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.

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.

Determination of anabasine, anatabine, and nicotine biomarkers in wastewater by enhanced direct injection LC-MS/MS and evaluation of their in-sewer stability.

Wastewater-based epidemiology (WBE) has been used to estimate tobacco use in the population. However, the increased use of nicotine replacement therapies and e-cigarettes contributes to the load of nicotine metabolites in wastewater, causing over-estimation of tobacco use if nicotine metabolites were used in WBE back-estimation. This study aims to develop a rapid method for determining the tobacco-specific biomarkers, anabasine and anatabine, in wastewater and to evaluate their in-sewer stability for better estimation of tobacco use by WBE. An enhanced direct injection LC-MS/MS was developed to quantify anabasine and anatabine as well as nicotine biomarkers (nicotine, cotinine and hydroxycotinine). The method was optimal when wastewater was filtered through 0.2 µm RC syringe filters and a pre-conditioned SPE cartridge (Oasis HLB 1 cc, 30 mg) before 50 µL was injected into the LC-MS/MS system. Limits of quantification varied between 2.7 and 54.9 ng/L with recoveries from 76% to 103% for all five compounds. In sewer reactors, anabasine and anatabine were less stable than cotinine and hydroxycotinine. They were more stable in the gravity sewer reactor with <20% loss in 12 h than in the rising main sewer reactor with ~30% loss in the same period. We then applied the new method to 42 daily wastewater influent samples collected from an Australian wastewater treatment plant. The five biomarkers were detected in all samples with concentrations ranging from 9.2 to 7430 ng/L. All five compounds were positively correlated with one another. Our results suggested a high throughput analytical method for feasible application in anabasine and anatabine as biomarkers of tobacco use in routine wastewater monitoring.

Attitudes toward E-Cigarettes, Reasons for Initiating E-Cigarette Use, and Changes in Smoking Behavior after Initiation: A Pilot Longitudinal Study of Regular Cigarette Smokers.

OBJECTIVES: We examined 1) changes in smoking and vaping behavior and associated cotinine levels and health status among regular smokers who were first-time e-cigarette purchasers and 2) attitudes, intentions, and restrictions regarding e-cigarettes. METHODS: We conducted a pilot longitudinal study with assessments of the aforementioned factors and salivary cotinine at weeks 0, 4, and 8. Eligibility criteria included being ≥18 years old, smoking ≥25 of the last 30 days, smoking ≥5 cigarettes per day (cpd), smoking regularly ≥1 year, and not having started using e-cigarettes. Of 72 individuals screened, 40 consented, 36 completed the baseline survey, and 83.3% and 72.2% were retained at weeks 4 and 8, respectively. RESULTS: Participants reduced cigarette consumption from baseline to week 4 and 8 (p's < 0.001); 23.1% reported no cigarette use in the past month at week 8. There was no significant decrease in cotinine from baseline to week 4 or 8 (p's = ns). At week 8, the majority reported improved health (65.4%), reduced smoker's cough (57.7%), and improved sense of smell (53.8%) and taste (50.0%). The majority believed that e-cigarettes versus regular cigarettes have fewer health risks (97.2%) and that e-cigarettes have been shown to help smokers quit (80.6%) and reduce cigarette consumption (97.2%). In addition, the majority intended to use e-cigarettes as a complete replacement for regular cigarettes (69.4%) and reported no restriction on e-cigarette use in the home (63.9%) or car (80.6%). CONCLUSIONS: Future research is needed to document the long-term impact on smoking behavior and health among cigarette smokers who initiate use of e-cigarettes.