Dana Mowls Carroll1, Theodore L Wagener2, Lancer D Stephens3, Lacy S Brame4, David M Thompson5, Laura A Beebe5. 1. Tobacco Research Programs, University of Minnesota, United States. Electronic address: dcarroll@umn.edu. 2. Oklahoma Tobacco Research Center, University of Oklahoma Health Sciences Center, United States. 3. Oklahoma Shared Clinical and Translational Resources, Department of Health Promotion Sciences, University of Oklahoma Health Sciences Center, United States. 4. College of Osteopathic Medicine, Oklahoma State University Center for Health Sciences, United States. 5. Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, United States.
Abstract
BACKGROUND: In American Indian (AI) tobacco users from the southern plains region of the US, we examined the relationship between nicotine and carcinogen exposure and nicotine metabolism. METHODS: Smokers (n = 27), electronic nicotine delivery system (ENDS) users (n = 21), and dual users (n = 25) of AI descent were recruited from a southern plains state. Urinary biomarkers of nicotine metabolism (nicotine metabolite ratio [NMR]), nicotine dose (total nicotine equivalents [TNE]), and a tobacco-specific lung carcinogen (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides [total NNAL] were measured. RESULTS: The geometric mean of NMR was 3.35 (95% Confidence Interval(CI): 2.42, 4.65), 4.67 (95% CI: 3.39, 6.43), and 3.26 (95% CI: 2.44, 4.37) among smokers, ENDS users, and dual users. Each of the three user groups had relatively low levels of TNE, indicative of light tobacco use. Among smokers, there were inverse relationships between NMR and TNE (r = -0.45) and between NMR and NNAL (r = -0.50). Among dual users, NMR and TNE, and NMR and NNAL were not associated. Among ENDS users, NMR and TNE were not associated. CONCLUSIONS: AI tobacco users with higher NMR did not have higher TNE or NNAL exposure than those with lower NMR. This supports prior work among light tobacco users who do not alter their tobacco consumption to account for nicotine metabolism. IMPACT: The high prevalences of smoking and ENDS among AI in the southern plains may not be related to nicotine metabolism. Environmental and social cues may play a more important role in light tobacco users and this may be particularly true among AI light tobacco users who have strong cultural ties.
BACKGROUND: In American Indian (AI) tobacco users from the southern plains region of the US, we examined the relationship between nicotine and carcinogen exposure and nicotine metabolism. METHODS: Smokers (n = 27), electronic nicotine delivery system (ENDS) users (n = 21), and dual users (n = 25) of AI descent were recruited from a southern plains state. Urinary biomarkers of nicotine metabolism (nicotine metabolite ratio [NMR]), nicotine dose (total nicotine equivalents [TNE]), and a tobacco-specific lung carcinogen (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides [total NNAL] were measured. RESULTS: The geometric mean of NMR was 3.35 (95% Confidence Interval(CI): 2.42, 4.65), 4.67 (95% CI: 3.39, 6.43), and 3.26 (95% CI: 2.44, 4.37) among smokers, ENDS users, and dual users. Each of the three user groups had relatively low levels of TNE, indicative of light tobacco use. Among smokers, there were inverse relationships between NMR and TNE (r = -0.45) and between NMR and NNAL (r = -0.50). Among dual users, NMR and TNE, and NMR and NNAL were not associated. Among ENDS users, NMR and TNE were not associated. CONCLUSIONS: AI tobacco users with higher NMR did not have higher TNE or NNAL exposure than those with lower NMR. This supports prior work among light tobacco users who do not alter their tobacco consumption to account for nicotine metabolism. IMPACT: The high prevalences of smoking and ENDS among AI in the southern plains may not be related to nicotine metabolism. Environmental and social cues may play a more important role in light tobacco users and this may be particularly true among AI light tobacco users who have strong cultural ties.
Authors: Janet L Thomas; Hongfei Guo; Steven G Carmella; Silvia Balbo; Shaomei Han; Andrew Davis; Andrea Yoder; Sharon E Murphy; Larry C An; Jasjit S Ahluwalia; Stephen S Hecht Journal: Cancer Epidemiol Biomarkers Prev Date: 2011-04-05 Impact factor: 4.254
Authors: Loïc Le Marchand; Kiersten S Derby; Sharon E Murphy; Stephen S Hecht; Dorothy Hatsukami; Steven G Carmella; Maarit Tiirikainen; Hansong Wang Journal: Cancer Res Date: 2008-11-15 Impact factor: 12.701
Authors: David K Espey; Xiao-Cheng Wu; Judith Swan; Charles Wiggins; Melissa A Jim; Elizabeth Ward; Phyllis A Wingo; Holly L Howe; Lynn A G Ries; Barry A Miller; Ahmedin Jemal; Faruque Ahmed; Nathaniel Cobb; Judith S Kaur; Brenda K Edwards Journal: Cancer Date: 2007-11-15 Impact factor: 6.860
Authors: Dana M Carroll; Sharon E Murphy; Neal L Benowitz; Andrew A Strasser; Michael Kotlyar; Stephen S Hecht; Steve G Carmella; Francis J McClernon; Lauren R Pacek; Sarah S Dermody; Ryan G Vandrey; Eric C Donny; Dorothy K Hatsukami Journal: Cancer Epidemiol Biomarkers Prev Date: 2020-02-12 Impact factor: 4.254
Authors: Dana M Carroll; Carol Hernandez; Greg Braaten; Ellen Meier; Pamala Jacobson; Abbie Begnaud; Erin McGonagle; Linda Bane Frizzell; Dorothy K Hatsukami Journal: Per Med Date: 2020-12-17 Impact factor: 2.512
Authors: Nick Wilson; Jennifer A Summers; Driss Ait Ouakrim; Janet Hoek; Richard Edwards; Tony Blakely Journal: BMC Public Health Date: 2021-11-08 Impact factor: 4.135