Abstract
Previously, we documented that smoking-associated lung cancer (LC) risk is greater in Hawaiians and lower in Japanese, compared to whites. Nicotine metabolism by CYP2A6 varies across ethnicity/race and is hypothesized to affect smoking behavior. We investigated whether higher CYP2A6 activity results in the smoker extracting more nicotine (adjusting for cigarettes per day [CPD]) and being exposed to higher levels of tobacco-specific nitrosamine [4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)] and pyrene, a representative polycyclic aromatic hydrocarbon. We conducted a cross-sectional study of 585 smokers among the three main ethnic/racial groups in Hawaii, and examined whether differences in CYP2A6 activity correlate with the ethnic/racial differences in LC risk. We assessed CYP2A6 activity by nicotine metabolite ratio [total trans 3′-hydroxycotinine (3-HC) : total cotinine] and caffeine metabolite ratio (1,7-dimethyl uric acid : 1,7-dimethylxanthine) in 12-hour urine. We also measured urinary nicotine equivalents (sum of nicotine, cotinine and 3-HC and their respective glucuronides), a marker of nicotine dose, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and its glucuronide, markers of NNK exposure, and 1-hydroxypyrene (1-OHP), a marker of pyrene exposure. The nicotine metabolite ratio was higher in whites than in Japanese, and intermediate in Hawaiians (p’s <0.05). CPD-adjusted nicotine equivalents were lower in Japanese compared to Hawaiians or whites (p=0.005 and <0.0001, respectively), and greater in men than women (p<0.0001). Nicotine equivalents and total NNAL increased with CYP2A6 activity, indicating that smokers with greater nicotine metabolism smoke more extensively and have a higher internal NNK dose. The particularly low nicotine metabolism of Japanese smokers may contribute to their previously described decreased LC risk.
Introduction
The lung cancer risk due to cigarette smoking is known to differ across U.S. ethnic/racial groups and, possibly, between the sexes, even after taking into account smoking dose and duration. Specifically, compared to whites, African American and Native Hawaiian smokers have been shown to be at a greater risk of developing lung cancer, whereas Latino and Japanese American smokers are less likely to develop the disease (1,2). Female smokers have also been suggested to have a higher lung cancer risk than do men (3), although not all studies are in agreement on the gender differences in lung cancer risk. The mechanisms underlying these racial/ethnic and gender differences likely involve both genetic and behavioral factors.
Metabolism is the primary route of elimination of nicotine from the circulation. The cytochrome P450 2A6 enzyme (CYP2A6) metabolizes up to 80% of nicotine into cotinine via C-oxidation (4). Cotinine is further metabolized to trans 3′-hydroxycotinine (3-HC) by the same enzyme. Differences in the rate of nicotine metabolism could contribute to inter-individual, gender and ethnic/racial variation in smoking behavior (5–8) and, as a consequence, lung cancer risk (9–13). In order to achieve the desired psycho-pharmacological effects of nicotine, smokers adjust their cigarette consumption to maintain particular levels of nicotine in the circulation (14). A slower nicotine metabolism rate may result in a person needing to smoke less extensively (i.e., smoke fewer cigarettes per day or extract lower nicotine dose per cigarette) to reach the same plasma nicotine level as someone who metabolizes nicotine more quickly (15). The molar sum of nicotine, cotinine, 3-HC and their respective glucuronides has been used as a measure of total nicotine exposure and is referred to as nicotine equivalents (16).
Genotyping is one common method of assessing CYP2A6 enzymatic activity, as several reduced-activity polymorphisms have been identified, particularly in Asians (17). However, it has been shown that even among individuals with the wild-type genotype, there is variation in CYP2A6 activity (18). Phenotyping by measuring the ratio of nicotine metabolite over the parent compound has been suggested to be a convenient and accurate probe of CYP2A6 activity (18). CYP2A6 is also the primary enzyme responsible for the conversion of the caffeine metabolite 1,7-dimethylxanthine (17X) to 1,7-dimethyl uric acid (17U), and the ratio of these two caffeine metabolites measured in the urine has been used as another index of CYP2A6 activity (19).
We asked whether a high CYP2A6 activity, measured either by the nicotine metabolite ratio or the caffeine metabolite ratio, affects smoking behavior in a way that could result in a greater level of nicotine intake, adjusted for cigarettes per day (CPD), and, thereby, in an increased exposure to tobacco smoke carcinogens, possibly contributing to the unexplained differences in lung cancer risk that we observed among Japanese American, white, and Native Hawaiian smokers. Because other factors may also affect how extensively people smoke their cigarettes, we assessed whether nicotine equivalents varied by age, gender, body mass index and diet. We also measured urinary metabolites as markers of internal dose of the tobacco-specific lung carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and pyrene, a representative polycyclic aromatic hydrocarbon (PAH) [the sum of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and its glucoronide (NNAL-Gluc), and 1-hydroxypyrene (1-OHP), respectively] to assess whether greater nicotine equivalents adjusted for CPD was associated with higher exposure to these two classes of tobacco smoke carcinogens.
Methods
Study Population
Between 1993 and 1996, the Multiethnic Cohort Study (MEC) was established in Hawaii and Los Angeles to test hypotheses related to diet and cancer. The MEC consists of more than 215,000 men and women, drawing from five racial/ethnic populations: African Americans, Japanese Americans, Latinos, Native Hawaiians and whites (20). For the present cross-sectional study, two main sources of participants were used. The majority (88.1%) were randomly selected among all MEC Oahu cohort members who reported on their baseline questionnaire that they smoked at least 10 cigarettes per day, had no previous history of cancer and reported that both parents were of Japanese or Caucasian ethnicity, or of any amount of Hawaiian ancestry. Another source of subjects (11.9%) was the control groups for completed population-based case-control studies of various cancer types, conducted in Hawaii (21,22). The same inclusion/exclusion criteria as for the MEC were used to re-contact the participants of these studies. The overall target sample size was 100 in each sex and ethnic group. The study was approved by the University of Hawaii Committee on Human Subjects and all participants signed a consent form.
A total of 596 participants completed all aspects of the study. Eight participants were excluded for reporting to smoke fewer than 10 CPD during data collection, and 3 were excluded for missing BMI. Thus, 585 subjects were used in the final data analysis (see Table 1).
Table 1.
Main characteristics* of study participants by sex and race (n=585)
| Native Hawaiian | White | Japanese American | |
|---|---|---|---|
| Females | Median (Inter-quartile range) | Median (Inter-quartile range) | Median (Inter-quartile range) |
| N (%) | 99 (51.6%) | 99 (50.0%) | 97 (49.7%) |
| Age (years) | 60 (57, 66) | 61 (57, 66) | 61 (58, 67) |
| Cigarettes per Day | 20.0 (12.0, 16.0)‡ | 20.0 (18.7,30.0) | 16.0 (13.0, 20.0)‡ |
| Smoking Duration (years) † | 42.0 (37.0, 46.0) | 43.0 (39.0, 47.0) | 41.5 (38.0, 46.0) |
| BMI (kg/m2) | 28.2 (24.0, 31.5)‡ § | 24.1 (21.2, 29.0) | 24.9 (21.4, 27.6) |
| Total Urine Volume (mL) | 739.0 (499.0, 1059.0) | 874.0 (444.0, 1160.0) | 824.0 (529.0, 1133.0) |
| Fruits Intake (g/kcal/day) | 44.0 (2.35, 168.2)‡ § | 117.4 (64.6, 256.7) | 162.7 (41.4, 264.9) |
| Vegetables Intake (g/kcal/day) | 186.7 (101.4, 247.6)§ | 241.3 (118.0, 358.0) | 207.4 (152.2, 281.6) |
| Caffeine Intake (mg/kcal/day) | 196.2 (86.5, 304.3) | 224.6 (137.9, 348.9) | 221.5 (120.7, 334.7) |
| Alcohol Intake (g/kcal/day) | 0.28 (0.01, 0.71) | 0.45 (0.16, 24.0) | 0.31 (0.07, 0.54) |
|
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| Males
| |||
| N (%) | 93 (48.4%) | 99 (50.0%) | 98 (50.3%) |
| Age (years) | 59 (49, 65)‡ | 61 (58, 66) | 61 (57–66) |
| Cigarettes per Day | 20.0 (18.0,25.0)‡ | 25.0 (20.0, 40.0) | 20.0 (20.0, 25.0)‡ |
| Smoking Duration (years) † | 41.0 (32.0, 47.0)‡ § | 46.0 (41.0, 50.0) | 45.0 (40.0, 49.0) |
| BMI (kg/m2) | 28.3 (24.2,33.2)‡ § | 26.4 (23.8, 30.1) | 26.0 (23.1, 28.9) |
| Total Urine Volume (mL) | 774.0 (534.0, 1114.0)‡ § | 914.0 (674.0, 1394.0) | 998.0 (674.0, 1454.0) |
| Fruits Intake (g/kcal/day) | 45.9 (0.21, 133.0)‡ | 90.3 (4.76, 208.1) | 53.8 (0.29, 151.6)‡ |
| Vegetables Intake (g/kcal/day) | 169.2 (100.7, 235.3) | 160.7 (93.7, 237.5) | 170.3 (127.5, 257.6) |
| Caffeine Intake (mg/kcal/day) | 171.7 (69.6, 299.5)‡ § | 292.8 (171.7, 398.5) | 241.0 (181.5, 401.0) |
| Alcohol Intake (g/kcal/day) | 0.01 (0.00, 1.59)‡ § | 0.94 (0.00, 39.1) | 0.20 (0.00, 35.8) |
Values are medians and inter-quartile ranges, unless otherwise indicated. The number of cigarettes smoked per day (CPD) and dietary intake are estimated by averaging over the three days preceding the 12-hour urine collection.
Smoking duration is sum of years using filtered cigarettes, non-filtered cigarettes, cigars, pipes, and chewing tobacco.
P-value for comparison to whites is <0.05 (except for comparison of total fruits intake among men between Native Hawaiians and whites and between Japanese Americans and whites, where p=0.06).
P-value for comparison to Japanese Americans is <0.05 (except for comparison of total vegetable intake among females, where p=0.06).
Data Collection
Interviews were conducted at home. The initial interview was to explain the study, obtain a history of lifetime tobacco and alcohol use, and lung cancer-related occupational exposures, as well as a food frequency questionnaire. At that time, the interviewer also provided instructions on how to keep a three-day food record and a diary of all medications and dietary supplements taken, as well as how to conduct the 12-hour (overnight) urine collection and the caffeine test. The food, medication, and supplement records were kept during the 3 days preceding the blood draw and 12-hour urine collection. The day before the second appointment, the overnight urine collection started between 5 pm and 9 pm (depending on subject) and included all urine passed during the night and the first morning urine, to cover a period of 12 hours. The urine was kept on ice in a cooler until pick-up the following morning. For the caffeine test, the subjects were instructed to consume two cups of coffee (Maxwell House instant coffee) (~100 mg caffeine) upon rising (after 12-hour collection), maintain fasting for another two hours, abstain from other caffeine consumption, and collect their urine during the fifth hour after caffeine dosage. At this second home visit, the interviewer/phlebotomist administered a short questionnaire (including tobacco use during the previous three days), measured weight and height, obtained the 12-hour and caffeine test urine samples and collected the blood sample. The biospecimens were kept on ice until processing, which occurred within 4 hours. Samples were stored at −80°C until analysis.
Laboratory Analysis
Analysis of total urinary nicotine, cotinine, and trans 3′-hydroxycotinine concentration was done by GC/MS (gas chromatography/mass spectrometry). To assay urine for total nicotine (free plus nicotine N-glucuronide) and total cotinine (free plus cotinine N-glucuronide) concentration, samples were treated with base to cleave the glucuronide conjugates, then nicotine and cotinine were quantified by GC/MS analysis as previously described (23). Total trans 3′-hydroxycotinine (3-HC plus its glucuronide) was analyzed by the sample first being treated with β-glucuronidase then analyzing trans 3′-hydroxycotinine by GC/MS as previously described (24). The sum of nicotine, cotinine, 3-HC, and the respective glucuronides accounts for about 81% of nicotine and its metabolites (25). Based on 65 blind duplicate pairs analyzed with the study samples for nicotine and cotinine, and 6 pairs for 3-HC, the intraclass correlation coefficient for total nicotine, total cotinine and total 3-HC was 0.98, 0.96 and 0.62, respectively.
NNAL and NNAL-Gluc were determined as described with slight modifications (26). 1-OHP was determined as previously described with modifications (27). In brief, urine mixed with pH 7 triethylamine buffer was incubated overnight with arylsulfatase and glucuronidase and using coumarin 138 (7-Dimethylaminocyclopenta [c] coumarin, Sigma), as internal standard. 1-OHP was partitioned into diethyl ether and analyzed by HPLC with a Supelcosil LC-PAH column (100×4.6 mm; 3 μm; Supelco, Bellefonte, PA) applying a linear water-methanol gradient and fluorescence monitoring (344 nm for excitation and 380 nm for emission). Inter-assay CV ranged from 3 to 4%.
CYP2A6 is responsible for 8-hydroxylation of 1,7-dimethylxanthine (17X) to form 1,7-dimethylurate (17U). The ratio of these urinary metabolites of caffeine (17U:17X) is used as a measure of CYP2A6 activity and phenotype (28). 17X and 17U were determined as previously described (29), in a modification of the method by Butler et al. (30), by partitioning from urine mixed with ammonium sulfate and acetaminophen as internal standard into chloroform/isopropanol (19:1; v/v) followed by HPLC analysis with a Supelcosil LC18 column (250×4.6 mm; 5μm), a linear methanol/0.09% aq. acetic acid gradient, and monitoring at 280 nm. Inter-day coefficients of variation ranged from 6–12% depending on the analyte concentration.
Data Analysis
Food group intakes were calculated by summing the intakes of the appropriate foods for each group, as reported on the three-day food record. Food group intakes were then adjusted for average daily calorie consumption, and expressed as grams or mg/kcal/day. Urinary metabolites were expressed in moles per milliliter of urine. The data analysis was conducted using SAS 9.0 software (SAS Inc., Cary, NC).
Because the dependent variables were not normally distributed, a Box-Cox transformation test was performed for each model to identify the most appropriate transformation. All models were log-transformed. Values presented in the tables are back-transformed to their natural scale for ease of interpretation. To determine differences between groups, least-square means were computed for each racial/ethnic group (total sample, and by gender subgroup) using the general linear model (GLM) procedure. Ninety-five percent confidence limits were computed for the means, as were p-values for differences for each group comparison.
Multivariate linear regression models were used to predict levels of total nicotine, total cotinine, total 3HC, nicotine equivalents, the nicotine metabolite ratio and the caffeine metabolite ratio. Age, race (with white as the reference), sex (with female as the reference), 12-hour urine volume, number of cigarettes per day during the previous 3 days (CPD), body mass index (BMI), and dietary intakes of all fruits, all vegetables (or specifically cruciferous vegetables), soy, caffeine, alcohol and processed meats were used as independent variables. Cruciferous vegetables and processed meats were not significant in any of the models and, therefore, were excluded from further analyses. The cumulative R2 value was used to assess the percentage of variation of the dependent variable accounted for by the independent variables.
A general linear model was also used to predict levels of nicotine equivalents for each CYP2A6 activity group (categorized as ≤ or > median or by tertiles), as assessed, successively, by the urinary nicotine metabolite ratio and caffeine metabolite ratio, and adjusting for age only (and sex and race, when appropriate), then further for additional covariates (see Table 4). Tests of interaction were conducted between nicotine metabolite ratio and, sequentially, age, BMI, and CPD to identify modifying effects. The interaction terms were not statistically significant and, therefore, were not included in the final models. The Spearman nonparametric correlation coefficient was used to assess the correlation between continuous variables.
Table 4.
Geometric mean for nicotine equivalents* (95% confidence limits) by CYP2A6 activity level
| Nicotine Metabolite Ratio † | Caffeine Metabolite Ratio † | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| ≤ Median | > Median | p-valuea | ≤ Median | > Median | p-valuea | |||||
| Age- and Urine Volume- Adjusted Means | n | Mean (95% CI) | n | Mean (95% CI) | n | Mean (95% CI) | n | Mean (95% CI) | ||
|
| ||||||||||
| Female | ||||||||||
| All ‡ | 142 | 28.2 (25.4–31.2) | 153 | 34.7 (31.5–38.4) | 0.005 | 138 | 29.3 (26.4–32.6) | 157 | 33.4 (30.3–36.9) | 0.08 |
| Native Hawaiian | 45 | 28.6 (23.0–35.6) | 54 | 37.7 (31.0–45.8) | 0.06 | 42 | 32.0 (25.5–40.2) | 57 | 34.3 (28.3–41.7) | 0.65 |
| White | 33 | 33.5 (27.3–41.0) | 66 | 38.7 (33.5–44.8) | 0.25 | 36 | 32.1 (26.3–39.3) | 63 | 39.8 (34.3–46.3) | 0.10 |
| Japanese American | 64 | 23.7 (20.9–26.8) | 33 | 28.4 (24.0–33.7) | 0.09 | 60 | 23.5 (20.8–26.7) | 37 | 28.2 (24.0–33.2) | 0.08 |
| Male | ||||||||||
| All ‡ | 151 | 36.8 (32.8–41.4) | 139 | 39.2 (34.7–44.3) | 0.48 | 154 | 32.7 (29.2–36.6) | 136 | 45.0 (39.9–50.8) | 0.0002 |
| Native Hawaiian | 49 | 38.7 (30.9–48.5) | 44 | 46.1 (36.3–58.4) | 0.30 | 50 | 34.1 (27.5–42.2) | 43 | 53.7 (42.6–67.7) | 0.01 |
| White | 39 | 40.0 (32.7–48.9) | 60 | 47.0 (40.5–54.5) | 0.20 | 38 | 41.2 (34.0–50.0) | 61 | 46.5 (39.9–54.1) | 0.34 |
| Japanese American | 63 | 31.3 (26.3–37.4) | 35 | 25.8 (19.9–33.5) | 0.22 | 66 | 25.9 (21.8–30.8) | 32 | 38.6 (30.1–49.6) | 0.01 |
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| Multivariate-Adjusted Means§ | ||||||||||
|
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| Female | ||||||||||
| All‡ | 28.4 (25.8–31.4) | 34.4 (31.3–37.9) | 0.01 | 29.9 (27.1–33.0) | 32.8 (29.8–36.1) | 0.20 | ||||
| Native Hawaiian | 28.4 (23.0–35.0) | 37.9 (31.5–45.7) | 0.04 | 32.7 (26.3–40.7) | 33.9 (28.1–40.8) | 0.81 | ||||
| White | 33.7 (27.8–41.0) | 38.6 (33.5–44.4) | 0.27 | 33.0 (27.1–40.0) | 39.3 (34.0–45.4) | 0.16 | ||||
| Japanese American | 23.9 (21.2–27.0) | 27.9 (23.6–32.9) | 0.15 | 23.9 (21.1–27.1) | 27.5 (23.5–32.3) | 0.17 | ||||
| Male | ||||||||||
| All‡ | 36.9 (32.8–41.5) | 39.2 (34.6–44.3) | 0.51 | 32.9 (29.4–36.8) | 44.7 (39.7–50.5) | 0.0004 | ||||
| Native Hawaiian | 39.1 (31.2–49.1) | 45.5 (35.8–57.9) | 0.37 | 34.2 (27.5–42.7) | 53.4 (42.1–67.8) | 0.01 | ||||
| White | 40.5 (32.9–49.8) | 46.7 (40.1–54.3) | 0.29 | 40.8 (33.5–49.7) | 46.8 (40.1–54.5) | 0.29 | ||||
| Japanese American | 31.1 (26.1–37.0) | 26.4 (20.3–34.2) | 0.31 | 26.4 (22.2–31.4) | 37.2 (28.8–47.9) | 0.03 | ||||
Nicotine equivalents is the sum of urinary total nicotine, total cotinine, and total trans-3′-hydroxycotinine in nmol/mL.
Medians were based on all participants, and median values are 0.73 and 1.27 for nicotine metabolite ratio and caffeine metabolite ratio, respectively.
Further adjusted for race.
Further adjusted for cigarettes per day and calorie-adjusted caffeine intake by multiple covariance analysis.
P-values were obtained in a model comparing nicotine equivalents as a continuous variable (with the independent variables described in the table) between low and high nicotine metabolite ratio or caffeine metabolite ratio, for each subgroup analysis.
Results
The main characteristics of the participants are provided in Table 1. Native Hawaiian women had a greater BMI, smoked fewer CPD and ate fewer total fruits compared to white women (p=0.003, 0.01 and 0.01, respectively). Japanese American women smoked fewer CPD than white women (p<0.0001) and had a lower BMI and greater total fruits intake, compared to Native Hawaiian women (p=0.003 and 0.0004, respectively). Within men, compared to whites, Native Hawaiians were younger, had a greater BMI, smoked fewer CPD and had been smoking for fewer years, and had lower total intake of caffeine and alcohol (all p’s ≤0.03). Compared to Japanese American men, Native Hawaiian men had a greater BMI, had been smoking for fewer years and had a lower intake of caffeine and alcohol (all p’s ≤0.04). Japanese American men smoked fewer CPD and had a lower total fruits intake than white men (p=0.0004 and 0.06, respectively).
The geometric means for urinary levels of nicotine and its metabolites, adjusted for age, sex, race and 12-hour urine volume are shown in Table 2. The effect of further adjusting mean nicotine equivalents for CPD is also shown. Mean total nicotine, total cotinine and nicotine equivalents (with and without CPD adjustment) were significantly higher in males than in females (all p’s <0.0001). Urinary levels of total 3-HC were significantly different across ethnic groups, with the mean highest in whites, followed by Native Hawaiians, and lowest in Japanese Americans (all p’s <0.001). Mean total cotinine was lower in Japanese Americans than in Native Hawaiians or whites (p= 0.001 and <0.0001, respectively). Nicotine equivalents (with and without CPD adjustment) followed this same ethnic/racial pattern (with CPD adjustment: p=0.005 and <0.0001, respectively). Within each sex, the racial/ethnic comparisons of nicotine and its metabolites are similar to those of the total sample. When nicotine equivalents are adjusted for CPD, these values represent the total amount of nicotine absorbed and are used as a measure of smoking intensity.
Table 2.
Geometric means* (95% confidence limits) for urinary total nicotine, total cotinine, total trans 3′-hydroxycotinine, nicotine equivalents, nicotine metabolite ratio, and caffeine metabolite ratio
| Native Hawaiian | White | Japanese American | ||||
|---|---|---|---|---|---|---|
| Females | n | Mean (95% CI) | n | Mean (95% CI) | n | Mean (95% CI) |
| Total Nicotine‡ | 99 | 8.18 (6.99–9.58) | 99 | 6.86 (5.84–8.04) | 97 | 7.95 (6.76–9.34)§ |
| Total Cotinine‡ | 12.4 (10.8–14.1)a | 12.4 (10.9–14.2) | 9.50 (8.30–10.9)§ | |||
| Total Trans 3′-hydroxycotinine‡ | 8.29 (6.64–10.4)§ a | 12.4 (9.91–15.5) | 4.58 (3.65–5.75)§ | |||
| Nicotine equivalents‡ | 32.6 (28.8–36.9)a | 37.5 (33.2–42.4) | 25.4 (22.4–28.7)§ | |||
| Nicotine equivalents adjusted for CPD‡ | 32.6 (28.9–36.7)a | 35.8 (31.7–40.4) | 26.7 (23.6–30.1)§ | |||
| Nicotine metabolite ratio‡ | 0.67 (0.55–0.81)§ a | 1.00 (0.82–1.22) | 0.48 (0.40–0.59)§ | |||
| Caffeine metabolite ratio‡ | 1.72 (1.49–1.98)a | 1.79 (1.55–2.06) | 1.04 (0.90–1.20)§ | |||
|
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| Males
| ||||||
| Total Nicotine | 93 | 9.54 (7.88–11.6) | 99 | 9.99 (8.29–12.0) | 98 | 9.89 (8.26 –11.9) |
| Total Cotinine | 13.9 (11.8–16.3)a | 16.8 (14.3–19.6) | 11.4 (9.79–13.3)§ | |||
| Total Trans 3′–hydroxycotinine | 9.76 (7.77–12.3)§ a | 15.2 (12.2–19.0) | 5.92 (4.78–7.35)§ | |||
| Nicotine equivalents | 37.0 (31.7–43.0)§ | 47.2 (40.9–54.5) | 31.3 (27.1–36.2)§ | |||
| Nicotine equivalents adjusted for CPD | 37.8 (32.4–43.9) | 45.2 (39.1–52.5) | 32.0 (27.7–37.0)§ | |||
| Nicotine metabolite ratio | 0.70 (0.59–0.84)a | 0.91 (0.76–1.08) | 0.52 (0.44–0.62)§ | |||
| Caffeine metabolite ratio | 1.35 (1.17–1.56)a | 1.35 (1.26–1.67) | 1.01 (0.88–1.16)§ | |||
|
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| All†
| ||||||
| Total Nicotine | 192 | 8.95 (7.92–10.1) | 198 | 8.21 (7.27–9.27) | 195 | 8.84 (7.83–9.97) |
| Total Cotinine | 13.3 (12.0 –14.7)a | 14.3 (12.9–15.8) | 10.4 (9.37–11.5)§ | |||
| Total Trans 3′- hydroxycotinine | 8.29 (6.64–10.4)§ a | 12.4 (9.91–15.5) | 4.58 (3.65–5.75)§ | |||
| Nicotine equivalents | 35.0 (31.8–38.5)§ a | 41.9 (38.2–46.1) | 28.1 (25.6–30.9)§ | |||
| Nicotine equivalents adjusted for CPD | 35.2 (32.2–38.9)a | 40.0 (36.4–43.9) | 29.2 (26.5–32.0)§ | |||
| Nicotine metabolite ratio | 0.68 (0.60–0.78)§ a | 0.95 (0.84–1.09) | 0.50 (0.44–0.57)§ | |||
| Caffeine metabolite ratio | 1.53 (1.38–1.70)a | 1.60 (1.44–1.77) | 1.03 (0.93–1.14)§ | |||
Means are adjusted for age, number of cigarettes per day (except for nicotine equivalents model) and 12-hour urine volume (except caffeine metabolite ratio, adjusted for caffeine test urine volume), by multiple covariance analysis.
Further adjusted for sex.
Total nicotine (free plus nicotine N-glucuronide), total cotinine (free plus cotinine N-glucuronide), total 3-HC (free plus 3-HC N-glucuronide) and nicotine equivalents are expressed in nmol/mL. The ratios are unitless.
P-value for comparison to whites is <0.05 (except for nicotine metabolite ratio in Native Hawaiian men, where p=0.06).
P-value for comparison to Japanese Americans is <0.05.
As also shown in Table 2, mean CYP2A6 activity (measured by either the nicotine metabolite ratio or the caffeine metabolite ratio) was significantly lower in Japanese Americans than in Native Hawaiians (p=0.001 and <0.0001, respectively) or whites (p’s <0.0001). The nicotine metabolite ratio, but not the caffeine metabolite ratio, was also significantly lower in Native Hawaiians compared to whites (p=0.001). Although no gender difference was observed for the nicotine metabolite ratio, the mean caffeine metabolite ratio was significantly higher in women than in men (p=0.04).
We performed a multivariate regression to determine the associations of age, race, sex, CPD, BMI, total urine volume, and calorie-adjusted intakes of fruits, vegetables, soy, caffeine and alcohol with, successively, nicotine equivalents, the nicotine metabolite ratio and the caffeine metabolite ratio (Table 3). The variables explained 32.9% of the variation in nicotine equivalents. CPD, male sex, and caffeine intake were associated with an increase in nicotine equivalents, whereas Japanese race/ethnicy (compared to whites) and 12-hour urine volume were associated with a decrease in nicotine equivalents.
Table 3.
emographic and lifestyle determinants of nicotine equivalents and CYP2A6 activity (n=585)*
| Dependent Variable | R2 (%) |
Independent Variables | Regression coefficient | P-value |
|---|---|---|---|---|
| Nicotine Equivalents† | 32.9 | Age (years) | −0.041 | 0.26 |
| Native Hawaiian vs. white | −0.067 | 0.12 | ||
| Japanese Am. vs. white | −0.193 | <0.0001 | ||
| Male vs. female | 0.143 | 0.0001 | ||
| Cigarettes per day | 0.157 | <0.0001 | ||
| BMI (kg/m2) | 0.017 | 0.65 | ||
| Urine Volume (mL) | −0.510 | <0.0001 | ||
| Tot Fruits (g/kcal/day) | −0.023 | 0.52 | ||
| Tot Vegetables (g/kcal/day) | −0.025 | 0.50 | ||
| Soy (g/kcal/day) | 0.028 | 0.45 | ||
| Caffeine (mg/kcal/day) | 0.088 | 0.01 | ||
| Alcohol (g/kcal/day) | 0.022 | 0.58 | ||
|
| ||||
| Nicotine Metabolite Ratio‡ | 14.4 | Age (years) | −0.030 | 0.47 |
| Native Hawaiian vs. white | −0.121 | 0.01 | ||
| Japanese Am. vs. white | −0.321 | <0.0001 | ||
| Male vs. female | −0.002 | 0.96 | ||
| Cigarettes per day | 0.116 | 0.01 | ||
| BMI (kg/m2) | −0.142 | 0.001 | ||
| Urine Volume (mL) | −0.153 | 0.0003 | ||
| Tot Fruits (g/kcal/day) | −0.035 | 0.38 | ||
| Tot Vegetables (g/kcal/day) | 0.030 | 0.47 | ||
| Soy (g/kcal/day) | 0.032 | 0.45 | ||
| Caffeine (mg/kcal/day) | 0.0003 | 0.99 | ||
| Alcohol (g/kcal/day) | 0.102 | 0.02 | ||
|
| ||||
| Caffeine Metabolite Ratio§ | 12.2 | Age (years) | 0.113 | 0.01 |
| Native Hawaiian vs. white | −0.096 | 0.05 | ||
| Japanese Am. Vs. white | −0.284 | <0.0001 | ||
| Male vs. female | −0.128 | 0.002 | ||
| Cigarettes per day | 0.055 | 0.19 | ||
| BMI (kg/m2) | 0.104 | 0.02 | ||
| Urine Volume (mL) | −0.096 | 0.03 | ||
| Tot Fruits (g/kcal/day) | −0.123 | 0.003 | ||
| Tot Vegetables (g/kcal/day) | −0.048 | 0.25 | ||
| Soy (g/kcal/day) | 0.064 | 0.14 | ||
| Caffeine (mg/kcal/day) | −0.005 | 0.91 | ||
| Alcohol (g/kcal/day) | 0.008 | 0.86 | ||
Data are for three separate multiple linear regression models.
Nicotine equivalents estimated by the sum of urinary levels of total nicotine, total cotinine, and total 3′-hydroxycotinine (3-HC); expressed as nmol/mL.
Nicotine metabolite ratio is the ratio of total 3-HC to total cotinine measured in a 12-hour urine sample.
Caffeine metabolite ratio is the ratio of 1,7-dimethyl uric acid (17U) to 1,7-dimethylxanthine (17X) measured 5-hours after caffeine administration.
The same model for the nicotine metabolite ratio explained 14.4% of the variation (Table 3). CPD and alcohol consumption were directly, and Hawaiian and Japanese ethnicities, BMI, and 12-hour urine volume were inversely, associated with the nicotine metabolite ratio. Similarly, 12.2% of the variation in caffeine metabolite ratio was explained by the model. Age and BMI were directly, and Native Hawaiian and Japanese ethnicity/race, male sex, 12-hour urine volume, and total fruits intake were inversely associated with the caffeine ratio. Both ratios (and nicotine equivalents) were particularly strongly inversely associated with being of Japanese ancestry.
To determine if CYP2A6 activity (measured by either the nicotine metabolite ratio or the caffeine metabolite ratio) was associated with a higher amount of exposure to nicotine equivalents, we first compared the age-, race-, and urine volume-adjusted mean nicotine equivalents by CYP2A6 activity (activity groups were divided at the median based on the distribution for the total population) in each sex (Table 4). In females, the mean nicotine equivalents was significantly greater in the high, compared to the low, CYP2A6 activity group when assessed by the nicotine metabolite ratio, as is borderline significant when assessed by the caffeine metabolite ratio. This pattern was observed in each race/ethnic group, although none of the differences reach statistical significance (see Table 4). In males, the mean nicotine equivalents was significantly higher in the high CYP2A6 activity group, compared to the low activity group, when measured by the caffeine metabolite ratio, overall (p=0.0002) and in Hawaiians and Japanese (p=0.01), but not when measured by the nicotine metabolite ratio (p=0.48), although the increasing trends were mostly consistent. As shown in the lower half of Table 4, very similar results were obtained after further adjusting the means for CPD and the other variables associated with nicotine equivalents in Table 3.
We further explored the difference in nicotine equivalents in women by CYP2A6 activity (measured by nicotine metabolite ratio) in stratified analyses, dividing, successively, the female subjects at the median age (60 years), BMI (25.4 kg/m2) and CPD (20.0 cigarettes per day). We found that nicotine equivalents were significantly greater in the high versus low CYP2A6 activity group among the older and high BMI categories (p=0.01 and 0.005, respectively), as well as among women who smoke ≤ 20 CPD (p=0.02), but not in female in the low age and BMI categories (p=0.20 and 0.55, respectively), and female smokers of greater than 20 CPD (p=0.20). For all ages, BMIs, and smoking levels among men, the difference in nicotine equivalents by CYP2A6 activity group was non-statistically significant. None of the interaction tests for the variables above were statistically significant.
To determine whether smokers with greater nicotine metabolism were also exposed to a greater amount of carcinogens after adjusting for CPD, we compared mean urinary 1-OHP and total NNAL (sum of NNAL and NNAL-glucuronide) by tertile of nicotine metabolite ratio level (Table 5). The model with total NNAL as the dependent variable was adjusted for age, sex and race (where appropriate), CPD and 12-hour urine volume, as these variables were associated with total NNAL in our study. Likewise, the model with 1-OHP as the dependent variable was also adjusted for age, race, sex, CPD and 12-hour urine volume. Mean total NNAL was significantly greater with increasing CYP2A6 activity among all subjects (p for trend=0.03). Mean 1-OHP also shows an overall positive trend by CYP2A6 activity, but this trend did not reach statistical significance. The race-specific analyses show similar patterns but none of the trends were statistically significant. We also re-ran the model for total NNAL by tertile of nicotine metabolite ratio with values expressed as absolute amount (in nmol), instead of adjusting for urine volume, and the conclusions were similar.
Table 5.
Geometric means* (95% confidence limits) for total NNAL† and 1-hydroxypyrene by nicotine metabolite ratio level
| Nicotine Metabolite Ratio† |
P for trend§ | ||||||
|---|---|---|---|---|---|---|---|
| Lower Tertile | Second Tertile | Upper Tertile | |||||
| Total NNAL (pmol/mL) | n | Mean (95% CI) | n | Mean (95% CI) | n | Mean (95% CI) | |
|
| |||||||
| All‡ | 193 | 0.70 (0.64–0.76) | 192 | 0.79 (0.72–0.85) | 193 | 0.80 (0.73–0.87) | 0.03 |
| Native Hawaiian | 65 | 0.61 (0.53–0.71) | 62 | 0.72 (0.62–0.84) | 61 | 0.69 (0.59–0.81) | 0.17 |
| White | 34 | 0.95 (0.78–1.16) | 72 | 0.90 (0.79–1.03) | 91 | 0.99 (0.88–1.12) | 0.61 |
| Japanese American | 94 | 0.63 (0.56–0.71) | 58 | 0.73 (0.63–0.86) | 41 | 0.74 (0.62–0.89) | 0.16 |
|
| |||||||
| 1-Hydroxypyrene (pmol/mL)
| |||||||
| All‡ | 192 | 0.40 (0.31–0.51) | 188 | 0.34 (0.26–0.43) | 190 | 0.50 (0.39–0.65) | 0.47 |
| Native Hawaiian | 65 | 0.37 (0.23–0.59) | 60 | 0.35 (0.22–0.56) | 60 | 0.50 (0.31–0.81) | 0.65 |
| White | 34 | 0.40 (0.23–0.68) | 71 | 0.31 (0.22–0.45) | 90 | 0.52 (0.37–0.71) | 0.58 |
| Japanese American | 93 | 0.42 (0.30–0.60) | 57 | 0.35 (0.22–0.54) | 40 | 0.52 (0.31–0.88) | 0.54 |
Mean total NNAL (NNAL+NNAL-glucuronide) and mean 1-hydroxypyrene were adjusted for age, sex, cigarettes per day, and 12-hour urine volume by multiple covariance analysis.
Tertile categorization for nicotine metabolite ratio was based on all participants. First tertile: ≤ 0.484; second tertile: 0.484 to 1.10; third tertile: > 1.10.
Further adjusted for race.
P-values for trend were obtained by treating nicotine metabolite ratio as a continuous variable.
We also compared NNK and 1-OHP exposure by nicotine equivalents level, to assess whether smokers who smoke more intensively are exposed to higher levels of these two tobacco smoke carcinogens. As was expected, total NNAL and 1-OHP increased with nicotine equivalents in both sexes (after adjusting for CPD and other covariates) (within each sex, p-for-trend <0.0001) (data not shown). The sum of NNAL and NNAL-Gluc was significantly correlated with nicotine equivalents in women and men (Spearman’s correlation coefficient: 0.66 and 0.68, respectively). However, the corresponding correlations for CPD were weaker (corresponding correlation coefficients of 0.21 and 0.26). 1-OHP was moderately correlated with nicotine equivalents in women and men (corresponding correlation coefficient: 0.41 and 0.50), but the corresponding correlations for CPD were low (corresponding correlation coefficient: 0.01 and 0.0003). These results suggest that total NNAL is a better surrogate for total nicotine exposure than 1-OHP. This is consistent with findings by Jacob et al. (31) suggesting that excretion of fluorene metabolites is a better indicator than 1-OHP of smoking status.
Discussion
We measured urinary nicotine and its metabolites and the extent of nicotine metabolism in a large sample of Native Hawaiian, Japanese and white smokers in Hawaii. Our hypothesis was that ethnic/racial differences in nicotine metabolism may affect smoking behavior in a way that could result in greater carcinogen exposure, adjusted for average daily cigarette consumption, and, thereby, may help explain the ethnic/racial differences in lung cancer risk that we have documented in this population (1,2). Indeed, case-control and prospective data in Hawaii have demonstrated that Native Hawaiian smokers have a greater risk, and Japanese American smokers a lower risk, compared to white smokers, even after taking into account smoking dose (specifically, CPD) and duration, and other potential confounders (1,2). Sobue et al. (32) also observed in a prospective study that the magnitudes of the smoking relative risks for different histologic types of lung cancer were substantially lower in Japan, compared to those reported for the U.S. or Europe, particularly for adenocarcinoma. We were also interested in comparing nicotine equivalents, adjusted for CPD, across ethnic groups, as other population-specific factors may affect how extensively people smoke and, consequently, their exposure to tobacco carcinogens. To our knowledge, there are no previous studies assessing nicotine equivalents (especially including the glucuronated metabolites) in these three ethnic/racial populations, or the nicotine metabolite ratio in Native Hawaiians.
In this study, we used two urinary phenotypic markers of CYP2A6 activity, namely, the nicotine metabolite ratio and the caffeine metabolite ratio. We gave preference to the nicotine metabolite ratio, because we were specifically interested in the ability to metabolize nicotine, not caffeine; however, we sought to examine the internal consistency of the findings by having a second, independent measure of CYP2A6 activity. The correlation between the two ratios was moderate but statistically significant (Spearman’s correlation coefficient=0.29; p<0.0001). Previous studies have used the ratio of 3-HC : cotinine measured in plasma (8,18,33,34), saliva (10,18) or urine (15,35) to assess ability to metabolize nicotine. The use of this ratio is based on the premise that the metabolism of cotinine to 3-HC is a measure of CYP2A6 activity (18). In all of these studies except one (35), the 3-HC : cotinine ratio does not include the glucuronide conjugates of these metabolites. The nicotine metabolite ratio used in the present study is total 3-HC : total cotinine. We believe that in urine, this ratio may be a better measure of CYP2A6-catalyzed cotinine metabolism than is the free 3-HC to free cotinine ratio. More than half the cotinine excreted is excreted as its glucuronide conjugate (36,37). Therefore, total urinary cotinine may better reflect the amount of cotinine available for CYP2A6 catalyzed metabolism. All the excreted 3-HC, glucuronidated or not, is the product of this metabolism, and therefore is included in the numerator.
In the white female and male smokers in our study, the unadjusted mean value of the total 3-HC : total cotinine ratio was 1.51 and 1.19, respectively (Supplementary table), whereas in the study by Kandel and colleagues (15) the mean 3-HC : cotinine ratio was 5.48. The large difference between these two values may in part be explained by the use of total cotinine in the denominator. Also, the previous studies used spot urine collected at time of interview (15,35). In contrast, our urine specimens were collected in a standardized manner over a 12-hour period. Our measurements, therefore, integrate tobacco exposure and metabolism over a longer period and, thus, are likely to be more stable. Nevertheless, in either study the nicotine metabolite ratio in Asians (Japanese in our study) was 40 to 50% lower than in Whites. This was true in our study whether adjusted for CPD or not (Table 2, Supplementary table).
We found that urinary levels of total cotinine, total 3-HC and nicotine equivalents, adjusted for CPD, were higher in men than in women; these metabolites were also significantly lower in Japanese Americans compared to Native Hawaiians and, in turn, lower in the latter group than in whites. Particularly striking was the markedly lower levels of nicotine equivalents (even after adjusting for CPD), nicotine metabolite ratio, and caffeine metabolite ratio in Japanese American smokers, a pattern which is consistent with their lower lung cancer risk. Because Native Hawaiians have a higher lung cancer risk than whites, however, the ethnic/racial differences in nicotine equivalents per cigarette dose and in CYP2A6 activity do not parallel the known differences in risk of the disease. A lower nicotine metabolism (measured by 3-HC : COT in saliva) has already been reported for Polynesians, compared to whites, in New Zealand, although these findings were based on 6 Maori and 6 European female smokers (10).
Kandel et al. (15) also found no correlation between nicotine metabolism and the previously documented lung cancer risk across the populations studied. In their data, the ratio of urinary 3-HC to cotinine was higher in whites and Hispanics than in African Americans and Asians. However, compared to whites, we have shown that the lung cancer risk due to smoking is greater in African Americans and lower in Hispanics and Japanese Americans (2). In another study using the ratio of plasma cotinine to nicotine as the nicotine metabolite ratio, no ethnic difference was found among whites, blacks, and Koreans (34). However, a significantly lower metabolic ratio was observed in Japanese subjects compared with the other populations. Taken together, the data suggest that whites consistently have higher nicotine metabolite ratios (no matter how nicotine metabolism is measured) than do Asians, particularly Japanese. This finding is supported by genotyping studies that have identified low-activity CYP2A6 alleles, the frequencies of which are much higher in Asians than in whites (34).
Some previous studies have reported ethnic/racial differences in cotinine levels, in particular higher levels among African Americans smokers. Specifically, serum cotinine was reported to be higher in African American smokers than in white (6) or Mexican American smokers (5). This has led to the suggestion that the observed increased cancer risk in this population is due to an increased dose of tobacco smoke. Pérez-Stable et al. (38) found that serum cotinine concentration per CPD was higher among African American than white smokers, and attributed this result to a lower overall clearance of cotinine and a higher intake of nicotine per cigarette (which is metabolized to its proximate metabolite, cotinine) in African Americans. Similarly, in the study by Kandel and colleagues of 900 daily smokers aged 18–26 and averaging 12 cigarettes per day, nicotine exposure per cigarette (measured by mean urinary cotinine per cigarette) was found to be greatest in African Americans, lowest in whites, and intermediate in Asians and Hispanics (15). This is in contrast to our study in which total nicotine equivalents was significantly lower in Japanese. These dissimilar findings may be explained by the younger population studied by Kandel et al. (15), the lower number of CPD for their participants and their different measure of nicotine equivalents per cigarette (urinary free cotinine vs. the sum of urinary free and conjugated nicotine, cotinine and 3-HC in our study).
There was no significant difference in the nicotine metabolite ratio by sex in our study. This was surprising in the context of previous studies, most of which report nicotine metabolism to be higher in women than in men (15,33,34,35). Estrogens have been shown to have a stimulatory effect on nicotine metabolism by both inducing CYP2A6 and accelerating glucuronide conjugation (39–41). Interestingly, Benowitz et al. (40) found no significant difference in nicotine metabolism between men and peri-menopausal or post-menopausal women. Since the median age for women in our study was 60, the absence of a gender difference in the ratio could be because the great majority of our female subjects were post-menopausal. Although there was no gender difference in mean nicotine metabolite ratio in our study, nicotine equivalents adjusted for CPD was significantly higher in men than in women. In an ethnic-specific analysis, this trend was consistent across groups, but did not reach statistical significance.
Women with a high nicotine (or caffeine) metabolite ratio had a statistically significantly elevated amount of nicotine equivalents with CPD-adjustment, compared to women with a low nicotine metabolite ratio. In men, this comparison was not significant according to the nicotine metabolite ratio, but was significant when measured by the caffeine metabolite ratio. This suggests that, at least in women, a faster nicotine metabolism could result in exposure to a higher level of nicotine equivalents and, therefore, to higher internal doses of tobacco carcinogens, possibly leading to an elevated risk of lung cancer, compared to women with a slow nicotine metabolism. This is supported by the fact that we observed a statistically significant relationship between the nicotine metabolite ratio and our biomarker of NNK internal dose. The association between nicotine metabolite ratio and 1-OHP was not significant, but displayed a positive relationship. Although not completely consistent across sexes and metabolite ratios, overall, these data suggest that smokers with high a CYP2A6 activity have a greater nicotine exposure and are exposed to a greater internal dose of tobacco smoke carcinogens, especially NNK. An association between total NNAL exposure and lung cancer incidence in smokers has recently been reported (42).
We had also hypothesized that differences in diet between gender and racial groups may also contribute to differences in CYP2A6 activity. Numerous studies suggest an inverse association between vegetable and fruit consumption and lung cancer risk (43–45). An intervention study among Jordanian non-smokers showed an increase in CYP2A6 activity (measured by the caffeine metabolite ratio) with broccoli consumption (46). We found that total fruits intake was negatively associated with the caffeine metabolite ratio, but not with the nicotine metabolite ratio. There was no association between vegetable intake or, specifically, cruciferous vegetable intake and CYP2A6 activity. Well-controlled feeding studies are needed to determine whether fruits or vegetables affect CYP2A6 activity. Surprisingly, we also found an inverse association between nicotine equivalents adjusted for CPD and caffeine intake in this study. This may be due to chance, as we do not see any possible explanation for such a relationship.
Age and BMI were also associated with CYP2A6 activity in our study, but these results were not consistent across the two metabolite ratios. Age was directly associated only with the caffeine metabolite ratio. Johnstone et al. (33) also reported a direct association (p=0.04) between the ratio of plasma 3-HC : cotinine and age. In contrast, an earlier study by Molander et al. (47) suggested that nicotine metabolism decreases with age. Unexpectedly, BMI was associated in opposite direction with our two markers of CYP2A6 activity. We found the nicotine metabolite ratio to be associated with number of cigarettes smoked per day; however, previous studies were inconsistent in showing this relationship (15,35).
Exploring the ethnic/racial and gender differences observed for smoking behavior, nicotine metabolism and lung cancer risk might provide important insight into the pathogenesis of this neoplasm. A better understanding of nicotine metabolism may also lead to more individualized and effective smoking cessation programs. Our study suggests that a high nicotine metabolism results in a greater nicotine exposure after adjustment for cigarette consumption and, consequently, a greater exposure to tobacco carcinogens, such as NNK and PAHs. It is important to note, however, that we sought to assess exposure to only two smoking-related carcinogens; yet, over 40 established carcinogens are found in cigarette smoke (48). However, it seems reasonable to assume that exposure to all, or most, carcinogens would also be increased. Finally, although the ethnic/racial differences in nicotine metabolism and nicotine equivalents per cigarette dose do not correlate directly with those for lung cancer risk in our three populations, the results for Japanese American smokers suggest that lower nicotine metabolism and exposure may contribute to their decreased lung cancer risk.
Supplementary Material
Acknowledgments
This research was supported in part by grant R01 CA 85997 from the National Cancer Institute. We thank Elizabeth Thompson and Nicole Thomson for carrying out the analysis of total nicotine, total cotinine, and total trans 3′-hydroxycotinine.
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