Paul Mendes

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.

The relationship between smoking machine derived tar yields and biomarkers of exposure in adult cigarette smokers in the US

UNLABELLED Comprehensive data on human exposure to smoke constituents from different machine-measured tar yield cigarettes is limited. METHODS This study used a stratified, cross-sectional, multi-center design to estimate biomarkers of exposure (BOE) from nicotine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), pyrene, CO, acrolein, and 1,3-butadiene and their relationship to tar yield categories of cigarette in adult smokers in the U.S. 3625 adults smokers were enrolled into four tar categories < or =2.9 mg (T1), 3.0-6.9 mg (T2), 7.0-12.9 mg (T3), and > or =13.0mg (T4). Biomarkers were measured in blood (carboxyhemoglobin, 4-aminobiphenyl-hemoglobin (4-ABP-Hb)-adducts, serum cotinine) and 24h urine (nicotine and five metabolites, calculated as nicotine equivalents (NE), NNAL, 1-OH-pyrene, 3-HPMA, MHBMA and DHBMA). Data were analyzed using analysis of covariance (ANCOVA). RESULTS Tar was a significant factor for most biomarkers in the ANCOVA models. The largest least square mean differences between tar categories was 35% for NE per day, 28% for NE per cigarette, 36% for serum cotinine, 42% for NNAL per day, 29% for NNAL per cigarette, 26% for 1-OHP, 24% for COHb, 14% for 3-HPMA and 40% for 4-ABP-Hb. Variability in BOE ranged from 41% to 154% CV. CONCLUSIONS There was a statistically significant effect of machine-measured tar yield on most BOE, which were generally lower with lower tar yield.

A randomized, controlled exposure study in adult smokers of full flavor Marlboro cigarettes switching to Marlboro Lights or Marlboro Ultra Lights cigarettes.

Rationale. To date no state-of-the-art clinical study has been conducted to address the question as to whether switching to lower tar cigarettes reduces exposure to smoke constituents in humans. Methods. Randomized, controlled, forced switching study in 225 adult smokers of full flavor Marlboro (MFF) cigarettes for 8 days with a 24-week follow-up. Subjects smoked MFF (a 15-mg Federal Trade Commission (FTC) tar cigarette) at baseline and were randomized to smoke 11-mg Marlboro Lights (ML) or 6-mg Marlboro Ultra Lights (MUL) cigarettes. Biomarkers of exposure to nicotine, 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), pyrene, CO, benzene, acrolein, and mutagenic substances were measured. Results. In the short-term phase, switching from MFF to ML showed statistically significant decreases in nicotine exposure (-13%) and non-significant increases in CO exposure (+6%), while switching from MFF to MUL showed statistically significant decreases in nicotine (-27%) and CO (-13%) exposure. Both nicotine and CO biomarkers trended similarly in the 24-week follow-up as in the short-term phase. The other biomarkers of cigarette smoke constituents followed the same trend as nicotine at the end of the 24-week follow-up. Conclusions. Switching smokers to lower FTC tar yield cigarettes, on average, reduces nicotine and other biomarkers considered surrogates of tar exposure.

The effect of menthol containing cigarettes on adult smokers' exposure to nicotine and carbon monoxide.

UNLABELLED There is limited information comparing biomarkers of exposure (BOE) to cigarette smoke in menthol (MS) and non-menthol cigarette smokers (NMS). OBJECTIVE To compare BOE to nicotine and carbon monoxide in MS and NMS. METHODS Cross-sectional, observational, ambulatory, multi-centre study in 3341 adult cigarette smokers. Nicotine equivalents (NE) in 24h urine, NE/cigarette, COHb and serum cotinine were measured. Statistical analyses included analysis of variance and Wilcoxon test. RESULTS Analyses of variance revealed no statistically significant effects of mentholated cigarettes on NE/24h, COHb, serum cotinine and NE/cigarette. On average MS smoked 15.0 and NMS 16.8 cigarettes/day. The unadjusted mean differences were as follows: MS had lower NE/24h (5.4%) and COHb (3.2%), higher serum cotinine (3.0%) and NE/cigarette (5.7%) than NMS. African-Americans MS smoked 40% fewer cigarettes, showed lower NE/24h (24%) and COHb (10%) and higher NE/cig (29%) and serum cotinine (8%) levels than their White counterparts. CONCLUSIONS Smoking mentholated cigarettes does not increase daily exposure to smoke constituents as measured by NE and COHb. These findings are consistent with the majority of epidemiological studies indicating no difference in smoking related risks between MS and NMS.

Short term effects of reduced exposure to cigarette smoke on white blood cells, platelets and red blood cells in adult cigarette smokers.

UNLABELLED Previous studies indicate that cigarette smokers have a 5-30% higher white blood cell counts (WBC) compared to non-smokers and higher red blood cell counts. METHODS This study was to pool hematology data from three similar studies and analyze the data for effects on WBC, its subpopulations, platelets, red blood cell count (RBC) and hematocrit in adult cigarette smokers three days after using an electrically heated cigarette smoking system (EHCSS) as a potential reduced exposure product (PREP) or no-smoking compared to smoking a conventional cigarette. RESULTS Lower exposure to cigarette smoke in adult, long term smokers, by using an EHCSS or stopping smoking, leads to statistically significant decreases of up to 9% in WBC, neutrophils, lymphocytes, platelets, RBC and hematocrit within three days. Switching from CC-smoking to EHCSS-smoking or no-smoking resulted in lower WBC and vice versa within 3 days. CONCLUSION This clinical model may be used as a screening tool to find new technologies that could provide insights on changes in inflammation resulting from the change in cigarette smoke.

Heart Rate Variability Increases With Reductions in Cigarette Smoke Exposure After 3 Days

Background: Smoking has been shown to influence the tone of the autonomic nervous system as reflected by heart rate variability (HRV). To date, no information is available as to whether 24-hour HRV might differentiate users of different tobacco products. Objective: To assess the differences in HRV derived from the 24-hour electrocardiogram (ECG) following the use of 2 tobacco products of potentially different exposures. Methods: Thirty adult Caucasian male smokers (mean age: 42.8 + 5.7 years) smoking 20 to 40 cigarettes/ day were randomized in a 3-way crossover study design to either smoke a conventional cigarette (CC, tar: 11 mg, Nic: 0.8 mg), to use the Electrically Heated Cigarette Smoking System (EHCSS: tar: 5 mg, Nic: 0.3 mg, according to the Federal Trade Commission [FTC]), or to stop smoking (NS) for 3 days each. The 24 hours ECGs were recorded during the last 24 hours of each exposure period. Results: A 24-hour ECG showed highest mean values for standard deviation of all normal-to-normal heart beat (NN) intervals (SDNN), standard deviation of all 5-minute averaged NN intervals in a 24-hour period (SDANN), mean of the standard deviations of the NN intervals calculated from all 5-minute segments in a 24-hour period (SDNNI), percentage (P) of all NN intervals that differ by 50 milliseconds of all NN (PNN50%), the square root of the mean of all squared differences between adjacent NN intervals in 24-hour period (RMSSD), and total number of all NN intervals divided by the height of the histogram of all NN intervals measured on a discrete scale with bins of 7 × 8125 ms (1/128 seconds; HRVTI) when participants stopped smoking followed by the use of the reduced exposure product and CC. Conclusion: Heart rate variability tended to increase with reduced smoke 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.

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.