BACKGROUND: This study was conducted to determine sex/gender differences in smoke exposure and to quantify the role of potential predictors including puffing behaviors, nicotine dependence, and non-nicotinic factors. METHODS: The Pennsylvania Adult Smoking Study (PASS) of 332 adult cigarette smokers utilized portable handheld topography devices to capture the smokers' profiles in a naturalistic environment. Sex/gender differences in salivary biomarkers were modeled using ANCOVA to account for measures of dependence (Fagerstrom Test for Nicotine Dependence, nicotine metabolite ratio [3-hydroxycotinine/cotinine]), and nondependence covariates including anthropomorphic factors and stress. The Blinder-Oaxaca method was used to decompose the sex/gender differences in nicotine uptake due to covariates. RESULTS: Men had significantly higher cotinine levels (313.5 ng/mL vs. 255.8 ng/mL, p < 0.01), cotinine +3-hydroxycotinine levels, (0.0787 mol/L vs. 0.0675 mol/L, p = 0.01), puff volumes (52.95 mL vs. 44.77 mL, p < 0.01), and a lower nicotine metabolite ratio (0.396 vs. 0.475, p = 0.01) than women. The mean Fagerström Test for Nicotine Dependence score did not differ between men and women (p = 0.24). Women had a higher mean Hooked on Tobacco Checklist score than men (7.64 vs. 6.87, p < 0.01). In multivariate analysis, nicotine metabolite levels were not significantly different by sex. Decomposition results show that ten predictors can explain 83% of the sex/gender differences in cotinine uptake. Height was the greatest contributor to these differences, followed by average puff volume. Conclusion and Impact: The higher levels of nicotine metabolites in men, compared to women, can be explained by height, weight, puff volume, and nicotine metabolism.
BACKGROUND: This study was conducted to determine sex/gender differences in smoke exposure and to quantify the role of potential predictors including puffing behaviors, nicotine dependence, and non-nicotinic factors. METHODS: The Pennsylvania Adult Smoking Study (PASS) of 332 adult cigarette smokers utilized portable handheld topography devices to capture the smokers' profiles in a naturalistic environment. Sex/gender differences in salivary biomarkers were modeled using ANCOVA to account for measures of dependence (Fagerstrom Test for Nicotine Dependence, nicotine metabolite ratio [3-hydroxycotinine/cotinine]), and nondependence covariates including anthropomorphic factors and stress. The Blinder-Oaxaca method was used to decompose the sex/gender differences in nicotine uptake due to covariates. RESULTS:Men had significantly higher cotinine levels (313.5 ng/mL vs. 255.8 ng/mL, p < 0.01), cotinine +3-hydroxycotinine levels, (0.0787 mol/L vs. 0.0675 mol/L, p = 0.01), puff volumes (52.95 mL vs. 44.77 mL, p < 0.01), and a lower nicotine metabolite ratio (0.396 vs. 0.475, p = 0.01) than women. The mean Fagerström Test for Nicotine Dependence score did not differ between men and women (p = 0.24). Women had a higher mean Hooked on Tobacco Checklist score than men (7.64 vs. 6.87, p < 0.01). In multivariate analysis, nicotine metabolite levels were not significantly different by sex. Decomposition results show that ten predictors can explain 83% of the sex/gender differences in cotinine uptake. Height was the greatest contributor to these differences, followed by average puff volume. Conclusion and Impact: The higher levels of nicotine metabolites in men, compared to women, can be explained by height, weight, puff volume, and nicotine metabolism.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Nicolle M Krebs; Allshine Chen; Junjia Zhu; Dongxiao Sun; Jason Liao; Andrea L Stennett; Joshua E Muscat Journal: Am J Epidemiol Date: 2016-06-16 Impact factor: 4.897
Authors: Joseph R DiFranza; Robert J Wellman; Judith A Savageau; Ariel Beccia; W W Sanouri A Ursprung; Robert McMillen Journal: ISRN Addict Date: 2012-11-22
Authors: Pongkwan Yimsaard; Ann McNeill; Hua-Hie Yong; K Michael Cummings; Janet Chung-Hall; Summer Sherburne Hawkins; Ann C K Quah; Geoffrey T Fong; Richard J O'Connor; Sara C Hitchman Journal: Nicotine Tob Res Date: 2021-03-19 Impact factor: 4.244
Authors: Melissa D Blank; Jennifer Pearson; Caroline O Cobb; Nicholas J Felicione; Marzena M Hiler; Tory R Spindle; Alison Breland Journal: Tob Control Date: 2019-11-04 Impact factor: 7.552
Authors: Connie S Sosnoff; Kevin Caron; J Ricky Akins; Kristin Dortch; Ronald E Hunter; Brittany N Pine; June Feng; Benjamin C Blount; Yao Li; Dana M van Bemmel; Heather L Kimmel; Kathryn C Edwards; Maciej L Goniewicz; Dorothy K Hatsukami; B Rey de Castro; John T Bernert; Stephen Arnstein; Nicolette Borek; Ying Deng-Bryant; Elena Mishina; Charles Lawrence; Andrew Hyland; Stephen S Hecht; Kevin P Conway; James L Pirkle; Lanqing Wang Journal: Nicotine Tob Res Date: 2022-03-26 Impact factor: 4.244
Authors: Thomas F Northrup; Angela L Stotts; Robert Suchting; Amir M Khan; Charles Green; Michelle R Klawans; Penelope J E Quintana; Eunha Hoh; Melbourne F Hovell; Georg E Matt Journal: Nicotine Tob Res Date: 2021-01-22 Impact factor: 4.244
Authors: Jaclyn Parks; Kathleen E McLean; Lawrence McCandless; Russell J de Souza; Jeffrey R Brook; James Scott; Stuart E Turvey; Piush J Mandhane; Allan B Becker; Meghan B Azad; Theo J Moraes; Diana L Lefebvre; Malcolm R Sears; Padmaja Subbarao; Tim K Takaro Journal: J Expo Sci Environ Epidemiol Date: 2021-06-26 Impact factor: 5.563