INTRODUCTION Evidence suggests that cigarette smokers who switch to electronic nicotine delivery systems (ENDS) reduce their exposure to harmful toxicants and carcinogens. It is unclear if dual-use is associated with decreases in exposure to toxicants. METHODS This parallel-group confinement study assessed changes in biomarkers of exposure (BOEs) over six days among healthy adult smokers who were randomized into 1 of 11 study groups: eight JUUL-brand System (JUUL) groups (4 JUUL flavors [Virginia Tobacco, Menthol, Mint, Mango] × 2 nicotine concentrations [5.0% or 3.0% by weight]); Dual-Use group used preferred JUUL flavor (5.0% nicotine) and ≤50% usual brand (UB) cigarettes/day; UB Cigarette group and one group abstained from all tobacco/nicotine product use (Abstinence group). Urine and blood analysis assessed changes in primary BOE endpoints (NNAL, 3-HPMA, MHBMA, S-PMA COHb) and secondary BOE endpoints (NNN, HMPMA, CEMA, 1-OHP, O-toluidine, 2-NA, 4-ABP) among 279 adult smokers. RESULTS In JUUL groups, median percent reductions in primary BOEs (Day 6-Baseline) were 90%-≥100% of Abstinence; there were no significant differences between JUUL groups and Abstinence. All reductions in JUUL groups were substantially and statistically significantly greater than reductions in the UB Cigarette group (ps < 0.025). Median reductions in primary BOEs in the Dual-Use group were 43%-55% of Abstinence. Similar results were observed for secondary BOEs. CONCLUSION This study suggests that the use of JUUL as a complete or partial substitute (i.e., dual-use with ≥50% reduction in cigarette consumption) for combustible cigarettes can substantially reduce exposure to multiple toxins associated with cigarette smoking. IMPLICATIONS This study adds to the growing body of evidence supporting the utility of ENDS products as potentially reduced-harm alternatives to cigarettes for adult smokers. Adult smokers who switched completely from cigarette smoking to use of the JUUL System ("JUUL") in two nicotine concentrations (5.0% and 3.0%) and four flavors significantly reduced their exposure to multiple classes of cigarette-related toxicants. Additionally, smokers who used JUUL and continued smoking but reduced their daily cigarette consumption by ≥50% (dual users) also significantly reduced their toxicant exposure compared to cigarette smoking.

/pdf/changes-in-biomarkers-of-cigarette-smoke-exposure-after-6-51yrx7ncvc.pdf

Changes in Biomarkers of Cigarette Smoke Exposure After 6 Days of Switching Exclusively or Partially to Use of the JUUL System with Two Nicotine Concentrations: A Randomized Controlled Confinement Study in Adult Smokers.

Background The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers will extend biomarker studies to cohorts without measured biomarkers and enable tobacco-related behavioral and exposure research. Methods We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants and age, ethnicity and sex, and cigarettes per day (CPD) (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2,239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles, and evaluated external validity using behavioral outcomes in 1,864 treatment-seeking smokers in two ethnic groups. Results The genomic regions with the most selected and trained variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68), and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and trained. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD, and predicted TNE (without CPD) with CPD, Time-To-First-Cigarette, and Fagerstrom total score. Conclusions Nicotine metabolites, genome-wide data and statistical learning approaches develop novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations help define genetically-influenced components of nicotine dependence. IMPLICATIONS We demonstrate development of robust models and multiethnic prediction of the urinary nicotine metabolite ratio and total nicotine equivalents using statistical and machine learning approaches. Trained variants in models for both biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites and related disease. Association of the two predicted nicotine biomarkers with Fagerstr□m Test for Nicotine Dependence items support models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

/pdf/multiethnic-prediction-of-nicotine-biomarkers-and-276p13yzt1.pdf

Multiethnic Prediction of Nicotine Biomarkers and Association with Nicotine Dependence

INTRODUCTION The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers. AIMS AND METHODS We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups. RESULTS The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerstrom total score. CONCLUSIONS Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence. IMPLICATIONS We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerstrom Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

/pdf/multiethnic-prediction-of-nicotine-biomarkers-and-3mfs2l55nh.pdf

Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence.

Abstract Introduction Tumour cell apoptosis is a crucial indicator for judging the antiproliferative effects of anti-cancer drugs. The detection of optical and macromolecular biomarkers is the most common method for assessing the level of apoptosis. We aimed to explore the anti-tumour mechanisms of 6-methoxyflavone. Materials and methods Three optical methods, including the percentage of apoptotic cells, cell morphology, and subcellular ultrastructure changes, were obtained using flow cytometry, inverted fluorescence microscopy, and transmission electron microscope imaging. The mRNA or protein expression of macromolecular biomarkers related to common apoptotic pathways was determined via polymerase chain reactions or western blot assays. The functional role of the core gene biomarker was investigated through overexpression, knockdown, and phosphorylation inhibitor (GSK2656157). Results Transcriptome sequencing and the optical biomarkers assays demonstrated that 6-methoxyflavone could induce apoptosis in HeLa cells. The expression of macromolecular biomarkers indicated that 6-methoxyflavone induced apoptosis through the PERK/EIF2α/ATF4/CHOP pathway. Phosphorylated PERK was identified as the core biomarker of this pathway. Both overexpression and GSK2656157 significantly altered the expression level of phosphorylated PERK in 6-methoxyflavone-treated HeLa cells. Discussion and conclusion Macromolecular biomarkers, such as phosphorylated PERK and phosphorylated EIF2α are of great significance for assessing the therapeutic effects of 6-methoxyflavone.

https://figshare.com/articles/journal_contribution/The_effect_and_apoptosis_mechanism_of_6-methoxyflavone_in_HeLa_cells/19625379/1/files/34858500.pdf

The effect and apoptosis mechanism of 6-methoxyflavone in HeLa cells

BACKGROUND
Biomarkers of exposure are tools for understanding the impact of tobacco use on health outcomes if confounders like demographics, use behavior, biological half-life and other sources of exposure are accounted for in the analysis.


METHODS
We performed multiple regression analysis of longitudinal measures of urinary biomarkers of alkaloids, tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, volatile organic compounds (VOC), and metals to examine the sample-to-sample consistency in Waves 1 and 2 of the Population Assessment of Tobacco and Health Study including demographic characteristics and use behavior variables of persons who smoked exclusively. Regression coefficients, within- and between-person variance, and intra-class correlation coefficients were compared to biomarker smoking-nonsmoking population mean ratios and biological half-lives.


RESULTS
Most biomarkers were similarly associated with sex, age, race/ethnicity, and product use behavior. The biomarkers with larger smoking-nonsmoking population mean ratios had greater regression coefficients related to recency of exposure. For VOC and alkaloid metabolites, longer biological half-life was associated with lower within-person variance. For each chemical class studied, there were biomarkers that demonstrated good intra-class correlation coefficients.


CONCLUSIONS
For most of the biomarkers of exposure reported in the PATH Study, for people who smoke cigarettes exclusively, associations are similar between urinary biomarkers of exposure and demographic and use behavior covariates. Biomarkers of exposure within-subject consistency is likely associated with non-tobacco sources of exposure and biological half-life.


IMPACT
Biomarkers measured in the PATH Study provide consistent sample-to-sample measures from which to investigate the association of adverse health outcomes with the characteristics of cigarettes and their use.

Influence of half-life and smoking/nonsmoking ratio on biomarker consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study.

INTRODUCTION We illustrate the differential impact of common analysis approaches to handling urinary creatinine, a measure for urine dilution, on relationships between race, gender, and biomarkers of exposure measured in spot urine. METHODS In smokers, spot urine levels of total nicotine equivalents (TNE, sum of total nicotine, total cotinine, and total 3'-hydroxycotinine) and total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) overall and per cigarette were examined. Relationships between race (African Americans [AA] n = 373, Whites n = 758) or gender (males n = 629, females n = 502) and TNE or NNAL were examined using the following approaches to handling creatinine: (1) unadjusted/unstandardized; (2) standardization; (3) adjustment as a covariate. Significance was considered at p < .05. RESULTS Creatinine was higher in AA versus Whites (1.19 vs. 0.96 mg/mL; p < .0001) and in males versus females (1.21 vs. 0.84 mg/mL; p < .0001). Independent of how creatinine was handled, TNE was lower among AA than Whites (TNE ratios AA vs. Whites: 0.67-0.84; p's < .05). Unadjusted TNE per cigarette was higher among AA versus Whites (ratio 1.12; p = .0411); however, the relationship flipped with standardization (ratio 0.90; p = .0360) and adjustment (ratio 0.95; p = .3165). Regarding gender, unadjusted TNE was higher among males versus females (ratio 1.13; p = .0063), but the relationship flipped with standardization (ratio 0.79; p < .0001) or adjustment (ratio 0.89; p = .0018). Unadjusted TNE per cigarette did not differ across gender (ratio 0.98; p = .6591), but lower levels were found in males versus females with standardization (ratio 0.68; p < .0001) and adjustment (ratio 0.74; p < .0001). NNAL displayed similar patterns. CONCLUSIONS Relationships between race, gender, and spot urine levels of biomarkers of exposure can vary greatly based on how creatinine is handled in analyses. IMPLICATIONS Lack of appropriate methods can lead to discrepancies across reports on variability of urinary biomarkers by race and gender. We recommend that for any analyses of biomarkers of exposure measure in spot urine samples across race, gender, or other population subgroups that differ in urinary creatinine levels, sensitivity analyses comparing the different methods for handling urinary creatinine should be conducted. If methods result in discrepant findings, this should be clearly noted and discussed.

Relationships Between Race, Gender, and Spot Urine Levels of Biomarkers of Tobacco Exposure Vary Based on How Creatinine Is Handled in Analyses.

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Relationships Between Race, Gender, and Spot Urine Levels of Biomarkers of Tobacco Exposure Vary Based on How Creatinine Is Handled in Analyses.