A Comparison of Direct and Indirect Analytical Approaches to Measuring Total Nicotine Equivalents in Urine

Association of Urinary N7-(1-hydroxyl-3-buten-1-yl) Guanine (EB-GII) Adducts and Butadiene-Mercapturic Acids with Lung Cancer Development in Cigarette Smokers

Approximately 10% of smokers will develop lung cancer. Sensitive predictive biomarkers are needed to identify susceptible individuals. 1,3-Butadiene (BD) is among the most abundant tobacco smoke carcinogens. BD is metabolically activated to 3,4-epoxy-1-butene (EB), which is detoxified via the glutathione conjugation/mercapturic acid pathway to form monohydroxybutenyl mercapturic acid (MHBMA) and dihydroxybutyl mercapturic acid (DHBMA). Alternatively, EB can react with guanine nucleobases of DNA to form N7-(1-hydroxyl-3-buten-1-yl) guanine (EB-GII) adducts. We employed isotope dilution LC/ESI-HRMS/MS methodologies to quantify MHBMA, DHBMA, and EB-GII in urine of smokers who developed lung cancer (N = 260) and matched smoking controls (N = 259) from the Southern Community Cohort (white and African American). The concentrations of all three biomarkers were significantly higher in smokers that subsequently developed lung cancer as compared to matched smoker controls after adjusting for age, sex, and race/ethnicity (p < 0.0001 for EB-GII, p < 0.0001 for MHBMA, and p = 0.0007 for DHBMA). The odds ratio (OR) for lung cancer development was 1.63 for MHBMA, 1.37 for DHBMA, and 1.97 for EB-GII, with a higher OR in African American subjects than in whites. The association of urinary EB-GII, MHBMA, and DHBMA with lung cancer status did not remain upon adjustment for total nicotine equivalents. These findings reveal that urinary MHBMA, DHBMA, and EB-GII are directly correlated with the BD dose delivered via smoking and are associated with lung cancer risk.

Simultaneous Measurement and Distribution Analysis of Urinary Nicotine, Cotinine, Trans-3'-Hydroxycotinine, Nornicotine, Anabasine, and Total Nicotine Equivalents in a Large Korean Population

Measurement of multiple nicotine metabolites and total nicotine equivalents (TNE) might be a more reliable strategy for tobacco exposure verification than measuring single urinary cotinine alone. We simultaneously measured nicotine, cotinine, 3-OH cotinine, nornicotine, and anabasine using 19,874 urine samples collected from the Korean National Health and Nutrition Examination Survey. Of all samples, 18.6% were positive for cotinine, 17.4% for nicotine, 17.3% for nornicotine, 17.6% for 3-OH cotinine, and 13.2% for anabasine. Of the cotinine negative samples, less than 0.3% were positive for all nicotine metabolites, but not for anabasine (5.7%). The agreement of the classification of smoking status by cotinine combined with nicotine metabolites was 0.982-0.994 (Cohen's kappa). TNE3 (the molar sum of urinary nicotine, cotinine, and 3-OH cotinine) was most strongly correlated with cotinine compared to the other nicotine metabolites; however, anabasine was less strongly correlated with other biomarkers. Among anabasine-positive samples, 30% were negative for nicotine or its metabolites, and 25% were undetectable. Our study shows that the single measurement of urinary cotinine is simple and has a comparable classification of smoking status to differentiate between current smokers and non-smokers relative to the measurement of multiple nicotine metabolites. However, measurement of multiple nicotine metabolites and TNE3 could be useful for monitoring exposure to low-level or secondhand smoke exposure and for determining individual differences in nicotine metabolism. Geometric or cultural factors should be considered for the differentiation of tobacco use from patients with nicotine replacement therapy by anabasine.

Associations between urinary biomarkers of oxidative stress and biomarkers of tobacco smoke exposure in smokers

Oxidative stress can contribute to the development of diseases, and may originate from exposures to toxicants commonly found in air pollution and cigarette smoke such as polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs). Yet, associations between these exposures and oxidative stress biomarkers are poorly characterized. We report here novel associations between 14 exposure biomarkers of PAHs and VOCs, and two oxidative stress biomarkers; 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) and 8-isoprostaglandin F_2α (8-isoprostane) in urine obtained from smokers participating in an ongoing clinical study (ESTxENDS, NCT03589989). We also assessed associations between six biomarkers of tobacco smoke exposure (metabolites of nicotine and tobacco-specific nitrosamines (TSNAs)) and both oxidative stress biomarkers. We then quantified the relative importance of each family of the 20 exposure biomarkers on oxidative stress. Participating smokers (153 men and 117 women, median age 44 years) had on average smoked 25 [2-62] years and smoked about 17 [5-40] cigarettes per day at the time of the study. Multiple linear regression results showed an association between 8-oxodG concentrations and the following metabolites in decreasing relative importance: PAHs (beta coefficient β = 0.105, p-value <0.001, partial R² = 0.15) > VOCs (β = 0.028, p < 0.001, partial R² = 0.09) > nicotine (β = 0.226, p < 0.001, partial R² = 0.08); and between 8-isoprostane concentrations and metabolites of PAHs (β = 0.117, p < 0.001, partial R² = 0.14) > VOCs (β = 0.040, p < 0.001, partial R² = 0.14) > TSNAs (β = 0.202, p = 0.003, partial R² = 0.09) > nicotine (β = 0.266, p < 0.001, partial R² = 0.08). Behavioral factors known to contribute to oxidative stress, including sleep quality, physical activity, and alcohol consumption, did not play a significant role. Exposures to PAHs and VOCs among smokers were significantly associated with oxidative stress.

Predicting nicotine metabolism across ancestries using genotypes

BACKGROUND

There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight).

RESULTS

We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry.

CONCLUSIONS

We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.

Application of HPLC-QQQ-MS/MS and New RP-HPLC-DAD System Utilizing the Chaotropic Effect for Determination of Nicotine and Its Major Metabolites Cotinine, and trans-3'-Hydroxycotinine in Human Plasma Samples

The routine techniques currently applied for the determination of nicotine and its major metabolites, cotinine, and trans-3′-hydroxycotinine, in biological fluids, include spectrophotometric, immunoassays, and chromatographic techniques. The aim of this study was to develop, and compare two new chromatographic methods high-performance liquid chromatography coupled to triple quadrupole mass spectrometry (HPLC-QQQ-MS/MS), and RP-HPLC enriched with chaotropic additives, which would allow reliable confirmation of tobacco smoke exposure in toxicological and epidemiological studies. The concentrations of analytes were determined in human plasma as the sample matrix. The methods were compared in terms of the linearity, accuracy, repeatability, detection and quantification limits (LOD and LOQ), and recovery. The obtained validation parameters met the ICH requirements for both proposed procedures. However, the limits of detection (LOD) were much better for HPLC-QQQ-MS/MS (0.07 ng mL⁻¹ for trans-3′-hydroxcotinine; 0.02 ng mL⁻¹ for cotinine; 0.04 ng mL⁻¹ for nicotine) in comparison to the RP-HPLC-DAD enriched with chaotropic additives (1.47 ng mL⁻¹ for trans-3′-hydroxcotinine; 1.59 ng mL⁻¹ for cotinine; 1.50 ng mL⁻¹ for nicotine). The extraction efficiency (%) was concentration-dependent and ranged between 96.66% and 99.39% for RP-HPLC-DAD and 76.8% to 96.4% for HPLC-QQQ-MS/MS. The usefulness of the elaborated analytical methods was checked on the example of the analysis of a blood sample taken from a tobacco smoker. The nicotine, cotinine, and trans-3′-hydroxycotinine contents in the smoker’s plasma quantified by the RP-HPLC-DAD method differed from the values measured by the HPLC-QQQ-MS/MS. However, the relative errors of measurements were smaller than 10% (6.80%, 6.72%, 2.04% respectively).

The Role of Pharmacogenetics in Smoking

Smoking continues to be the leading preventable contributor to death worldwide. Twin studies have suggested a significant genetic contribution underlying most smoking behaviors (40-70% heritability estimates). Candidate gene studies of smoking phenotypes have identified several pharmacogenes implicated in nicotine's pharmacokinetics (CYP2A6, CYP2B6, CYP2A13, FMOs, UGTs, and OCT2), and nicotine's pharmacodynamic response in the central nervous system (nicotinic acetylcholine receptors, as well as through the dopaminergic and serotonergic systems). Subsequent genome-wide association studies (GWAS) have confirmed the role of certain pharmacogenes through hypothesis-free approaches. Furthermore, pharmacogenes that alter the efficacy of smoking cessation pharmacotherapies, including nicotine replacement therapies, bupropion, and varenicline, may also impact quitting success. In this brief review we highlight the role of pharmacogenes in smoking behaviors, such as smoking status, consumption, nicotine dependence, spontaneous quitting, and altered abstinence to pharmacotherapies; We provide examples from initial candidate gene associations and subsequent GWAS. The genes CYP2A6 and the CHRNA5-A3-B4 confer the most replicated sources of genetic variation in smoking behaviors, likely due to their importance in nicotine's pharmacology. We will also provide examples of genetic scoring approaches, and the role of rare variants in explaining a portion of the missing heritability in smoking behaviors.

Sensitive Quantification of Nicotine in Bronchoalveolar Lavage Fluid by Acetone Precipitation Combined With Isotope-Dilution Liquid Chromatography-Tandem Mass Spectrometry

The United States experienced an outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) that began in August 2019. Patient diagnosis and treatment sometimes involved bronchoscopy and collection of the bronchoalveolar lavage (BAL) fluid. Although this matrix has been useful for understanding some chemical exposures in the lungs, no methods existed for measuring the nicotine content. Therefore, we developed a simple and sensitive method for measuring nicotine in the BAL fluid. Nicotine was extracted from the BAL fluid using acetone precipitation in a 96-well plate format to increase the sample throughput (200 samples/day). We optimized liquid chromatography column conditions (e.g., mobile phase, column temperature) and mass spectrometry parameters to improve the signal-to-noise ratio and lower limits of detection (LOD) for measuring nicotine in the BAL fluid. The LOD for nicotine in the BAL fluid was 0.050 ng/mL at a sample volume of 40 μL of the BAL fluid. The within-day and between-day imprecision and bias were less than 10%. This method detected nicotine in 15 of 43 BAL fluids from EVALI case patients. This method is useful for understanding recent inhalational exposure to nicotine as part of characterizing EVALI or similar illnesses.

Nicotine metabolite ratio: Comparison of the three urinary versions to the plasma version and nicotine clearance in three clinical studies

BACKGROUND

Variation in CYP2A6 activity influences tobacco smoking behaviors and smoking-related health outcomes. Plasma Nicotine Metabolite Ratio (NMR) is a robust phenotypic biomarker of CYP2A6 activity and nicotine clearance. In urine, the NMR has been calculated as a ratio of free trans-3'-hydroxycotinine to free cotinine (NMR_F/F), total trans-3'-hydroxycotinine to free cotinine (NMR_T/F), or total trans-3'-hydroxycotinine to total cotinine (NMR_T/T). We evaluated these three urinary NMR versions relative to plasma NMR and nicotine clearance and elucidated mechanisms of discrepancies among them.

METHODS

Baseline plasma and urine biomarker data were available from two smoking cessation clinical trials and one nicotine pharmacokinetic study (total N = 768). NMRs were compared using Pearson correlations, linear regressions and ANOVA analyses. UGT2B10 and UGT2B17 were genotyped.

RESULTS

Urinary NMR_T/F was the most highly related to plasma NMR (R² = 0.70, P <2.2e-16) followed by NMR_F/F (R² = 0.68, P <2.2e-16), while NMR_T/T was less strongly related (R² = 0.60, P <2.2e-16); consistent across study, ethnicity, sex, heaviness of smoking, and analyte analysis. Controlling for cotinine glucuronidation, as a phenotype or UGT2B10 genotype, corrected the NMR_T/T discordance with plasma NMR (P_anova<0.001). Similar findings were obtained for relationships of nicotine clearance with plasma NMR > urinary NMR_T/F > NMR_F/F > NMR_T/T (R² = 0.41 > 0.37 > 0.35 > 0.25 respectively).

CONCLUSION

Urinary NMR_T/F followed by NMR_F/F are the best urinary alternatives to plasma NMR or nicotine clearance. NMR_T/T has the least utility as it is influenced substantially by variation in cotinine glucuronidation.

IMPACT

This work highlighted the variation in urinary NMRs, and identified mechanisms for disparities among them, which facilitates their use in predicting smoking-related outcomes.

Nicotine Exposure by Device Type among Adult Electronic Nicotine Delivery System Users in the Population Assessment of Tobacco and Health Study, 2015-2016

BACKGROUND

Previous studies have examined the characteristics of open and closed system electronic nicotine delivery system (ENDS) users, but population-level information on nicotine exposure among these users has not been available.

METHODS

We analyzed nicotine biomarker and survey data from Wave 3 of the Population Assessment of Tobacco and Health (PATH) study collected from October 2015 to October 2016. We identified 277 exclusive ENDS users and 468 dual cigarette and ENDS users and analyzed concentrations of nicotine and its metabolites obtained from urine samples by device type and other characteristics, such as frequency of use and e-liquid flavor.

RESULTS

Among exclusive ENDS users, open system users had higher levels of total nicotine exposure (TNE-2) than closed system users [8.8 μmol/g creatinine (95% confidence interval [CI] = 5.3-14.8 μmol/g vs. 2.0 μmol/g (95% CI = 0.7-5.4 μmol/g)]. However, TNE-2 concentrations were similar when open and closed system users were stratified as daily [26.4 μmol/g (95% CI = 20.1-34.7 μmol/g) vs. 27.1 μmol/g (95% CI = 16.4-44.9 μmol/g)] and nondaily [0.5 μmol/g (95% CI = 0.1-1.9 μmol/g) vs. 0.2 μmol/g (95% CI = 0.0-0.7 μmol/g)] ENDS users. Dual users generally had higher nicotine exposure than exclusive users.

CONCLUSIONS

Nicotine exposure was observed to be higher among exclusive open system ENDS users compared with closed system users, but levels were similar when users were stratified by frequency of use.

IMPACT

These results suggest that exclusive ENDS users with similar use patterns receive comparable levels of nicotine, regardless of whether they use open or closed system devices.

A review of the analysis of biomarkers of exposure to tobacco and vaping products

Quantification of exposure to different chemicals from both combustible cigarettes and vaping products is important in providing information on the potential health risks of these products. To assess the exposure to tobacco products, biomarkers of exposure (BOEs) are measured in a variety of biological matrices. In this review paper, current knowledge on analytical methods applied to the analysis of biomarkers of exposure to tobacco products is discussed. Numerous sample preparation techniques are available for the extraction and sample clean up for the analysis of BOEs to tobacco and nicotine delivery products. Many tobacco products-related exposure biomarkers have been analyzed using different instrumental techniques, the most common techniques being gas and liquid chromatography coupled with mass spectrometry (GC-MS, GC-MS/MS and LC-MS/MS). To assess exposure to emerging tobacco products and study exposure in dual tobacco users, the list of biomarkers analyzed in urine samples has been expanded. Therefore, the current state of the literature can be used in preparing a preferred list of biomarkers based on the aim of each study. The information summarized in this review is expected to be a handy tool for researchers involved in studying exposures to tobacco products, as well as in risk assessment of biomarkers of exposure to vaping products.

Changes in Nicotine Metabolite Ratio Among Daily Smokers Receiving Treatment for Alcohol Use Disorder

INTRODUCTION

Alcohol may influence the nicotine metabolite ratio (NMR), an index of the rate of nicotine metabolism that is associated smoking level and lapses. We examined if NMR changes during alcohol use disorder (AUD) treatment and how changes in NMR relate to reductions in drinking.

METHODS

Using an observational design, 22 daily smokers [63.64% male, Mage = 46.77 (11.37)] receiving AUD treatment completed baseline and follow-up appointments 3 weeks apart. At each appointment, daily alcohol and cigarette use, salivary and urinary NMR, nicotine exposure via urinary total nicotine equivalents, and carbon monoxide were assessed. Multilevel models examined the change over time in NMR and its within-person relations with changes in drinks per week. Sex differences were evaluated.

RESULTS

There were significant reductions in both salivary and urinary NMR over time for men (p = .02; p = .01, respectively) but not for women (p = .54; p = .90, respectively). There were no changes over time in total nicotine equivalents (p = .09), carbon monoxide (p = .44), or cigarette use (p = .44) in either sex. Drinks per week were significantly reduced for men (29.12 drink reduction, p < .001) but not for women (2.28 drink reduction, p = .80); however, within-person changes in drinking were not associated with changes in salivary or urinary NMR (p = .99; p = .19).

CONCLUSIONS

The reduction in alcohol use and NMR in men provides indirect support for alcohol increasing NMR. In contrast, the low baseline drinking and lack of alcohol reduction likely underlie the lack of change in NMR in females. Reasons for NMR reductions during AUD treatment and its effects on smoking require further study.

IMPLICATIONS

Three weeks of alcohol use disorder treatment among daily smokers coincided with a significant reduction in both alcohol use and NMR for men; however, neither drinking level nor NMR changed for women. The findings indirectly support that heavy drinking increases NMR, which is reversed with reduced drinking. Additional research is needed to establish if these changes in NMR correlate with smoking and cessation outcomes.

Pregnancy-Induced Increases in the Nicotine Metabolite Ratio: Examining Changes During Antepartum and Postpartum

INTRODUCTION

Pregnancy-induced increases in nicotine metabolism may contribute to difficulties in quitting smoking during pregnancy. However, the time course of changes in nicotine metabolism during early and late pregnancy is unclear. This study investigated how pregnancy alters the nicotine metabolite ratio (NMR), a common biomarker of nicotine metabolism among nonpregnant smokers.

METHODS

Urinary NMR (trans-3'-hydroxycotinine [3HC]/cotinine [COT]) was assessed using total (free + glucuronide) and free compounds among women (N = 47) from a randomized controlled trial for smoking cessation who self-reported smoking and provided a urine sample during early pregnancy (M ± SD = 12.5 ± 4.5 weeks' gestation), late pregnancy (28.9 ± 2.0 weeks' gestation), and 6 months postpartum (24.7 ± 1.2 weeks since childbirth). Urine samples were analyzed using liquid chromatography-tandem mass spectrometry and NMR were calculated as Total 3HC/Free COT, Free 3HC/Free COT, and Total 3HC/Total COT.

RESULTS

NMR was significantly higher during early and late pregnancy compared to postpartum and significantly increased from early to late pregnancy as measured by Total 3HC/Free COT (0.76, 0.89, 0.60; all p's < .05) and Free 3HC/Free COT (0.68, 0.80, 0.51; all p's < .05). Total 3HC/Total COT did not vary over time (p = .81).

CONCLUSIONS

Total 3HC/Free COT and Free 3HC/Free COT increased in the first trimester and continued to increase throughout pregnancy, suggesting a considerable increase in nicotine metabolism over gestation. Future analyses are needed to interpret the changes in NMR in the context of nicotine pharmacokinetics, as well as its impact on changes in smoking behavior and cessation outcomes.

IMPLICATIONS

We observed that the NMR was significantly higher as early as 12 weeks' gestation and increased further as a function of gestational age. Among nonpregnant smokers, elevated NMR is associated with smoking phenotypes such as smoking more cigarettes per day and poorer response to nicotine patch; therefore, pregnancy-induced increases in the NMR may contribute to smoking during the first trimester of pregnancy and reducing or quitting smoking may become more challenging as the rate of nicotine metabolism accelerates over the course of pregnancy.

Cigarette consumption and biomarkers of nicotine exposure during pregnancy and postpartum

BACKGROUND AND AIMS

Smokers can regulate their nicotine intake by altering the number of cigarettes smoked per day (CPD) and their smoking intensity. The current study aimed to compare the utility of self-reported CPD, total nicotine equivalents (TNE) and urinary cotinine to estimate nicotine intake during pregnancy.

DESIGN

Longitudinal smoking behavior and biomarker data were collected at early pregnancy, late pregnancy and at postpartum as part of a smoking cessation trial to examine voucher-based incentives for decreasing smoking.

SETTING

Obstetric practices in Burlington, Vermont, United States.

PARTICIPANTS

A subset of participants (n = 47) from the parent trial, recruited between December 2006 and June 2012, who provided a urine sample at each assessment during early pregnancy, late pregnancy and postpartum.

MEASUREMENTS

Smoking was assessed using self-reported CPD, TNE, TNE/CPD and urinary cotinine.

FINDINGS

Pregnant smokers reported smoking 10.4 CPD at early pregnancy, 7.2 CPD at late pregnancy (a 31% reduction at late pregnancy, P = 0.001) and 8.6 CPD at postpartum (a 19% increase from late pregnancy, P = 0.08). TNE exposure was 41% (P = 0.07) and 48% (P = 0.03) lower at early and late pregnancy, respectively, compared to postpartum. TNE/CPD was on average 167% higher at late pregnancy compared to early pregnancy (P = 0.01) and remained high at postpartum, where it was 111% higher compared to early pregnancy (P = 0.007). Uriniary cotinine underestimated nicotine intake by 55% during early pregnancy and by 65% during late pregnancy compared to postpartum (P_interaction  < 0.001); the underestimation was greater in slower (P_interaction  < 0.001) versus faster (P_interaction  = 0.04) nicotine metabolizers.

CONCLUSIONS

Neither cigarettes smoked per day (CPD) nor cotinine provides an accurate estimate of nicotine exposure during pregnancy. CPD underestimates nicotine intake substantially due to under-reporting and/or higher intensity of smoking, while cotinine underestimates nicotine intake markedly due to accelerated nicotine (and cotinine) metabolism during pregnancy.