Supplemental Figure 1 shows the analytic sample flow chart.

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

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

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

INTRODUCTION:Cotinine (nicotine exposure) and cyanoethyl mercapturic acid (2CyEMA, smoke exposure) are known biomarkers for classifying tobacco use status. The increasing use of multiple tobacco products, including dual use of e-cigarettes and cigarettes, drives the need for the assessment of these biomarkers, both individually and in combination to distinguish various product use groups. METHODS:We evaluated the 2CyEMA-to-cotinine ratio of urine samples as a metric for differentiating tobacco use status (cigarettes only, e-cigarette only, and dual use) in participants from the Exhale study. Tobacco use status was determined from questionnaire data of participant's product use in the 48 hours prior to urine collection. Descriptive statistics and linear regression modeling were used to determine which measures (2CyEMA-to-cotinine ratio or the individual components) could distinguish the different tobacco use patterns. RESULTS:Compared to the dual use reference group, the 2CyEMA-to-cotinine ratio differentiated cigarettes only (p=.0332) and e-cigarette only (p<.0001) use statuses. Cotinine was not able to distinguish any of the three use statuses and 2CyEMA was only able to differentiate the e-cigarette only users. The 2CyEMA-to-cotinine ratio remains relatively constant across a wide range of smoking frequency, underscoring the efficacy of this smoking metric compared with 2CyEMA or cotinine alone. CONCLUSIONS:The 2CyEMA-to-cotinine ratio is an effective measure for classifying tobacco use status among cigarette only, e-cigarette only, and dual use users. IMPLICATIONS:The novel approach of examining the 2CyEMA-to-cotinine ratio can serve as a potential measure to distinguish between cigarette only users, e-cigarette only users, and cigarette and e-cigarette dual users.

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.

Urinary phenyl mercapturic acid (PhMA) is a specific biomarker of benzene exposure that has been widely used in biomonitoring of the general population and in occupational exposure studies. However, previous research has identified significant interlaboratory variation in urinary PhMA concentrations due to differences in the acidity of the sample treatment conditions. This variation arises from the need to convert the benzene's precursor metabolite 6-hydroxy-2,4-cyclohexadienyl mercapturic acid (pre-PhMA) to PhMA. In this study, we systematically examined the influence of sample treatment pH on this reaction across various acidic treatment conditions representative of the reported PhMA assays. The resulting pre-PhMA and PhMA levels were quantified using an established liquid chromatography-tandem mass spectrometry assay. PhMA levels increased with more acidic treatment conditions until pH -0.6 when PhMA formation was the greatest at 53.1% formation of the total pre-PhMA. The formation of PhMA was dependent on sample treatment pH. Thus, a quadratic regression was modeled on PhMA formation vs pH across all acid types. The resulting regression model (y = 0.874 × pH2 - 12.146 × pH + 41.99, R2 = 0.978) can be used to determine the extent of PhMA formation for a specific treatment pH, improving the ability to compare PhMA results among studies with different analytical methods or to absolute health-based cutoffs such as the biological exposure index. To explain the incomplete PhMA formation, we utilized gas chromatography-mass spectrometry to identify and quantify the formation of benzene as a major byproduct of the acid-derived dehydration of pre-PhMA. Further, base-derived dehydration of pre-PhMA to PhMA was performed to validate the benzene formation mechanism observed with acid dehydration.

While electronic cigarettes (ECIG) may have lower toxicant delivery than cigarettes, ECIG-liquids and aerosols still contain toxicants that can potentially disrupt lung lipid homeostasis. Participants from two studies underwent bronchoscopy and bronchoalveolar lavage (BAL). Ninety-eight participants (21-44 years old) were included in a cross-sectional study, with 17 ECIG users, 52 non-smokers, and 29 smokers. In the four-week clinical trial, 30 non-smokers were randomly assigned to use nicotine-free, flavorless ECIG or no use. A panel of 75 quantifiable lipid species and 7 lipid classes were assessed in the BAL using two tandem mass spectrometry (MS/MS) platforms. Ten cytokines and lipid-laden macrophages (LLM) were analyzed using the V-PLEX Plus Proinflam Combo 10 panel and Oil Red O staining, respectively. In the cross-sectional study, 43 lipids were associated with smoking status at FDR<0.1, including two between ECIG and non-smokers (PC(14:0/18:1) and PC(18:0/14:0)) in pairwise follow-up analyses (Bonferroni-adjusted p<0.017). Associations between lipid species and cotinine, inflammatory markers, including IL-1β and IL-8, and LLM were also identified, as well as differences in lipid classes between smokers and the other groups. Smokers had higher saturated lipids, including ceramide (CER), sphingomyelin (SM), and diacylglycerol (DAG) than that of non-smokers and ECIG users. No significant associations were identified in the 4-week clinical trial. Smoking was associated with altered lipid levels, as compared to both non-smokers and ECIG users; the majority were downregulated and ECIG effects tend to be smaller in magnitude than smoking effects, although some were different than those in the smokers group. This is a novel study of healthy individuals examining lipidomic differences between smokers, ECIG users, and non-smokers, indicating potential roles of smoking and ECIG-related lipid alterations in pulmonary disease. The study was approved by The OSU Institutional Review Board (OSU-2015C0088) in accordance with its ethical standards, the Helsinki declaration, and the Belmont Report, and is registered on Clinicaltrials.gov (NCT02596685; 2015-11-04).

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.

Exposure assessment of hazardous volatile organic compounds (VOCs) requires accurate quantification of internal dose when establishing limits or identifying significant differences within and among populations. Even though accurate internal dose can be directly measured in blood, it is not always practical or possible to collect a suitable blood specimen. This work studies the relationship between blood and urine levels for certain smoke biomarkers (e.g., tobacco, marijuana) measured in self-reported cigarette smokers. Urine and blood specimens were collected as matched pairs from individuals at the same time. We used our latest specimen collection and VOC analysis protocols to minimize sample collection, handling, and analysis biases. From these analyses, unmetabolized urine benzene, furan, 2,5-dimethylfuran, isobutyronitrile, and benzonitrile levels were found to trend with blood levels. In addition, we measured urine creatinine levels, which were found to be significantly associated with all blood analyte concentrations (p-value ranging from <0.0063 to <0.0001) except for isobutyronitrile (p = 0.3347). For the analytes that were associated with urine creatinine levels, the ratios of urine-to-blood concentrations were substantially higher than those predicted from the urine/blood partition coefficients (Kurine/blood), which should occur if VOCs can freely equilibrate (i.e., passive diffusion) between the blood and urine. The urine isobutyronitrile concentration, which was the only analyte that was not associated with the urine creatinine level, had a urine-to-blood ratio similar to Kurine/blood. These results suggest either that urine VOC levels for certain VOCs do not equilibrate with blood levels in the urinary tract or that there is a conversion of conjugated to free forms, increasing urine VOC levels. Nevertheless, these deviations from partition theory (e.g., Henry's Law) are analyte-specific and require characterization to establish a relationship between blood and urine levels.

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

BACKGROUND:Household mold is a major problem in communities which face natural disasters such as hurricanes or flooding, and in homes with other sources of significant water intrusion; a biomarker for exposure to indoor mold could support public health investigations. METHODS:We analyzed serum from 132 children with asthma living in government-subsidized housing for six microbial volatile organic compounds (2-ethyl-1-hexanol, 2-heptanone, 2-hexanone, 3-methylfuran, 3-octanone, and geosmin) using GC-MS. Fewer than 10% of the samples for three compounds (2-ethyl-1-hexanol, 2-heptanone, and 2-hexanone) were quantified below the limit of detection. Associations between mold/water damage variables and microbial volatile organic compounds (mVOCs) were assessed via regression analyses, adjusting for urinary cotinine and self-reported home characteristics. RESULTS:Children with household mold (assessed by occupant report of visual mold, mold odor, or water damage) had 32% higher serum concentrations of 2-hexanone than those living in homes without reported mold or water damage. We investigated indoor tobacco use via urinary cotinine analysis of a "first morning void spot sample" (FMV) and found that children with higher urinary cotinine had significantly higher serum 2-ethyl-1-hexanol. We found that children in homes where residents reported tobacco smoking indoors had significantly higher serum 2-ethyl-1-hexanol compared with those without reported household smoke exposure. Tobacco smoke, indoor painting, gas stoves, and carpets were not confounders in the relationship between mVOCs and mold/water damage variables. CONCLUSION:2-hexanone, along with an index variable which included all detectable mVOCs in our panel, are promising biomarkers of recent mold exposure that could be used in concert with other detection methods.

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.