Mohamadi Sarkar

Population estimates for biomarkers of exposure to cigarette smoke in adult U.S. cigarette smokers

INTRODUCTION There are about 4,800 different chemical constituents in cigarette smoke. Therefore, the total systemic exposure evaluation of the population of smokers to cigarette smoke is challenging. Measurement of biomarkers as surrogates of cigarette smoke constituents is a realistic approach to assess exposure. OBJECTIVE To estimate cigarette smoke exposure of the U.S. smoker population. METHODS Stratified, cross-sectional, multicenter design (39 sites in 31 states); 3,585 adult cigarette smokers and 1,077 nonsmokers. Biomarkers were determined from 24-hr urine collections or blood samples. Population estimates were generated by weighting sample data with weights from a large U.S. probability sample (Behavioral Risk Factor Surveillance System). RESULTS The adult smoker population estimates for tobacco-specific biomarkers were nicotine equivalents 13.3 mg/24 hr (SE 0.14), serum cotinine 184 ng/ml (1.8), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol 439 ng/24 hr (5.5). The population estimates for smokers and nonsmokers for nontobacco-specific biomarkers were 1-hydroxypyrene 317 (6.8) and 110 (7.1) ng/24 hr, 4-aminobiphenyl Hb adducts 43.1 (1.04) and 11.4 (1.5) pg/g Hb, carboxyhemoglobin 5.26(0.04) in percent of hemoglobin saturation and 1.45(0.02), 3-hydroxypropylmercapturic acid 2,030 (24) and 458 (17) microg/24 hr, monohydroxy-butenyl-mercapturic acid 3.61 (0.1) and 0.30 (0.02) microg/24 hr, and dihydroxy-butyl-mercapturic acid 556 (4.9) and 391 (5.5) microg/24 hr. On average, young adult smokers had lower exposure than older smokers; female smokers had lower exposure than males, and Black smokers had lower exposure than Whites. DISCUSSION This study estimated the population exposure to cigarette smoke constituents in adult U.S. smokers and identified significant differences between subpopulations. The data may serve as a reference for monitoring the impact of changes in cigarette consumption and the introduction of potentially reduced exposure cigarettes.

Biomarkers of Potential Harm Among Adult Smokers and Nonsmokers in the Total Exposure Study

INTRODUCTION There is overwhelming medical and scientific consensus that cigarette smoking causes lung cancer, heart disease, emphysema, and other serious diseases in smokers. In the Total Exposure Study, 29 biomarkers of potential harm (BOPH) were measured in a cross-sectional sample of 3,585 adult smokers (AS) and 1,077 nonsmokers (NS). The BOPH included markers of oxidative stress, inflammation, platelet activation, endothelial function, lipid metabolism, hematology, metabolism, the cardiovascular system, lung function, kidney function, and liver function. METHODS Multiple stepwise regression was used to examine the effect of demographic factors (age, gender, body mass index [BMI], and race) and smoking (number of cigarettes smoked per day or nicotine equivalents [NE] per 24 hr and smoking duration) on each BOPH. RESULTS As compared with NS, AS had >10% higher levels of 8-epi-prostaglandin F(2α) (8-epi-PG F(2α), 42%), 11-dehydrothromboxane B₂ (11-DHTB, 29%), white blood cell (WBC) count (19%), high-sensitivity C-reactive protein (15%), triglycerides (16%), and alkaline phosphatase (11%) and had 18% lower total bilirubin. Multiple stepwise regression revealed that although NE (milligrams per 24 hours) was statistically significant for 18 of the 29 BOPH, it was the most important factor only for WBCs and 11-DHTB. Smoking duration was the most important factor for forced expiratory volume in 1 second. In contrast, BMI was the most important factor for 12 BOPH. CONCLUSIONS These results contribute to the understanding of the relationship between tobacco smoking and potential biological effects.

Relationship between Biomarkers of Cigarette Smoke Exposure and Biomarkers of Inflammation, Oxidative Stress, and Platelet Activation in Adult Cigarette Smokers

Background: Cigarette smoking is a risk factor for several diseases, including cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer, but the role of specific smoke constituents in these diseases has not been clearly established. Methods: The relationships between biomarkers of potential harm (BOPH), associated with inflammation [white blood cell (WBC), high sensitivity C-reactive protein (hs-CRP), fibrinogen, and von Willebrand factor (vWF)], oxidative stress [8-epi-prostaglandin F2α (8-epiPGF2α)] and platelet activation [11-dehydro-thromboxin B2 (11-dehTxB2)], and machine-measured tar yields (grouped into four categories), biomarkers of exposure (BOE) to cigarette smoke: nicotine and its five metabolites (nicotine equivalents), 4-methylnitrosamino-1-(3-pyridyl)-1-butanol (total NNAL), carboxyhemoglobin, 1-hydroxypyrene, 3-hydroxypropylmercapturic acid, and monohydroxybutenyl-mercapturic acid, were investigated in 3,585 adult smokers and 1,077 nonsmokers. Results: Overall, adult smokers had higher levels of BOPHs than nonsmokers. Body mass index (BMI), smoking duration, tar category, and some of the BOEs were significant factors in the multiple regression models. Based on the F value, BMI was the highest ranking factor in the models for WBC, hs-CRP, fibrinogen, and 8-epiPGF2α, respectively, and gender and smoking duration for 11-dehTxB2 and vWF, respectively. Conclusions: Although several demographic factors and some BOEs were statistically significant in the model, the R2 values indicate that only up to 22% of the variability can be explained by these factors, reflecting the complexity and multifactorial nature of the disease mechanisms. Impact: The relationships between the BOEs and BOPHs observed in this study may help with the identification of appropriate biomarkers and improve the design of clinical studies in smokers. Cancer Epidemiol Biomarkers Prev; 20(8); 1760–9. ©2011 AACR.

Evaluation of biomarkers of exposure in adult cigarette smokers using Marlboro Snus

INTRODUCTION It has been reported that adult smokers (AS) may be considering smokeless tobacco products as an alternative to smoking. The objective of this study was to evaluate the change in exposure in AS using Marlboro snus (MSNUS) (a tobacco pouch product in test market in June 2007). METHODS AS were randomized into the following groups--CS: subjects (n = 30) continue smoking their own brand; DU: subjects (n = 60) reduced their daily cigarette consumption by >or=50% and were allowed to use MSNUS; SN: subjects (n = 15) stopped smoking their cigarettes but were allowed to use MSNUS; NT: subjects (n = 15) were not allowed to use any tobacco products for the entire duration of the 8-day study. Biomarkers of smoke exposure (BOE) measured at baseline and postbaseline were 24-hr urinary excretion of metabolites of N-nitrosamines, nicotine (urine and plasma), aromatic amines, benzene, and polycyclic aromatic hydrocarbon; urine mutagenicity; and carboxyhemoglobin at various timepoints. RESULTS Statistically significant (p < .05) reductions in all the urinary BOE were observed in the DU group compared with the CS group. After correcting for the residual effect, a proportionate reduction (approximately 50%) in most of the biomarkers was observed. Even larger reductions, similar to the NT group, were observed in the SN group. DISCUSSION The proportionate reduction in exposure when reducing the number of cigarettes by 50% and using MSNUS, under the consumption patterns observed, suggest that the AS did not appear to alter their smoking behavior. The added exposure from MSNUS usage in this group was minimal. The AS sustained substantial reductions in exposure when using MSNUS exclusively.

Determination of methyl-, 2-hydroxyethyl- and 2-cyanoethylmercapturic acids as biomarkers of exposure to alkylating agents in cigarette smoke

Alkylating agents occur in the environment and are formed endogenously. Tobacco smoke contains a variety of alkylating agents or precursors including, among others, N-nitrosodimethylamine (NDMA), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), acrylonitrile and ethylene oxide. We developed and validated a method for the simultaneous determination of methylmercapturic acid (MMA, biomarker for methylating agents such as NDMA and NNK), 2-hydroxyethylmercapturic acid (HEMA, biomarker for ethylene oxide) and 2-cyanoethylmercapturic acid (CEMA, biomarker for acrylonitrile) in human urine using deuterated internal standards of each compound. The method involves liquid/liquid extraction of the urine sample, solid phase extraction on anion exchange cartridges, derivatization with pentafluorobenzyl bromide (PFBBr), liquid/liquid extraction of the reaction mixture and LC-MS/MS analysis with positive electrospray ionization. The method was linear in the ranges of 5.00-600, 1.00-50.0 and 1.50-900 ng/ml for MMA, HEMA and CEMA, respectively. The method was applied to two clinical studies in adult smokers of conventional cigarettes who either continued smoking conventional cigarettes, were switched to test cigarettes consisting of either an electrically heated cigarette smoking system (EHCSS) or having a highly activated carbon granule filter that were shown to have reduced exposure to specific smoke constituents, or stopped smoking. Urinary excretion of MMA was found to be unaffected by switching to the test cigarettes or stop smoking. Urinary HEMA excretion decreased by 46 to 54% after switching to test cigarettes and by approximately 74% when stopping smoking. Urinary CEMA excretion decreased by 74-77% when switching to test cigarettes and by approximately 90% when stopping smoking. This validated method for urinary alkylmercapturic acids is suitable to distinguish differences in exposure not only between smokers and nonsmokers but also between smoking of conventional and the two test cigarettes investigated in this study.

The relationship between nicotine dependence scores and biomarkers of exposure in adult cigarette smokers.

BACKGROUND Tobacco dependence is a multidimensional phenomenon. The Fagerström Test for Nicotine Dependence (FTND) is a widely administered six-item questionnaire used as a measure of nicotine dependence. It has been suggested that this test may not represent the entire spectrum of factors related to dependence. Also the relationship of this test with biomarkers of exposure to cigarette smoke has not been extensively studied. METHODS Data from a multi-center, cross-sectional, ambulatory study of US adult smokers (the Total Exposure Study, TES) was analyzed. The FTND score and a number of additional questions related to smoking behavior, from an adult smoker questionnaire (ASQ) completed by 3585 adult smokers in the TES were analyzed. The 24-h urine nicotine equivalents, serum cotinine and blood carboxyhemoglobin were measured as biomarkers of exposure (BOE) to nicotine and carbon monoxide. Cigarette butts returned were collected during the 24-h urine collection period. RESULTS The FTND showed moderate correlations with BOE, while selected questions from ASQ although statistically significant, had weaker correlations. FTND scores showed substantially weaker correlations without the question about cigarettes smoked per day (CPD). CPD and time to first cigarette (TTFC) had the most impact on BOE. CONCLUSION Additional questions from ASQ did not appear to contribute towards refining the FTND test. The correlation of the FTND scores with nicotine and carbon monoxide seems to be primarily driven by CPD. CPD and TTFC were the most important factors correlating with exposure.

Is 24h nicotine equivalents a surrogate for smoke exposure based on its relationship with other biomarkers of exposure

Nicotine and its 5 major metabolites (Nicotine equivalents, NE) may serve as a surrogate biomarker for smoke exposure. Objective: To investigate the relationship between nicotine equivalents (NE) and biomarkers of exposure (BOE) to cigarette smoke. Methods: Data from nine controlled studies in 916 adult smokers were used. BOEs to nicotine, NNK, pyrene, acrolein, benzene, 1,3-butadiene and CO were used. Results: Among all the factors investigated (NE, cigarette type, age, gender, BMI and study), NE was the most statistically significant factor for all biomarker relationships. Weak to moderate relationships (0.32 ≤ R2 ≤ 0.65) were found between NE and the BOEs. Conclusions: Based on the relationships with BOEs, NE may be considered as a surrogate biomarker of total cigarette smoke exposure.

Down-regulation of hepatic CYP3A in chronic renal insufficiency.

AbstractPurpose. The objective of this study was to investigate the mechanisms underlying the decrease in hepatic clearance of some drugs metabolized by CYP450 enzymes in chronic renal insufficiency (CRI). Methods. CRI was induced in male Sprague-Dawley rats (n = 7) by the remnant kidney model (RKM); control animals (C) (n = 12) underwent sham surgery, of which n = 6 rats were pair-fed (CPF) with CRI rats and others (n = 6) had free access to food. Serum creatinine (Scr) and urea nitrogen (SUN) were monitored every 2 weeks. On day 36, livers were isolated, and microsomes were prepared. Catalytic activities were measured through O-demethylation (CYP2D) and N-demethylation of dextromethorphan (CYP3A) and O-deethylation of 7-ethoxyresorufin (CYP1A2). CYP450 protein and mRNA levels were also measured. Results. Compared with CPF, Scr and SUN levels in CRI rats were increased twofold (p < 0.01) and 2.5-fold (p < 0.01), respectively. No effect on CYP1A2 and CYP2D activities, mRNA, or protein levels was observed between the groups. There was a reduction (41.8 ± 20%, p < 0.01) in CYP3A activity, mRNA (p < 0.05), and protein levels (p < 0.05) in CRI rats compared to CPF. Conclusions. CRI induced by RKM does not have an effect on hepatic CYP1A2 and CYP2D enzymes but does reduce CYP3A activity, probably through down-regulation of CYP3A2.

Factors affecting exposure to nicotine and carbon monoxide in adult cigarette smokers.

Exposure to cigarette smoke among smokers is highly variable. This variability has been attributed to differences in smoking behavior as measured by smoking topography, as well as other behavioral and subjective aspects of smoking. The objective of this study was to determine the factors affecting smoke exposure as estimated by biomarkers of exposure to nicotine and carbon monoxide (CO). In a multi-center cross-sectional study of 3585 adult smokers and 1077 adult nonsmokers, exposure to nicotine and CO was estimated by 24h urinary excretion of nicotine and five of its metabolites and by blood carboxyhemoglobin, respectively. Number of cigarettes smoked per day (CPD) was determined from cigarette butts returned. Puffing parameters were determined through a CreSS® micro device and a 182-item adult smoker questionnaire (ASQ) was administered. The relationship between exposure and demographic factors, smoking machine measured tar yield and CPD was examined in a statistical model (Model A). Topography parameters were added to this model (Model B) which was further expanded (Model C) by adding selected questions from the ASQ identified by a data reduction process. In all the models, CPD was the most important and highest ranking factor determining daily exposure. Other statistically significant factors were number of years smoked, questions related to morning smoking, topography and tar yield categories. In conclusion, the models investigated in this analysis, explain about 30-40% of variability in exposure to nicotine and CO.

Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers.

BackgroundThis article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F2α (EPI8), 24 h urine 11-dehydro-thromboxane B2 (DEH11), and high-density lipoprotein cholesterol (HDL).MethodsRandom Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical modelsResultsThe analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39).ConclusionsLevels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors togather explain 29%-41% of the variability in the BOPH.