participants who smoked cigarette exclusively and nonusers of tobacco products, 2013-2014 NHANES

INTRODUCTION:Evaluating nicotine exposure (total nicotine equivalents-2; TNE-2) changes over time can provide data on the public health impact of electronic nicotine delivery systems (ENDS). This study describes TNE-2 levels of those who use ENDS with or without cigarettes from 2013-2019, and models how changing ENDS use behavior impacts change in TNE-2. AIMS AND METHODS:Creatinine-corrected TNE-2 was assessed for exclusive ENDS use and dual ENDS and cigarette use from Waves (W) 1-5 of the Population Assessment of Tobacco and Health Study. Exploratory analyses using generalized estimated equations modeled how changing ENDS use (ie, frequency of use, flavor use, device type) between wave pairs (W1-W2, W2-W3, etc.) impacted changes in TNE-2. RESULTS:For exclusive ENDS use at each wave, TNE-2 levels increased from 10.1 µmol/g at W1 to 18.4 µmol/g at W5, a positive linear trend (p = .03). Among those who exclusively used ENDS at all waves, TNE-2 levels peaked at W3 and then decreased at W5, exhibiting a significant quadratic trend (p = .02). Switching from non-daily to daily use (n = 15) was associated with a greater increase in TNE-2 than continued daily use (n = 304). For dual use, TNE-2 levels remained relatively flat, and there were no significant effects of changing ENDS behavior on TNE-2. CONCLUSIONS:For exclusive ENDS use, TNE-2 levels over time differ when looking within-subjects versus repeated longitudinal assessments, and frequency of use was the only significant predictor of change in TNE-2. TNE-2 from dual-use did not significantly change from 2013-2019 and was not impacted by change in ENDS use behavior. IMPLICATIONS:Exclusive ENDS use was associated with a positive linear trend in nicotine exposure between 2013 and 2019, which may reflect how newer generations of ENDS are better at delivering nicotine. When limiting analysis to within-subject use at all waves the trend was quadratic, with nicotine exposure peaking at W3 and returning toward W1 levels by W5. This may be related to people trying to titrate their nicotine exposure in response to changes in ENDS characteristics. Dual ENDS and cigarette use had more consistent levels of exposure over time, which could be due to the greater ease of nicotine titration via cigarettes.

Supplementary Figure S1. Chemical structures of five AAs. Analytes that were monitored for isomeric separation but not quantified are indicated with an asterisk.