33 research outputs found
Association of CYP2A6 activity with lung cancer incidence in smokers: The multiethnic cohort study
<div><p>While smoking is the primary cause of lung cancer, only 11–24% of smokers develop the malignancy over their lifetime. The primary addictive agent in tobacco smoke is nicotine and variation in nicotine metabolism may influence the smoking levels of an individual. Therefore, inter-individual variation in lung cancer risk among smokers may be due in part to differences in the activity of enzymes involved in nicotine metabolism. In most smokers, cytochrome P450 2A6 (CYP2A6)-catalyzed C-oxidation accounts for >75% of nicotine metabolism, and the activity of this enzyme has been shown to correlate with the amount of nicotine and carcinogens drawn from cigarettes. We prospectively evaluated the association of urinary biomarkers of nicotine uptake (total nicotine equivalents [TNE]) and CYP2A6 activity (ratio of urinary total trans-3′-hydroxycotinine to cotinine) with lung cancer risk among 2,309 Multiethnic Cohort Study participants who were current smokers at time of urine collection; 92 cases were diagnosed during a mean follow-up of 9.5 years. We found that higher CYP2A6 activity and TNE was associated with increased lung cancer risk after adjusting for age, sex, race/ethnicity, body mass index, smoking duration, and urinary creatinine (p’s = 0.002). The association for CYP2A6 activity remained even after adjusting for self-reported cigarettes per day (CPD) (Hazard Ratio [HR] per unit increase in log-CYP2A6 activity = 1.52; p = 0.005) and after adjusting for TNE (HR = 1.46; p = 0.01). In contrast, the association between TNE and lung cancer risk was of borderline statistical significance when adjusted for CPD (HR = 1.53; p = 0.06) and not statistically significant when further adjusted for CYP2A6 activity (HR = 1.30; p = 0.22). These findings suggest that CYP2A6 activity provides information on lung cancer risk that is not captured by smoking history or a (short-term) biomarker of dose. CYP2A6 activity should be further studied as a risk biomarker for smoking-related lung cancer.</p></div
Age-standardized<sup>a</sup> distributions of risk factors with associated RRs of mortality from AMI and OHD among women in the MEC.
a<p>Age standardized (5-year age groups) to the total female population included in the study.</p>b<p>RRs for all risk factors shown estimated simultaneously (i.e. adjusted for each other).</p>c<p>RRs compared to “No Hypertension”, or “No Diabetes”.</p>d<p>Per 5 years of use.</p><p>Abbreviations: eth = ethyl alcohol; Nat = natural menopause; Ooph = oophorectomy; P<sub>t</sub> = P for trend.</p
Associations of biomarkers of nicotine uptake (TNE) and nicotine metabolism (CYP2A6 activity) with lung cancer incidence (92 cases).
<p>Associations of biomarkers of nicotine uptake (TNE) and nicotine metabolism (CYP2A6 activity) with lung cancer incidence (92 cases).</p
Associations of various characteristics at time of urine collection with lung cancer incidence (n = 92 cases).
<p>Associations of various characteristics at time of urine collection with lung cancer incidence (n = 92 cases).</p
Characteristics of MEC current smokers at time of urine collection and incident lung cancer cases identified in this group during follow-up.
<p>Characteristics of MEC current smokers at time of urine collection and incident lung cancer cases identified in this group during follow-up.</p
Risk factors for sepsis mortality for all MEC participants and cancer patients only<sup>*</sup>.
<p>Risk factors for sepsis mortality for all MEC participants and cancer patients only<sup><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178374#t002fn001" target="_blank">*</a></sup>.</p
Number of deaths and risk of sepsis death within the Multiethnic Cohort.
<p>Number of deaths and risk of sepsis death within the Multiethnic Cohort.</p
Partial Correlations Among SPMA and Other Biomarkers
<p>Partial Correlations Among SPMA and Other Biomarkers</p
Geometric least square means of SPMA by population and percent variation explained by smoking and GST genotypes.
<p>Geometric least square means of SPMA by population and percent variation explained by smoking and GST genotypes.</p